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 |
|---|---|---|---|---|
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
A_ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, ... | 609 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_availab... | 609 | 1 |
'''simple docstring'''
from __future__ import annotations
import copy
import tempfile
import unittest
from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
DUMMY_UNKNOWN_IDENTIFIER,
S... | 318 |
'''simple docstring'''
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTe... | 318 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Un... | 87 |
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
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
_lowerCamelCase : ... | 87 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case )
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : str = ... | 315 |
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 import MvpTokenizer
__lowercase... | 315 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Optional[int] = {
'facebook/timesformer': 'https://huggingface.co/facebook/timesformer/resolve/main/... | 328 |
'''simple docstring'''
from pathlib import Path
import fire
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : int = Path(UpperCAmelCase_ )
_UpperCamelCase : str = Path(UpperCAmelCase_ )
dest_di... | 195 | 0 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError('Input value must be an \'int\' type' )
__lowerCamelCase : Optional[Any] = 0
while number:
position += 1
number >>= 1
return position
if __na... | 230 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
deprecate(
... | 230 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_attentio... | 291 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModel... | 272 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 516 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class lowerCAmelCase__ ( __magic_name__ ):
'''simple docstring'''
def __init__( self , ... | 516 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@requir... | 472 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/... | 472 | 1 |
import argparse
from collections import defaultdict
import yaml
__a: Optional[Any] = '''docs/source/en/_toctree.yml'''
def _SCREAMING_SNAKE_CASE ( __snake_case ) -> Union[str, Any]:
_UpperCAmelCase = defaultdict(__snake_case )
_UpperCAmelCase =... | 712 |
__a: int = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
__a: List[str] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case , __snake_case ) -> list[int]:
_UpperCAmelCase = ... | 402 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatureEx... | 583 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script f... | 583 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def A_( A , A ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(A , A ) ) )
def A_( A , A ):
if dataset.ndim != value_arr... | 700 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : List[Any] = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise ... | 486 | 0 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('''dataset_size''' ,[None, 4_00 * 2**20, 6_00 * 2**20] )
@pytest.mark.parametrize('''input_in_memory_max_size''' ,['''default''', 0, 1_00 * 2**20, 9_00 * 2**20] )
def ... | 550 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch... | 550 | 1 |
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : str ) -> int:
assert column_title.isupper()
SCREAMING_SNAKE_CASE_ : List[Any] =0
SCREAMING_SNAKE_CASE_ : Union[str, Any] =len(UpperCAmelCase_ ) - 1
SCREAMING_SNAKE_CASE_ : ... | 719 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : list , UpperCAmelCase_ : int ) -> Tuple:
# Checks if the entire collection has been sorted
if len(UpperCAmelCase_ ) <= 1 or n <= 1:
return
insert_next(UpperCA... | 431 | 0 |
from __future__ import annotations
import math
import random
from typing import Any
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: Dict ):
lowercase__ : list[Any] = []
lowercase__ : int = 0
lower... | 266 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __lowerCAmelCase :
'''simple docstring'''
def __init__( self: List[str], lowerCamelCase_: Optional[Any], lowerCamelCase_: int, lowerCamelCase_: int ):
... | 266 | 1 |
'''simple docstring'''
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import Neste... | 459 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_lowercase )
class UpperCAmelCase ( _lowercase ):
UpperCAmelCase : str = field... | 459 | 1 |
__UpperCamelCase : Tuple = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" ... | 80 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lower... | 80 | 1 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowercase = logging.getLogger(__name__)
@dataclass
class __lowercase ... | 701 | def lowerCamelCase_ ( UpperCamelCase__ : int = 100 ):
'''simple docstring'''
UpperCamelCase__ = (n * (n + 1) // 2) ** 2
UpperCamelCase__ = n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print... | 591 | 0 |
"""simple docstring"""
# Copyright 2021 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... | 34 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device... | 238 | 0 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils imp... | 567 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'bigcode/gpt_bigcode-santacoder': 'https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json',
}
class lowerCAmelCase_ (... | 567 | 1 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequence... | 637 |
from __future__ import annotations
from typing import Any
class __lowercase :
'''simple docstring'''
def __init__( self : Dict , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : float = 0 ):
... | 637 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, ... | 712 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
'''microsoft/focalnet-tiny''': '''https... | 455 | 0 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A__ ( __SCREAMING_SNAKE_CASE ):
... | 154 |
import math
from numpy import inf
from scipy.integrate import quad
def lowerCAmelCase ( UpperCAmelCase ) ->float:
"""simple docstring"""
if num <= 0:
raise ValueError('''math domain error''' )
return quad(UpperCAmelC... | 154 | 1 |
'''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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
... | 713 |
'''simple docstring'''
from math import factorial
A_ = {str(digit): factorial(digit) for digit in range(10)}
def A ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
if not isinstance(_UpperCAmelCase ,_UpperCAmelCase ):
raise TypeErr... | 123 | 0 |
"""simple docstring"""
from __future__ import annotations
def lowerCamelCase_ ( _lowerCamelCase : list[int] , _lowerCamelCase : list[int] , _lowerCamelCase : int ):
lowerCamelCase_ = list(range(len(_lowerCamelCase ) ) )
lowerCamelCase_ ... | 142 |
"""simple docstring"""
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
__lowercase : int = {
"""<""": operator.lt,
"""<=""": operator.le,
"""==""": operator.eq,
"""!=""": operator.ne,
""">=""": oper... | 142 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, 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... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase ) -> int:
while a != 0:
_A , _A = b % a, a
return b
def a__ ( __lowercase , __lowercase ) -> int:
if gcd(__lowercase , __lowercase ) != 1:
_A = f"... | 621 | 1 |
"""simple docstring"""
