code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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,
... | 57 |
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.... | 57 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils ... | 525 | def __A ( _A ):
"""simple docstring"""
__a = []
for data in source_data:
for i, el in enumerate(_A ):
if len(_A ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(_A ) )
return data_lists
def __A ( _A , _A ):
... | 525 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( __lowerCAmelCase : list[int] ) -> list[int]:
if len(__lowerCAmelCase ) == 0:
return array
snake_case , snake_case = min(__lowerCAmelCase ), max(__low... | 369 |
'''simple docstring'''
from manim import *
class _lowerCAmelCase ( A__ ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[Any] )-> Union[str, Any]:
snake_case = Rectangle(height=0.5 , width=0.5 ... | 369 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_A = logging.get_logger(__name__)
_A = "▁"
_A = {"vocab_file": "senten... | 705 |
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
_A = get_logger(__name__)
_A = R"\n Args:\n input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n Indices of input... | 279 | 0 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
snake_case_ = str(bin(SCREAMING_SNAKE_CASE__ ) )
binary_number += "0" * shi... | 39 | '''simple docstring'''
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE_ = 2_99_79_24_58
# Symbols
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = symbols('... | 523 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
lowercase : Dict = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
... | 94 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : str) -> None:
'''simple docstring'''
__UpperCamelCase , __UpperCamelCase : ... | 94 | 1 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tor... | 542 |
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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEA... | 542 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_... | 406 |
"""simple docstring"""
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
snake_case = logging.get_logger(__name__)
snake_case = R'\n Args:\n input_i... | 406 | 1 |
'''simple docstring'''
import re
def A_( A : str):
if len(re.findall('[ATCG]' , A)) != len(A):
raise ValueError('Invalid Strand')
return dna.translate(dna.maketrans('ATCG' , 'TAGC'))
if __name__ == "__main__":
import doctest
... | 3 |
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 im... | 556 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _UpperCAmelCase :
a = 42
a = None
a = None
def ... | 481 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging
logging.set_verb... | 481 | 1 |
'''simple docstring'''
import enum
import shutil
import sys
a__ , a__ : Any = shutil.get_terminal_size()
a__ : Optional[int] = {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}
class __snak... | 368 |
'''simple docstring'''
from math import loga
def __lowerCamelCase ( UpperCAmelCase_ ) ->int:
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise TypeError('Input value ... | 368 | 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, prepare_image_inputs
if is_torch_available():
import torch... | 703 |
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 lowerCamelCase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
'''... | 501 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
cl... | 39 |
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if any(not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) or x < 0 for x in sequence ):
raise TypeError('''Sequence must be list of non-negative integers''' )
for _ in range(len(SCREAMING_SNAKE_CASE_... | 39 | 1 |
"""simple docstring"""
def snake_case_ ( A_ : int ):
'''simple docstring'''
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowerCamelCase : Any = 1
_lowerCamelCase : List[Any] = 1
... | 714 |
"""simple docstring"""
def snake_case_ ( A_ : int ):
'''simple docstring'''
_lowerCamelCase : list[list[int]] = [[0 for _ in range(A_ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
_lowerCamelCase : Union[str, Any] ... | 598 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
