code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 309 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTest... | 309 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__UpperCamelCase : int = ['''small''', '''medium''', '''large''']
__UpperCamelCase : str = '''lm_head.decoder.weight'''
__UpperCamelCase : Dict = '''lm_hea... | 309 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__UpperCamelCase : List[str] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , **_snake_case ... | 309 |
"""simple docstring"""
__UpperCamelCase : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : dict[int, list[int]] , _UpperCAmelCase : ... | 309 | 1 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultiste... | 309 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def _SCREAMING_SNAKE... | 309 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in ... | 309 | 1 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CA... | 309 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__UpperCamelCase : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
''... | 309 | 1 |
"""simple docstring"""
from timeit import timeit
__UpperCamelCase : Optional[Any] = {
'''MALAYALAM''': True,
'''String''': False,
'''rotor''': True,
'''level''': True,
'''A''': True,
'''BB''': True,
'''ABC''': False,
'''amanaplanacanalpanama''': True, # "a man a p... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETR... | 309 | 1 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class a ( a__ ):
snake_case__ = (KDPMaDiscreteScheduler,)
snake_case__ = 1_0
def UpperCamelCase... | 309 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , ... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, s... | 309 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCamelCase : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wo... | 309 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int ):
return int((input_a, input_a).count(1 ) != 0 )
def _SCREAMING_SNAKE_CASE ():
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , ... | 309 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = ''
lowerCAmelCase = ''
lowerCAmelCase = []
l... | 309 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__Up... | 309 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1337 , num_examples... | 309 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration... | 309 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 309 | 1 |
"""simple docstring"""
import os
import sys
__UpperCamelCase : Any = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
A... | 309 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import 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_av... | 309 | 1 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
f... | 309 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_laun... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Union[str, Any] = {
'''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''... | 309 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation on... | 309 | 1 |
"""simple docstring"""
from PIL import Image
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Image , _UpperCAmelCase : float ):
def brightness(_UpperCAmelCase : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
raise ValueError('level must be between -... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctc... | 309 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chann... | 309 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : Dict = logging.... | 309 | 1 |
"""simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
cla... | 309 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase ... | 309 | 1 |
"""simple docstring"""
from manim import *
class a ( a__ ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase = Rectangle(height=0.46 , wid... | 309 |
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main_... | 309 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class a ( unittest.TestCase ):
d... | 309 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets b... | 309 | 1 |
"""simple docstring"""
import pprint
import requests
__UpperCamelCase : Any = '''https://zenquotes.io/api'''
def _SCREAMING_SNAKE_CASE ():
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def _SCREAMING_SNAKE_CASE ():
return requests.get(API_ENDPOINT_URL + '/random' ).jso... | 309 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[int] , _UpperCAmelCase : str ):
lowerCAmelCase = int(_UpperCAmelCase )
# Initialize Result
lowerCAmelCase = []
# Traverse through all denomination
for denomination in reversed(_UpperCAmelCa... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Union[str, Any] = {'''configuration_ibert''': ['''IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''IBertConfig''', '''IBertOnnxConfig''']}
t... | 309 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, i... | 309 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : int ):
lowerCAmelCase = len(_UpperCAmelCase )
lowerCAmelCase = [[0] * n for i in range(_UpperCAmelCase )]
for i in range(_UpperCAmelCa... | 309 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Dict = {'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}... | 309 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__UpperCamelCase : int = ['''small''', '''medium''', '''large''']
__UpperCamelCase : str = '''lm_head.decoder.weight'''
__UpperCamelCase : Dict = '''lm_hea... | 309 | 1 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__... | 309 |
"""simple docstring"""
__UpperCamelCase : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : dict[int, list[int]] , _UpperCAmelCase : ... | 309 | 1 |
"""simple docstring"""
from math import loga
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('Input value must be a \'int\' type' )
return 0 if... | 309 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 309 | 1 |
"""simple docstring"""
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class a ( a__ ):
