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 |
|---|---|---|---|---|
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 639 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
... | 639 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Any = logging.get_logger(__name__)
a : Optional[Any] = {
'''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-... | 639 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCAmelCase = None
__UpperCAmelCase = False
__UpperCAmelCase = F... | 639 | 1 |
import os
from distutils.util import strtobool
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
for e in env_keys:
__lowercase = int(os.environ.get(_UpperCamelCase , -1 ) )
if val >= 0:
return val
return default
... | 639 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__Up... | 639 | 1 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError('''multiplicative_persistence() only accepts integral values''' )
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept... | 639 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 639 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
import math
import sys
import cva
import numpy as np
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = math.sqrt(_UpperCamelCase )
__lowercase = 1 / (sigma * math.sqrt(2 * math.pi ))
return cons * np.exp(-((img / sigma) ... | 639 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 639 | 1 |
# 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
#
# Unless required by applic... | 639 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : List[Any] = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': ['''TapasTokenize... | 639 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 639 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPA... | 639 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__l... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 | 1 |
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
def A ( self , snake_case_ ) -> List[Any]:
'''simple docstring'''
... | 639 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 | 1 |
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, DP... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Dict = logging.get_logger(__name__)
a : Union[str, Any] = {
'''junnyu/roformer_chinese_small... | 639 |
from maths.prime_factors import prime_factors
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCamelCase )
... | 639 | 1 |
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
def get_matched_characters(_UpperCamelCase , _UpperCamelCase ) -> str:
__lowercase = []
__lowercase = min(len(_stra ) , len(_stra ) ) // 2
for i, l in en... | 639 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_=0.2 , ... | 639 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 1 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ... | 639 |
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 tran... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
a : int = {
'''configuration_clip''': [
'''CLIP_PR... | 639 |
a : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 639 | 1 |
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 639 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 639 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder... | 639 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 639 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPo... | 639 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 1 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
a : ... | 639 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
tr... | 639 | 1 |
from __future__ import annotations
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
create_state_space_tree(_UpperCamelCase , [] , 0 , [0 for i in range(len(_UpperCamelCase ) )] )
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ,... | 639 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenize... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : int = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
... | 639 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
... | 639 | 1 |
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 .tokenization_xlnet import XLNe... | 639 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCAmelCase = None
__UpperCAmelCase = False
__UpperCAmelCase = F... | 639 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
... | 639 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__Up... | 639 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase = ["torch", "scipy"]
def __init__( self , *snake_case_ , **snake_case_ ) -> List[... | 639 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 639 | 1 |
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.sche... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models... | 639 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 639 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_... | 639 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 1 |
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__lowercase = str(bin(_UpperCamelCase ) )[2:] # remove the leading "0b"
__lowercase = str(bin... | 639 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 639 | 1 |
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : int = '''src/transformers'''
# This is to make sure the tr... | 639 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__l... | 639 | 1 |
from __future__ import annotations
a : int = list[list[int]]
# assigning initial values to the grid
a : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5... | 639 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 | 1 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
a : int... | 639 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 | 1 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if num <= 0:
raise ValueError('''Input must be a positive integer''' )
__lowercase = [True] * (num + 1)
__lowercase = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , nu... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
import socket
def lowercase_ ( ):
'''simple docstring'''
__lowercase = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
__lowercase = socket.gethostname()
__lowercase = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' )
w... | 639 |
from maths.prime_factors import prime_factors
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCamelCase )
... | 639 | 1 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format... | 639 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 1 |
import requests
a : Optional[Any] = '''''' # <-- Put your OpenWeatherMap appid here!
