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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Dict = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
... | 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'''
def _A ( snake_case__ : list[int] , snake_case__ : str ):
snake_case__ : Tuple = int(snake_case__ )
# Initialize Result
snake_case__ : List[str] = []
# Traverse through all denomination
for denomination in reversed(snake_case__ ):
# Fin... | 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
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ... | 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'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configurati... | 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 cva import destroyAllWindows, imread, imshow, waitKey
def _A ( snake_case__ : Union[str, Any] ):
# getting number of pixels in the image
snake_case__ ,snake_case__ : Union[str, Any] = img.shape[0], img.shape[1]
# converting each pixel's color to its neg... | 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 argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf i... | 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 json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common imp... | 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 typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : str = {
"huggingface/time-series-transformer... | 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'''
_lowerCAmelCase : Dict = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def _A ( ):
snake_case__ : Dict = input('''Enter message: ''' )
snake_case__ : Union[str, Any] = input('''Enter key [alphanumeric]: ''' )
snake_case__ : List[Any] = in... | 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 os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_lowerCAmelCase : Any = 4
_lowerCAmelCase : str = ... | 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 inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_model... | 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 TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : Tuple = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"... | 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 gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)... | 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 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 |
'''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 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 |
'''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'''
def _A ( snake_case__ : float , snake_case__ : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
snake_case__ : Tuple = sum(
... | 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 json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common ... | 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'''
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, re... | 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 ...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 |
'''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'''
def _A ( snake_case__ : str , snake_case__ : str ):
snake_case__ : Union[str, Any] = len(snake_case__ )
snake_case__ : Any = []
for i in range(len(snake_case__ ) - pat_len + 1 ):
snake_case__ : Optional[Any] = True
for j in ... | 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_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : str = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_A... | 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 binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_lowerCAmelCase : Optional[Any] = {
# 1536-bit
... | 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 math
def _A ( snake_case__ : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are i... | 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 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 |
'''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'''
def _A ( snake_case__ : str , snake_case__ : str = " " ):
snake_case__ : List[Any] = []
snake_case__ : Union[str, Any] = 0
for index, char in enumerate(snake_case__ ):
if char == separator:
split_words.append(string[last_index:index] )
... | 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'''
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerat... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : str = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG... | 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 random import randint
from tempfile import TemporaryFile
import numpy as np
def _A ( snake_case__ : Tuple , snake_case__ : Optional[int] , snake_case__ : int ):
snake_case__ : int = 0
if start < end:
snake_case__ : Union[str, Any] = ran... | 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'''
_lowerCAmelCase : List[str] = 2_5_6
# Modulus to hash a string
_lowerCAmelCase : Union[str, Any] = 1_0_0_0_0_0_3
def _A ( snake_case__ : str , snake_case__ : str ):
snake_case__ : List[str] = len(snake_case__ ... | 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 faiss # 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 requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn #... | 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 itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
_lowerCAmelCase : int = "\\n@misc{chen2021evaluating,\n... | 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 unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_... | 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 argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCAmelCase : Any = logging.get_logger("transformers.models.speecht5")
def _A ( snake_case__ : Tu... | 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 json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
_lowerCAmelCase : int = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in ... | 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 __future__ import annotations
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase ) -> None:
"""simple docstring"""
snake_case__ : Optional[int] = order
# a_{0} ... a_{k}
snake_ca... | 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 torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = (KDPMaDiscreteScheduler,)... | 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 unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 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 __future__ import annotations
import time
import numpy as np
_lowerCAmelCase : int = [8, 5, 9, 7]
_lowerCAmelCase : Optional[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_lowerCAmelCa... | 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
def _A ( snake_case__ : np.ndarray , snake_case__ : np.ndarray , snake_case__ : float = 1E-12 , snake_case__ : int = 1_00 , ):
assert np.shape(snake_case__ )[0] == np.shape(snake_case__ )[1]
# Ensure proper dimensionality.
assert... | 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'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
_lowerCAmelCase : An... | 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 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 |
'''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 os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
... | 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 sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
_lowerCAmelCase : Dict = 2_9_9_7_9_2_4_5_8
# Symbols
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] ... | 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'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simpli... | 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 , snake_case__ : list ):
_validate_point(snake_case__ )
_validate_point(snake_case__ )
if len(snake_case__ ) != len(snake_case__ ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
... | 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[list[int]] ):
snake_case__ : Dict = len(snake_case__ )
