code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from __future__ import annotations
from random import random
class __a :
def __init__( self : str , UpperCAmelCase : int = None ):
lowerCAmelCase_ : Any = value
lowerCAmelCase_ : List[str] = random()
low... | 600 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 0 |
import string
import numpy
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , UpperCamelCase__ ):
return b if a == 0 else greatest_common_divisor(b % a , lowercase_ )
class _lowerCamelCase :
"""simple docstring"""
SCREAMING_SNAKE_CASE_ = string.ascii_uppercase... | 285 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 0 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
SCREAMING_SNAKE_CASE__:str = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Optional[Any] = """\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Fa... | 528 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 0 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class lowerCamelCase_ ( unittest.TestCase ):
_lowercase : Dict = JukeboxTokenizer
_lowercase : Any = {
'''artist''': '''Zac Brown Band''',
'''genres... | 17 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorTy... | 114 | 0 |
import operator
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ = False , snake_case__ = None ) -> Optional[int]:
lowerCAmelCase = operator.lt if reverse else operator.gt
lowerCAmelCase = solution or []
if not arr:
re... | 312 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 0 |
"""simple docstring"""
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 156 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 0 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module
import numpy as np
from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available,... | 213 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 0 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowercase_ ():
snake_case__ : int = HfArgumentParser(lowercase_ )
snake_case__ : int = parser.parse_args_into_dataclasses()[0]
snake_case__ : Tu... | 478 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 114 | 0 |
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 GPTaTokenizer
if TY... | 542 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: Optional[Any] ) -> Any:
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("partitions can... | 300 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 0 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_env... | 351 |
import unittest
import numpy as np
import requests
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_available... | 114 | 0 |
def __UpperCamelCase ( lowercase__ : List[str] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ : List[Any] = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def __UpperCamelCase ( lowercase__ ... | 600 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase ={"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP... | 285 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowerCamelCase = transfo... | 114 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__:Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__:Optional[Any] = ... | 528 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 0 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from tra... | 17 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 0 |
from __future__ import annotations
from collections import namedtuple
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__ ) -> Any:
lowerCAmelCase = namedtuple('''result''' , '''name value''' )
if (voltage, current, power).count(0 ) !... | 312 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import Counter
from random import random
class __lowerCamelCase :
def __init__(self ):
'''simple docstring'''
_lowerCAmelCase = {}
def A__ (self , lowerCa... | 156 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import... | 114 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
_a : str = {
'edbeeching/decision-transformer-gym-hopper-medium': (
'https://huggingface.co/edbeeching/decision-trans... | 213 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 0 |
from math import sqrt
def lowercase_ (A : Union[str, Any] = 1_0_0_0_0_0_0 ):
snake_case__ : int = 0
snake_case__ : int = 0
snake_case__ : int
while num_cuboids <= limit:
max_cuboid_size += 1
for su... | 478 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeature... | 542 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 0 |
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... | 300 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 0 |
import torch
from diffusers import StableDiffusionPipeline
UpperCAmelCase__ = '''path-to-your-trained-model'''
UpperCAmelCase__ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')
UpperCAmelCase__ = '''A photo of sks dog in a bu... | 351 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'vocab_file': 'vocab.json',
'tokenizer_config_file': 'tokenizer_... | 600 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 0 |
lowerCamelCase ="\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
lowerCamelCase =[{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase ={
"{processor_class}": "Fake... | 285 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 0 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils impor... | 528 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplin... | 17 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorTy... | 114 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...... | 312 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class __lowerCamelCase ( lowerCamelCase__ ):
def __init__(self , *... | 156 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 213 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 0 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
Data... | 478 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 114 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def snake_case__ ( lowerCamelCase_ ):
if "cls_token" in name:
A : str = name.replace('''cls... | 542 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokeniz... | 300 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 0 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_commo... | 351 |
import unittest
import numpy as np
import requests
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_available... | 114 | 0 |
import doctest
from collections import deque
import numpy as np
class __a :
def __init__( self : Any ):
lowerCAmelCase_ : Any = [2, 1, 2, -1]
lowerCAmelCase_ : Optional[int] = [1, 2, 3, 4]
def A ( self : Tupl... | 600 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 0 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase =[int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def SCREAMING_SNAKE_CASE_ ( ):
UpperCamelCase__ : List[str] = os.path.dirname(os.path.realpath(lowercase_ ) )
