code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from math import loga
def lowerCamelCase__ ( snake_case_ : int ) -> int:
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(snake_case_ , snake_case_ ):
raise TypeError('''Input value mus... | 24 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipelin... | 24 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
def _lowerCamelCase(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> List[str]:
_lowerCAmelCase =F'''{file}_{class_name}_{test_name}'''
done_test[... | 341 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( __magic_name__ ):
'''simple docstring'''
lowerCamelCase = ['''image_processor''', '''tokenizer''']
l... | 341 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {
'''configuration_vision_encoder_decoder''': ['''VisionEncoderDecoderC... | 108 |
def snake_case_ ( snake_case , snake_case ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 196 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCamelCase_ = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"PoolFormerOnnxConfig... | 354 |
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def A ( __UpperCAmelCase ) -> Dict[str, torch.Tensor]:
'''simple docstring'''
UpperCAmelCase_ = []
UpperC... | 344 | 0 |
from __future__ import annotations
from typing import Any
class A :
def __init__(self : Union[str, Any] , __UpperCAmelCase : int , __UpperCAmelCase : int , __UpperCAmelCase : float = 0 ) -> List[str]:
"""simple ... | 65 |
"""simple docstring"""
# Copyright 2022 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/LI... | 332 | 0 |
'''simple docstring'''
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
... | 322 |
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase = 4_000_000 ):
_UpperCAmelCase : List[Any] = []
_UpperCAmelCase , _UpperCAmelCase : Dict = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__lowerCAmelCase )
_UpperCAmelCase ... | 322 | 1 |
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class __magic_name__ ( UpperCAmelCase__ ):... | 218 | '''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
__UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __lowerCAme... | 67 | 0 |
'''simple docstring'''
import argparse
import datetime
def UpperCAmelCase ( lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """We... | 8 |
'''simple docstring'''
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__A : Tuple = logging.get_logge... | 8 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Blend... | 19 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from ... | 333 | 0 |
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import Gradie... | 213 |
def UpperCAmelCase__ (UpperCamelCase_ ,UpperCamelCase_ ,UpperCamelCase_ ):
"""simple docstring"""
snake_case = len(UpperCamelCase_ )
snake_case = [[0] * n for i in range(UpperCamelCase_ )]
for i in range(UpperCamelCase_ ):
... | 213 | 1 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase =logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
warnings.warn(
... | 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def A_ ( ) -> tuple[list[int], int]:
a__ : Tuple = [randint(-1000 , 1000 ) for i in range(10 )]
a__ : Dict = randint(-5000 , ... | 225 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine impor... | 225 | 1 |
'''simple docstring'''
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
__SCREAMING_SNAKE_CASE : Optional[int] = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company... | 31 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCH... | 31 | 1 |
"""simple docstring"""
import math
def snake_case ( A__ ,A__ = 0 ,A__ = 0 ):
UpperCAmelCase_ : Union[str, Any] = end or len(A__ )
for i in range(A__ ,A__ ):
UpperCAmelCase_ : Tuple = i
UpperCAmelCase_ : Tuple ... | 253 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 253 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A (unittest.TestCase ):
'''... | 274 | from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils imp... | 348 | 0 |
"""simple docstring"""
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGe... | 357 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf imp... | 263 | 0 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase__ = """src/transformers"""
# Matches is_xxx_available()
lowercase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-l... | 96 |
'''simple docstring'''
def lowercase_ ( lowerCAmelCase__ : str ):
"""simple docstring"""
return credit_card_number.startswith(("""34""", """35""", """37""", """4""", """5""", """6""") )
def lowercase_ ( lowerCAmelCase__ : str ):
"... | 254 | 0 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_con... | 367 |
"""simple docstring"""
def lowerCamelCase (a_ :int) -> None:
lowercase :Tuple = generate_pascal_triangle(a_)
for row_idx in range(a_):
# Print left spaces
for _ in range(num_rows - row_idx - 1):
print(end=''' ''')
... | 172 | 0 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_UpperCamelCase : Union[str, Any] = logging.getLogger()
@unittest.skip("... | 220 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=__snake_case , defaul... | 220 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,... | 357 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
'''configuration_layoutlmv3''': [
'''L... | 125 | 0 |
from typing import Dict
from .base import GenericTensor, Pipeline
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def UpperCamelCase_ ( self : int ,A : Optional[int]=None ,A : Tuple=None ,A : List[str]=None ,**A ... | 15 |
SCREAMING_SNAKE_CASE :Any = 256
# Modulus to hash a string
SCREAMING_SNAKE_CASE :Union[str, Any] = 100_0003
