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 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ : Dict = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_available():
... | 51 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 39 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __A ):
def __init__( self , *A_ , **A_ ):
warn... | 643 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if any(not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(SCREAMING_SNAKE_CASE_... | 39 | 0 |
# 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... | 699 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 39 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class _A ( ... | 36 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
snak... | 39 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : List[Any] = "The quick brown fox jumps over the lazy dog" , ) -> str:
lowercase : Dict =set()
# Replace all the whitespace in our sentence
lowercase : Any =input_str.replace(''' ''' , '... | 92 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase_ = {
'''... | 39 | 0 |
'''simple docstring'''
def _a ( _lowerCamelCase , _lowerCamelCase ) -> int:
"""simple docstring"""
__snake_case : Union[str, Any] = [[] for _ in range(SCREAMING_SNAKE_CASE__ )]
__snake_case : Any = key... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise Op... | 39 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCAmelCase_ : List[Any] ,lowerCAmelCase_ : Any ) -> Any:
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE__ ) < k or k < 0:
raise ValueError('Invalid Input' )
... | 220 |
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 ..t... | 39 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_clap""": [
"""CLAP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ClapAudioConfig""",
"""ClapConfig""",
""... | 437 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ = '''naver-clova-ix/donut-base'''
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Any:
... | 39 | 0 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowercase__ ( lowercase_ ) -> Optional[... | 624 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not nums:
raise ValueError('''List is empty''' )
return sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
d... | 39 | 0 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _a (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 1 / sqrt(2 ) ):
"""simple docstring"""
_UpperCamelCase =tau * frequency / samplerate
... | 404 |
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 impo... | 39 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests... | 431 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/hug... | 39 | 0 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.da... | 51 |
import cmath
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = math.radians(SCREAMING_SNAKE_CASE__ )
snake_case_ = math.radians(SCREAMING_SNAKE_C... | 39 | 0 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE__ : str = logging.getLogger(__name__)
@dataclass
class _SCREAMING_SNAKE_CASE ( __A ):... | 643 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 39 | 0 |
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = len(SCREAMING_SNAKE_CASE__ )
while cur > 1:
# Find the maximum number in arr
A = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
A = arr[mi::-1] + a... | 699 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
snake_case_ = (boundary[1] - boundary[0]) / steps
snake_case_ = boundary[0]
snake_case_ = boundary... | 39 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multiple repository ... | 36 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 39 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
UpperCamelCase_ = logging.get_lo... | 92 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config... | 39 | 0 |
'''simple docstring'''
import inspect
import unittest
from math import floor
from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 26 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 39 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 220 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series... | 39 | 0 |
"""simple docstring"""
from __future__ import annotations
a_ = 1.6021e-19 # units = C
def UpperCAmelCase_ ( __a : int , __a : Dict , __a : Optional[Any] , ):
'''simple docstring'''
if (conductivity, electron_conc, mobility).coun... | 437 |
from math import factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
... | 39 | 0 |
"""simple docstring"""
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
lowerCamelCase__ = "sshleifer/bart-ti... | 624 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 39 | 0 |
'''simple docstring'''
from timeit import timeit
__lowerCamelCase : Tuple = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test data... | 404 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Dic... | 39 | 0 |
from __future__ import annotations
import math
def __UpperCamelCase ( _A ):
if num <= 0:
lowerCAmelCase_ = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(SCREAMING_SNAKE_CASE__ )
lowerCAmelCase_ = [True] * (num + 1)... | 431 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-43... | 39 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 51 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 39 | 0 |
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
SCREAMING_SNAKE_CASE__ : str = '__DUMMY_TRANSFORMERS_USER__'
SCREAMING_SNAKE_CASE__ : Optional[int] = 'Dummy User'
SCREAMI... | 643 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if any(not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(SCREAMING_SNAKE_CASE_... | 39 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase (lowerCAmelCase : str = 3 ) -> List[Any]:
if isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ):
raise TypeError('n... | 699 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 39 | 0 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class _A ( __A ):
'''simple docstring'''
__lowerCamelCase : Optional[Any] =... | 36 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
snak... | 39 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""sh... | 92 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase_ = {
'''... | 39 | 0 |
'''simple docstring'''
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
__UpperCamelCase = logging.getLogger(__name__)
def _a ( ) -> Dict:
"""simple docstring... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise Op... | 39 | 0 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
__SCREAMING_SNAKE_CASE = {
'text_branch': 'text_model',
'audio_branch': 'audio_model.audio_encoder',
'attn': 'attention.self',
'self.proj': 'out... | 220 |
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 ..t... | 39 | 0 |
"""simple docstring"""
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 Backb... | 437 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ = '''naver-clova-ix/donut-base'''
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Any:
... | 39 | 0 |
"""simple docstring"""
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
fro... | 624 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not nums:
raise ValueError('''List is empty''' )
return sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
d... | 39 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCAmelCase :
"""simple docstring"""
lowerCAmelCase_ = 42
lowerCAme... | 404 |
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 impo... | 39 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_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_copies # noqa: E402
# This is the reference code that wi... | 431 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/hug... | 39 | 0 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for te... | 51 |
import cmath