UpperCamelCase__ : Option... | 285 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowerCamelCase = transfo... | 114 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__:Dict = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenization... | 528 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( a__ : Union[str, Any] ,a__ : Optional[Any] ,a__ : List[Any] ,) -> int:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" ... | 17 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 0 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowercase__ : Tuple = '''\\n\n'''
lowercase__ : Any = '''\nPerplexity (PPL) i... | 312 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# f... | 156 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import... | 114 | 0 |
"""simple docstring"""
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 : Dict = logging.get_logger(__name__)
_... | 213 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 0 |
def lowercase_ (A : Optional[Any] ):
snake_case__ : str = [0] * len(lowercase_ )
snake_case__ : Optional[int] = []
snake_case__ : Tuple = [1] * len(lowercase_ )
for values in graph.values():
for i in... | 478 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowercase : Any = {
"facebook/mask2former-swin-small-coco-instance": (
"https://huggingface.co/facebook/mask2form... | 542 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'kssteven/ibert-roberta-base': 'ht... | 300 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/... | 351 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 0 |
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def __UpperCamelCase ( lowercase__ : Tuple , lowercase__ : Any , lowercase__ : Tuple=1024 , ... | 600 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 0 |
from __future__ import annotations
lowerCamelCase =tuple[int, int, int]
lowerCamelCase =tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
lowerCamelCase ="ABCDEFGHIJKLMNOPQRSTUVWXYZ"
# -------------------------- default selection --------------------------
# rotors --... | 285 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def _lowerCamelCase( a ):
__a = SwinConfig()
... | 528 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import... | 17 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorTy... | 114 | 0 |
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ ) -> Dict:
lowerCAmelCase = int(lowercase_ )
# Initialize Result
lowerCAmelCase = []
# Traverse through all denomination
for denomination in reversed(lowercase_ ):
... | 312 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 0 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import Paddi... | 156 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_a : List[str] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that... | 213 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_ten... | 478 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 114 | 0 |
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
A : List[Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
A : Any = n - k
# Calculate C(n,k)
for i in range(lo... | 542 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 0 |
import os
def __SCREAMING_SNAKE_CASE ( ) -> Any:
_UpperCAmelCase : Union[str, Any] = os.path.dirname(os.path.realpath(lowercase_ ) )
_UpperCAmelCase : Tuple = os.path.join(lowercase_ , "triangle.txt" )
with open(lowercase_ ) as f:
_UpperCA... | 300 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 0 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
UpperCAmelCase__ = get_logger(__name__)
UpperCAmelCase__ = r'''\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_siz... | 351 |
import unittest
import numpy as np
import requests
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_available... | 114 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__UpperCAmelCase = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__UpperCAmelCase = typing.Union[np.floataa, int, float] # noqa: UP007
def __UpperCamelCase ... | 600 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 0 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require... | 285 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowerCamelCase = transfo... | 114 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_fl... | 528 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class lowerCamelCase_ ( lowerCamelCase__ ):
_lowercase : int = '''MCTCTFeatureExtractor'''
_lowercase : Any = '''AutoTokenizer'''
def __init__( self : ... | 17 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hug... | 312 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 0 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from .... | 156 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import... | 114 | 0 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
_a : Any = logging.get_logger(__name__)
class __A ... | 213 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 0 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType... | 478 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 0 |
from __future__ import annotations
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ = None , lowerCamelCase_ = None ):
if start is None:
A : Union[str, Any] = 0
if end is None:
A : Union[str, Any]... | 542 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 0 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Tuple , lowerCAmelCase: Union[str, Any] , **lowerCAmelCase: Any ) -> Optional[int]:
_UpperCAmelCase : Optional[Any] = AutoConfig.from_pretraine... | 300 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAme... | 351 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 0 |
__UpperCAmelCase = [
(10_00, 'M'),
(9_00, 'CM'),
(5_00, 'D'),
(4_00, 'CD'),
(1_00, 'C'),
(90, 'XC'),
(50, 'L'),
(40, 'XL'),
(10, 'X'),
(9, 'IX'),
(5, 'V'),
(4, 'IV'),
(1, 'I'),
]
def __UpperCamelCase ( lowercase__ : List[Any] )... | 600 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 0 |
import math
from numpy import inf
from scipy.integrate import quad
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
if num <= 0:
raise ValueError('''math domain error''' )
return quad(lowercase_ , 0 , lowercase_ , args=(lowercase_) )[0]
def SCREAMING_SNAKE_CASE_ ... | 285 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
fr... | 528 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 0 |
def __SCREAMING_SNAKE_CASE ( a__ : Tuple ) -> List[str]:
if not isinstance(lowercase_ ,lowercase_ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(lowercase_ ) == 0:
raise ValueError("""Input list must be a non empty list""... | 17 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorTy... | 114 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def SCREAMING_SNAKE_CASE_ ( snake_case__ ) -> List[Any]:
def decorator(snake_case__ ):
lowerCAmelCase = getattr(lowercase_ , '''handle_key''' , [] )
handle += [key]
... | 312 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 0 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
