code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_... | 162 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__A : List[str] = '''examples/'''
__A : int = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
... | 33 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 362 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = {"""voca... | 295 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class _a (unittest.TestCase ):
... | 192 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
... | 55 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ ) -> list:
'''simple docstring'''
snake_case : Any = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case : Optional[int] = collection[i]
snak... | 83 |
'''simple docstring'''
print((lambda quine: quine % quine)("print((lambda quine: quine %% quine)(%r))"))
| 83 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( _lowercase : str , _lowercase : Tuple , _low... | 170 |
import math
import os
import sys
def lowercase_ ( _A : str ):
"""simple docstring"""
lowerCamelCase__ : str = ""
try:
with open(_A , "rb" ) as binary_file:
lowerCamelCase__ : Union[str, Any] = binary_... | 184 | 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
d... | 368 |
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def U... | 164 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[int] ) -> list[int]:
"""simple docstring"""
if len(UpperCamelCase__ ) == 0:
return array
__lowerCamelCase , __lowerCamelCase ... | 90 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase_ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
el... | 12 | 0 |
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...tes... | 356 |
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 lowerCAmelCase_ ( lowerCamelCase__ ):
'''simple docstring'''
__snake_case = None... | 267 | 0 |
'''simple docstring'''
from __future__ import annotations
import requests
_UpperCamelCase = set(
'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post cat... | 208 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPText... | 208 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class ... | 370 |
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Any , UpperCAmelCase_ : int) ->None:
'''simple docstring'''
lowerCamelCase__: int =num_of_nodes
lowerCamelCase__... | 273 | 0 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import sh... | 329 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.mark.parametrize('revision' ... | 329 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pip... | 290 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokeni... | 290 | 1 |
from __future__ import annotations
import math
def lowerCAmelCase_ ( __A, __A ) -> float:
'''simple docstring'''
UpperCAmelCase__ = u
for i in range(1, __A ):
UpperCAmelCase__ = temp * (u - i)
return temp
... | 65 | import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def lowerCAmelCase_ ( __A ) -> Dict:
'''simple docstring'''
UpperCAmelCase__ = [
"encoder.version",
"dec... | 65 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 356 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFM... | 38 | 0 |
def lowerCAmelCase_ ( __A ) -> bool:
'''simple docstring'''
UpperCAmelCase__ = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCAmelCase__ = set()
return any(
node not in visit... | 65 |
"""simple docstring"""
import os
# Precomputes a list of the 100 first triangular numbers
lowerCamelCase_ : List[str] = [int(0.5 * n * (n + 1)) for n in range(1, 1_01)]
def UpperCAmelCase__ ( ):
"""simple docstring"""
A_ : Union[str, Any] = os... | 286 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__lowercase: str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__lowercase: list[int] = [ord(let... | 363 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : list ) -> float:
'''simple docstring'''
UpperCamelCase__ = 0
while len(_UpperCamelCase ) > 1:
UpperCamelCase__ = 0
# Consider two files with minimum cost to be me... | 31 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowerCAmelCase__ = datasets.u... | 153 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, ... | 110 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __lowerCamelCase ( __lowerCAmelCase : O... | 3 |
'''simple docstring'''
_SCREAMING_SNAKE_CASE = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_SCREAMING_SNAKE_CASE = ["a", "b", "c", "d", "e"]
def __lowerCamelCase ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : str , __... | 3 | 1 |
'''simple docstring'''
from manim import *
class _snake_case ( lowercase_ ):
def lowerCAmelCase__ ( self ) -> str:
'''simple docstring'''
snake_case_ = Rectangle(height=0.5 , width=0.5 )
snake_case_ = ... | 85 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCamelCase : Tuple = {
"""config""": [
"""EXTERNAL_DATA_FORMAT_SIZE_LIMIT""",
"""OnnxConfig""",
"""OnnxConfigWithPast""",
"""OnnxSeq2SeqConfigWithPast""",
"""Patc... | 307 | 0 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def snake_case ( A__ ):
return getitem, k
def snake_case ( A__ ,A__ ):
return setitem, k, v
def snake_case ( A__... | 363 |
"""simple docstring"""
def snake_case ( A__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(A__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 253 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
fr... | 86 |
"""simple docstring"""
import numpy as np
def __lowerCAmelCase (_UpperCamelCase ):
return 1 / (1 + np.exp(-vector ))
def __lowerCAmelCase (_UpperCamelCase ):
return vector * sigmoid(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 86 | 1 |
"""simple docstring"""
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_prop... | 40 |
"""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 _Upper... | 40 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string seq... | 23 |
'''simple docstring'''
from math import isclose, sqrt
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> tuple[float, float, float]:
UpperCAmelCase : Optional[int] = point_y /... | 23 | 1 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
