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 |
|---|---|---|---|---|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceCl... | 61 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_avail... | 61 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from tran... | 710 |
'''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
from ..auto import CONFIG_MAPPING
_lowercase = logging.get_logger(__name__... | 162 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__UpperCamelCase : Union[str, Any] = {'''vocab_file''': '''vocab.... | 4 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_tapas""": ["""TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TapasConfig"""],
"""tokenization_tapas""": ["""TapasT... | 548 | 0 |
def _snake_case (__lowercase):
UpperCamelCase_ = int(__lowercase)
if n_element < 1:
UpperCamelCase_ = ValueError('a should be a positive number')
raise my_error
UpperCamelCase_ = [1]
UpperCamelCase_ , UpperCamelCase_ , ... | 618 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _a ( unittest.TestCase ):
"""simple docstri... | 618 | 1 |
import random
def _snake_case ( __snake_case , __snake_case , __snake_case = False ):
_UpperCamelCase = {i: [] for i in range(__snake_case )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_gr... | 10 |
def __snake_case ( __UpperCamelCase : list ,__UpperCamelCase : int = 0 ):
"""simple docstring"""
A_ = length or len(__UpperCamelCase )
A_ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
... | 86 | 0 |
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
Wav... | 718 |
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,
)
UpperCamelCase_ = {
'configuration_albert': ['ALBERT_PRETRAINE... | 142 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_... | 531 |
'''simple docstring'''
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_m... | 531 | 1 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class SCRE... | 703 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between che... | 25 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCamelCase = {
"configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}
try:... | 71 | '''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase ( UpperCamelCase__ : int ):
"""simple docstring"""
# A local function to see if a dot lands in the circle.
de... | 262 | 0 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import ... | 655 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Reg... | 655 | 1 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
__A = {'''UserAgent''': UserAgent().random}
def __a ( lowerCAmelCase_ : int ) -> dict:
'''simple docstring'''
UpperCAmelCas... | 593 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import e... | 593 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_o... | 393 |
"""simple docstring"""
def UpperCAmelCase__ ( SCREAMING_SNAKE_CASE : int ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError("""Input value must be an 'int' type""" )
lowerCAmelCa... | 393 | 1 |
"""simple docstring"""
def _lowerCamelCase( a ):
return " ".join(
"".join(word[::-1] ) if len(a ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 528 | """simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_comm... | 528 | 1 |
'''simple docstring'''
from math import isclose, sqrt
def _lowerCamelCase ( lowercase : float , lowercase : float , lowercase : float ) -> tuple[float, float, float]:
_a = point_y / 4 / point_x
_a = 2 * normal_gradient / (... | 521 |
'''simple docstring'''
import math
import sys
def _lowerCamelCase ( lowercase : str ) -> str:
_a = ""
try:
with open(lowercase , "rb" ) as binary_file:
_a = binary_file.read()
for dat in data:
_a ... | 521 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Tuple = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_available... | 519 |
"""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
if is_... | 506 | 0 |
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 import TensorType
class snake_ca... | 102 |
# 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... | 102 | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
t... | 47 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTest... | 179 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
class lowerCamelCase__ :
def __init__( self : Optional[int] , _lowercase : int ):
A = size
# approximate the overall size of segment tree with given value
A ... | 716 |
"""simple docstring"""
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 91 | 0 |
'''simple docstring'''
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
a__ : Dict = '... | 51 |
"""simple docstring"""
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors i... | 470 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class UpperCAmelCase_ :
lowerCamelCase : int
lowerCamelCase : int
class UpperCAmelCas... | 513 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__snake_case ={
"""configuration_chinese_clip""": [
"""CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""C... | 513 | 1 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNA... | 40 |
def _SCREAMING_SNAKE_CASE ( a ) -> list:
if len(a ) <= 1:
return lst
__A : Any = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
__A , __A : str = lst[i]... | 239 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
... | 350 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, Truncation... | 350 | 1 |
"""simple docstring"""
import math
import sys
def __magic_name__ ( _lowerCamelCase : str ):
__a : Optional[int] = """"""
try:
with open(_lowerCamelCase , """rb""" ) as binary_file:
__a : Union[str, ... | 581 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType... | 581 | 1 |
#
# 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-distributed-g... | 155 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class __snake_case ( ctypes.Structure ):
'''simple docstring'''
lowerCAmelCase__ = [("""size""", ctyp... | 155 | 1 |
'''simple docstring'''
import sys
import turtle
def __snake_case ( lowerCAmelCase : tuple[float, float] , lowerCAmelCase : tuple[float, float] ):
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __snake_case ( lowerCAmelCase : tuple[float, float] , low... | 396 | '''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.conversational... | 396 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : str = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_torch_availabl... | 702 |
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import... | 208 | 0 |
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
_lowerCAmelCase = logging.get_logger("transformers.models.speecht5")
def _snake_case ( __snake_case , __snake_case , __snake_case ):
... | 10 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNL... | 626 | 0 |
'''simple docstring'''
import numpy as np
def __UpperCAmelCase ( a_: List[Any], a_: Optional[int], a_: List[Any], a_: Any, a_: Tuple ):
_UpperCAmelCase : List[Any] = int(np.ceil((x_end - xa) / h ) )
_UpperCAmelCase : str ... | 257 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : Dict , *lowerCA... | 257 | 1 |
"""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_mask
... | 223 |
"""simple docstring"""
import math
class lowerCAmelCase_ :
"""simple docstring"""
def __init__(self , SCREAMING_SNAKE_CASE__=0 ) -> str: # a graph with Node 0,1,...,N-1
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : ... | 223 | 1 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under ... | 704 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils imp... | 176 | 0 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from trans... | 216 | import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
generate_identified_filename,
infer_shapes... | 216 | 1 |
"""simple docstring"""
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, torc... | 721 |
"""simple docstring"""
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
lo... | 439 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
... | 119 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmel... | 119 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)... | 385 |
'''simple docstring'''
from manim import *
class _lowerCAmelCase ( __A ):
"""simple docstring"""
def UpperCAmelCase_ ( self ) -> List[str]:
A_ : Optional[Any] = Rectangle(height=0.5 , width=0.5 )
A_ : List[str]... | 385 | 1 |
'''simple docstring'''
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : str , a__ : List[Any] , a__ : Optional[Any] , a__ : int ):
UpperCAmelCase = name
UpperCAmelCase = ... | 51 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective... | 638 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def UpperCAmelCase ( A__ ) -> Any:
_snake_case : int = SwinConfig(image_size=1_92 )
if "base" in model_name:
_s... | 519 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ = {
'''configuration_blip_2''': [
'''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Blip2Config''',
'''Blip2QFormerConfig''',
'''Blip2Visio... | 519 | 1 |
'''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... | 210 |
'''simple docstring'''
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling... | 210 | 1 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipel... | 706 |
__UpperCamelCase : List[Any] = 256
# Modulus to hash a string
__UpperCamelCase : Union[str, Any] = 100_0003
def _a ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
UpperCamelCase__ : Optio... | 106 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : List[Any] = logging.get_logger(__name__)
class a ( UpperCamelCase_ ):
__lowercase = """encoder-decoder"""
__lowercase = True
... | 416 |
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 floats_tensor, is_xfo... | 416 | 1 |
'''simple docstring'''
import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname
import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary
from transformers import FSMTConfig, FSMTForConditionalGenera... | 721 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp impor... | 691 | 0 |
'''simple docstring'''
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__snake_case: Tuple = {
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.102 Sa... | 577 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms imp... | 71 | 0 |
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
lowerCAmelCase__ = logging.getLogger(__name__)
@dataclass
class a__ ... | 626 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class a__ :
"""simple docstring"""
__lowerCamelCase = field(
metadata={'h... | 626 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 194 |
a_ : str = 6_55_21
def __a ( __UpperCAmelCase ):
a__ = 1
a__ = 0
for plain_chr in plain_text:
a__ = (a + ord(__UpperCAmelCase )) % MOD_ADLER
a__ = (b + a) % MOD_ADLER
return (b << 16) | a
| 194 | 1 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
SCREAMING_SNAKE_CASE_ = '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'
SCREAMING_SNAKE_CASE_ = '\nArgs:\n ... | 717 | '''simple docstring'''
from __future__ import annotations
SCREAMING_SNAKE_CASE_ = 10
def UpperCamelCase__ ( _lowercase : list[int] ) -> list[int]:
__UpperCAmelCase: Union[str, Any] = 1
__UpperCAmelCase: Optional[Any] = max(_lowercase )
while placement <= ma... | 466 | 0 |
def lowercase ( _lowerCAmelCase ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
UpperCAmelCase__ = [True] * (num + 1)
UpperCAmelCase__ = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , _lowerCAmelCase... | 392 |