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = math.radians(SCREAMING_SNAKE_CASE__ )
snake_case_ = math.radians(SCREAMING_SNAKE_C... | 39 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logge... | 643 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 39 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def __UpperCamelCase (lowerCAmelCase : Tuple, lowerCAmelCase : Dict, lowerCAmelCase : List[str], lowerCAmelCase : Tuple, lowerCAmelCase : Optional[Any], lowerCAmelCase : Tuple... | 699 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
snake_case_ = (boundary[1] - boundary[0]) / steps
snake_case_ = boundary[0]
snake_case_ = boundary... | 39 | 0 |
__lowercase : List[Any] = range(2, 20 + 1)
__lowercase : Dict = [10**k for k in range(ks[-1] + 1)]
__lowercase : str = {}
def lowercase ( __A : str , __A : Any , __A : Union[str, Any] , __A : Optional[int] ) -> List[str]:
... | 36 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 39 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"""huggingface/informer-tourism-monthly""": (
"""https:... | 92 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config... | 39 | 0 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
fro... | 26 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 39 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root o... | 220 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series... | 39 | 0 |
"""simple docstring"""
def UpperCAmelCase_ ( __a : str , __a : str ):
'''simple docstring'''
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_lowerCamelCase : List[str] = str(bin(SCREAMING_SNAKE_... | 437 |
from math import factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
... | 39 | 0 |
"""simple docstring"""
# Copyright 2021 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-... | 624 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 39 | 0 |
'''simple docstring'''
from __future__ import annotations
def _a (__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =[]
_UpperCamelCase =[]
_UpperCamelCase =0
_UpperCamelCase =sum(SCREAMING_SNAKE_... | 404 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Dic... | 39 | 0 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logg... | 431 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-43... | 39 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a__ : Optional[Any] = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Pix2S... | 51 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 39 | 0 |
from math import factorial
def a__ ( snake_case__ : Optional[int] , snake_case__ : Optional[Any] ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
raise ... | 643 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if any(not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(SCREAMING_SNAKE_CASE_... | 39 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_commo... | 699 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 39 | 0 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def lowercase ( __A : Dict ) -> Dict:
'''simple docstring'''
def wrapper(*__A : Optional[Any] , **__A : Any ):
... | 36 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
snak... | 39 | 0 |
'''simple docstring'''
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
UpperCamelCase_ = {
# 1536-bit
5: {
"""pr... | 92 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase_ = {
'''... | 39 | 0 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def _a ( _lowerCamelCase ) -> List[Any]:
"""simple docstring"""
__snake_case , __snake_case , __snake_case : Optional[int] ... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise Op... | 39 | 0 |
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 import BatchFeature
from ...ut... | 220 |
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 ..t... | 39 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVa... | 437 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ = '''naver-clova-ix/donut-base'''
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Any:
... | 39 | 0 |
"""simple docstring"""
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCamelCase__ = "\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n ... | 624 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not nums:
raise ValueError('''List is empty''' )
return sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
d... | 39 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
log... | 404 |
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 impo... | 39 | 0 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class A ( __A , unittest.TestCase ):
__snake_case = TransfoXLTokenizer
__snake_case ... | 431 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/hug... | 39 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.... | 51 |
import cmath
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = math.radians(SCREAMING_SNAKE_CASE__ )
snake_case_ = math.radians(SCREAMING_SNAKE_C... | 39 | 0 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE__ : List[str] = logging.getLogge... | 643 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 39 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import M... | 699 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
snake_case_ = (boundary[1] - boundary[0]) / steps
snake_case_ = boundary[0]
snake_case_ = boundary... | 39 | 0 |
from itertools import product
def lowercase ( __A : List[Any] , __A : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case : Any = sides_number
snake_case : Dict = max_face_number * dice_number
snake_case : Optional[A... | 36 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 39 | 0 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCamelCase_ = version.pa... | 92 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config... | 39 | 0 |
'''simple docstring'''
import copy
import unittest
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration... | 26 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 39 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeature... | 220 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series... | 39 | 0 |
"""simple docstring"""
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCAmelCase_ ( __a : Optional[Any] , ... | 437 |
from math import factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
... | 39 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ = {"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAv... | 624 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 39 | 0 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slo... | 404 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Dic... | 39 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A = {
'''ut/deta''': '''https://huggingface.co/ut/deta/resolve/main/config.json''',
}
class A ( __A ):
__snake... | 431 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-43... | 39 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : Dict = logging.get_logger(__name__)
a__ : Union[str, Any] = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/re... | 51 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 39 | 0 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
SCREAMING_SNAKE_CASE__ : Union[str, Any] = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __A ):
def __init__( self , *A_ , **A_ ):
... | 643 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if any(not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(SCREAMING_SNAKE_CASE_... | 39 | 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 TYPE_CHECKING:
... | 699 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 39 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _A ( __A , unittest.TestCase... | 36 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