__lowerCAmelCase : Any = TypeVar("T")
__lowerCAmelCase : Dict = Union[List[T], Tuple[T, ...]]
__lowerCAmelCase : Optional[Any] = Union[T, List[T], Dict[str, T]]
__lowerCAmelCase ... | 509 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( __a : bytes ,__a : int ) -> np.array:
"""simple docstring"""
_a : int = F"""{sampling_rate}"""
_... | 14 | 0 |
'''simple docstring'''
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
... | 35 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE__ = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
... | 35 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have ... | 33 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , a = 6):
lowercase__ : Node | None = None
lowercase__ : Node | None = None
self.create_linked_list(a)
def snake_case_ ( self , a)... | 164 | 0 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeli... | 31 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 31 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
"""ucl... | 178 |
"""simple docstring"""
from manim import *
class _lowerCAmelCase ( snake_case_ ):
def lowerCamelCase ( self ) -> Any:
'''simple docstring'''
snake_case : List[str] = Rectangle(height=0.5 , width=0.5 )
snake_case ... | 178 | 1 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
A__ : List[Any]= get_logger(__name__)
class __lowerCamelCase :
def __init__( self , snake_case_ , snake_case_=None ) -> List[str]:
UpperCamelCase__ ... | 20 |
"""simple docstring"""
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__ : Any= """src/diffusers"""
# Matches is_xxx_available()
A__ : Tuple= re.c... | 20 | 1 |
import operator as op
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> Dict:
_a = []
_a = lambda _UpperCAmelCase , _UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation
_a = {
'^': op.pow,
'*': o... | 562 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import... | 562 | 1 |
def _SCREAMING_SNAKE_CASE ( snake_case_ : list[list[int]] , snake_case_ : int , snake_case_ : int , snake_case_ : set ):
__magic_name__ , __magic_name__ = len(snake_case_ ), len(grid[0] )
if (
min(snake_case_ , snake_case_ ) < 0
or row == row... | 678 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _SCREAMING_SNAKE_CASE ( snake_case_ : Tuple , snake_case_ : Union[str, Any] , snake_case_ : List[str] , snake_case_ : Union[str, Any] ):
__magic_name__ = {
'''en''': '''Machine learni... | 678 | 1 |
'''simple docstring'''
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
a = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
... | 350 |
from math import loga
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("Input value must be a 'int' type" )
return 0... | 276 | 0 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf... | 701 |
'''simple docstring'''
lowerCAmelCase_ = "Alexander Joslin"
import operator as op
from .stack import Stack
def lowerCAmelCase( a__ : str ):
'''simple docstring'''
lowerCamelCase__ = {"*": op.mul, "/": op.truediv, "+": op.add, "... | 426 | 0 |
"""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 lowercase ( lowerCAmelCase_... | 29 |
'''simple docstring'''
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"microsoft/xprophetnet-large-wiki100-cased": (
"https://huggingface.co/microsoft/xprophetnet-large-wiki100-... | 42 | 0 |
def snake_case_ ( lowerCAmelCase_ : int , lowerCAmelCase_ : int ):
return int((input_a, input_a).count(1 ) != 0 )
def snake_case_ ( ):
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) =... | 649 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from .... | 649 | 1 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase__ = logging.getLogger(__name__)
if __name__ == "__main__":
lowerCamelCase__ ... | 612 |
"""simple docstring"""
import warnings
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... | 247 | 0 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute... | 708 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCamelCase__( lowerCAmelCase ):
__magic_name__ : Tuple = ["image_processor", "tokenizer"]
__magic_name__ : Any = "ViT... | 50 | 0 |
"""simple docstring"""
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowerCAmelCase_ : str = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=F... | 673 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import flo... | 673 | 1 |
'''simple docstring'''
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is tr... | 709 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
# Initialise PyTorch model
... | 652 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _snake_case ( ) -> Tuple:
lowerCAmelCase__ = HfArgumentParser(A )
lowerCAmelCase__ = parser.parse_args_into_dataclasses()[0]
... | 90 |
def a_ ( SCREAMING_SNAKE_CASE__ : bytes ):
'''simple docstring'''
return "".join([hex(SCREAMING_SNAKE_CASE__ )[2:].zfill(2 ).upper() for byte in list(SCREAMING_SNAKE_CASE__ )] )
def a_ ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
if (l... | 464 | 0 |
'''simple docstring'''
from math import factorial
lowerCamelCase_ : List[Any] = {str(d): factorial(d) for d in range(10)}
def __magic_name__( _A ):
'''simple docstring'''
return sum(DIGIT_FACTORIAL[d] for d in str(_A ) )
def __magic_name__( ):
''... | 265 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_C... | 265 | 1 |
import random
class _a :
@staticmethod
def __snake_case (SCREAMING_SNAKE_CASE_ ) -> tuple[list[int], list[int]]:
UpperCAmelCase_: List[str] = [ord(SCREAMING_SNAKE_CASE_ ) for i in text]
UpperCAmelCase_: List[Any] ... | 556 |
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distr... | 556 | 1 |
from __future__ import annotations
import math
def _lowerCAmelCase ( _lowerCAmelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of... | 481 |
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_confi... | 481 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
lowercase = logging.get_logger(__name__)
class __A( _SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = 'upernet'