import numpy
# List of input, output pairs
snake_case__ : Optional[Any] = (
((5, 2, 3), 1_5),
((6, 5, 9), 2_5),
((1_1, 1_2, 1_3), 4_1),
((1, 1, 1), 8),
((1_1, 1_2, 1_3), 4_1),
)
snake_case__ : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9),... | 392 | 1 |
"""simple docstring"""
from string import ascii_uppercase
__SCREAMING_SNAKE_CASE = {str(ord(c) - 55): c for c in ascii_uppercase}
def A_ ( __lowercase , __lowercase ):
if isinstance(__lowercase , __lowercase ):
raise TypeError('int() can\'t convert non-string with explicit base... | 710 |
"""simple docstring"""
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,
)
if is_sentencepiece_available():
from ..ta.tok... | 395 | 0 |
"""simple docstring"""
import argparse
import json
import os
import torch
from transformers.file_utils import has_file
from diffusers import UNetaDConditionModel, UNetaDModel
A: Optional[int] = False
A: List[Any] = True
A: List[Any] = False
if __name__ == "__main__... | 160 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...tes... | 160 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def a_ ( lowerCamelCase : int , lowerCamelCase : int , lowerCamelCase : bool , lowerCamelCase : list[int] , lowerCamelCase : float ):
if depth < 0:
raise Va... | 711 |
'''simple docstring'''
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
)
| 513 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_:Tuple = {
"""configuration_distilbert""": [
"""DISTILBERT_PRETRAINE... | 662 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@s... | 662 | 1 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class __snake_case:
'''simple docstring'''
_UpperCAmelCase ... | 708 | """simple docstring"""
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCamelCase : ... | 237 | 0 |
def _snake_case ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
_lowerCAmelCase = generate_large_matrix()
_lowerCAmelCase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
... | 10 |
import string
def lowerCamelCase_ ( lowerCAmelCase: str )-> str:
_snake_case : str = ''
for i in sequence:
_snake_case : Tuple = ord(lowerCAmelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1... | 411 | 0 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a__ = get_logger(__name__)
class _lowerCAmelCase ( enum.Enum ):
"""simple docstring"""
_lowercase : Tuple ... | 713 |
def _UpperCAmelCase ( a : int = 400_0000 ):
snake_case__ = [0, 1]
snake_case__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
snake_case__ = 0
... | 99 | 0 |
from importlib import import_module
from .logging import get_logger
_lowercase : Optional[Any] =get_logger(__name__)
class UpperCamelCase_ :
def __init__( self : str , lowerCamelCase : Optional[int] , lowerCamelCase ... | 364 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : Any ,... | 494 | 0 |
"""simple docstring"""
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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 507 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_A = """src/transformers"""
# This is to ma... | 507 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def __lowercase ( __lowerCAmelCase : str , __lowerCAmelCase : str=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split('.' )[n_... | 335 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'vocab... | 433 | 0 |
'''simple docstring'''
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
UpperCAmelCase = logging.getLogger(__name__)
class __snake_case:
'''simple docstring'... | 713 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common imp... | 344 | 0 |
"""simple docstring"""
def lowercase_ ( _lowerCamelCase: List[Any] ) -> Tuple:
'''simple docstring'''
if not head:
return True
# split the list to two parts
__lowerCamelCase , __lowerCamelCase : List[Any] = head.next, head
while fast and fa... | 646 | """simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__A = logging.get_logger(__name__)
__A = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.j... | 646 | 1 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( UpperCamelCase , UpperCamelCase , UpperCamelCase , U... | 720 |
"""simple docstring"""
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import di... | 158 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class lowercase_ ( metaclass=__lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : str , ... | 7 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Optional[int] = (KDPMaDis... | 7 | 1 |
import itertools
import string
from collections.abc import Generator, Iterable
def __lowerCAmelCase ( __snake_case , __snake_case ):
__lowerCAmelCase = iter(__snake_case )
while True:
__lowerCAmelCase = tuple(itertoo... | 702 |
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor
from accelerate import Ac... | 290 | 0 |
import warnings
from ..trainer import Trainer
from ..utils import logging
__magic_name__ : Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
def __init__( self : Optional[int] , __lowerCamelCase : Dict=None ,... | 615 |
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
__magic_name__ : Optional[int] = get_te... | 615 | 1 |
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_mask
from ...test_pipeline_mix... | 708 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __a ( __UpperCAmelCase : int ) -> str:
"""simple docstring"""
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("Undefined for non-integers" )
... | 253 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeli... | 467 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class UpperCAmelCase ( nn.Module ):
def __init__( self : int , __lowerCamelCase : int = 1_6 , __lowerCamelCase ... | 467 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase__ = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
