code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : str = logging.get_logger(__name__)
_lowerCAmelCase : Any ... | 694 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
fro... | 694 | 1 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transf... | 694 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
f... | 694 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def _A ( snake_case__ : int = 1_00_00_00 , snake_case__ : int = 10 ):
snake_case__ : defaultdict = defaultdict(snake_case__ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
i... | 694 |
'''simple docstring'''
def _A ( snake_case__ : int = 4_00_00_00 ):
snake_case__ : int = []
snake_case__ ,snake_case__ : Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(snake_case__ )
snake_case__ ,snake_case__ : Any = b, a + b
return s... | 694 | 1 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class snake_case ( __lowerCamelCase ... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 694 | 1 |
'''simple docstring'''
def _A ( snake_case__ : int = 50 ):
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_numbe... | 694 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case ( __low... | 694 | 1 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : List[Any] = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbra... | 694 |
'''simple docstring'''
from math import factorial
def _A ( snake_case__ : int = 20 ):
snake_case__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case__ : Union[str, Any] = n // 2
return int(factorial(snake_case__ ... | 694 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_lowerCAmelCase : int = input("Enter image url: ").strip()
print(F'''Downloading image from {url} ...''')
_lowerCAmelCase : List[str] = ... | 694 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = (EulerDiscreteScheduler,)... | 694 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
fro... | 694 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageRes... | 694 | 1 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_m... | 694 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''... | 694 | 1 |
'''simple docstring'''
def _A ( snake_case__ : float , snake_case__ : float ):
return price * (1 + tax_rate)
if __name__ == "__main__":
print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
print(F'''{price_plus_tax(1_25.50, 0.05) = }''')
| 694 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
snake_case__ : Optional[Any] = namedtuple('''result''' , '''name value''' )
if (voltage, current... | 694 | 1 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _A ( snake_case__ : int ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest... | 694 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCAmelCase : Union[str, Any] = "\nimport os\n"
_lowerCAmelCase : Optional[int] = "\ndef foo():\n import os\n return False\n"
_lowerCAmelCase : ... | 694 | 1 |
'''simple docstring'''
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> List[str]:
"""simple docstring"""
snake_case__ : Any = name
snake_case__ : Any ... | 694 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 694 |
'''simple docstring'''
def _A ( snake_case__ : float ):
return 10 - x * x
def _A ( snake_case__ : float , snake_case__ : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(snake_case__ ) * equation(snake_case__ ) >= 0:
... | 694 | 1 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_availa... | 694 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ ,snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1... | 694 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__na... | 694 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_av... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
_lowerCAmelCase : List[str] = "2020.9.26"
_lowerCAmelCase : Any = "xcodz-dot, cclaus, dhruvmanila"
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : floa... | 694 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = ... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_at... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)... | 694 | 1 |
'''simple docstring'''
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_lowerCAmelCase : Any = None
try:
import msvcrt
except ImportError:
_lowerCAmelCase : Tuple = None
try:
import fcntl
except ImportError:
... | 694 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCAmelCase : str = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
pars... | 694 | 1 |
'''simple docstring'''
import math
def _A ( snake_case__ : list , snake_case__ : int = 0 , snake_case__ : int = 0 ):
snake_case__ : Optional[int] = end or len(snake_case__ )
for i in range(snake_case__ , snake_case__ ):
snake_case__ : str = i
snake_ca... | 694 |
'''simple docstring'''
import socket
def _A ( ):
snake_case__ : Any = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case__ : str = socket.gethostname()
snake_case__ : Union[str, Any] = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' ... | 694 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise Opti... | 694 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 694 | 1 |
'''simple docstring'''
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered... | 694 |
'''simple docstring'''
from math import isqrt
def _A ( snake_case__ : int ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case__ ) + 1 ) )
def _A ( snake_case__ : int = 10**6 ):