... | 272 |
"""simple docstring"""
def __a ( a = 6_0_0_8_5_1_4_7_5_1_4_3 ):
"""simple docstring"""
try:
_a = int(a )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0... | 388 | 0 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformer... | 712 | def lowerCAmelCase( __lowerCamelCase ):
__a = len(__lowerCamelCase )
while cur > 1:
# Find the maximum number in arr
__a = arr.index(max(arr[0:cur] ) )
# Reverse from 0 to mi
__a = arr[mi::-1] + arr[mi + 1 : len(__lowerCa... | 246 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
... | 35 |
def A__( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ):
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(__lowerCAmelCase ) )
def A__( __lowerCAmelCase , __lowerCAmelCase , ... | 304 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Optional[int] = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if not i... | 713 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | 178 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def _lowerCAmelCase ( __snake_case : str = "AAPL" ) -> str:
__A : Optional[Any] = f'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
__A : Optional[int] =... | 8 |
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,
resize,
to_channel_dimension_forma... | 461 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Optional[int] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig''',... | 713 |
import argparse
import gc
import json
import os
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 Accelerato... | 315 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
UpperCamelCase : List[str] = r'\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can... | 50 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention... | 526 | 0 |
"""simple docstring"""
# 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... | 244 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
A__ : Optional[Any] = logging.get_logger(__name__)
A__ : Tuple ... | 244 | 1 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgument... | 533 |
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... | 590 | 0 |
# 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 __SCREAMING_SNAKE_CASE ... | 365 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compressi... | 365 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 97 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 0 |
from __future__ import annotations
from math import pow, sqrt
def UpperCamelCase (lowercase_: float , lowercase_: float , lowercase_: float ) -> dict[str, float]:
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0... | 64 |
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A_ : Union[str, Any] = logging.get_logger(__name__)
class _a (__magic_name__ ):
'''simple docstring'''
def __init__( self , *A__ , **A__ ):
... | 64 | 1 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[int]:
return np.maximum(0 , lowerCamelCase_ )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 89 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.... | 89 | 1 |
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 ...test... | 709 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 353 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( __SCREAMING_SNAKE_CASE = 200_0000 ) -> List[str]:
snake_case__ : Optional[Any] = [0 for i in range(n + 1 )]
snake_case__ : List[Any] = 1
snake_case__ : Optional[Any] = 1
fo... | 270 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] ... | 275 | 0 |
import cva
import numpy as np
class __SCREAMING_SNAKE_CASE:
def __init__( self: Dict , UpperCamelCase: float , UpperCamelCase: int ) -> List[Any]:
if k in (0.04, 0.06):
snake_case__ = k
snake_ca... | 718 |
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedule... | 372 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def _UpperCamelCase ( lowerCAmelCase__: int ,lowerCAmelCase__: int ,lowerCAmelCase__: bool ,lowerCAmelCase__: list[int] ,lowerCAmelCase__: float ) -> int:
if depth < 0:
... | 294 |
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = 0 # The first color of the flag.
SCREAMING_SNAKE_CASE : List[str] = 1 # The second color of the flag.
SCREAMING_SNAKE_CASE : Optional[Any] = 2 # The third color of the flag.
SCREAMING_SNAKE_CASE ... | 294 | 1 |
def UpperCAmelCase ( _lowerCamelCase ):
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 17 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class lowerCamelCase_ :
'''simple docstring'''
... | 17 | 1 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisio... | 265 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
AutoConfig,
AutoFeatureExtractor,
WavaVecaConfig,
WavaVecaFeatureExtractor,
)
from transformers.t... | 265 | 1 |
__a : int = {str(digit): digit**5 for digit in range(1_0)}
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(lowercase ) )
def UpperCAmelCase ( ):
"""simple d... | 522 | 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, valid_ima... | 522 | 1 |
'''simple docstring'''
lowercase : List[Any] = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __a ( A__ , A__ , A__ , A__ ... | 649 |
'''simple docstring'''
from __future__ import annotations
def __a ( A__ , A__ = None , A__ = None , A__ = False , ) -> tuple[int, float, str]:
lowerCAmelCase = cipher_alphabet or [chr(A__ ) for i in range(97 , 123 ... | 649 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase__ = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
"MobileNetV2OnnxConfig... | 594 | from __future__ import annotations
from collections import Counter
from random import random
class _a :
"""simple docstring"""
def __init__( self ):
_lowercase ={}
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
_lowercase ={}
def __lowerCAmelCase ( ... | 594 | 1 |
'''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 impor... | 22 |
"""simple docstring"""
def snake_case ( _a: int )-> int:
'''simple docstring'''
if not isinstance(_a , _a ):
