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'''
def _A ( snake_case__ : Any = 1_00 ):
snake_case__ : Any = (n * (n + 1) // 2) ** 2
snake_case__ : Dict = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(F'''{solution() = }''')
| 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 pprint
import requests
_lowerCAmelCase : int = 'https://zenquotes.io/api'
def _A ( ):
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def _A ( ):
return requests.get(API_ENDPOINT_URL + '''/random''' ).json()
if _... | 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 __future__ import annotations
def _A ( snake_case__ : Optional[int] ):
if len(__UpperCamelCase ) == 0:
return array
snake_case__ ,snake_case__ : str = min(__UpperCamelCase ), max(__UpperCamelCase )
# Compute the variables
snake_case_... | 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'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def _A ( snake_case__ : Optional[Any] ):
snake_case__ : Optional[Any] = []
snake_case__ : List[str] = []
... | 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'''
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 RealmT... | 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'''
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIM... | 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'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Dict = {
'''configuration_efficientnet''': [
'''EFFICIENTNET_... | 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 collections
import inspect
import unittest
from transformers import FocalNetConfig
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_backbone_co... | 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'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_re... | 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 numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _A ( snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int , snake_case__ : int ):
# pre... | 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'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 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'''
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=__a ):
"""simple docstring"""
_lowerCAmelCase = ['transformers', 'torch', 'note_seq']
def __init__( self , *lowerCamelCase , **lowerCamelCase ) ... | 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 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_sub... | 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 typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Any = {
"configuration_roberta": ["RO... | 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'''
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class snake_case ( a__ ):
"""simple docstr... | 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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, loggin... | 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 |
def _A ( snake_case__ : List[str]=2_81_23 ):
snake_case__ : Tuple = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs[k * i] += k + i
snake_case__ : Any = set()
snake_case__... | 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 os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed... | 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 os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 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 unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCA... | 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'''
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
cl... | 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'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _A ( snake_case__ : Any ):
# A local function to see if a dot lands in the circle.
def is_in_circle(snake_case__ : Dict , snake_case__ : ... | 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 os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin... | 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 zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _A ( snake_case__ : Any , snake_case__ : Dict=7 ):
snake_case__ : Union[str, Any] = None
if token is not None:
snake_case__ : Any ... | 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'''
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_lowerCAmelCase : Dict = models.... | 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 copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowercase )
class snake_case ( __lowercase ):
"""simple docstring"""
_lowerCAmelC... | 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'''
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokeni... | 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'''
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
_lowerCAmelCase : List[str] = re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@total_orderi... | 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 logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_lowerCAmelCase : Union[str, Any] = logging.getLogger(__name__)
class snake_case ( UpperCame... | 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 json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import Threade... | 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'''
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
_lowerCAmelCase : Dict = input("Enter image url: ").strip()
print(F'Downloading image from {url} ...')
_lowerCAmelCase : str = Beautiful... | 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'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : Optional[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalD... | 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 requests
def _A ( snake_case__ : str , snake_case__ : str ):
snake_case__ : int = {'''Content-Type''': '''application/json'''}
snake_case__ : List[Any] = requests.post(lowerCAmelCase__ , json={'''text''': message_body} , headers=lowerCAmelCas... | 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'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase : Union[str, Any] = {
"""configuration_mobilevit""": ["""MOBILEVIT... | 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 importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
import tor... | 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
import sys
from collections import deque
from typing import Generic, TypeVar
_lowerCAmelCase : Union[str, Any] = TypeVar("T")
class snake_case ( Generic[T] ):
"""simple docstring"""
_lowerCAmelCase = ... | 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 OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _A ( snake_case__ : Optional[int] , snake_case__ : List[Any] , snake_case__ : Dict ):
snake_case__ : Union[str, Any] = OmegaCo... | 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 functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)
_lowerCAmelCase : str = {
"microsoft/wavlm-base": "https://huggingface.... | 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 multiprocessing
from typing import TYPE_CHECKING, Optional, Union
from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream
if TYPE_CHECKING:
import sqlitea
import sql... | 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 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 : List[Any] ... | 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'''
from __future__ import annotations
def _A ( snake_case__ : str , snake_case__ : Optional[int] , snake_case__ : Optional[Any] ):
if days_between_payments <= 0:
raise ValueError('''days_between_payments must be > 0''' )
if daily_interest_rate < 0:
ra... | 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 |
from pathlib import Path
import torch
from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter
_lowerCAmelCase : Optional[Any] = "Create a default config file for Accelerate wi... | 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__ : int = 10_00 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 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 unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import Tokeniz... | 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 os
from collections.abc import Iterator
def _A ( snake_case__ : List[str] = "." ):
for dir_path, dir_names, filenames in os.walk(snake_case__ ):
snake_case__ : Optional[Any] = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for file... | 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 argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
_lowerCAmelCase : Union[str, Any] = argparse.ArgumentParser(