_UpperCAmelCase = ... | 369 |
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 lowerCAmelCase_ ( U... | 328 | 0 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowerCAmelCase__( lowercase : Option... | 326 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
_UpperCamelCase = 4
_UpperCamelCase = 3
class _lowerCamelCase ( a ):
"""s... | 326 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTe... | 366 |
def __UpperCamelCase ( lowerCAmelCase__ : int = 1_0_0_0 ):
__a : int = -1
__a : Any = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
__a : List[Any] = (n * n - 2 * a * n) // (2 * n - 2 * a)
__a : ... | 90 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int ):
"""simple docstring"""
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
... | 331 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common impor... | 331 | 1 |
'''simple docstring'''
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_... | 351 | '''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case : list[int | float] , snake_case : int , snake_case : int ) -> int | float:
"""simple docstring"""
if len(snake_case ) =... | 345 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def UpperCamelCase_( snake_case : Optional[int]... | 85 |
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
__a = 'sshleifer/bart-tiny-random'
__a = 'pa... | 30 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...to... | 370 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowercase_ = 5_0_0_0_0
lowercase_ = 5_0_0_0
lowercase_ ,lowercase_ = os.path.split(__file__)
lowercase_ = os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace... | 194 | 0 |
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .sch... | 46 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
while b:
lowerCAmelCase , lowerCAmelCase = b, a % b
return a
def UpperCAmel... | 46 | 1 |
from PIL import Image
def __magic_name__ ( __a : Image ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ = image.size
UpperCamelCase__ = 0
UpperCamelCase__ = image.load()
for i in range(__a ):
for j in range(__a ... | 359 |
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( __a : Dict , __a ... | 178 | 0 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowercase_ ( _A : Tuple , _A : List[Any] , _A : ... | 184 |
def lowercase_ ( _A : int , _A : list ):
"""simple docstring"""
_enforce_args(_A , _A )
if n == 0:
return 0
lowerCamelCase__ : Union[str, Any] = float("-inf" )
for i in range(1 , n + 1 ):
... | 184 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase : Tuple , UpperCAmelCase : str ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCAmelCase ( UpperCAmelCase : Optional[int] ,... | 157 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
Roberta... | 157 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( __UpperCamelCase ):
__lowercase : List[str] = len(__UpperCamelCase )
for i in range(__UpperCamelCase ):
for j in range(i + 1 , __UpperCamelCase ):
if numbers[j] < numbers[i]:
... | 249 |
# 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 ... | 38 | 0 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__A : Optional[Any] = {
"n_samples": 64,
"horizon": 32,
"num_inference_steps": 20,
"n_guide_steps": 2, # can set to 0 for faster sampling, does not use value netw... | 371 |
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 _SCREAMING_SNAKE_CASE ( lowerCAmelCase__):
def __init__( self , _SCRE... | 49 | 0 |
from ...configuration_utils import PretrainedConfig
class snake_case_ (snake_case__ ):
UpperCAmelCase__ : Dict = "bert-generation"
def __init__( self :str ,__snake_case :str=5_03_58 ,__snake_case :int=10_24 ,__snake_case :Optional[Any]=24 ,__snake... | 240 | '''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n... | 31 | 0 |
lowercase : Optional[Any] = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
lowercase : str = ["a", "b", "c", "d", "e"]
def UpperCAmelCase_ (_lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int ... | 171 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampli... | 171 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCAmelCase_ ( snake_case__ , snake_case__ ,... | 3 |
'''simple docstring'''
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, s... | 3 | 1 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase__ ( lowerCAmelCase__ :np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 366 | """simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> int:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise TypeError("""only integers accepted as input""" )
else:
lowercase ... | 32 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenizati... | 153 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class _lowerCamelCase ( _lowercase ):
def __init__(self , *__a , **__a ... | 153 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_AR... | 247 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
... | 247 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def lowercase ( A_="ro" , A_="en" , A_="wmt16" , A_=None )-> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportErro... | 40 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase ... | 40 | 1 |
'''simple docstring'''
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_a : int = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safi... | 46 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tenso... | 46 | 1 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def __magic_name__ ( A ... | 107 |
def A_ ( snake_case : int ) -> None:
'''simple docstring'''
__UpperCamelCase = generate_pascal_triangle(snake_case )
for row_idx in range(snake_case ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 )... | 328 | 0 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def lowercase (_A , _A , _A ):
"""simple docstring"""
_lowerCAmelCase : List[str] = x
_lowerCAmelCase : ... | 25 |
'''simple docstring'''
import argparse
import os
import re
lowerCAmelCase : Tuple = """src/transformers"""
