code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : Union[str, Any] = {
"configuration_electra": ["ELECTRA_PR... | 21 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__SCREAMING_SNAKE_CASE :Optional[int] = TypeVar('''T''')
class A_ ( Generic[T] ):
def __init__( self : List[Any] , snake_case_ ... | 22 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
... | 23 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 0 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
snake_case_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class SCREAMING_SNAKE_CA... | 24 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 0 |
"""simple docstring"""
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 25 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.se... | 26 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 0 |
'''simple docstring'''
import unittest
from transformers import XLMConfig, 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 imp... | 27 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 0 |
'''simple docstring'''
def __lowerCamelCase ( A__ , A__ ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase = str(bin(A__ ) )[2:] # remove the leading "0b"... | 28 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
__UpperCAmelCase = (720, 1280) # Height, Width
__UpperCAmelCase = (0.4, 0.6) # if height or width lower than this scale, drop it.
__UpperCAmelCa... | 29 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagem... | 30 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_a... | 31 |
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, prepare_image_inputs
if is... | 14 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ ... | 32 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 0 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def lowercase ( __snake_case : str ):
def decorator(__snake_case : int ):
lowercase_ : int = getattr(__snake_case , '''handle_key''' , [] )
ha... | 33 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 0 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
A ='\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation... | 34 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 0 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowercase = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained.... | 35 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class UpperCAmelCase_ ( a):
def __init__( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[str... | 36 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 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
... | 37 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from ... | 38 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 0 |
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
_a = logging.get_logger(__name__)
class __lowerCamelCase :
"""simple docstring"""... | 39 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowercase ( A_ )-> Optional[int]:
'''simple docstring'''
if not is_accelerate_available(... | 40 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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_common ... | 14 | 0 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig... | 41 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
_snake_case = 1
_snake_case = 2
while i * i <= n:
_snake_case = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_div... | 42 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''facebook/wav2vec2-base-960h''': '''https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json''',... | 43 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 0 |
"""simple docstring"""
_a : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_... | 44 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 0 |
"""simple docstring"""
import string
def lowercase ( lowerCAmelCase__ : str ) -> str:
__a = ''''''
for i in sequence:
__a = ord(lowerCAmelCase__ )
if 65 <= extract <= 90:
output += chr(155 - extract )
elif 97 <= extract <... | 45 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_rober... | 46 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 0 |
'''simple docstring'''
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class A__ ( A__ ):
def A ( self : Optional[Any] ) -> str:
'''simple docstring'''
return [
{"col_1": 3... | 47 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 0 |
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 .tokeniz... | 48 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
loggi... | 49 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 0 |
import argparse
import os
import re
import packaging.version
_UpperCAmelCase : Optional[int] = """examples/"""
_UpperCAmelCase : List[str] = {
"""examples""": (re.compile(R"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
"... | 50 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 0 |
def A (__A : int = 1000000 ) -> int:
"""simple docstring"""
UpperCAmelCase_ = limit + 1
UpperCAmelCase_ = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __A , ... | 51 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 0 |
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
assert isi... | 52 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : List[Any] =logging.get_logger(__name__)
a__ : List[Any] ={
'''BAAI/AltCLIP''': '''https://huggingface.co/BAAI/AltCLIP/resolve... | 53 |
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, prepare_image_inputs
if is... | 14 | 0 |
"""simple docstring"""
from PIL import Image
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = image.size
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = image.load(... | 54 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .... | 55 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of i... | 56 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 0 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( lowerCAmelCase__ ):
'''s... | 57 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 0 |
'''simple docstring'''
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase_ = logging.get_... | 58 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 0 |
from __future__ import annotations
def UpperCamelCase ( __lowerCamelCase : dict , __lowerCamelCase : str ):
snake_case , snake_case : Union[str, Any] = set(__lowerCamelCase ), [start]
while stack:
snake_case : str ... | 59 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 0 |
"""simple docstring"""
def _snake_case ( _snake_case : list ):
if len(_snake_case ) <= 1:
return lst
lowerCAmelCase : Any = 1
while i < len(_snake_case ):
if lst[i - 1] <= lst[i]:
i += 1
else:
lowerCAmel... | 60 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 0 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, Lis... | 61 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 0 |
from math import factorial
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : int = 1_00 ):
return sum(map(SCREAMING_SNAKE_CASE__ , str(factorial(SCREAMING_SNAKE_CASE__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 62 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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_common ... | 14 | 0 |