a : List[Any] = '''https://api.openweathermap.org/data/2.5/'''
def lowercase_ ( _UpperCamelCase = "Chicago" , _UpperCamelCase = APPID ):
'''simple docstring'''
r... | 639 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from ... | 639 |
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 tran... | 639 | 1 |
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
a : int = '''src/transformers'''
a : Dict = '''doc... | 639 |
a : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 639 | 1 |
def lowercase_ ( _UpperCamelCase = 4_00_00_00 ):
'''simple docstring'''
__lowercase = []
__lowercase , __lowercase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_UpperCamelCase )
__lowercase , __lowercase = b, a + b
ret... | 639 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 639 | 1 |
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, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available()... | 639 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 639 | 1 |
import sys
import turtle
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ... | 639 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 1 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if len(_UpperCamelCase ) <= 1:
return lst
__lowercase = 1
while i < len(_UpperCamelCase ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__lowercase , __lowercase = lst[i], lst[i... | 639 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
tr... | 639 | 1 |
from heapq import heappop, heappush
import numpy as np
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , ):
'''simple docstring'''
__lowercase , __lowercase = grid.shape
__lowercase = ... | 639 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenize... | 639 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowercase_ ( ):
'''simple docstring'''
__lowercase = 9
__lowercase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
... | 639 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a : Tuple = {'''configuration_yolos''': ['''YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''YolosConfig''', '''YolosOnnxConfig''']}
try:
if not is_vision_avail... | 639 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCAmelCase = None
__UpperCAmelCase = False
__UpperCAmelCase = F... | 639 | 1 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feat... | 639 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__Up... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : Tuple = {
'''configuration_lxmert''': ['''LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LxmertConfig''']... | 639 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 639 | 1 |
def lowercase_ ( _UpperCamelCase = 50 ):
'''simple docstring'''
__lowercase = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length]... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 639 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 639 | 1 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowercase_ ( _UpperCamelCase = 3 ):
'''simple docstring'''
if isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''number of qubits ... | 639 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
a : Union[str, Any] = 0
a : Dict = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0... | 639 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 639 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca ... | 639 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__l... | 639 | 1 |
from cva import destroyAllWindows, imread, imshow, waitKey
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase , __lowercase = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(_UpperCamelCase ):
for j in ran... | 639 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : Dict = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
class lowerCamelCase_ ( ... | 639 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 | 1 |
# 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 ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet impo... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
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_mvp import MvpTokenizer
a : Tu... | 639 |
from maths.prime_factors import prime_factors
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCamelCase )
... | 639 | 1 |
from __future__ import annotations
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = str(_UpperCamelCase )
return len(_UpperCamelCase ) == 9 and set(_UpperCamelCase ) == set('''123456789''' )
def lowercase_ ( ):
'''simple docstring'''
for bas... | 639 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
a : Tuple = logging.get_logger(__name__)
class ... | 639 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 1 |
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
a : Optional[int] = logging.getLogger(__name__)
a : Any = 50 # m... | 639 |
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 tran... | 639 | 1 |
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> List[str]:
'''simple docstring'''
__lowercase = 0
__lowercase = 0
__lowercase = {}
def A ( self , snake_case_ ... | 639 |
a : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 639 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__Up... | 639 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 639 | 1 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 639 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 639 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
a : Optional[int] = logging.get_logger(__name__)
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , snake_case_=None , **sna... | 639 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
a : Union[str, Any] = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''Group... | 639 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
tr... | 639 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , snake_cas... | 639 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenize... | 639 | 1 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
... | 639 | 1 |
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 tran... | 639 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCAmelCase = None
__UpperCAmelCase = False
__UpperCAmelCase = F... | 639 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : List[Any] = {
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.j... | 639 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__Up... | 639 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_=2 , snake_case_=3 , snake_case_=6_4 , s... | 639 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 639 | 1 |