snake_case__ : str = 0
snake_case__ : List... | 694 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis... | 694 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_lowerCAmelCase : List[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_lowerCAmelCase : ... | 694 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
def __... | 694 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
fro... | 694 | 1 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..mode... | 694 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
f... | 694 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_visi... | 694 |
'''simple docstring'''
def _A ( snake_case__ : int = 4_00_00_00 ):
snake_case__ : int = []
snake_case__ ,snake_case__ : Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(snake_case__ )
snake_case__ ,snake_case__ : Any = b, a + b
return s... | 694 | 1 |
'''simple docstring'''
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def _A ( snake_case__ : Union[str, Any] ):
return getitem, k
def _A ( snake_case__ : Any , snake_case__ : Dict ):
return setitem... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 694 | 1 |
'''simple docstring'''
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_lowerCAmelCase : Optional[Any] = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by def... | 694 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case ( __low... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase ) -> None:
"""simple docstring"""
snake_case__ : Any = num_of_nodes
snake_case_... | 694 |
'''simple docstring'''
from math import factorial
def _A ( snake_case__ : int = 20 ):
snake_case__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case__ : Union[str, Any] = n // 2
return int(factorial(snake_case__ ... | 694 | 1 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
f... | 694 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = (EulerDiscreteScheduler,)... | 694 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
_lowerCAmelCase : List[str] = logging... | 694 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageRes... | 694 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : List[str] = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTex... | 694 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''... | 694 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Dict = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"T... | 694 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
snake_case__ : Optional[Any] = namedtuple('''result''' , '''name value''' )
if (voltage, current... | 694 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : List[str] = logging.get_logger(__name__)
_lowe... | 694 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCAmelCase : Union[str, Any] = "\nimport os\n"
_lowerCAmelCase : Optional[int] = "\ndef foo():\n import os\n return False\n"
_lowerCAmelCase : ... | 694 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_lowerCAmelCase : Any = "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that deve... | 694 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-... | 694 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_av... | 694 |
'''simple docstring'''
def _A ( snake_case__ : float ):
return 10 - x * x
def _A ( snake_case__ : float , snake_case__ : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(snake_case__ ) * equation(snake_case__ ) >= 0:
... | 694 | 1 |
'''simple docstring'''
import argparse
import os
import re
_lowerCAmelCase : List[str] = "src/diffusers"
# Pattern that looks at the indentation in a line.
_lowerCAmelCase : Optional[int] = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in gro... | 694 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ ,snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list ):
if not nums:
raise ValueError('''List is empty''' )
return sum(snake_case__ ) / len(snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 694 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_av... | 694 | 1 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 694 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = ... | 694 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Optional[Any] = {
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Tabl... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)... | 694 | 1 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_s... | 694 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCAmelCase : str = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
pars... | 694 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import... | 694 |
'''simple docstring'''
import socket
def _A ( ):
snake_case__ : Any = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case__ : str = socket.gethostname()
snake_case__ : Union[str, Any] = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' ... | 694 | 1 |
'''simple docstring'''
import random
def _A ( snake_case__ : int , snake_case__ : float , snake_case__ : bool = False ):
snake_case__ : dict = {i: [] for i in range(snake_case__ )}
# if probability is greater or equal than 1, then generate a complete graph
if probabi... | 694 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase = 6 ) -> None:
"""simple docstring"""
snake_case__ : Node | None = None
snake_c... | 694 |
'''simple docstring'''
from math import isqrt
def _A ( snake_case__ : int ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case__ ) + 1 ) )