def __init__( self , ... | 309 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in ... | 309 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , **_snake... | 309 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__UpperCamelCase : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
''... | 309 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_b... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETR... | 309 | 1 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. 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.... | 309 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , ... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Any = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
''... | 309 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCamelCase : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wo... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
'''configuration_x_clip''': [
'''XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''XCLIPConfig''',
... | 309 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = ''
lowerCAmelCase = ''
lowerCAmelCase = []
l... | 309 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : List[Any] ):
lowerCAmelCase = [0 for i in range(r + 1 )]
# nc0 = 1
lowerCAmelCase = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
... | 309 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1337 , num_examples... | 309 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformer... | 309 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 309 | 1 |
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acce... | 309 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import 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_av... | 309 | 1 |
"""simple docstring"""
__UpperCamelCase : Tuple = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[Any] , _UpperCAmelCase ... | 309 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_laun... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class a :
snake_case__ = 42
snake_case__ = None
snake_case__ = None
__UpperCamelCase : Tuple = namedtuple('''Coi... | 309 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation on... | 309 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedT... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctc... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import _LazyModule
__UpperCamelCase : Union[str, Any] = {'''tokenization_tapex''': ['''TapexTokenizer''']}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
__UpperCa... | 309 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : Dict = logging.... | 309 | 1 |
"""simple docstring"""
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_imag... | 309 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase ... | 309 | 1 |
"""simple docstring"""
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a ( a__ ):
snake_case__ = (D... | 309 |
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main_... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list ):
if len(_UpperCAmelCase ) != 2 or len(a[0] ) != 2 or len(_UpperCAmelCase ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' ... | 309 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets b... | 309 | 1 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__UpperCamelCase : Optional[int] = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
__UpperCamelCase : Any = None
def _SCREAMING_SNAKE_CASE ... | 309 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[int] , _UpperCAmelCase : str ):
lowerCAmelCase = int(_UpperCAmelCase )
# Initialize Result
lowerCAmelCase = []
# Traverse through all denomination
for denomination in reversed(_UpperCAmelCa... | 309 | 1 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__UpperCamelCase : int = ['''small''', '''medium''', '''large''']
__UpperCamelCase : str = '''lm_head.decoder.weight'''
__UpperCamelCase : Dict = '''lm_hea... | 309 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, i... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
__UpperCamelCase : int = '''#'''
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = {}
def UpperCamelCase__ ( self , _snake_case ):
... | 309 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__... | 309 | 1 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathli... | 309 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__UpperCamelCase : int = ['''small''', '''medium''', '''large''']
__UpperCamelCase : str = '''lm_head.decoder.weight'''
__UpperCamelCase : Dict = '''lm_hea... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__UpperCamelCase : Union[str, Any] = numpy.array([0, 0])
__UpperCamelCase : int = numpy.array([0.5, 0.8_66_02_54])
__UpperCa... | 309 |
"""simple docstring"""
__UpperCamelCase : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : dict[int, list[int]] , _UpperCAmelCase : ... | 309 | 1 |
"""simple docstring"""
import argparse
import os
import re
__UpperCamelCase : List[str] = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
__UpperCamelCase : int = re.compile(R'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
__Uppe... | 309 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 309 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
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 impo... | 309 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in ... | 309 | 1 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 309 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__UpperCamelCase : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
''... | 309 | 1 |
"""simple docstring"""
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
__UpperCamelCase : Tuple = lo... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETR... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
return np.maximum(0 , _UpperCAmelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 309 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , ... | 309 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_... | 309 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCamelCase : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wo... | 309 | 1 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class a ( a__ ):