description=(
"Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM fo... | 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'''
def _A ( snake_case__ : Optional[Any] = 1_00_00_00 ):
snake_case__ : Union[str, Any] = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , lowerCamelCase_ ):
phi[j] -= phi[... | 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 re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_lowerCAmelCase : Tuple = {
"text_branch": "text_model",
"audio_branch": "audio_model.audio_encoder",
"attn": "atten... | 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'''
class snake_case :
"""simple docstring"""
def __init__( self , lowerCamelCase = "" , lowerCamelCase = False ) -> Optional[int]:
"""simple docstring"""
snake_case__ : dict[str, RadixNode] = {}
# A node wil... | 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'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_lowerCAmelCase : Optional[int] = logging.getLogger(__name__)
@dataclass
class snake_case ... | 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'''
def _A ( snake_case__ : int , snake_case__ : int , snake_case__ : list[list[int]] ):
def update_area_of_max_square(snake_case__ : int , snake_case__ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case__ : List[Any... | 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'''
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked ... | 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'''
from __future__ import annotations
def _A ( snake_case__ : Any ):
if not nums:
raise ValueError('''List is empty''' )
return sum(snake_case__ ) / len(snake_case__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_d... | 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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
_lowerCAmelCase : str = {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.... | 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'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
_lowerCAmelCase : Optional[Any] = "\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks ... | 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 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 lowercase_... | 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 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... | 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'''
from __future__ import annotations
import numpy as np
def _A ( snake_case__ : Optional[Any] ):
snake_case__ ,snake_case__ : Any = np.shape(UpperCamelCase__ )
if rows != columns:
snake_case__ : Dict = (
'''\'table\' has to be of square... | 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 numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _A ( snake_case__ : List[str] , snake_case__ : List[str] , snake_case__ : Tuple , snake_case__ : Union[str, Any] , snake_case__ : Tuple , snake_case__... | 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 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__ : Optional[Any] ):
snake_case__ : Di... | 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'''
def _A ( snake_case__ : str ):
snake_case__ : Any = [0 for i in range(len(_lowerCamelCase ) )]
# initialize interval's left pointer and right pointer
snake_case__ : Optional[Any] = 0, 0
for i in range(1 , len(_lowerCamelCase ) ):
... | 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'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_u... | 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 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 transformers.utils impor... | 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 numpy as np
def _A ( snake_case__ : np.array ):
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _A ( snake_case__ : Uni... | 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'''
def _A ( snake_case__ : int ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 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 webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
_lowerCAmelCase : str = "%20".join(argv[1:]) if len(argv) > 1 else quote(str... | 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 __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
_lowerCAmelCase : List[str] = TypeVar("T")
class snake_case ( Generic[T] ):
"""simple docstring"""
_lowerCAmelCase = 4_2... | 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 unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def lowercase__ ( self ) -> 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'''
import unittest
from transformers import LiltConfig, 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_modeling_common i... | 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'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, R... | 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 json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_lowerCAmelCase : str = loggi... | 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'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 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'''
# Copyright 2022 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... | 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'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCAmelCase : Dict = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE... | 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 unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowerCAmelCase ... | 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'''
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_confi... | 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'''
_lowerCAmelCase : Optional[int] = [
"VerificationMode",
"Version",
"disable_progress_bar",
"enable_progress_bar",
"is_progress_bar_enabled",
"experimental",
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enab... | 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 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... | 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 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... | 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 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... | 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.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=__lowerCamelCase , ... | 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 __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... | 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'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRobertaXLConfig",
"XLMRo... | 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 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_case__ : Dict ):... | 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'''
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
... | 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'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1:
return all_n... | 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'''
_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... | 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 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... | 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'''
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__ ... | 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'''
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 : ... | 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'''
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:... | 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'''
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 bin... | 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 torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class snake_case ( __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = (EulerDiscreteScheduler,)... | 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'''
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... | 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'''
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... | 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'''
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... | 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'''
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... | 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'''
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... | 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 |
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