raise ValueError('Input must be an integer' )
if input_num <= 0:
raise ValueError('Input must be positive' )
return sum... | 510 | 0 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE ):
# vision encoder
if "img_encoder.pos_embed" in name:
lowerCAmelCase... | 708 |
'''simple docstring'''
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 _snake_case ( lowerCAmel... | 305 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAna... | 75 |
"""simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
... | 49 | 0 |
'''simple docstring'''
_UpperCAmelCase : Any = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
_UpperC... | 717 |
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def UpperCamelCase ( ) -> None:
'''simple docstring'''
lowercase =input('''Enter message: ''' )
lowercase =input('''Enter key [alphanumeric]: ''' )
lowercase =i... | 145 | 0 |
"""simple docstring"""
# 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 ... | 357 |
"""simple docstring"""
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 RoFo... | 357 | 1 |
import mpmath # for roots of unity
import numpy as np
class snake_case__ :
'''simple docstring'''
def __init__( self : Optional[Any] , lowerCAmelCase_ : Optional[int]=None , lowerCAmelCase_ : List[Any]=None ) -> List[str]:... | 719 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def _lowerCAmelCase ( __magic_name__ :list , __magic_name__ :list , __magic_name__ :list , ... | 407 | 0 |
'''simple docstring'''
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> None:
'''simple docstring'''
_UpperCamelCase : Optional[int] = len(UpperCAmelCase )
print("The following activities are selected:" )
# The first activ... | 435 | '''simple docstring'''
from __future__ import annotations
import time
lowerCAmelCase_ : Any = list[tuple[int, int]]
lowerCAmelCase_ : List[str] = [
[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,... | 435 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowercase : int , __lowercase : int ) -> int:
'''simple docstring'''
return 1 if input_a == input_a else 0
def UpperCAmelCase_ ( ) -> None:
'''simple docstring'''
assert xnor_gate(0 ... | 119 |
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNet... | 119 | 1 |
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
def update_area_of_max_square(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
A_ : Tupl... | 590 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_... | 663 | 0 |
'''simple docstring'''
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def snake_case_ ( _lowerCAm... | 528 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: Optional[Any] = logging.get_logger(__name__)
UpperCamelCase__: Tuple = {
"huggingface/t... | 528 | 1 |
"""simple docstring"""
def lowercase__ ( lowerCamelCase : int , lowerCamelCase : int ) -> int:
return int(input_a == input_a == 0 )
def lowercase__ ( ) -> None:
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input ... | 308 |
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... | 269 | 0 |
'''simple docstring'''
from collections import deque
class snake_case :
"""simple docstring"""
def __init__( self : Dict , __A : str , __A : int , __A : int ):
__UpperCamelCase = process_name # process name
__UpperCamelCase = ... | 434 |
'''simple docstring'''
def lowercase__ ( __lowercase : list[int] , __lowercase : list[int] ) -> None:
"""simple docstring"""
__UpperCamelCase = len(__lowercase )
print('The following activities are selected:' )
# The first ac... | 434 | 1 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : List[str] = logging.get_logger(__name__)
A_ : Optional[Any] = {
'facebook/encodec_24khz': 'https://hugging... | 265 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ = logging.getLogger(__... | 532 | 0 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__lowercase : Tuple = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operat... | 93 | """simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_configuration_comm... | 93 | 1 |
from math import sqrt
def A ( __UpperCamelCase = 1_000_000 ) -> List[str]:
A__ = 0
A__ = 0
A__ = 42
while num_cuboids <= limit:
max_cuboid_size += 1
for sum_shortest_sides in range(2 , 2 * max_cuboid_size + 1 ):
if sqrt(sum_short... | 9 |
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_available... | 70 | 0 |
'''simple docstring'''
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... | 666 |
'''simple docstring'''
import sys
__lowerCAmelCase = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'1254069874715852386305071569329096329522... | 666 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ... | 49 |
"""simple docstring"""
_snake_case = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
1_0: '''a''',
1_1: '''b''',
1_2: '''c''',
1_3: '''d''',
1_4: '''e''... | 580 | 0 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def _SCREAMING_SNAKE_CASE ( _lowercase... | 717 |
"""simple docstring"""
import baseaa
def _SCREAMING_SNAKE_CASE ( _lowercase : str ) ->bytes:
'''simple docstring'''
return baseaa.aaaencode(string.encode("utf-8" ) )
def _SCREAMING_SNAKE_CASE ( _lowercase : ... | 31 | 0 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( UpperCamelCase : int = 5000_0000 ):
A__ = set()
A__ = int((limit - 24) ** (1 / 2) )
A__ = set(range(3 , prime_square_limit + 1 , 2 ) )
primes.add(2 ... | 574 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _UpperCamelCase ( __snake_case):
__lowerCamelCase = "M-CLIP"
def __init__(self , lowerCamelCase__=1_0_2_4 , lowerCamel... | 574 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(... | 716 |
def UpperCamelCase__( UpperCamelCase__ : int = 50 )->int:
A__ = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block... | 212 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_m... | 91 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import ... | 388 | 0 |
"""simple docstring"""
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