'''simple docstring'''
import math
def _lowerCamelCase ( lowercase : int = 100 ) -> int:
_a = sum(i * i for i in range(1 , n + 1 ) )
_a = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of... | 63 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 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_availa... | 64 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
UpperCamelCase__ = {'configuration_dpt': ['DPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPTConfig']}
try:
... | 65 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 0 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self: Optional[Any] ) -> Union[str, Any]... | 66 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 0 |
'''simple docstring'''
def __lowerCAmelCase ( UpperCamelCase__ ) -> int:
__lowerCamelCase = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __lowerCAmelCase ( UpperCamelCase__ = 1_00 ) -> int:
__lowerCamelCase = 1
__lowerCa... | 67 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 0 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = 42
__lowerCamelCase ... | 68 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 0 |
"""simple docstring"""
import sys
__UpperCamelCase = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''6... | 69 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class UpperCAm... | 70 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_devi... | 71 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 0 |
"""simple docstring"""
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def snake_case_ ( A_ : str = "isbn/0140328726" ):
'''simple docstring'''
_lowerCamelCase : List[str] = olid.strip().str... | 72 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 0 |
# Function to print upper half of diamond (pyramid)
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> str:
for i in range(0 , lowerCamelCase__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(' ' , end='' )
for _ in range(0 , i + 1 ): # printing stars
... | 73 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 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 Tokenize... | 74 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ : Optional[Any] = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOn... | 75 |
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, prepare_image_inputs
if is... | 14 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Tuple = get_failure_array(_a)
# 2) Step through text searching for pattern
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = 0, 0 # index into text, patte... | 76 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 0 |
"""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, prepa... | 77 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 0 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
... | 78 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 0 |
'''simple docstring'''
def __lowercase ( __lowercase ) -> bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 79 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 80 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 0 |
"""simple docstring"""
import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pi... | 81 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available... | 82 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 0 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : List[str] = abs(UpperCAmelCase_ )
_UpperCamelCase : int = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def A__ ( UpperCAmelCase_ ):
... | 83 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.ut... | 84 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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_common ... | 14 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def UpperCamelCase_( snake_case : Callable[[int | float], int | float] , snake_case : int | float , snake_case : int | float , snake... | 85 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 0 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax impor... | 86 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_A... | 87 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging im... | 88 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/r... | 89 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 0 |
from math import sqrt
def lowerCamelCase_ ( UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) and (
number >= 0
), "'number' must been an int and positi... | 90 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
"""asapp/sew-d-tiny-100k""": """https://huggingface.c... | 91 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 0 |
from __future__ import annotations
import numpy as np
def _a ( SCREAMING_SNAKE_CASE_ : list[float] ):
return np.maximum(0 , SCREAMING_SNAKE_CASE_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 92 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 0 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(import... | 93 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 0 |
def __lowerCamelCase ( UpperCAmelCase_ : list[int] ):
"""simple docstring"""
if not numbers:
return 0
if not isinstance(UpperCAmelCase_ , (list, tuple) ) or not all(
isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) for number in numbers ... | 94 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 0 |
# 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
#
# Unless r... | 95 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 0 |
"""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.skipUnl... | 96 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
sm... | 97 |
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, prepare_image_inputs
if is... | 14 | 0 |
"""simple docstring"""
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowerCAmelCase__ : str = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and... | 98 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 0 |
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowercase : str = logging.get_logger(__name__)
class A__ ( ... | 99 |
from ...processing_utils import ProcessorMixin
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''SpeechT5FeatureExtractor'''
UpperCAmelCase__ = '''SpeechT5Tokenizer'''
def __init__( self : Any , UpperC... | 14 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _lowerCAmelCase ( UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = analyze_text(UpperCamelCase_ )
__SCREAMING_S... | 100 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Tuple = logging.get_logger(__name__)
_lowerCamelCase : str = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-... | 14 | 0 |
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from... | 101 |
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> str:
"""simple docstring"""
A__ = BeautifulSoup(requests.get(lowercase_ , params=lowercase_ ).content , '''html.parser''' )
A__ ... | 14 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _UpperCAmelCase ( __snake_case )... | 102 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[Any]:
"""simple docstring"""
A__ ... | 14 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def UpperCamelCase( __UpperCamelCase : int ):