# Pattern that looks at the indentation in a line.
lowerCAmelCase : str = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key`... | 25 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common... | 99 |
def _a ( lowerCamelCase ):
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
lowerCamelCase : Any = 4
lowerCamelCase : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
lowerCamelCase : List[Any] = ((s... | 287 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( _Up... | 352 |
from jiwer import compute_measures
import datasets
lowercase_ = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for con... | 282 | 0 |
def __UpperCamelCase ( _A ):
lowerCAmelCase_ = [int(_A ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(_A ) == 4 and all(0 <= int(_A ) <= 254 for octet in octets )
if __name__ == "__main__":
_A = input().strip()
_A = '''valid''' if is... | 278 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def __UpperCamelCase ( _A = 3 ):
if isinstance(_A , _A ):
raise TypeError('''number of qubits must be a integer.''' )
if number_of_qubits <= 0:
... | 278 | 1 |
'''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_accelerate... | 352 |
'''simple docstring'''
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def UpperCamelCase ( a , a , a , a=1024 ) -> Union[str, Any]:
'''simple docstring'''
__magic_name__ , __magic_n... | 98 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
__lowercase = get_logger(__name__)
class lowerCamelCase_ ( enum.Enum ):
'''simple docstring'''
a__ : Union[str, Any] = """all_check... | 43 |
'''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__( __UpperCamelCase: Union[d... | 251 | 0 |
'''simple docstring'''
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class lowerCAmelCase_:
'''simple docstring'''
__lowercase : Optional[Union[str, Path]] = None
__lowercase : bool ... | 184 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inpu... | 184 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmen... | 184 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.mo... | 178 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
'configuration_longformer': [
'LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'LongformerCo... | 194 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator... | 194 | 1 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
_snake_case = logging.get_logger(__name__)
def _UpperCamelCase ( snake_case__, snake_case__ ) -> Tuple:
__UpperCAmelCase : Opt... | 157 | import argparse
import os
import re
_snake_case = '''src/transformers/models/auto'''
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_snake_case = re.compile(r'''[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDi... | 157 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_te... | 221 |
from maths.prime_factors import prime_factors
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
lowerCAmelCase__ = F'Input value of [number={number}] must be an int... | 221 | 1 |
"""simple docstring"""
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers import MarianMTModel... | 108 |
import logging
from transformers.configuration_utils import PretrainedConfig
__snake_case :Any = logging.getLogger(__name__)
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : Optional[Any] = '''masked_bert'''
def __init__( self : str ... | 49 | 0 |
"""simple docstring"""
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
lowerCAmelCase__ = yaml.safe_load(
'''\
name: ""
allow_empty: false
allow_empty_text: true
sub... | 244 |
"""simple docstring"""
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 3 ):
"""simple docstring"""
UpperCamelCase = min(_SCREAMING_SNAKE_CASE )
UpperCamelCase = max(_SCREAMING_SNAKE_CASE )
# normalize data
return ... | 244 | 1 |
"""simple docstring"""
import operator as op
def a__ ( __SCREAMING_SNAKE_CASE ) -> Tuple:
__lowerCAmelCase: Any = []
__lowerCAmelCase: int = lambda __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int(x / y ) # noqa: E731 integer division ope... | 217 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : int = logging.get_logger(__name__)
__A : Tuple = {
"google/bigbird-roberta-base": "https://huggin... | 273 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impor... | 297 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __lowerCamelCase ( snake_case_ , snake_case_ ):
"""s... | 297 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(__A , __A ):
raise TypeError('''only integers accepted as input''' )
else:
snake_case__ : Union[str, Any] = str(abs(__A ) )
... | 230 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 32 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCAmelCase__ ( _a : List[str] , _a : Optional[int]=10_00 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case_ : str = n - 1
snake_case_ : ... | 36 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCAmelCase__ ( ):
snake_case_ : str = ArgumentParser(
description=(
"PyTorch TPU distributed training l... | 36 | 1 |