def UpperCAmelCase ( a_ , a_ ) -> bool:
"""simple docstring"""
__A = len(a_ )
__A = len(a_ )
if p_len > t_len:
... | 15 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowercase__ ( _UpperCAmelCase ) -> Dict:
'''simple docstring'''
return DownloadCommand(args.model , args.cache_dir , args.... | 351 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Imag... | 53 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase = 0
__UpperCamelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
... | 69 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''Yi... | 319 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_... | 327 |
# 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 ap... | 327 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
SCREAMING_SNAKE_CASE : List[str] = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig"""... | 102 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to ... | 176 | 0 |
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''') | 228 |
'''simple docstring'''
from __future__ import annotations
import math
from collections.abc import Callable
def _snake_case ( A , A , A , A = 100 , ) -> float:
lowerCAmelCase__ = x_start
lowerCAmelCase__ = fnc(A )
... | 228 | 1 |
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 ):
... | 308 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logg... | 308 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : Tuple ={
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_... | 262 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassif... | 262 | 1 |
from __future__ import annotations
from random import random
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _A = None ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = value
__SCREAMING_SNAKE_CASE ... | 257 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCAmelCase__ : List[Any] =input('''Enter image url: ''').strip()
print(F'''Downloading image from {url} ...''')
lowerCAmelCase__ : int =BeautifulSoup(requests.get(u... | 257 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer
lowerCamelCase_ : List[Any] = logging.get_logger(__name__)
... | 362 | from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 197 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_UpperCamelCase = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
_UpperCamelCase ... | 326 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase__)
class _lowercase ( lowercase__):
"""simple docstring"""
A__ ... | 184 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : List[str] =loggi... | 358 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
c... | 118 | 0 |
def lowerCAmelCase ( lowerCAmelCase_ = 4_000_000 )-> int:
lowerCAmelCase_ : Tuple = [0, 1]
lowerCAmelCase_ : List[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lo... | 262 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
fro... | 262 | 1 |
import os
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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : int = '▁'
Upp... | 368 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCAmelCase_ : str = logging.... | 120 | 0 |
import importlib
import os
import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry
from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem
from .utils import require_lza, require_z... | 82 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effect... | 34 | 0 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Dict , snake_case_ : list[tuple[float, float]] ):
UpperCamelCase_: List[str] = list_of_points
# Degree determines t... | 360 |
def A__ ( lowerCamelCase , lowerCamelCase ) -> list:
UpperCamelCase_: Optional[int] = word.split()
def justify(lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> str:
UpperCamelCase_: Tuple = max_width - width
UpperCamelCase_: ... | 223 | 0 |
from __future__ import annotations
import math
def UpperCAmelCase__ ( _A : int ):
'''simple docstring'''
if num <= 0:
a__ =F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(_A )
a__ =[True] * (num + 1)
a__ =[]
a__ =2
a__ ... | 188 |
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...... | 188 | 1 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
fro... | 114 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_UpperCamelCase ... | 114 | 1 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
fro... | 57 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 1 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class UpperCAmelCase_ ( _UpperCamelCase ):
def __lt__( self : Dict , A : Tuple ):
... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCAmelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 202 | 0 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
__a ... | 312 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=snake_case_ )
class _a ( snake_case_ ):
"""simple docstring"""
_lowerCamel... | 312 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
A : str = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
A : List[str] = [file for file in filepaths if file !... | 146 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A ( UpperCAmelCase__ ):
'''s... | 146 | 1 |
"""simple docstring"""
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_... | 269 |
_a = 65_521
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = 1
lowerCamelCase__ = 0
for plain_chr in plain_text:
lowerCamelCase__ = (a + ord(__snake_case )) % MOD_ADLER
lowerCamelCase__ = ... | 209 | 0 |
'''simple docstring'''
def __magic_name__ ( A = 1_0 , A = 2_2 ) -> int:
snake_case = range(1 , A )
snake_case = range(1 , A )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if _... | 332 |