def _A ( snake_case__ : int = 10**6 ):
snake_case__ : str = 0
snake_case__ : List... | 694 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
... | 694 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_lowerCAmelCase : List[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_lowerCAmelCase : ... | 694 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageRes... | 694 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
fro... | 694 | 1 |
'''simple docstring'''
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( snake_case__ : Optional[Any] , snake_case__ : List[str] , snake_case__ : Dict ):
... | 694 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
f... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowerCAmelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowerCAmelCase : list[int] = ... | 694 |
'''simple docstring'''
def _A ( snake_case__ : int = 4_00_00_00 ):
snake_case__ : int = []
snake_case__ ,snake_case__ : Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(snake_case__ )
snake_case__ ,snake_case__ : Any = b, a + b
return s... | 694 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_lowerCAmelCase : Union[str, Any] = ""
_lowerCAmelCase : Dict = ""
_lowerCAmelCase : str = ""
_lowerCAmelCase : int ... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 694 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _A ( snake_case__ : Callable , snake_case__ : float , snake_case__ : float , snake_case__ : float , snake_case__ : float ):
snake_case__ : Dict = int(np.ceil((x_end - xa) / step_size... | 694 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case ( __low... | 694 | 1 |
'''simple docstring'''
def _A ( snake_case__ : int , snake_case__ : int ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
snake_case__ : List[Any] = str(bin(snake_case__ ) )[2:] # remove the leading "0b"
snake_case__ : Any ... | 694 |
'''simple docstring'''
from math import factorial
def _A ( snake_case__ : int = 20 ):
snake_case__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case__ : Union[str, Any] = n // 2
return int(factorial(snake_case__ ... | 694 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""s... | 694 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = (EulerDiscreteScheduler,)... | 694 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmToke... | 694 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageRes... | 694 | 1 |
'''simple docstring'''
from math import factorial
def _A ( snake_case__ : int = 1_00 ):
return sum(map(snake_case__ , str(factorial(snake_case__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 694 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''... | 694 | 1 |
'''simple docstring'''
def _A ( snake_case__ : int ):
if n == 1 or not isinstance(snake_case__ , snake_case__ ):
return 0
elif n == 2:
return 1
else:
snake_case__ : Tuple = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
... | 694 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
snake_case__ : Optional[Any] = namedtuple('''result''' , '''name value''' )
if (voltage, current... | 694 | 1 |
'''simple docstring'''
def _A ( snake_case__ : list[list[int | float]] ):
snake_case__ : int = len(snake_case__ )
snake_case__ : Union[str, Any] = len(matrix[0] )
snake_case__ : List[Any] = min(snake_case__ , snake_case__ )
for row in range(snake_case__ ... | 694 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCAmelCase : Union[str, Any] = "\nimport os\n"
_lowerCAmelCase : Optional[int] = "\ndef foo():\n import os\n return False\n"
_lowerCAmelCase : ... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ ,snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1... | 694 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-... | 694 | 1 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 694 |
'''simple docstring'''
def _A ( snake_case__ : float ):
return 10 - x * x
def _A ( snake_case__ : float , snake_case__ : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(snake_case__ ) * equation(snake_case__ ) >= 0:
... | 694 | 1 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class snake_case ( __lowerCamelCase , __lowerC... | 694 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ ,snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1... | 694 | 1 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCAmelCase : str = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
pars... | 694 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_av... | 694 | 1 |
'''simple docstring'''
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : List[str] = None
snake_case... | 694 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = ... | 694 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCriteria... | 694 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)... | 694 | 1 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import float... | 694 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCAmelCase : str = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
pars... | 694 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 694 |
'''simple docstring'''
import socket
def _A ( ):
snake_case__ : Any = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case__ : str = socket.gethostname()
snake_case__ : Union[str, Any] = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' ... | 694 | 1 |
