code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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,
AutoModelForMultipleChoice,
AutoTokenizer,
... | 100 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return 10 - x * x
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if equation(UpperCamelCase__ ) * equation(UpperCamelCase__ ) >= 0:
raise Val... | 657 | 0 |
def a__ ( A__, A__ ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = str(bin(A__ ) )[2:] # remove the leading "0b"
SCREAMING_SNAKE_CASE_ : Dict = str(bin(A__ ... | 101 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
c... | 657 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
# Checks if the entire collection has been sorted
if len(SCREAMING_SNAKE_CASE ) <= 1 or n <= 1:
return
insert_... | 102 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__magic_name__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''... | 657 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require... | 103 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''YituTech/conv-bert-ba... | 657 | 0 |
"""simple docstring"""
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase_, UpperCAmelCase_ ):
raise ValueError("Input must be an integer" )
if input_num <= 0:
raise Value... | 104 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if (len(UpperCam... | 657 | 0 |
from __future__ import annotations
def __UpperCAmelCase ( lowerCamelCase_ : list[int] ) -> int:
"""simple docstring"""
if not nums:
return 0
SCREAMING_SNAKE_CASE_ : List[str] = nums[0]
SCREAMING_SNAKE_CASE_ : Any = 0
for ... | 105 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
try:
_UpperCAmelCase = float(UpperCamelCase__ )
except ValueError:
raise ValueError("Please enter a valid number" )
_UpperCAmelCase = decimal - int(UpperCamelCase__ )
if fractional_part == ... | 657 | 0 |
from math import sqrt
def lowerCamelCase_ ( lowerCAmelCase__ : int ) -> bool:
'''simple docstring'''
assert isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
A = ... | 106 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = {
"en": "Machine learning is grea... | 657 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
_UpperCAmelCase : Dict = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
_UpperCAmelCase : ... | 107 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowerCamelCase ):
lowercase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self , *a_ , **a_ ) -> Optional[int]:
requires_backends(self ,... | 657 | 0 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test... | 108 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Optional[Any]... | 657 | 0 |
'''simple docstring'''
def __magic_name__ ( __UpperCAmelCase ) -> int:
'''simple docstring'''
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
ra... | 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__magic_name__ = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__,... | 657 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_ber... | 620 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 657 | 0 |
from ...configuration_utils import PretrainedConfig
lowercase : Tuple = {
"""google/tapas-base-finetuned-sqa""": (
"""https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json"""
),
"""google/tapas-base-finetuned-wtq""": (
"""https://huggingface.co/go... | 302 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simpl... | 657 | 0 |
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers... | 365 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datase... | 657 | 0 |
'''simple docstring'''
def lowerCamelCase__ ( A : int , A : Optional[int] ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(UpperCamelCase__ , int(b / 2 ) ) * actual_power(UpperCamelCase__ , int(b / 2 ) )
el... | 210 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowerCAmelCase ( unittest.TestCase ):
def _a ( self ) -> Optional[Any]:
_Uppe... | 657 | 0 |
from math import factorial
def __a ( __UpperCAmelCase : Dict = 100 ) -> int:
"""simple docstring"""
return sum(int(UpperCamelCase__ ) for x in str(factorial(UpperCamelCase__ ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the... | 488 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINE... | 657 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self : Optional[int] , *UpperCAmel... | 62 |
"""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_tokenizers
@require_sente... | 657 | 0 |
class snake_case__ : # Public class to implement a graph
def __init__( self : Tuple , _lowerCamelCase : Tuple , _lowerCamelCase : List[str] , _lowerCamelCase : Any ):
snake_case__ : Tuple = row
snake_case__ : O... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_UpperCAmelCase = f"Input value of [number={number}] must be an integer"
raise TypeError(UpperCamelCase__ )
if number < 0:
return Fals... | 657 | 0 |
import os
from pathlib import Path
def lowerCAmelCase__ ( _a : str , _a : List[str] , _a : str , _a : Dict ):
snake_case_ : Tuple = {
"en": "Machine learning is great, isn't it?",
"ru": "Машинное обучение - это здо... | 568 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
... | 657 | 0 |
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,
PILImageResampl... | 117 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def merge(UpperCamelCase__ , UpperCamelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
ret... | 657 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not ... | 475 |
"""simple docstring"""
import unittest
from transformers import 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 import ModelTesterM... | 657 | 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 ImageProcessingSavingTes... | 168 |
"""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,
AdamW,
get_constant_schedu... | 657 | 0 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO,
)
UpperCamelCase__ = ... | 620 |
"""simple docstring"""
# Copyright 2021 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
#
# Unle... | 657 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Dict = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
try:
if not is_torc... | 302 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return 10 - x * x
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if equation(UpperCamelCase__ ) * equation(UpperCamelCase__ ) >= 0:
raise Val... | 657 | 0 |
'''simple docstring'''
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _lowerCamelCase ( snake_case_ , snake_case_ ):
'''simple docstr... | 365 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
c... | 657 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransfor... | 210 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__magic_name__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''... | 657 | 0 |
def __a ( __UpperCAmelCase : int ) -> Any:
"""simple docstring"""
return 10 - x * x
def __a ( __UpperCAmelCase : List[str] , __UpperCAmelCase : Dict ) -> int:
"""simple docstring"""
if equation(UpperCamelCase_... | 488 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''YituTech/conv-bert-ba... | 657 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/... | 62 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if (len(UpperCam... | 657 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase : int = {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}
try:
if not is... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
try:
_UpperCAmelCase = float(UpperCamelCase__ )
except ValueError:
raise ValueError("Please enter a valid number" )
_UpperCAmelCase = decimal - int(UpperCamelCase__ )
if fractional_part == ... | 657 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : List[Any] = {
'''configuration_altclip''': [
'''ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AltCLIPConfig''',
'''... | 568 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = {
"en": "Machine learning is grea... | 657 | 0 |
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 (
cen... | 117 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowerCamelCase ):
lowercase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self , *a_ , **a_ ) -> Optional[int]:
requires_backends(self ,... | 657 | 0 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
fr... | 475 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Optional[Any]... | 657 | 0 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copie... | 168 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__magic_name__ = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__,... | 657 | 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,
)
UpperCamelCase__ = {
'configuration_electra': ['ELECTRA_PRETRAINED_C... | 620 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 657 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def A_ ( A__ , A__ , A__ = "x" , A__ = 10**-10 , A__ = 1 , ) -> Optional[Any]:
a__ : Union[str, Any] = symbols(UpperCamelCase__ )
a__ : List... | 302 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simpl... | 657 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( UpperCamelCase__ : Optional[Any] ):
if len(UpperCamelCase__ ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
r... | 365 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datase... | 657 | 0 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.... | 210 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowerCAmelCase ( unittest.TestCase ):
def _a ( self ) -> Optional[Any]:
_Uppe... | 657 | 0 |
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class snake_case_ ( __A ):
'''simple docstring'''
@require_torch
def __SCREAMING_SNAKE_CASE ( self... | 488 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINE... | 657 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Huggin... | 62 |
"""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_tokenizers
@require_sente... | 657 | 0 |
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase : Any = logging.getLogger()
def lowercase__( A ):
... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_UpperCAmelCase = f"Input value of [number={number}] must be an integer"
raise TypeError(UpperCamelCase__ )
if number < 0:
return Fals... | 657 | 0 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowercase : str = parse(importlib.metadata.version('''torch'''))
def lowerCAmelCase__ ( _a : List[str] , _a : Tuple , ... | 568 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
... | 657 | 0 |
import sys
from pathlib import Path
UpperCAmelCase__ = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools # noqa
import json # noqa
import os # noqa
import unittest # noqa
from co... | 117 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def merge(UpperCamelCase__ , UpperCamelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
ret... | 657 | 0 |
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self : List[Any] , _lowercase : List[Any] , _lowercase : int ... | 475 |
"""simple docstring"""
import unittest
from transformers import 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 import ModelTesterM... | 657 | 0 |
'''simple docstring'''
from manim import *
class __A (__magic_name__ ):
def _snake_case ( self ):
__UpperCAmelCase : Any = Rectangle(height=0.5 , width=0.5 )
__UpperCAmelCase : Tuple = Rec... | 168 |
"""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,
AdamW,
get_constant_schedu... | 657 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
UpperCamelCase__ = {
'configuration_speech_to_text': ['SPEECH_T... | 620 |
"""simple docstring"""
# Copyright 2021 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
#
# Unle... | 657 | 0 |
class A__ :
"""simple docstring"""
def __init__( self) -> Any:
'''simple docstring'''
a__ : Any = {}
def __lowercase ( self) -> None:
'''simple docstring'''
print(self.vertex)
for i... | 302 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return 10 - x * x
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if equation(UpperCamelCase__ ) * equation(UpperCamelCase__ ) >= 0:
raise Val... | 657 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
UpperCAmelCase_ : List[str] = '\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'
UpperCAmelCase_ : int ... | 365 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
c... | 657 | 0 |
'''simple docstring'''