snake_case__ = '''MCTCTFeatureExtractor'''
snake_case__ = '''AutoTokenizer'''
def __init__( self , _snake_case ... | 309 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = ''
lowerCAmelCase = ''
lowerCAmelCase = []
l... | 309 | 1 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
d... | 309 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1337 , num_examples... | 309 | 1 |
"""simple docstring"""
# Algorithm for the pigeonhole sorting
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Tuple ):
lowerCAmelCase = min(_UpperCAmelCase ) # min() finds the minimum value
lowerCAmelCase = max(_UpperCAmelCase ) # max() finds the maximum value
lowerCAmelCase ... | 309 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCamelCase : Union[str, Any] = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', ''... | 309 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import 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_av... | 309 | 1 |
"""simple docstring"""
import math
import unittest
from transformers import BioGptConfig, 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_mode... | 309 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_laun... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__UpperCamelCase : int = 10
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , ... | 309 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation on... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
__UpperCamelCase : Union[str, Any] = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctc... | 309 | 1 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Optional[Any] ):
lowerCAmelCase = {}
lowerCAmelCase = job['started_at']
lowerCAmelCase = job['compl... | 309 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : Dict = logging.... | 309 | 1 |
"""simple docstring"""
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 (
... | 309 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase ... | 309 | 1 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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... | 309 |
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main_... | 309 | 1 |
"""simple docstring"""
import functools
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[int] , _UpperCAmelCase : list[int] ):
# Validation
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for day in d... | 309 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets b... | 309 | 1 |
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main_... | 309 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[int] , _UpperCAmelCase : str ):
lowerCAmelCase = int(_UpperCAmelCase )
# Initialize Result
lowerCAmelCase = []
# Traverse through all denomination
for denomination in reversed(_UpperCAmelCa... | 309 | 1 |
"""simple docstring"""
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class a ( a__ ):
# to overwrite at feature extractactor specific tes... | 309 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, i... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctc... | 309 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__... | 309 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_... | 309 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__UpperCamelCase : int = ['''small''', '''medium''', '''large''']
__UpperCamelCase : str = '''lm_head.decoder.weight'''
__UpperCamelCase : Dict = '''lm_hea... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase ... | 309 |
"""simple docstring"""
__UpperCamelCase : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : dict[int, list[int]] , _UpperCAmelCase : ... | 309 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__UpperCamelCase : Dict = None
try:
import msvcrt
except ImportError:
__UpperCamelCase : Optional[int] = None
try:
import fcn... | 309 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 309 | 1 |
"""simple docstring"""
# 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 ..controlne... | 309 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in ... | 309 | 1 |
"""simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : Union[str, Any] ):
# ===== initialization =====
lowerCAmelCas... | 309 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__UpperCamelCase : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
''... | 309 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union
__UpperCamelCase : Any = TypeVar('''T''')
__UpperCamelCase : Dict = Union[List[T], Tuple[T, ...]]
__UpperCamelCase : str = Union[T, List[T], Dict[str, T]]
__UpperCamelCase ... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETR... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Any = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 309 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , ... | 309 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a ( a__ ):
snake_case__ = (IPNDMScheduler,)
snake_case__ = (('''num_inference_steps''', 5_0),)
def UpperCamelCase__ ... | 309 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCamelCase : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wo... | 309 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extracti... | 309 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = ''
lowerCAmelCase = ''
lowerCAmelCase = []
l... | 309 | 1 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
__UpperCamelCase : int = '''src/transformers'''
# Matches is_xxx_available()
__UpperCamelCase : str = re.compile(R'''is\_([a-z_]*)_available()''')
# Catches a one-line _... | 309 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1337 , num_examples... | 309 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int ):
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
lowerCAmelCase = str(bin(_UpperCAmelCase ) )[2:] # remove the leading "0b"
lowerCAmelCase ... | 309 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 309 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import 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_av... | 309 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import 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_av... | 309 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( a__ ):