'''simple docstring'''
import numpy
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase , lowerCamelCase ) -> None:
"""simple docstring"""
snake_case__ : List[str] = input_array
# Random initial weights ... | 694 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 694 | 1 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
_lowerCAmelCase : Optional[int] = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .loggi... | 694 |
'''simple docstring'''
from math import isqrt
def _A ( snake_case__ : int ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case__ ) + 1 ) )
def _A ( snake_case__ : int = 10**6 ):
snake_case__ : str = 0
snake_case__ : List... | 694 | 1 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
def __init__( self , lower... | 694 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_lowerCAmelCase : List[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_lowerCAmelCase : ... | 694 | 1 |
'''simple docstring'''
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBa... | 694 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
fro... | 694 | 1 |
'''simple docstring'''
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVision... | 700 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
f... | 694 | 0 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401... | 701 |
'''simple docstring'''
def _A ( snake_case__ : int = 4_00_00_00 ):
snake_case__ : int = []
snake_case__ ,snake_case__ : Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(snake_case__ )
snake_case__ ,snake_case__ : Any = b, a + b
return s... | 694 | 0 |
'''simple docstring'''
from __future__ import annotations
_lowerCAmelCase : Dict = [
[-1, 0], # left
[0, -1], # down
[1, 0], # right
[0, 1], # up
]
def _A ( snake_case__ : List[Any] , snake_case__ : List[Any] , snake_case__ : Union[str, Any] , sna... | 702 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 694 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
_lowerCAmelCase : Optional[int] = False
_lowerCAmelCase : Optional[int] = True
_lowerCAm... | 703 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case ( __low... | 694 | 0 |
'''simple docstring'''
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def lowercas... | 704 |
'''simple docstring'''
from math import factorial
def _A ( snake_case__ : int = 20 ):
snake_case__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case__ : Union[str, Any] = n // 2
return int(factorial(snake_case__ ... | 694 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : Optional[int] = {
... | 705 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = (EulerDiscreteScheduler,)... | 694 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _A ( snake_case__ : Dict ):
sn... | 706 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageRes... | 694 | 0 |
'''simple docstring'''
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"vocab_file": "vo... | 707 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''... | 694 | 0 |
def _A ( snake_case__ : str , snake_case__ : str ):
if len(__A ) != len(__A ):
raise ValueError('''String lengths must match!''' )
snake_case__ : List[Any] = 0
for chara, chara in zip(__A , __A ):
if chara != chara:
count += 1
return count
if __... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
snake_case__ : Optional[Any] = namedtuple('''result''' , '''name value''' )
if (voltage, current... | 694 | 0 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _A ( snake_case__ : Union[str, Any] ):
snake_case__ : Union[str, Any] = [
'''encoder.version''',
'''decoder.version''',
''... | 709 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCAmelCase : Union[str, Any] = "\nimport os\n"
_lowerCAmelCase : Optional[int] = "\ndef foo():\n import os\n return False\n"
_lowerCAmelCase : ... | 694 | 0 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passes --user <user> --... | 710 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-... | 694 | 0 |
'''simple docstring'''
import math
_lowerCAmelCase : Any = 1_0
_lowerCAmelCase : List[str] = 7
_lowerCAmelCase : int = BALLS_PER_COLOUR * NUM_COLOURS
def _A ( snake_case__ : int = 20 ):
snake_case__ : Optional[int] = ... | 711 |
'''simple docstring'''
def _A ( snake_case__ : float ):
return 10 - x * x
def _A ( snake_case__ : float , snake_case__ : float ):
# Bolzano theory in order to find if there is a root between a and b
if equation(snake_case__ ) * equation(snake_case__ ) >= 0:
... | 694 | 0 |
'''simple docstring'''
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class snake_case ( unittest.TestCase ):
"""simple docstring"""
... | 712 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ ,snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1... | 694 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=_snake_case ):
"""simple docstring"""
_lowerCAmelCase = ['sentencepiece']
def __init__( self , *lowerCamelCase , **lowerCamelCase ) -> Optio... | 713 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Any = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not is_torch_av... | 694 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _A ( snake_case__ : str ):
snake_case__ : Tuple ... | 714 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = ... | 694 | 0 |
'''simple docstring'''
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_ima... | 715 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)... | 694 | 0 |
