code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def a(lowercase__ , lowercase__ = False ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
snake_case_ = f"""Expected string as input, found {type(lowercase__ )}"""
raise ValueError(lowercase__ )
if not isinstance(lowercase__ , lowercase__ ):
snake_case_ = f"""... | 46 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# Initialise PyTorch model
snake_case_ ... | 46 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 applicab... | 46 | 1 |
import re
def a(lowercase__ ):
'''simple docstring'''
if len(re.findall('[ATCG]' , lowercase__ ) ) != len(lowercase__ ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' , 'TAGC' ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 1 |
from __future__ import annotations
def a(lowercase__ ):
'''simple docstring'''
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(lowercase__ ) ):
matrix[i][0] += matrix[i - 1][0]
# upd... | 46 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 1 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 46 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
A = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
A = [ord(letter) for letter in string.ascii_lowercase]
A = {ord(char) for... | 46 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 1 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 |
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_determinism()
class SCREA... | 46 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
embed.append(
(... | 46 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin impo... | 46 | 1 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 1 |
import os
import sys
import unittest
A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # ... | 46 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_logger(__name__)
A = {'vocab_file': 's... | 46 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_tokenization_common import To... | 46 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 1 |
import math
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = [True] * n
snake_case_ = False
snake_case_ = False
snake_case_ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
snake_case_ = i * 2
while index < n:
snake_case_ = False
snake... | 46 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 |
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, slow
from ..test_modeling_t... | 46 | 1 |
import os
def a():
'''simple docstring'''
with open(os.path.dirname(lowercase__ ) + '/p022_names.txt' ) as file:
snake_case_ = str(file.readlines()[0] )
snake_case_ = names.replace('"' , '' ).split(',' )
names.sort()
snake_case_ = 0
snake_case_ = 0
for i, name in e... | 46 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 1 |
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __snake_ca... | 46 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
A = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\... | 46 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTest... | 46 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
... | 46 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 1 |
# 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-2.0
#
# Unless required by ap... | 46 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import ... | 46 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.jso... | 46 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOn... | 46 | 1 |
from typing import Union
import fire
import torch
from tqdm import tqdm
def a(lowercase__ , lowercase__ = "cpu" , lowercase__ = None ):
'''simple docstring'''
snake_case_ = torch.load(lowercase__ , map_location=lowercase__ )
for k, v in tqdm(state_dict.items() ):
if not isinstance(lowerca... | 46 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 | 1 |
from __future__ import annotations
from math import gcd
def a(lowercase__ , lowercase__ = 2 , lowercase__ = 1 , lowercase__ = 3 , ):
'''simple docstring'''
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('The input value cannot be less than 2' )
... | 46 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 46 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 applicab... | 46 | 1 |
def a(lowercase__ = 100 ):
'''simple docstring'''
snake_case_ = set()
snake_case_ = 0
snake_case_ = n + 1 # maximum limit
for a in range(2 , lowercase__ ):
for b in range(2 , lowercase__ ):
snake_case_ = a**b # calculates the current power
collect_powers.add(lowe... | 46 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
... | 46 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 1 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def a(lowercase__ , lowercase__ , lowercase__ , lowercase__... | 46 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB 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
#
# Unles... | 46 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
A = TypeVar('T')
class SCREAMING_SNAKE_CASE ( Generic[T] ):
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = data
snake... | 46 |
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_determinism()
class SCREA... | 46 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A = {
'configuration_canine': ['CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CanineConfig'],
'tokenization_canine': ['CanineTokenizer'],
}
try:
if not i... | 46 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin impo... | 46 | 1 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 1 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class S... | 46 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 1 |
A = 'Alexander Joslin'
import operator as op
from .stack import Stack
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
snake_case_ = Stack()
snake_case_ = Stack()
for i in equation:
if i.isdigit()... | 46 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 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 a(lowercase__ ):
'''simple docstring'''
... | 46 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 1 |
import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.utils import write_basic_config
from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.u... | 46 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 1 |
from math import factorial
def a(lowercase__ = 100 ):
'''simple docstring'''
return sum(map(lowercase__ , str(factorial(lowercase__ ) ) ) )