'''simple docstring'''
from __future__ import annotations
from math import ceil, floor, sqrt
def __magic_name__ ( A = 2_0_0_0_0_0_0 ) -> int:
snake_case = [0]
snake_case = 42
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_numbe... | 332 | 1 |
'''simple docstring'''
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
a_ ... | 75 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class __A ( A ):
'''simple docstring'''
__lowerCamelCase : str
__lowerCamelCase : int
def lowerCAmelCase (__A):
"""simple docstring"""
if not isinstance(__A... | 211 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from .env import EnvironmentCommand
def UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
__magic_name__ = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''... | 98 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def UpperCamelCase ( a , a ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def Up... | 98 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def lowerCAmelCase__ ( ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = 9
_UpperCAmelCase = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, ... | 329 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ :List[Any] = logging.get_logger(__name__)
lowerCAmelCase__ :Tuple = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __a ( UpperCAmelCase ):
_a : ... | 329 | 1 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
lowercase__ : Optional[Any] = 'us-east-1' # defaults region
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
_snake_case : str
_snake_case : Any = 'ar... | 287 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,... | 287 | 1 |
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
if is_torch_available():
import t... | 71 |
UpperCAmelCase__ = {}
def A ( _UpperCAmelCase : int , _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
'''simple docstring'''
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late ... | 339 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
class __A (__lowercase):
'''simple docstring'''
__lowercase: Any = ... | 350 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
fro... | 233 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvB... | 330 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 122 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
import diffusers
from diffusers import (
AutoencoderKL,
EulerDiscreteScheduler,
StableDiffusionLatentUpscalePipeline,
StableDiff... | 8 |
'''simple docstring'''
import functools
def UpperCAmelCase ( lowerCamelCase_ :str , lowerCamelCase_ :str ):
'''simple docstring'''
snake_case_ : List[str] = len(lowerCamelCase_ )
snake_case_ : Dict = len(lowerCamelCase_ )
@funct... | 8 | 1 |
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
_lowercase : List[Any] = re.compile(r"([A-Z]+)([A-Z][a-z])")
_lowercase : int = re.compile(r"([a-z\d])([A-Z])")
_lowercase : Union[str, Any] = re.compile(r"(?<!_)_(?!_)")
_lowercas... | 238 |
"""simple docstring"""
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_... | 238 | 1 |
"""simple docstring"""
import math
import os
import sys
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : int = ''''''
try:
with open(__lowerCamelCase , '''rb''' ) as binary_file:
lowercase__ : in... | 302 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transfor... | 302 | 1 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def __UpperCAmelCase ( __UpperCamelCase ):
def decorator(__UpperCamelCase ):
__lowercase : Optional[Any] = getattr(SCREAMING_SNAKE_CASE__... | 249 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before token... | 199 | 0 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''MCTCTFeatureExtractor'''
__SCREAMING_SNAKE_CASE = '''AutoTokenizer'''
... | 364 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@requ... | 39 | 0 |
def __A ( __lowerCAmelCase )-> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase = []
_UpperCAmelCase = []
_UpperCAmelCase = {
'^': 3,
'*': 2,
'/': 2,
'%': 2,
'+'... | 39 | '''simple docstring'''
def snake_case__ ( _A: str ) -> list[int]:
'''simple docstring'''
lowerCAmelCase = [0 for i in range(len(_A ) )]
# initialize interval's left pointer and right pointer
lowerCAmelCase , lowerCAmelCase = 0, 0
for i in range(1 , le... | 272 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""... | 54 | """simple docstring"""
from __future__ import annotations
UpperCAmelCase = 8.988E9 # units = N * m^s * C^-2
def lowercase ( a__ : float , a__ : float , a__ : float , a__ : float ) -> dict[str, float]:
_UpperCamelCase = abs(chargea ... | 54 | 1 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowerCAmelCase : Optional[Any] = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
... | 156 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __lowerCAmelCase ( lowerCAmelCase_ ):
"""simple docstring"""
A__ : Any = '''EncodecFeatureExtractor'''
A__ : ... | 156 | 1 |
import pytest
__A = """__dummy_dataset1__"""
__A = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation... | 364 |
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
if isinstance(__a , __a ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(__a , __a ):
raise TypeError("'str' object cannot be interpreted as an integer" )
if ... | 273 | 0 |
"""simple docstring"""
def A__ ( UpperCamelCase ):
A = [0] * len(UpperCamelCase )
A = []
A = [1] * len(UpperCamelCase )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(Upper... | 292 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def A ( lowercase ) -> str:
'''simple docstring'''