... | 299 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Imag... | 299 | 1 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration... | 236 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def UpperCAmelCase_ ( __lowercase : Optional[Any] ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = tf.convert_to_tensor(__lowercase )
_UpperCAmelCase = 0.5 *... | 236 | 1 |
"""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
fr... | 702 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__ = 10, SCREAMING_SNAKE_CASE__ = 1_000, SCREAMING_SNAKE_CASE__ = True ) -> int:
assert (
isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ )
and isinstance(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ... | 370 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_ma... | 609 |
"""simple docstring"""
import string
import numpy
def UpperCAmelCase__ (snake_case__ : int , snake_case__ : int ):
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case__ )
class lowercase:
... | 609 | 1 |
'''simple docstring'''
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir('fixtures/te... | 715 |
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def A__ ( A : Optional[int] , A : List[str] , A : int , A : Any):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = ... | 435 | 0 |
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_available():
from ..mod... | 242 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CAS... | 242 | 1 |
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
... | 719 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class _UpperCamelCase( __lowerCamelCase ):
... | 577 | 0 |
"""simple docstring"""
from __future__ import annotations
_lowercase = []
def _snake_case ( snake_case__ : list[list[int]] , snake_case__ : int , snake_case__ : int ):
for i in range(len(snake_case__ ) ):
if board[row][i] == 1:
return False
for i in range(len(snake_... | 91 |
'''simple docstring'''
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_token... | 98 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS models at htt... | 613 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from ... | 613 | 1 |
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope='session' )
def _lowercase( ):
a__ =10
a__ ... | 20 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_availabl... | 475 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : Union[str, Any] ={
'''c... | 661 | import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_lowercase : Optional[Any] =WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def A__ ( lowe... | 661 | 1 |
'''simple docstring'''
from math import loga
def lowerCamelCase__ ( __lowerCamelCase : int ):
'''simple docstring'''
if a < 0:
raise ValueError('Input value must be a positive integer' )
elif isinstance(__lowerCamelCase , __lowerCamelCase ):
raise... | 446 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : str = "The quick brown fox jumps over the lazy dog" , ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] =set()
# Replace all the whitespace in our sentence
_UpperCAmelCase : Dict ... | 446 | 1 |
"""simple docstring"""
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_... | 494 |
"""simple docstring"""
def __lowerCamelCase ( SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
_UpperCAmelCase = ... | 494 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
... | 302 | """simple docstring"""
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __A :
def __init__( self , a__ , a__ , a__ ):
if dst_width < 0 or dst_height < 0:
raise ValueError("""Destination width/height should be > 0""" )
... | 213 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_C... | 576 | import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
Aut... | 576 | 1 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def a__ ( _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Tuple=7 ) -> Any:
"""simple docstring"""
U... | 71 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils im... | 14 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Any = {
'configuration_electra': ['ELEC... | 350 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils impo... | 350 | 1 |
from __future__ import annotations
class _a :
"""simple docstring"""
def __init__( self : List[Any] , UpperCAmelCase : str , UpperCAmelCase : str ):
A_ , A_ = text, pattern
A_ , A_ = l... | 86 |
'''simple docstring'''
import inspect
import unittest
from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import Config... | 405 | 0 |
"""simple docstring"""
from math import factorial
lowercase__ : Union[str, Any] = {str(d): factorial(d) for d in range(10)}
def __lowercase ( _a ):
return sum(DIGIT_FACTORIAL[d] for d in str(_a ) )
def __lowercase ( ):
snake_case_ : Option... | 713 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowercase ( _a , _a , _a ):
snake_case_ : Tuple = AutoConfig.from_pretrained(_a )
snake_case_ : Tuple = FlaxAutoModelF... | 485 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_to... | 600 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class __a ( __UpperCamelCase ):
__snake_cas... | 600 | 1 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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_c... | 64 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def UpperCamelCase (lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: np.ndarray , lowercase_: int , lowercase_: int ) -> np.ndarray:
A__ : Any ... | 64 | 1 |
'''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: List[str] = logging.get_logger(__name__... | 152 |
'''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_cu... | 120 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def SCREAMING_SNAKE_CASE ( lowerCAmelCase : ArgumentParser ) -> List[str]:
... | 218 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def snake_case_ (__A : int ) -> str:
__lowerCAmelCase : str = int(__A )
... | 218 | 1 |
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