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def lowerCamelCase__ ( *A : Union[str, Any] ):
'''simple docstring'''
with open(UpperCamelCase__ , '''r''' ) as fh:
fcntl.flock(UpperCamelCase__ , fcntl.LOCK_EX ... | 210 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__magic_name__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''... | 657 | 0 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
snake_case_ : Any = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_... | 488 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''YituTech/conv-bert-ba... | 657 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
snake_case = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
sna... | 62 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if (len(UpperCam... | 657 | 0 |
def lowercase__( A ):
snake_case__ : Optional[Any] = len(UpperCamelCase__ )
snake_case__ : Any = len(matrix[0] )
snake_case__ : Tuple = min(UpperCamelCase__ , UpperCamelCase__ )
for row in range(UpperCamelCase__ ... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
try:
_UpperCAmelCase = float(UpperCamelCase__ )
except ValueError:
raise ValueError("Please enter a valid number" )
_UpperCAmelCase = decimal - int(UpperCamelCase__ )
if fractional_part == ... | 657 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[A... | 568 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = {
"en": "Machine learning is grea... | 657 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
... | 117 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowerCamelCase ):
lowercase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self , *a_ , **a_ ) -> Optional[int]:
requires_backends(self ,... | 657 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
A = logging.get_logger(__name__)
def __UpperCAmelCase ( __A=None , __A=None ) -> str:
... | 475 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Optional[Any]... | 657 | 0 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_a : Dict ... | 168 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__magic_name__ = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__,... | 657 | 0 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
UpperCamelCase__ = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
'JH 8S TH AH QH',
'TS KS 5S 9S AC',
'KD 6... | 620 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 657 | 0 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase : Any = transforms... | 302 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simpl... | 657 | 0 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.rou... | 365 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datase... | 657 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features... | 210 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowerCAmelCase ( unittest.TestCase ):
def _a ( self ) -> Optional[Any]:
_Uppe... | 657 | 0 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 488 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINE... | 657 | 0 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require... | 62 |
"""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_tokenizers
@require_sente... | 657 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-k... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_UpperCAmelCase = f"Input value of [number={number}] must be an integer"
raise TypeError(UpperCamelCase__ )
if number < 0:
return Fals... | 657 | 0 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
lowercase : Optional[int] = logging.get_logger(__name__)
def lo... | 568 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
... | 657 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from data... | 117 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def merge(UpperCamelCase__ , UpperCamelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
ret... | 657 | 0 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
A = {"tokenization_tapex": ["TapexTokenizer"]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
A = _LazyModule(__name__, globals()["__file__"], _import_structure)
... | 475 |
"""simple docstring"""
import unittest
from transformers import 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 import ModelTesterM... | 657 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.... | 168 |
"""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,
AdamW,
get_constant_schedu... | 657 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Generator
def __SCREAMING_SNAKE_CASE ( ):
"""simple docstring"""
lowercase_ : Optional[int] = {}
lowercase_ : List[Any] = 2
while True:
lowercase_ ... | 620 |
"""simple docstring"""
# Copyright 2021 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
#
# Unle... | 657 | 0 |
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command... | 302 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return 10 - x * x
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if equation(UpperCamelCase__ ) * equation(UpperCamelCase__ ) >= 0:
raise Val... | 657 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
'Visual-Attention-Network/van-base': (
'https://huggingface.co/Visual-Att... | 365 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
c... | 657 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class UpperCamelCase__( lowerCAmelCase ):
@staticmethod
@abstractmethod
def a__( lowerCAmelCase : Union[str, Any] )-> str:
"""simple docstring"""
raise ... | 210 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__magic_name__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''... | 657 | 0 |
def __a ( __UpperCAmelCase : str ) -> Optional[int]: # noqa: E741
"""simple docstring"""
lowerCamelCase_ : Optional[int] = len(UpperCamelCase__ )
lowerCamelCase_ : Tuple = 0
lowerCamelCase_ : Dict = ... | 488 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''YituTech/conv-bert-ba... | 657 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case = {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfi... | 62 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if (len(UpperCam... | 657 | 0 |
from statistics import mean
import numpy as np
def lowercase__( A , A , A , A ):