'''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 transforme... | 1 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class UpperCAmelCase_ ( A_ ):
lowercase__ = ['''image_processor''', '''feature_extractor''']
lowercase__ = '''TvltImageProcessor'''
lowercase__ = '''TvltFeatureExtracto... | 247 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( A__ : list[int] ):
'''simple docstring'''
if not nums:
return 0
lowerCAmelCase_ : Any = nums[0]
lowerCAmelCase_ : Tuple = 0
... | 89 |
'''simple docstring'''
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
... | 89 | 1 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..tab... | 46 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase )
class lowercase ( _UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = field(def... | 46 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowercase_ ( unittest.TestCase , lowercase ):
'''simple docstring'''
def __lowerCAmelCase ( self : str ) ->str:
... | 354 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
UpperCAmelCase__ = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:
if not is... | 26 | 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__ : str = logging.get_logger(__nam... | 25 |
"""simple docstring"""
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
... | 25 | 1 |
import os
import tempfile
import unittest
from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(_a), "Tatoeba directo... | 315 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a ( ):
"""simple docstring"""
UpperCamelCase : Any = ArgumentParser('''Diffusers CLI tool''' , usage='''diffusers-cli <command> [<args>]''' )
... | 315 | 1 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase_ ( _a : Union[str, Any] , _a : Any , _a : List[Any] ):
'''si... | 345 |
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
UpperCamelCase_ = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_pytorch''': '''htt... | 345 | 1 |
def a__ ( lowerCAmelCase__ ) -> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 362 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : int , *_A : Tuple , **_A : ... | 299 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> Union[str, Any]:
snake_case__ : List[str] = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def __s... | 35 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler... | 194 | 0 |
"""simple docstring"""
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-au... | 368 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowercase__ = logging.get_logger(__name__)
class lo... | 12 | 0 |
'''simple docstring'''
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils i... | 27 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils impo... | 336 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProc... | 48 |
'''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 OptionalDepend... | 48 | 1 |
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> list:
UpperCamelCase__ : List[str] = len(__lowerCAmelCase )
for _ in range(__lowerCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
... | 189 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCamelCase : ... | 189 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines... | 352 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, r... | 40 | 0 |
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_effective_axis_dimension
from ...ut... | 300 | import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __lowercase ( lowerCamelCase : str , lowerCamelCase : str , **lowerCamelCase : List[Any] ):
UpperCamelCase_ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase ... | 175 | 0 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE_:Union[str, Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
'''simple docstring'''
__lowerCamelCa... | 115 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase ) -> list[int]:
"""simple docstring"""
A : Tuple = 2
A : List[Any] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
... | 115 | 1 |
from scipy.stats import spearmanr
import datasets
lowercase_ : Optional[int] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correl... | 133 |
'''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, slow
f... | 265 | 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_accel... | 355 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase_ : str = get_tests_dir('''fixture... | 62 | 0 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_utils... | 249 |
from __future__ import annotations
lowerCamelCase__ = list[list[int]]
# assigning initial values to the grid
lowerCamelCase__ = [
[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... | 212 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_ten... | 371 | """simple docstring"""
import math
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[int]:
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_SCREAMING_SNAKE_CASE )
else:
if x == 0: # 0 ... | 203 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, ... | 36 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
... | 36 | 1 |
'''simple docstring'''
import datasets