... | 6 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
lowerCAmelCase__ = 3
def __UpperCAmelCase ( lowerCamelCase_) -> int:
print('Generating primitive root o... | 6 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_... | 332 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase = 1_0
def __magic_name__ ( lowercase , lowercase , ... | 409 | 0 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
re... | 253 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __a ( __UpperCAmelCase : int ) -> str:
"""simple docstring"""
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ):
raise TypeError("Undefined for non-integers" )
... | 253 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerT... | 83 |
import pprint
import requests
__UpperCAmelCase = 'https://zenquotes.io/api'
def __UpperCamelCase ( ) -> list:
'''simple docstring'''
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __UpperCamelCase ( ) -> list:
'''simple docs... | 600 | 0 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's eas... | 482 |
"""simple docstring"""
from collections import deque
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[Any] , _snake_case : str , _snake_case : int , _snake_case : int ) -> None:
"... | 482 | 1 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class a__ :
'''simple do... | 90 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A_ ( unittest.TestCas... | 67 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hugg... | 185 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedul... | 185 | 1 |
"""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_available():
from .token... | 636 |
"""simple docstring"""
import baseaa
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return baseaa.baaencode(string.encode("utf-8" ) )
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
return baseaa.baadecode(_UpperCamelCase ).decode("utf-... | 636 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class __magic_name__ ( unittest.TestCase):
def _UpperCAmelCase ( self : List[Any] ):
UpperCAmelCase ... | 707 |
def __UpperCamelCase ( _lowerCAmelCase = 10 ):
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or n < 0:
raise ValueError("Invalid input" )
UpperCAmelCase = 10**n
UpperCAmelCase = 2_84_33 * (pow(2 , 7_83_... | 405 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
_... | 346 |
"""simple docstring"""
def a__ ( ) -> list[list[int]]:
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
__A = generate_large_matrix()
__A = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, ... | 346 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Optional[Any] = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_A... | 705 |
from functools import lru_cache
@lru_cache
def _lowerCAmelCase ( __magic_name__ :int ):
if num < 0:
raise ValueError('''Number should not be negative.''' )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doc... | 407 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> str:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError('''Undefined for non-integers... | 462 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUn... | 462 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
... | 718 | import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ : Dict = logging.get_logger(__name__)
class __snake_case ( _lowerCamelCase ):
def __init__( self , *__UpperCamelCase , **__Up... | 699 | 0 |
"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils... | 553 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __lowercase ( __snake_case ):
_A = (DEISMultistepSc... | 461 | 0 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
UpperCamelCase : Tuple = "docs/source/en/_toctree.yml"
def __snake_case ( UpperCamelCase__ ) -> List[str]:
"""simple docstring"""
A = defaultdict(UpperCa... | 715 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
UpperCamelCase : Optional[Any] ... | 91 | 0 |
"""simple docstring"""
from __future__ import annotations
def A ( snake_case__ , snake_case__ , snake_case__ , ):
'''simple docstring'''
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than ... | 196 |
"""simple docstring"""
A_ : Any = "0.21.0"
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from... | 196 | 1 |
lowerCamelCase__ = 9.80665
def lowerCAmelCase__ ( a__ , a__ , a__ = g ) ->float:
'''simple docstring'''
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Object volume" )
if gravity <= 0:
... | 701 | import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
... | 82 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : str ):
'''simple docstring'''
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase__ ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
... | 135 |
'''simple docstring'''
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, Truncati... | 135 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
lowerCamelCase : Any = {
'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
'albert-large-v1': 'https://huggingface... | 303 |
from math import ceil
def lowercase__( A = 1_0_0_1 ):
snake_case__ : Dict = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
snake_case__ : str = 2 * i + 1
snake_case__ : Any = 2 * i
snake... | 303 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( __lowerCamelCase )... | 79 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 536 | 0 |
'''simple docstring'''
import os
from collections.abc import Iterator
def UpperCAmelCase_ ( A = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(A ):
_a : List[Any] = [d for d in dir_names if d != 'scripts' and d[0] not in '._']
... | 424 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
UpperCAmelCase_ : str = TypeVar("T")