if __name__ == "__main__":
print(solution(int(input('Enter the Number: ').strip())))
| 46 |
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, slow
from ..test_modeling_t... | 46 | 1 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers i... | 46 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 1 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def a(lowercase__ , lowercase__=() , lowercase__=None , lowercase__="no" , lowercase__="29500" ):
'''simple docstring'''
s... | 46 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A = _LazyModule(__name__, globals()... | 46 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTest... | 46 | 1 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
snake_case_ = ''
snake_case_ = ''
snake_case_ = []
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simp... | 46 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 1 |
def a(lowercase__ ):
'''simple docstring'''
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(lowercase__ , lowercase__ ):
raise TypeError('\'str\' object cannot be interpreted as an integer' )
if num == 0:
return "0b... | 46 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 1 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 applicab... | 46 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 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 ... | 46 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOn... | 46 | 1 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_conf... | 46 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
def __init__( self , *__UpperCamelCase , **__UpperCame... | 46 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 1 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 46 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 applicab... | 46 | 1 |
def a(lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('check_bouncy() accepts only integer arguments' )
snake_case_ = str(lowercase__ )
snake_case_ = ''.join(sorted(lowercase__ ) )
return sorted_str_n != str_n and sorted_str_n[::-1] != ... | 46 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 1 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
A = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0... | 46 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = ["""image_processor""", """tokenizer"""]
__A = """AutoImageProcessor"""
__A = """AutoTok... | 46 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 1 |
from jiwer import compute_measures
import datasets
A = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connecte... | 46 |
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_determinism()
class SCREA... | 46 | 1 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
def get_matched_characters(lowercase__ , lowercase__ ) -> str:
snake_case_ = []
snake_case_ = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
snake_case_ = int(max(0 , i - limit ) )
snake_cas... | 46 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin impo... | 46 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 1 |
import os
from distutils.util import strtobool
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
for e in env_keys:
snake_case_ = int(os.environ.get(lowercase__ , -1 ) )
if val >= 0:
return val
return default
def a(lowercase__ , lowercase__=False ):
'''simple docstr... | 46 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelT... | 46 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A = {
'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}
try:
i... | 46 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A = logging.get_logger(__name__)
A = {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokeni... | 46 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotA... | 46 |
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, slow
from ..test_modeling_t... | 46 | 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 import BertTokenizer
A = logging.get_logger(__name__)
A = {'... | 46 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 1 |
from __future__ import annotations
from collections.abc import Callable
A = list[list[float | int]]
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = len(SCREAMING_SNAKE_CASE_ )
snake_case_ = [[0 for _ in range(size + 1 )] for _ in... | 700 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 0 |
import math
import sys
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = ''
try:
with open(__A , 'rb' ) as binary_file:
snake_case_ = binary_file.read()
for dat in data:
snake_case_ = f"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print... | 701 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTest... | 46 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class SCREAMING_SNAKE_CASE ( snake_case__ ... | 702 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 0 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 703 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
A = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A = ['''GPTS... | 704 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 0 |
from typing import List
import numpy as np
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {key: len(_A ) for key, value in gen_kwargs.items() if isinstance(_A , _A )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'Sharding is ambiguous for this da... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOn... | 46 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'go... | 706 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self , *__UpperCamelCase , **__UpperC... | 707 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
raise OptionalDependencyN... | 708 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 applicab... | 46 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_available():
raise Opti... | 709 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 0 |
import math
def a(lowercase__ ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWit... | 711 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 0 |
A = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = {"""I""": 1, """... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 0 |
'''simple docstring'''
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 713 |
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_determinism()
class SCREA... | 46 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