# We need to create solution object to save path.
snake_case__ : Any = [[0 for _ in range(snake_case__ )] for _ in range(s... | 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'''
def _A ( ):
snake_case__ : Optional[Any] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
snake_case__ : Tuple = 6
snake_case__ : str = 1
snake_case__ : str = 19_01
snake_case__ : Tuple = 0
while year < 20_01:
day += 7
if (year % 4 =... | 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'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase : str = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",... | 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 typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstring... | 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 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 |
'''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 os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel,... | 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'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ... | 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'''
def _A ( snake_case__ : str ):
return " ".join(
''''''.join(word[::-1] ) if len(snake_case__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("Hey wollef sroirraw... | 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 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 |
'''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 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 : Tuple = logging.get_logger(__name__... | 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 logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_lowerCAmelCase : Union[str, Any] = logging.getLogger(__n... | 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from di... | 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'''
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 |
'''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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : List[str] = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 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 os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(__lowerCamelCase )... | 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 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 |
'''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'''
def _A ( snake_case__ : int , snake_case__ : int ):
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def _A ( ):
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.r... | 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 unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 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'''
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 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 copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCAmelCase : int = {
"Intel/dpt-large": "https:/... | 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 .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 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'''
def _A ( snake_case__ : int ):
if not isinstance(snake_case__ , snake_case__ ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
snake_case__ : Any = str(snake_case__ )
snake_case__ : Optional[Any] = ''''''.join(sorte... | 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'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_lowerCAmelCase : Optional[Any] = TypeVar("KEY")
_lowerCAmelCase : Optional[int] = TypeVar("VAL")
@dataclass(frozen=__lowe... | 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
_lowerCAmelCase : Any = []
def _A ( snake_case__ : list[list[int]] , snake_case__ : int , snake_case__ : int ):
for i in range(len(snake_case__ ) ):
if board[row][i] == 1:
return False
... | 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'''
def _A ( snake_case__ : int = 10_00 ):
snake_case__ ,snake_case__ : List[str] = 1, 1
snake_case__ : List[str] = 2
while True:
snake_case__ : Dict = 0
snake_case__ : List[Any] = fa + fa
snake_case__ ,snake_case__ : Union[str, Any] ... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCAmelCase : str = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/m... | 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 inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoencoder... | 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 unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 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_speech_available, is_torch_available
_lowerCAmelCase : Tuple = {
"configuration_audio_spectrogram_transformer": [
"AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAIN... | 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 json
import os
import torch
from diffusers import UNetaDModel
os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)
def _A ( snake_cas... | 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'''
_lowerCAmelCase : Optional[int] = 6_5_5_2_1
def _A ( snake_case__ : str ):
snake_case__ : Dict = 1
snake_case__ : List[str] = 0
for plain_chr in plain_text:
snake_case__ : Tuple = (a + ord(snake_case__ )) % MOD_ADL... | 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'''
# Copyright 2023 The HuggingFace 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
#
# U... | 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 os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArgu... | 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 RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_f... | 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 unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_mod... | 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 datetime import datetime
import matplotlib.pyplot as plt
import torch
def _A ( snake_case__ : Optional[Any] ):
for param in module.parameters():
snake_case__ : List[Any] = False
def _A ( ):
snake_case__ : Union[str, Any] = '''cuda''' if... | 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'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
Conditiona... | 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'''
def _A ( snake_case__ : int = 1 , snake_case__ : int = 10_00 ):
snake_case__ : Union[str, Any] = 1
snake_case__ : List[str] = 0
for divide_by_number in range(snake_case__ , digit + 1 ):
snake_case__ : list[int] = []
snake_case__ : Any ... | 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 argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def _A ( snake_case__ : List[Any] , snake_case__ : int=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix... | 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 io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _A... | 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'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditional... | 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] ):
snake_case__ : Any = current_set.copy()
for row_index, row in enumerate(snake_case__ ):
snake_case__ : Union[str, Any] = row[0]
for column_index, column in enumerate(snake_case__ ):
if magnitude == 0:
... | 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'''
def _A ( snake_case__ : int , snake_case__ : int ):
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
snake_case__ : Any = str(bin(snake_case__ ) )
binary_number += "0" * shift_amount
return b... | 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'''
def _A ( snake_case__ : int = 10 , snake_case__ : int = 22 ):
snake_case__ : Union[str, Any] = range(1 , snake_case__ )
snake_case__ : Optional[Any] = range(1 , snake_case__ )
return sum(
1 for power in powers for base in bases if len(st... | 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 dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
def _A ( snake_case__ : Lis... | 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'''
# Lint as: python3
import itertools
import os
import re
_lowerCAmelCase : Tuple = re.compile(R"([A-Z]+)([A-Z][a-z])")
_lowerCAmelCase : int = re.compile(R"([a-z\d])([A-Z])")
_lowerCAmelCase : Tuple = re.compile(R"(?<!_)_(... | 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'''
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( snake_case__ : Optional[int] , snake_case__ : int , snake_case__ : List[Any] ... | 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 os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.... | 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 unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 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 doctest
from collections import deque
import numpy as np
class snake_case :
"""simple docstring"""
def __init__( self ) -> None:
"""simple docstring"""
snake_case__ : Optional[Any] = [2, 1, 2, -1]
snake_case__ : Tuple... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : int = logging.get_logger(__name__)
_lowerCAmelCase : Optional[Any] = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/... | 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 json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepar... | 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 os
import unicodedata
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 SPIECE_UNDERLINE, logging
_lowerCAmelCase : Dict ... | 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 |
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