# A local function to see if a dot lands in the circle.
def is_in_circle(__UpperCamelCase : float ,__UpperCamelCase :... | 103 |
import os
import pytest
from transformers.dynamic_module_utils import get_imports
_lowerCamelCase : Any = """
import os
"""
_lowerCamelCase : Optional[int] = """
def foo():
import os
return False
"""
_lowerCamelCase : List[Any] = """
def foo():
def ... | 14 | 0 |
'''simple docstring'''
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def _A ( A__ = 8 ):
"""simple docstring"""
__lowercase = ascii_letters + digits + punctuation
return "".join(secrets.choice... | 104 |
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def SCREAMING_SNAKE_CASE ( ) -> None:
"""simple docstring"""
print('''Truth Table of NOR Gate:''' )... | 14 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
Au... | 105 |
import os
import sys
import unittest
_lowerCamelCase : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model... | 14 | 0 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
__UpperCamelCase : int ... | 106 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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_common ... | 14 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__lowerCAmelCase : List[str] = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', '... | 107 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ ) -> tuple[float, list[float]]:
"""simple docstring"""
A__ = list(range(len(lowercase_ ) ) )
A__ = [v / w for v, w in zip(l... | 14 | 0 |
"""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"] )
@pyt... | 108 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ ) -> Optional[Any]:
"""simple docstrin... | 14 | 0 |
"""simple docstring"""
from __future__ import annotations
A: Dict = tuple[int, int, int]
A: Optional[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
A: List[str] = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default... | 109 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase : int = {
"""configuration_blip""": [
"""BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 14 | 0 |
import os
import sys
import unittest
lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, fin... | 110 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : List[str] = {"""configuration_vit_msn""": ["""VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMSNConfig"""]}
try:
if not is_torch_available():
raise Opti... | 14 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeq... | 146 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
A__ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
... | 14 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
A... | 254 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
A__ = args.pruning_method
A__ = ar... | 14 | 0 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preproc... | 320 |
_lowerCamelCase : Optional[int] = 65521
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> int:
"""simple docstring"""
A__ = 1
A__ = 0
for plain_chr in plain_text:
A__ = (a + ord(lowercase_ )) % MOD_A... | 14 | 0 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from acceler... | 230 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, ... | 14 | 0 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase = False ):
'''simple docstring'''
if radian_mode:
return [m... | 57 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Any = logging.get_logger(__name__)
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
UpperCAmelCase__ = '''encoder-decoder'''
... | 14 | 0 |
"""simple docstring"""
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require... | 165 |
def SCREAMING_SNAKE_CASE ( lowercase_ ) -> Any:
"""simple docstring"""
A__ = [0] * len(lowercase_ )
A__ = []
A__ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
... | 14 | 0 |
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchSampler,
SkipDataLoader,
... | 169 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_lowerCamelCase : Optional[Any] = datasets.ut... | 14 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Kandi... | 301 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
_lowerCamelCase : List[Any] = """sshleifer/bart-tiny... | 14 | 0 |
from ...processing_utils import ProcessorMixin
class lowercase_ ( UpperCAmelCase__ ):
A__ : str = """SpeechT5FeatureExtractor"""
A__ : Dict = """SpeechT5Tokenizer"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
... | 122 |
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, prepare_image_inputs
if is... | 14 | 0 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_UpperCamelCase : Any = 5_0_0_0_0_0
_UpperCamelCase : List[Any] = os.path.split(__file__)
_UpperCamelCase : Tuple = os.path.joi... | 220 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
_lowerCamelCase : str = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
_lowerCamelCase : Tuple = typing.Union[np.floataa, int, float] # noqa: UP007
... | 14 | 0 |
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