... | 714 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin impo... | 46 | 0 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, Parti... | 715 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 0 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(_lowerCamelCase ) == ... | 716 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 0 |
'''simple docstring'''
from __future__ import annotations
def a(lowercase__ , lowercase__ = None , lowercase__ = None ):
'''simple docstring'''
if start is None:
snake_case_ = 0
if end is None:
snake_case_ = len(_lowerCamelCase ) - 1
if start >= end:
return
snake_case_ ... | 717 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 0 |
'''simple docstring'''
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
A = [
"word_embeddings_laye... | 718 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 0 |
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
impor... | 719 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docst... | 46 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ... | 720 |
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, slow
from ..test_modeling_t... | 46 | 0 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
A = 50_0000
A = os.path.split(__file__)
A = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_du... | 721 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_a... | 46 | 0 |
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = len(snake_case__ )
snake_case_ = [[0] * n for i in range(snake_case__ )]
for i in range(snake_case__ ):
snake_case_ = y_points[i]
for i in range(2 , snake_case__ ):
for j in r... | 700 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a(lowercase__ , low... | 46 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
A ... | 701 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTest... | 46 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def a(lowercase__ ):
'''simple docstring'''
if num <= 0:
raise ValueError('math domain error' )
return quad(lowercase__ , 0 , lowercase__ , args=(lowercase__) )[0]
def a(lowercase__ , lowercase__ ):
'''simple... | 702 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
A = argparse.ArgumentParser('Stable Diffusion script with intel optimization', add_help=False)
parser.add_argument('--dpm', action='store_true', help='En... | 46 | 0 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=UpperCamelCase_ ):
"""simple docstring"""
__A = ["""torch""", """torchsde"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
... | 703 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolv... | 46 | 0 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from .... | 704 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 0 |
import operator as op
A = 'scaler.pt'
A = 'pytorch_model'
A = 'random_states'
A = 'optimizer'
A = 'scheduler'
A = 'pytorch_model.bin'
A = 'pytorch_model.bin.index.json'
A = 'model.safetensors'
A ... | 705 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOn... | 46 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A = {
'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_... | 706 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ):
raise ValueError('iterations must be defined as integers' )
if not isinstance(lowercase__ , lowercase__ ) or not number >= 1:
raise ValueError(
'starting number must be\n ... | 46 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class SCREAMING_SNAKE_CASE ( __snake_case ):
"""simple docstring"""
__A = """M-CLIP"""
def __init__( self , __UpperCamelCase=10_24 , __UpperCamelCase=7_68 , **__UpperCamelCase ... | 707 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = 1.5
snake_case_ ... | 46 | 0 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_commo... | 708 |
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# 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 applicab... | 46 | 0 |
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A = '''.'''
# Internal TensorFlow ops that can be safely igno... | 709 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 46 | 0 |
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if gpta_config_file == "":
... | 710 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
A = {
'configuration_audio_spectrogram_transformer': [
'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ASTConfig',
]
}
tr... | 46 | 0 |
from __future__ import annotations
from statistics import mean
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = [0] * no_of_processes
snake_case_ = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(_snake_case ):
... | 711 |
import operator as op
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = []
snake_case_ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
snake_case_ = {
'^': op.pow,
'*': op.mul,
'/': div,
'+': op.add,
'-': o... | 46 | 0 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa
import itertools #... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A = logging.get_logger(__name__)
A = {
'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/... | 46 | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def a():
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ... | 713 |
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_determinism()
class SCREA... | 46 | 0 |
A = {}
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we ... | 714 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin impo... | 46 | 0 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
snake_case_ = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leading "0b"
snake_case_ = str(bin(SCREAMING_SNAKE_CASE_ ) )[2:] # remove the leadi... | 715 |
from collections import defaultdict
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = first_str.lower().strip()
snake_case_ = second_str.lower().strip()
# Remove whitespace
snake_case_ = first_str.replace(' ' , '' )
snake_case_ = second_str.replac... | 46 | 0 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm ... | 716 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
... | 46 | 0 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTo... | 717 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils i... | 46 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCR... | 718 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_... | 46 | 0 |
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