... | 156 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 0 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __A ( lowerCamelCase_... | 213 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ :Tuple = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependenc... | 478 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 114 | 0 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distribut... | 542 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 0 |
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_mode... | 300 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 0 |
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class snake_case_ ( lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
... | 351 |
import unittest
import numpy as np
import requests
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_available... | 114 | 0 |
import cva
import numpy as np
class __a :
def __init__( self : List[str] , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any ):
if k in (0.04, 0.06):
lowerCAmelCase_ : Optional[Any] = k
lo... | 600 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 0 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCamelCase =WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ ):
UpperC... | 285 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowerCamelCase = transfo... | 114 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import gcd
def _lowerCamelCase( a , a = 2 , a = 1 , a = 3 , ):
# 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 ... | 528 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase = {
'configuration_clipseg': [
'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
'CLIPSegConfig',
'CLIPSegTextConfig',
'CLIPSegVisionCon... | 114 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoo... | 17 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import To... | 114 | 0 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
lowercase__ : List[Any] = logging.get_logger(__name__)
class lowercase_ ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( se... | 312 |
def __UpperCAmelCase( lowercase_ , lowercase_ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
_lowerCamelCase : int = (boundary[1] - boundary[0]) / steps
_lowerCamelCase : Any = boundary[0]
_lowerCamelCase : Tuple ... | 114 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : Any ) -> int:
"""simple docstring"""
if edge <= 0 or not isinstance(lowercase_ , lowercase_ ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2))... | 156 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import... | 114 | 0 |
"""simple docstring"""
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __A ( tf.keras.optimizers.schedules.LearningRateSchedule ... | 213 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __UpperCAmelCase( lowercase_ ):
# vision encoder
if "img_encoder.pos_embed" in name:
_lowerCamelCase : Tuple = name.replace(... | 114 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
a_ :int = logging.get_logger(__name__)
a_ :Union[str, Any] = {"vocab_file": ... | 478 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCame... | 114 | 0 |
from __future__ import annotations
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
... | 542 |
import os
def __UpperCAmelCase( ):
with open(os.path.dirname(lowercase_ ) + '''/p022_names.txt''' ) as file:
_lowerCamelCase : Optional[int] = str(file.readlines()[0] )
_lowerCamelCase : List[Any] = names.replace('''"''' , '''''' ).split(... | 114 | 0 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> int:
_UpperCAmelCase : str = len(lowercase_ )
for _ in range(lowercase_ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_UpperCAmelCase : Dict ... | 300 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Config... | 114 | 0 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser(
description=(
'''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
... | 351 |
import mpmath # for roots of unity
import numpy as np
class __A :
"""simple docstring"""
def __init__( self , a__=None , a__=None):
"""simple docstring"""
_lowerCamelCase : List[str] = list(poly_a or [0])[:]
... | 114 | 0 |
import logging
from transformers import PretrainedConfig
__UpperCAmelCase = logging.getLogger(__name__)
__UpperCAmelCase = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
class __a ( ... | 600 |
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
_lowerCamelCase = '\\n\n'
_lowerCamelCase = '\nPerplexity (PPL) is one of the most common metrics for eval... | 114 | 0 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ( UpperCamelCase__ , ... | 285 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __A :
"""simple docstring"""
def __init__( self , a__):
"""simple docstring"""
_lowerCamelCase : str = list_of_points
# ... | 114 | 0 |
"""simple docstring"""
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... | 528 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
... | 114 | 0 |
import os
import sys
import unittest
UpperCAmelCase_ : List[str] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_... | 17 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorTy... | 114 | 0 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE_ ( snake_case__ , snake_case__ , snake_case__=None... | 312 |
import doctest
from collections import deque
import numpy as np
class __A :
"""simple docstring"""
def __init__( self):
"""simple docstring"""
_lowerCamelCase : Any = [2, 1, 2, -1]
_lowerCamelCase : Optional[i... | 114 | 0 |
"""simple docstring"""
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Tuple = {
'''huggingface/autoformer-tourism-monthl... | 156 |
from __future__ import annotations
def __UpperCAmelCase( lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = False , ):
_lowerCamelCase : Tuple = cipher_alphabet or [chr(lowercase_ ) for i in range(97 , 1_23 )]
# If the argument is No... | 114 | 0 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 213 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at... | 114 | 0 |
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
a_ :Dict = logging.getLogger(__name__)
@da... | 478 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
... | 114 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase : Union[str, Any] = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is... | 542 |
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __A ( lowerCamelCase__ )... | 114 | 0 |
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE_ = TypeVar('KT')
SCREAMING_SNAKE_CASE_ = TypeVar('VT')
class a ( Generic[KT, VT] ):
def __init__( self , A_ = "root" , A_ = None ... | 300 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case_ ( metaclass=lowerCamelCase__ ):
"""simple docstring"""
snake_case__ = ["""torch""", """transformers""", """onnx"""]
def __init__(self: Optional[int] , *__UpperCAmelCase: ... | 351 |
import unittest
import numpy as np
import requests
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_available... | 114 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vis... | 600 |
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
_lowerCamelCase = argparse.ArgumentParser(
description=(
'Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Le... | 114 | 0 |
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