'''simple docstring'''
_lowerCAmelCase : str = {str(digit): digit**5 for digit in range(1_0)}
def _A ( snake_case__ : Optional[int] ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_lowerCamelCase ) )
def _A ( ):
return sum(
number
... | 716 |
'''simple docstring'''
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
_lowerCAmelCase : str = argparse.ArgumentParser("Stable Diffusion script with intel optimization", add_help=False)
pars... | 694 | 0 |
'''simple docstring'''
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumul... | 717 |
'''simple docstring'''
import socket
def _A ( ):
snake_case__ : Any = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case__ : str = socket.gethostname()
snake_case__ : Union[str, Any] = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' ... | 694 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from... | 718 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
raise ValueError('... | 694 | 0 |
'''simple docstring'''
def _A ( snake_case__ : Optional[int] ):
snake_case__ : str = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def _A ( snake_case__ : Any ):
snake_case__ : List[Any] = 0
while number > 0:
snake_case__ : Tuple = n... | 719 |
'''simple docstring'''
from math import isqrt
def _A ( snake_case__ : int ):
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case__ ) + 1 ) )
def _A ( snake_case__ : int = 10**6 ):
snake_case__ : str = 0
snake_case__ : List... | 694 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from ... | 720 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_lowerCAmelCase : List[Any] = "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"
_lowerCAmelCase : ... | 694 | 0 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def _A ( snake_case__ : str , snake_case__ : str , **snake_case__ : Optional[int] ):
snake_case__ : Optional[Any] = AutoConfig.from_pretrained(__UpperCamelCase , **__... | 721 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
fro... | 694 | 0 |
'''simple docstring'''
def _A ( snake_case__ : list[int] , snake_case__ : str ):
snake_case__ : List[str] = int(snake_case__ )
# Initialize Result
snake_case__ : Dict = []
# Traverse through all denomination
for denomination in reversed(snake_case__ ):
# Find... | 700 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
f... | 694 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or ... | 701 |
'''simple docstring'''
def _A ( snake_case__ : int = 4_00_00_00 ):
snake_case__ : int = []
snake_case__ ,snake_case__ : Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(snake_case__ )
snake_case__ ,snake_case__ : Any = b, a + b
return s... | 694 | 0 |
'''simple docstring'''
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils ... | 702 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:... | 694 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 703 |
'''simple docstring'''
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class snake_case ( __low... | 694 | 0 |
'''simple docstring'''
def _A ( snake_case__ : int ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError('''only integers accepted as input''' )
else:
snake_case__ : List[str] = str(abs(_lowerCamelCase ) )
snake_case__ : Optional[int] ... | 704 |
'''simple docstring'''
from math import factorial
def _A ( snake_case__ : int = 20 ):
snake_case__ : int = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
snake_case__ : Union[str, Any] = n // 2
return int(factorial(snake_case__ ... | 694 | 0 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case ( unittest.TestCase ):
"""simpl... | 705 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = (EulerDiscreteScheduler,)... | 694 | 0 |
'''simple docstring'''
from collections import deque
def _A ( snake_case__ : Optional[int] ):
snake_case__ : Dict = len(snake_case__ )
snake_case__ : List[Any] = deque()
snake_case__ : List[str] = [False for _ in range(snake_case__ )]
snake_case__ : List[... | 706 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageRes... | 694 | 0 |
'''simple docstring'''
import math
def _A ( snake_case__ : int ):
assert isinstance(__snake_case , __snake_case ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negative... | 707 |
'''simple docstring'''
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''... | 694 | 0 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
f... | 708 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def _A ( snake_case__ : float , snake_case__ : float , snake_case__ : float ):
snake_case__ : Optional[Any] = namedtuple('''result''' , '''name value''' )
if (voltage, current... | 694 | 0 |
'''simple docstring'''
def _A ( snake_case__ : Union[str, Any] , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : List[str] , snake_case__ : Union[str, Any] , snake_case__ : Union[str, Any] ):
if index == r:
for j in range(_lowercase )... | 709 |
'''simple docstring'''
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCAmelCase : Union[str, Any] = "\nimport os\n"
_lowerCAmelCase : Optional[int] = "\ndef foo():\n import os\n return False\n"
_lowerCAmelCase : ... | 694 | 0 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( snake_case__ : Dict , snake_case__ : str , s... | 710 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : Any = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-... | 694 | 0 |
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