snake_case__ = ['''image_processor''', '''tokenizer''']
snake_case__ = '''CLIPImageProcessor'''
snake_case__ ... | 309 |
"""simple docstring"""
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_laun... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation on... | 309 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[list[float]] ):
lowerCAmelCase = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation on... | 309 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCamelCase : Tuple = logging.get... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Dict = {
'''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
'''feature_extraction_mctc... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : str = {
'''configuration_bridgetower''': [
'''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 309 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : Dict = logging.... | 309 | 1 |
"""simple docstring"""
from collections.abc import Sequence
from queue import Queue
class a :
def __init__( self , _snake_case , _snake_case , _snake_case , _snake_case=None , _snake_case=None ):
"""simple docstring"""
lowerCAme... | 309 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : list[str] | None = None ):
lowerCAmelCase = word_bank or []
# create a table
lowerCAmelCase = len(_UpperCAmelCase ) + 1
lowerCAmelCase ... | 309 | 1 |
"""simple docstring"""
import math
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
lowerCAmelCase = []
lowerCAmelCase = 2
lowerCAmelCase = int(math.sqrt(_UpperCAmelCase ) ) # Size of every segment
lowerCAmelCase = [True] * (end + 1)
lowerCAmelCase ... | 309 |
"""simple docstring"""
import re
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
if len(re.findall('[ATCG]' , _UpperCAmelCase ) ) != len(_UpperCAmelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main_... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__UpperCamelCase : Any = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIV... | 309 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
__UpperCamelCase : List[Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets b... | 309 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 309 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[int] , _UpperCAmelCase : str ):
lowerCAmelCase = int(_UpperCAmelCase )
# Initialize Result
lowerCAmelCase = []
# Traverse through all denomination
for denomination in reversed(_UpperCAmelCa... | 309 | 1 |
"""simple docstring"""
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str , _UpperCAmelCase : complex , _UpperCAmelCase : str = "x" , _UpperCAmelCase : float = 10**-10 , ... | 309 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, i... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Dict = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViT... | 309 |
"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__... | 309 | 1 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__UpperCamelCase : int = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()... | 309 |
"""simple docstring"""
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__UpperCamelCase : int = ['''small''', '''medium''', '''large''']
__UpperCamelCase : str = '''lm_head.decoder.weight'''
__UpperCamelCase : Dict = '''lm_hea... | 309 | 1 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...m... | 309 |
"""simple docstring"""
__UpperCamelCase : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__UpperCamelCase : str = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : dict[int, list[int]] , _UpperCAmelCase : ... | 309 | 1 |
"""simple docstring"""
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
if is... | 309 |
"""simple docstring"""
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from... | 309 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__UpperCamelCase : Dict = logging.... | 309 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str = "." ):
for dir_path, dir_names, filenames in os.walk(_UpperCAmelCase ):
lowerCAmelCase = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
for filename in ... | 309 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase : Dict = 0
__UpperCamelCase : Optional[Any] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's ar... | 309 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
__UpperCamelCase : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
''... | 309 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list ):
if any(not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(_UpperCAmelCase ) ):
for i, (rod_upper... | 309 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Any = {
'''configuration_layoutlmv2''': ['''LAYOUTLMV2_PRETR... | 309 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class a ( a__ , ... | 309 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
class a ( a__ ):
def __init__( self , *_snake_case , ... | 309 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int ):
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise ValueError('check_bouncy() accepts only integer arguments' )
lowerCAmelCase = str(_UpperCAmelCase )
lowerCAmelCase = ''.join(sorted... | 309 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCamelCase : str = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wo... | 309 | 1 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.uti... | 309 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class a :
def __init__( self ):
"""simple docstring"""
lowerCAmelCase = ''
lowerCAmelCase = ''
lowerCAmelCase = []
l... | 309 | 1 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech... | 309 |
"""simple docstring"""
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'split_dict' , [
SplitDict(),
SplitDict({'train': SplitInfo(name='train' , num_bytes=1337 , num_examples... | 309 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : Dict = {
'''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCH... | 309 |
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when... | 309 | 1 |
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