snake_case__ : Tuple = 0
# Number of processes finished
snake_case__ : Optional[Any] = 0
# Displays the finished process.
# If it is 0, the ... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
try:
_UpperCAmelCase = float(UpperCamelCase__ )
except ValueError:
raise ValueError("Please enter a valid number" )
_UpperCAmelCase = decimal - int(UpperCamelCase__ )
if fractional_part == ... | 657 | 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 numpy as np
import tensorflow as tf
from transformers import TFXLMR... | 568 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = {
"en": "Machine learning is grea... | 657 | 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_blenderbot": [
"BLEN... | 117 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowerCamelCase ):
lowercase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self , *a_ , **a_ ) -> Optional[int]:
requires_backends(self ,... | 657 | 0 |
def __UpperCAmelCase ( __A = 1_0_0_0 ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ = -1
UpperCAmelCase__ = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b... | 475 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Optional[Any]... | 657 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils impor... | 168 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__magic_name__ = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__,... | 657 | 0 |
'''simple docstring'''
from math import ceil, sqrt
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase = 100_0000 ):
"""simple docstring"""
lowercase_ : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
lowerca... | 620 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 657 | 0 |
def A_ ( A__ ) -> int:
def merge(A__ , A__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
... | 302 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simpl... | 657 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
UpperCAmelCase_ : List[str] = logging.getLogger(__name__)
class _lowerCamelCase ( snake_... | 365 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datase... | 657 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCamelCase__( lowerCAmelCase , uni... | 210 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowerCAmelCase ( unittest.TestCase ):
def _a ( self ) -> Optional[Any]:
_Uppe... | 657 | 0 |
from __future__ import annotations
def __a ( __UpperCAmelCase : List[str] ) -> Any: # This function is recursive
"""simple docstring"""
lowerCamelCase_ : int = len(UpperCamelCase__ )
# If the array contains only one element, we return... | 488 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINE... | 657 | 0 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class SCREAMING_SNAKE_CASE ... | 62 |
"""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_tokenizers
@require_sente... | 657 | 0 |
import torch
from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool
class snake_case__ ( UpperCamelCase_ ):
_lowerCAmelCase ='''facebook/bart-large-mnli'''
_lowerCAmelCase =(
'''This is a tool that classifies an English text... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_UpperCAmelCase = f"Input value of [number={number}] must be an integer"
raise TypeError(UpperCamelCase__ )
if number < 0:
return Fals... | 657 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowercase : List[Any] = logging.get_log... | 568 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
... | 657 | 0 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax... | 117 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
def merge(UpperCamelCase__ , UpperCamelCase__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
ret... | 657 | 0 |
def __UpperCAmelCase ( __A = 1_0_0_0 ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase__ = 2**power
UpperCAmelCase__ = 0
while n:
UpperCAmelCase__ , UpperCAmelCase__ = r + n % 1_0, n // 1... | 475 |
"""simple docstring"""
import unittest
from transformers import 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 import ModelTesterM... | 657 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a : Union[str, Any] = logging.get_logger(__name__)
_a : ... | 168 |
"""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,
AdamW,
get_constant_schedu... | 657 | 0 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowercase_ : List[Any] = F"""Input value of [number={number}] must be an integer"""
raise TypeError... | 620 |
"""simple docstring"""
# Copyright 2021 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
#
# Unle... | 657 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, re... | 302 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return 10 - x * x
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if equation(UpperCamelCase__ ) * equation(UpperCamelCase__ ) >= 0:
raise Val... | 657 | 0 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
UpperCAmelCase_ : Optional[int] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
UpperCAmelCase_ :... | 365 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
c... | 657 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = HfArgumentParser(UpperCamelCase__ )
UpperCAmelCase = parser.parse_args_into... | 210 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__magic_name__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''... | 657 | 0 |
def __a ( __UpperCAmelCase : Dict ) -> Dict:
"""simple docstring"""
lowerCamelCase_ : Optional[Any] = generate_pascal_triangle(UpperCamelCase__ )
for row_idx in range(UpperCamelCase__ ):
# Print left spaces
... | 488 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
'''YituTech/conv-bert-ba... | 657 | 0 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCREAMING... | 62 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if (len(UpperCam... | 657 | 0 |
import os
def lowercase__( A ):
snake_case__ : Any = len(grid[0] )
snake_case__ : Dict = len(UpperCamelCase__ )
snake_case__ : List[str] = 0
snake_case__ : Union[str, Any] = 0
snake_case__ : Li... | 170 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
try:
_UpperCAmelCase = float(UpperCamelCase__ )
except ValueError:
raise ValueError("Please enter a valid number" )
_UpperCAmelCase = decimal - int(UpperCamelCase__ )
if fractional_part == ... | 657 | 0 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutp... | 568 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = {
"en": "Machine learning is grea... | 657 | 0 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.t... | 117 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowerCamelCase ):
lowercase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self , *a_ , **a_ ) -> Optional[int]:
requires_backends(self ,... | 657 | 0 |
def __UpperCAmelCase ( __A , __A ) -> Tuple:
'''simple docstring'''
UpperCAmelCase__ = 0
UpperCAmelCase__ = len(UpperCamelCase__ ) - 1
while left <= right:
# avoid divided by 0 during interpolation
... | 475 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__magic_name__ = logging.get_logger(__name__)
class _lowerCAmelCase ( lowerCamelCase ):
lowercase_ : Optional[Any]... | 657 | 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_avai... | 168 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__magic_name__ = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__magic_name__ = _LazyModule(__name__,... | 657 | 0 |
'''simple docstring'''
from jiwer import compute_measures
import datasets
UpperCamelCase__ = '\\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: impro... | 620 |
"""simple docstring"""
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
fro... | 657 | 0 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Any = {
"""post_extract_proj""": """feat... | 302 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simpl... | 657 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.