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.transforms.functional im... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
# 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
#
# Unless required by applic... | 639 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 639 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_... | 639 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase = ["flax", "transformers"]
def __init__( self , *snake_case_ , **snake_case_ ) -> ... | 639 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 639 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiffusionPipeline,
UNetaDCond... | 639 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__l... | 639 | 1 |
import numpy as np
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
return 1 / (1 + np.exp(-vector ))
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
... | 639 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 | 1 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
def A ( self , snake_case_ ) -> float:
... | 639 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 | 1 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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 required by a... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.tr... | 639 |
from maths.prime_factors import prime_factors
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(_UpperCamelCase )
... | 639 | 1 |
# 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 required by applic... | 639 |
a : Any = [
'''Audio''',
'''Array2D''',
'''Array3D''',
'''Array4D''',
'''Array5D''',
'''ClassLabel''',
'''Features''',
'''Sequence''',
'''Value''',
'''Image''',
'''Translation''',
'''TranslationVariableLanguages''',
]
from .audio import Audio
from .f... | 639 | 1 |
import qiskit
def lowercase_ ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowercase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
__lowercase = qiskit.QuantumCircuit(_UpperCamelCase ,... | 639 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
... | 639 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def lowercase_ ( _UpperCamelCase , _UpperCamelCase=1 ):
'''simple docstring'''
if n_shave_prefix_segments >= 0:
return ".".join(path.split('''.''' )[n_shave_prefix_se... | 639 |
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 tran... | 639 | 1 |
from __future__ import annotations
a : List[Any] = '''#'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = {}
def A ( self , snak... | 639 |
a : Any = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared +=... | 639 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Union[str, Any] = {
'''vocab_file''': '''vocab.json''',
'''tokeni... | 639 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTe... | 639 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transfor... | 639 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 639 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase... | 639 |
from __future__ import annotations
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ ) -> None:
'''simple docstring'''
__lowercase = order
# a_{0} ... a_{k}
__lowercase = [1.0]... | 639 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...te... | 639 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''' )
__lowercase = hex_num[0] == '''-'''
if is_negative:
__lowercase = hex_num[1:]
tr... | 639 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def lowercase_ ( _UpperCamelCase ):
'''simple... | 639 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGeneration, BartTokenize... | 639 | 1 |
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that t... | 639 |
import doctest
from collections import deque
import numpy as np
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self ) -> None:
'''simple docstring'''
__lowercase = [2, 1, 2, -1]
__lowercase = [1, 2, 3, 4]
... | 639 | 1 |
from collections.abc import Sequence
from queue import Queue
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_ , snake_case_ , snake_case_=None , snake_case_=None ) -> Optional[Any]:
... | 639 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCAmelCase = None
__UpperCAmelCase = False
__UpperCAmelCase = F... | 639 | 1 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingfac... | 639 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__Up... | 639 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a : Tuple = 500000
a , a : Tuple = os.path.split(__file__)
a : Optional[int] = os.path.join(RESULTS_BASEPATH, '''res... | 639 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_commo... | 639 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__ )
class lowerCamelCase_ ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase ... | 639 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCAmelCase = None
__UpperCAmelCase = False
__UpperCAmelCase = F... | 639 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 639 | 1 |
import pprint
import requests
a : int = '''https://zenquotes.io/api'''
def lowercase_ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + '''/today''' ).json()
def lowercase_ ( ):
'''simple docstring'''
return requests.get(API_ENDPOINT_URL +... | 639 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 | 1 |
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
from transf... | 639 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
fro... | 639 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerc... | 639 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
a : List[str] = logging.get_logger(__name__)
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__l... | 639 | 1 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
a : List[str] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow('''''', '''|''', '''|'''),
... | 639 |
from collections import Counter
from timeit import timeit
def lowercase_ ( _UpperCamelCase = "" , ):
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowercase_ ( _UpperCamelCase = "" ):
'''s... | 639 | 1 |
def lowercase_ ( _UpperCamelCase , _UpperCamelCase = 0 ):
'''simple docstring'''
__lowercase = length or len(_UpperCamelCase )
__lowercase = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
__lowercase , __lowercase ... | 639 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a : Optional[Any] = {
'''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', ''... | 639 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Dict = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google... | 639 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
a : Dict = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise ... | 639 | 1 |
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