if not isinstance(lowercase , lowercase ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('Undefined for non-natu... | 222 | 0 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE : List[Any] = "Input must be a string of 8 numbers plus letter"
_SCREAMING_SNAKE_CASE : Dict = "TRWAGMYFPDXBNJZSQVHLCKE"
def UpperCamelCase_( snake_case : str ):
'''simple docstring'''
... | 92 |
'''simple docstring'''
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCamelCase_( snake_case : Optional[int] , snake_case : Optional[int] ):
'''simple docstring'''
snake_case_ = int(snake_case )
a... | 92 | 1 |
'''simple docstring'''
def _lowercase ( __A = 50_000_000 ):
'''simple docstring'''
__UpperCamelCase = set()
__UpperCamelCase = int((limit - 24) ** (1 / 2) )
__UpperCamelCase = set(range(3 ,prime_square_limit + 1 ,2 ) )
pr... | 349 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.n... | 347 | 0 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class __A ( __lowerCAmelCase ):
def __init__(self : str , *__a : List[str] , **__a : Optional[Any] ):
super().__init__(*lowerCamelCase__ , **lowerCam... | 357 | '''simple docstring'''
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_: Any =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: ... | 106 | 0 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class UpperCAmelCase__ ... | 62 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfi... | 62 | 1 |
_lowerCamelCase : Tuple = [
(1_000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''),
(1, '''I'''),
]
def a... | 361 |
def a_ ( __lowercase : int = 50_000_000 ) -> int:
_snake_case = set()
_snake_case = int((limit - 24) ** (1 / 2) )
_snake_case = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 )
for p in range(3 , prime_square_limit + 1 , 2 ... | 130 | 0 |
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
__a = [0] * len(_SCREAMING_SNAKE_CASE )
__a = []
__a = [1] * len(_SCREAMING_SNAKE_CASE )
for values in graph.values():
for i in values:
... | 302 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class SC... | 302 | 1 |
"""simple docstring"""
from __future__ import annotations
def a__ ( SCREAMING_SNAKE_CASE : list[float] , SCREAMING_SNAKE_CASE : list[float] ):
'''simple docstring'''
lowerCAmelCase : List[str] = sorted(numsa + numsa )
lowerCAmelCase , lowe... | 133 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from .... | 133 | 1 |
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_visio... | 40 |
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def lowercase ( A_ , A_ , A_ , A_ )-> Union[str, Any]:
'''simple docstring'''
a : Any = sorted(zip(A_ , A_ ) , key=lambda A_ ... | 40 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class ... | 307 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,... | 307 | 1 |
"""simple docstring"""
import sys
from collections import defaultdict
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str ):
lowerCAmelCase = []
def __lowercase ( self : int , lowerCAmelCase : int ):
return sel... | 155 |
"""simple docstring"""
import argparse
import json
from tqdm import tqdm
def lowercase () -> Dict:
'''simple docstring'''
lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=s... | 155 | 1 |
"""simple docstring"""
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
fro... | 149 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_data2vec_audio": ["DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP", "Data2VecAudioConfig"],
"conf... | 149 | 1 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Union[str, Any] ):
'... | 260 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, Fl... | 257 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
'studio-ousia/luke-... | 238 |
def lowerCamelCase__ ( snake_case_ : int = 1000 ) -> int:
__snake_case = 2**power
__snake_case = str(snake_case_ )
__snake_case = list(snake_case_ )
__snake_case = 0
for i in list_num:
sum_of_num += int(snake... | 238 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase = {
"""configuration_mobilebert""": [
... | 40 |
'''simple docstring'''
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class a_ (_a ):
__lowerCAmelCase : Dict = ... | 309 | 0 |
from typing import Any
def UpperCamelCase ( __magic_name__ : list , __magic_name__ : list , __magic_name__ : dict , __magic_name__ : dict , __magic_name__ : dict , ) -> list:
"""simple docstring"""
_vali... | 146 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion impo... | 146 | 1 |
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> Tuple:
'''simple docstring'''
UpperCamelCase = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise Val... | 343 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 0 |
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCamelCase_( _lowerCamelCase = 3 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if isinstance(_lowerCamelC... | 340 |
"""simple docstring"""
_lowerCAmelCase : Tuple = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase... | 340 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class UpperCamelCase_ (UpperCAmelCase_ ):
def __init__( self : List[str] , *lowerCAmelCase_ ... | 268 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class A ( unittest.TestCase ):
def lo... | 65 | 0 |
def UpperCAmelCase_( a__ = 1_000 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = 1, 1
SCREAMING_SNAKE_CASE : str = []
for i in range(1 , n + 1 ):
SCREAMING_SNAKE_CASE : Any = pre... | 363 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils... | 19 | 0 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _a ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
UpperCamelCase__ : Dict ... | 146 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