class a ( Generic[T] ):
'''simple docstring'''
__lowerCAmelCase : deque[T] # Cache store of keys
... | 424 | 1 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _snake_case ( A , A ) -> str | Literal[False]:
lowerCAmelCase__ = list(A )
lowerCAmelCase__ = list(A )
... | 90 |
'''simple docstring'''
def _snake_case ( A ) -> int:
if n == 1 or not isinstance(A , A ):
return 0
elif n == 2:
return 1
else:
lowerCAmelCase__ = [0, 1]
for i in range(2 , n... | 90 | 1 |
import qiskit
def _A ( __snake_case :int , __snake_case :int ) -> qiskit.result.counts.Counts:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circuit acting on the q re... | 214 |
import re
def _A ( __snake_case :str ) -> str:
"""simple docstring"""
if len(re.findall("[ATCG]" , __snake_case ) ) != len(__snake_case ):
raise ValueError("Invalid Strand" )
return dna.translate(dna.maketrans("ATCG" , ... | 214 | 1 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=UpperCAmelCase__ ):
"""simple docstring"""
A_ = ["""flax"""]
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> Optional[Any]:
requi... | 23 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''huggingface/informer-tourism-monthly''': (
'''https://hugg... | 95 | 0 |
"""simple docstring"""
def lowerCAmelCase__ ( lowerCamelCase__ ) -> Tuple: # noqa: E741
lowerCamelCase = len(__A )
lowerCamelCase = 0
lowerCamelCase = [0] * n
lowerCamelCase = [False] * n
lowerCamelCase ... | 708 |
"""simple docstring"""
from __future__ import annotations
class UpperCAmelCase__ :
def __init__( self : Any , snake_case : list[list[int]] ) -> Union[str, Any]:
'''simple docstring'''
A = TypeError(
'Matrices must... | 109 | 0 |
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 to... | 479 | def _lowerCamelCase( __snake_case ) -> float:
if edge <= 0 or not isinstance(__snake_case , __snake_case ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _lowerCamelCase( __snake_case ) -> float:
... | 524 | 0 |
"""simple docstring"""
from typing import Any
import numpy as np
def lowerCamelCase_ ( _lowerCamelCase ):
return np.array_equal(_lowerCamelCase , matrix.conjugate().T )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
lowerCamelCase__ : U... | 714 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase ):
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_lowerCamelCase ) * abs(_lowerCamelCase )
if __name__ == "__main__":
import doctest
... | 696 | 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/... | 118 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowercase = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE... | 118 | 1 |
def _UpperCAmelCase ( UpperCamelCase: str ):
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(UpperCamelCase ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 376 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
"speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
# See all M-CTC-T models at https://huggingfac... | 376 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
Dat... | 170 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsM... | 170 | 1 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, requir... | 712 |
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=1_0_0_0 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
SCREAMING... | 406 | 0 |
import unittest
from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
from transformers.models.big_bird.model... | 64 |
from __future__ import annotations
import math
def a_ ( UpperCamelCase_ : float , UpperCamelCase_ : int ) -> float:
"""simple docstring"""
lowerCamelCase = u
for i in range(1 , UpperCamelCase_ ):
lowerCamelCase = temp * (u - ... | 246 | 0 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def a (_lowerCAmelCase , _lowerCAmelCase=None ):
SCREAMING_SNAKE_CASE_ = None
if token is not None:
SCREA... | 89 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class __magic_name__ ( __UpperCAmelCase):
'''simple docs... | 89 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( lowercase__ = 1_0_0_0_0_0_0 ) -> Any:
lowerCAmelCase__ : Tuple = limit + 1
lowerCAmelCase__ : List[Any] = [0] * limit
for first_term in range(1 , __UpperCamelCase ):
for n in range(__UpperCamelCase , _... | 453 |
'''simple docstring'''
import copy
import re
class lowerCamelCase__:
UpperCamelCase : Dict = "hp"
UpperCamelCase : Optional[Any] = {}
UpperCamelCase : str = None
@classmethod
def __magic_name__ ( cls , __UpperCAmelC... | 566 | 0 |
from collections import defaultdict
def lowerCAmelCase( __lowerCamelCase , __lowerCamelCase ):
__a = first_str.lower().strip()
__a = second_str.lower().strip()
# Remove whitespace
__a = first_str.replace(' ' , '' )
__a = ... | 702 | 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_video_inputs
if is_torch_available():
import... | 246 | 0 |
def UpperCamelCase ( __lowerCamelCase : Union[str, Any] ):
return "".join(chr(ord(_UpperCAmelCase ) - 32 ) if "a" <= char <= "z" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 204 |
'''simple docstring'''
def a ( _UpperCAmelCase , _UpperCAmelCase ) -> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def a ( ) -> None:
"""simple docstring"""
print('Truth Table of NOR Gate:' )
pri... | 697 | 0 |
from collections import defaultdict
from math import ceil, sqrt
def UpperCAmelCase_ ( _UpperCAmelCase = 1_0_0_0_0_0_0 , _UpperCAmelCase = 1_0 ):
lowerCamelCase_: defaultdict = defaultdict(_UpperCAmelCase )
for outer_width in range(3 , (t_limit // 4) + 2 ... | 584 | from graphs.minimum_spanning_tree_kruskal import kruskal
def UpperCAmelCase_ ( ):
lowerCamelCase_: str = 9
lowerCamelCase_: Tuple = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2]... | 584 | 1 |
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