from .nmt_bleu import compute_bleu # From: https://github.com/tensorflow/nmt/blob/master/nmt/scripts/bleu.py
lowerCAmelCase_ : Dict = '''\
@INPROCEEDINGS{Papineni02bleu:a,
author = {Kishore Papineni and Salim Roukos and Todd Ward and Wei... | 170 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def __A ( lowerCAmelCase_ , lowerCAmelCas... | 170 | 1 |
"""simple docstring"""
from collections.abc import Callable
class _A :
def __init__( self , __lowerCAmelCase = None ):
"""simple docstring"""
lowercase = []
# Stores indexes of each item for s... | 197 | """simple docstring"""
from ..utils import DummyObject, requires_backends
class _A ( metaclass=lowerCAmelCase ):
snake_case__ : str = ['torch', 'transformers', 'onnx']
def __init__( self , *__lowerCAmelCase , **__lowerCAmelCase ):
... | 197 | 1 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : int = logging.get_logger(__name__)
_lowerCamelCase : Dict = {
"BridgeTower/bridgetower-... | 249 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from tra... | 249 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A : Tuple = {
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxC... | 142 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/xmod-base": "https://huggingface.... | 26 | 0 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'''BAAI/AltCLIP''': '''https://hugging... | 217 |
'''simple docstring'''
from math import sqrt
def _lowerCAmelCase ( lowerCamelCase_ : int ):
__lowercase = 0
for i in range(1 , int(sqrt(lowerCamelCase_ ) + 1 ) ):
if n % i == 0 and i != sqrt(lowerCamelCase_ ):
total += i + n... | 217 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : str = {
"""configuration_trajectory_transformer""": [
"""TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIV... | 81 |
"""simple docstring"""
def _A ( lowercase , lowercase ):
"""simple docstring"""
while second != 0:
a =first & second
first ^= second
a =c << 1
return first
if __name__ == "__main__":
import doctest
... | 81 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : Optional[int] = logging.get_logger(__name__)
_lowercase : Tuple = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json'... | 359 |
"""simple docstring"""
def lowercase__ ( snake_case_ :int , snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def lowercase__ ( ... | 86 | 0 |
from ..utils import DummyObject, requires_backends
class __lowercase (metaclass=UpperCamelCase__ ):
"""simple docstring"""
_snake_case = ["""torch""", """transformers""", """onnx"""]
def __init__( self , *A , **A ) -> List[Any]:
... | 124 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> float:
if not nums: # Makes sure that the list is not empty
raise ValueError("""List is empty""" )
snake_case : Optional[Any] = sum(lowercase ) / len(lowercase ) # Calculate the average
return sum(abs(x -... | 124 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 357 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
UpperCAmelCase_ ... | 198 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load... | 88 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, vali... | 12 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class... | 361 |
'''simple docstring'''
import qiskit
def UpperCamelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
A__ = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q register
A__ = qiskit.QuantumCircuit(_lowerCamelCase , _l... | 123 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Dict = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
... | 48 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def A ( _SCREAMING_SNAKE_CASE ) -> tuple:
return (... | 48 | 1 |
'''simple docstring'''
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
_lowercase : List[Any] = """<<<<<<< This should probably be modified because it men... | 91 |
'''simple docstring'''
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... | 91 | 1 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert ... | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
""... | 40 | 0 |
"""simple docstring"""
import math
def SCREAMING_SNAKE_CASE__ ( snake_case : list , snake_case : int )-> int:
'''simple docstring'''
UpperCAmelCase__ : str = len(snake_case )
UpperCAmelCase__ : List[str] = int(math.floor(math.... | 353 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class lowerCAmelCase__ ( __magic_name__ ):
def __a ( self : List[Any] , snake_case__ : str ):
'''simple docstring'''
wi... | 298 | 0 |
"""simple docstring"""
UpperCAmelCase : List[str] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transform... | 115 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowerCamelCase__ ( ctypes.Structure ):
"""simple docstring"""
__a = ... | 115 | 1 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _lowerCamelCase( lowercase__ ) -> List[str]:
'''simple docstring'''
__lowercase= args.pruning_method
__lowercase= args.threshol... | 371 |
# 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 applic... | 304 | 0 |
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