snak... | 39 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name... | 92 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase_ = {
'''... | 39 | 0 |
'''simple docstring'''
from abc import ABC, abstractmethod
from typing import List, Optional
class _A ( __A ):
def __init__( self : int ) -> Optional[Any]:
"""simple docstring"""
self.test()
def lowercase__ ( self : int ... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise Op... | 39 | 0 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=__A ):
'''simple docstring'''
_lowercase = ["flax", "transformers"]
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ):
requires_backen... | 220 |
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 ..t... | 39 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
a_ = logging.get_logger(__name__)
a_ = {
"""Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
... | 437 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ = '''naver-clova-ix/donut-base'''
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Any:
... | 39 | 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 OptionalDependencyNotAvai... | 624 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not nums:
raise ValueError('''List is empty''' )
return sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
d... | 39 | 0 |
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...tes... | 404 |
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 impo... | 39 | 0 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArgumen... | 431 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/hug... | 39 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelera... | 51 |
import cmath
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = math.radians(SCREAMING_SNAKE_CASE__ )
snake_case_ = math.radians(SCREAMING_SNAKE_C... | 39 | 0 |
import numpy as np
def a__ ( snake_case__ : int ):
return 1 / (1 + np.exp(-vector ))
def a__ ( snake_case__ : Any ):
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 643 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common impor... | 39 | 0 |
def __UpperCamelCase (lowerCAmelCase : int ) -> int:
if any(not isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('Sequence must be list of non-negative integers' )
for _ in range(len(SCREAMING_SNAKE_CASE__ ) ):... | 699 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# "extended trapezoidal rule"
# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
snake_case_ = (boundary[1] - boundary[0]) / steps
snake_case_ = boundary[0]
snake_case_ = boundary... | 39 | 0 |
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
__lowercase ... | 36 |
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
fro... | 39 | 0 |
'''simple docstring'''
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, torc... | 92 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config... | 39 | 0 |
'''simple docstring'''
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_tenso... | 26 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transforme... | 39 | 0 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 220 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series... | 39 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_tor... | 437 |
from math import factorial
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
# If either of the conditions are true, the function is being asked
# to calculate a factorial of a negative number, which is not possible
if n < k or k < 0:
... | 39 | 0 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_tr... | 624 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, Tr... | 39 | 0 |
'''simple docstring'''
import re
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =re.compile(
r'''^(?:0|94|\+94|0{2}94)''' r'''7(0|1|2|4|5|6|7|8)''' r'''(-| |)''' r'''\d{7}$''' )
return bool(re.search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CA... | 404 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Dic... | 39 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_A = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', '''weight'''),
... | 431 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-43... | 39 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_torch_available,
)
a__ : Dict = {
'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARC... | 51 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging ... | 39 | 0 |
def a__ ( snake_case__ : Union[str, Any] , snake_case__ : Optional[Any] ):
_UpperCAmelCase : str = [0 for i in range(r + 1 )]
# nc0 = 1
_UpperCAmelCase : Optional[int] = 1
for i in range(1 , n + 1 ):
# to compute curre... | 643 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if any(not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(SCREAMING_SNAKE_CASE_... | 39 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCamelCase (lowerCAmelCase : Any, lowerCAmelCase : int ) -> Union[str, Any]:
A = f'''{sampling_rate}'''
A = '1'
A = 'f32le'
... | 699 |
import re
from filelock import FileLock
try:
import nltk
lowerCAmelCase_ = True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase_ = False
if NLTK_AVAILABLE:
with FileLock('''.lock''') as lock:
nltk.download('''punkt''', quiet=True)
... | 39 | 0 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
__lowercase : List[Any] = 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_copies # noqa: E402
# This is th... | 36 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = [0 for i in range(r + 1 )]
# nc0 = 1
snake_case_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
snak... | 39 | 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
if TYPE_CHECKING:
from trans... | 92 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase_ = {
'''... | 39 | 0 |
'''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def _a ( _lowerCamelCase = "laptop" ) -> Optional[int]:
"""simple docstring"""
__snake_case : Tup... | 26 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase_ = {'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}
try:
if not is_torch_available():
raise Op... | 39 | 0 |
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 lowerCAmelCase_ ( __A ):
'''simple docs... | 220 |
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 ..t... | 39 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_... | 437 |
import unittest
from transformers import DonutProcessor
lowerCAmelCase_ = '''naver-clova-ix/donut-base'''
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case__( self : Union[str, Any] ) ->Any:
... | 39 | 0 |
"""simple docstring"""
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowerCamelCase__ = datasets.logging.get_logger(__name__)
lowerCamelCase__ = "\\n@InProceedings{moosavi2019min... | 624 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if not nums:
raise ValueError('''List is empty''' )
return sum(SCREAMING_SNAKE_CASE__ ) / len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
import doctest
d... | 39 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Tuple = {
'configuration_longformer': [
'LONGFORMER_PRETRAIN... | 404 |
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 impo... | 39 | 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,
)
_A = {'''configuration_mbart''': ['''MBART_PRETRAINED_CONFIG_AR... | 431 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://huggingface.co/hug... | 39 | 0 |
'''simple docstring'''
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 __snake_case ( S... | 51 |
import cmath
import math
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = math.radians(SCREAMING_SNAKE_CASE__ )
snake_case_ = math.radians(SCREAMING_SNAKE_C... | 39 | 0 |
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