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_configur... | 146 | 1 |
from math import factorial
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
# 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 ValueError('Please enter positive intege... | 88 |
_SCREAMING_SNAKE_CASE = {
"""Pillow""": """Pillow""",
"""accelerate""": """accelerate>=0.11.0""",
"""compel""": """compel==0.1.8""",
"""black""": """black~=23.1""",
"""datasets""": """datasets""",
"""filelock""": """filelock""",
"""flax""": """flax>=0.4.1""",
"... | 88 | 1 |
'''simple docstring'''
from collections.abc import Sequence
def __A ( lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
return sum(c * (x**i) for i, c in enumerate(lowerCamelCase_ ) )
def __A ( lowerCamelCase_ , lowerCamelC... | 323 |
'''simple docstring'''
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..contr... | 323 | 1 |
import argparse
import os
import re
import packaging.version
UpperCAmelCase ="examples/"
UpperCAmelCase ={
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", r... | 355 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
... | 77 | 0 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowercase_ ( _snake_case ):
if not is_accelerate_available():
return method
SCREAMING_SNAKE_CASE__... | 25 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def lowercase_ ( _snake_case ):
# encoder.embeddings are double cop... | 25 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SqueezeBertConfig',
'Sq... | 116 |
import os
from collections.abc import Iterator
def snake_case( __magic_name__ = "." ) -> Iterator[str]:
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(__magic_name__ ):
lowercase : Tuple = [d for d in dir_na... | 116 | 1 |
'''simple docstring'''
from __future__ import annotations
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase ) -> list:
'''simple docstring'''
snake_case_ = []
snake_case_ = input_list[low:mid], input_list[m... | 56 |
"""simple docstring"""
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertFo... | 171 | 0 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCAmelCase ( A ):
UpperCamelCase = DistilBertTo... | 353 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 290 | 0 |
'''simple docstring'''
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def _lowerCAmelCase ( __snake_case : Tuple , __snake_case : str , __snake_case : Union[str, Any] ) -> str:
__... | 190 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all e... | 190 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Any = logging.get_logger(__name__)
_lowercase : Dict = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kinetic... | 354 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_availabl... | 86 | 0 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default... | 227 |
from __future__ import annotations
_lowercase: Tuple = list[list[int]]
# assigning initial values to the grid
_lowercase: Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5]... | 227 | 1 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
UpperCAmelCase... | 148 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __lowerCAmelCase ( UpperCamelCase__):
def __... | 148 | 1 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__lowerCAmelCase : str ='\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author = "Popovi{\'c}, Maja",\n booktitle = "Proceedings of t... | 9 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokeniz... | 9 | 1 |
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')
class _snake_case :
def __i... | 350 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decode... | 327 | 0 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable... | 293 |
"""simple docstring"""
from __future__ import annotations
__A = 1.6_021e-19 # units = C
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) ->tuple[str, float]:
"""simple docstring"""
if (conductivity, electron_conc, mobility).co... | 293 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=_a ):
"""simple docstring"""
UpperCAmelCase : Tuple = ["""speech"""]
def __init__( self : Tuple , *__UpperCAmelCase : Dict , **__UpperCAmelCa... | 365 |
"""simple docstring"""
from math import ceil, sqrt
def lowercase ( A_ = 1_000_000 )-> int:
'''simple docstring'''
a : Tuple = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
a : ... | 226 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a_ : Dict = {
"""configuration_squeezebert""": [
"""SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 75 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from ... | 116 | 0 |
"""simple docstring"""
from PIL import Image
def _lowerCAmelCase ( UpperCAmelCase : Image , UpperCAmelCase : float ):
'''simple docstring'''
def brightness(UpperCAmelCase : int ) -> float:
return 128 + level + (c - 128)
if no... | 157 |
"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/li... | 157 | 1 |
"""simple docstring"""
from typing import List
from .keymap import KEYMAP, get_character
def _snake_case ( UpperCamelCase : str ):
def decorator(UpperCamelCase : Union[str, Any] ):
UpperCAmelCase : List[str] = getattr(UpperCamelCase , """handle_key""" , [] ... | 109 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'''facebook/s2t-wav2vec2-large-en-de''': (
'''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/confi... | 119 | 0 |
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase (_snake_case ):
'''simple docstring'''
_snake_case : Dict = (KDPMaDiscreteSc... | 145 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__UpperCAmelCase = '\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplifica... | 145 | 1 |
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