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__A = TypeVar('''T''')
class _snake_case ( Generic[T] ):
snake_case__ = 42 # Cache store of keys
snake_case__ = 42 # References... | 646 |
'''simple docstring'''
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs t... | 296 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_ = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
... | 201 |
'''simple docstring'''
from math import pi, sqrt
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> float:
"""simple docstring"""
if num <= 0:
raise ValueError("""math domain error""" )
if num > 171.5:
raise OverflowError("""math range error""" ... | 201 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from tr... | 524 | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 524 | 1 |
'''simple docstring'''
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def snake_case_ (_a : BertModel , _a : str , _a : str ):
UpperCAmelCase = ('''dense.weight''', '''attent... | 358 |
'''simple docstring'''
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForCon... | 358 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase__ ( __snake_case ):
'''simple docstring'''
a : Any = ["image_processor", "tokenizer"]
a : List[Any] ... | 253 |
'''simple docstring'''
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
... | 585 | 0 |
"""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, require_flax, re... | 558 | """simple docstring"""
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE__ : Dict =True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE__ : Any =False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('pun... | 558 | 1 |
lowerCamelCase_ = '''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 .data_loader import skip_first_batches
from .lau... | 513 |
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 Regres... | 513 | 1 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE_ :
"""simple docstring"""
def __init__( self :Optional[Any] , __lowercase :str = "" , __lowercase :bool = False ):
# Mapping from the first character of the prefix of the node
... | 363 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
_UpperCamelCase = logging.get_logger(__name__)
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Union[tf.Tensor, n... | 363 | 1 |
import re
import string
import numpy as np
import datasets
UpperCAmelCase_ = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
UpperCAmelCase_ = "\nArgs:\n predictions: List of p... | 32 |
from __future__ import annotations
def _UpperCAmelCase ( UpperCAmelCase : str , UpperCAmelCase : str ):
"""simple docstring"""
__lowerCamelCase : int = get_failure_array(UpperCAmelCase )
# 2) Step through text searching fo... | 519 | 0 |
"""simple docstring"""
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 ImageProcessin... | 523 |
"""simple docstring"""
import copy
import random
from transformers import CLIPTokenizer
class UpperCAmelCase_ ( snake_case ):
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> Optional[Any]:
super().__init__(*UpperCamelCase_ , **UpperC... | 523 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def UpperCamelCase (lowercase_: int , lowercase_: str ) -> int:
# ===== initialization =====
A__ : Union[str, Any] = Mock()
A__ ... | 456 |
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - usef... | 456 | 1 |
# 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 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase = {
'''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
'''configuration_data2v... | 102 | 1 |
def _A ( SCREAMING_SNAKE_CASE__ : list ):
UpperCamelCase :str = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
UpperCamelCase :Optional[Any] = collection[i]
UpperCamelCase :Dict = 0
UpperCam... | 658 |
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
__snake_case = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"""text-classification""",
... | 658 | 1 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class __lowerCAmelCase ( unittest.T... | 704 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
... | 156 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def lowerCAmelCase__ ( __magic_name__ ) ->List[Any]:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
... | 118 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import ... | 118 | 1 |
from collections import UserDict
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():
... | 436 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig"""... | 436 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class A( lowerCamelCase__ ):
"""simple docstring"""
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ ) -> float... | 355 |
"""simple docstring"""
def A_ ( snake_case__ ) -> int:
_UpperCamelCase :Dict = 1
for i in range(1 , num + 1 ):
fact *= i
return fact
def A_ ( snake_case__ ) -> int:
_UpperCamelCase :Dict = 0
while number > 0:
_... | 355 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowerCamelCase ( UpperCAmelCase_ : List[Any] )... | 321 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm im... | 321 | 1 |
'''simple docstring'''
def __snake_case (__UpperCAmelCase , __UpperCAmelCase ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(__UpperCAmelCase , x % y )
def __snake_case (__UpperCAmelCase , __UpperCAmelCase ):
"""sim... | 501 |
'''simple docstring'''
import torch
def __snake_case ():
"""simple docstring"""
if torch.cuda.is_available():
lowerCamelCase_ : Optional[int] = torch.cuda.device_count()
else:
lowerCamelCase_ : str = 0
print(F"""Successfully ran on {num_gpus} GPUs""" ... | 501 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_... | 702 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
snake_case = {
"""configuration_convnext""": ["""CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""", ... | 568 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'''goo... | 7 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class snake_case__ ( __A ):
def __init__( self , *UpperCamelCase_ , **UpperCamelCase_ ) -> ... | 419 | 0 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
UpperCAmelCase_ : List[Any] = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n ... | 718 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
UpperCAmelCase_ : int = get_logger(__name__)
class lowercase__ ( enum.Enum ):
__UpperCamelCase = """all_checks"""
__UpperCamelCase ... | 440 | 0 |
'''simple docstring'''
from __future__ import annotations
snake_case_ : str = '''#'''
class A_ :
'''simple docstring'''
def __init__( self ):
_UpperCamelCase = {}
def a ( self , A_ ):
_UpperCamelCase = self._tri... | 138 |
'''simple docstring'''
def lowercase__( _UpperCamelCase : str )-> str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(_UpperCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.te... | 138 | 1 |
def __a ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE : str = ''
for i in table:
res += inp[i - 1]
return res
def __a ( __lowerCAmelCase ) -> Optional[Any]:
return data[1:] + data[... | 701 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_m... | 308 | 0 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase ( a__ , a__ , a__ , a__ , a__ ):
'''simple docstring'''
lowerCAmelCase :str = int(np.ceil((x_end - xa) / h ) )
lowerCAmelCase :int = np.zeros((n + 1,... | 553 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
UpperCamelCase_ : List[Any] = 3
def UpperCamelCase ( _UpperCAmelCase : int ) -> int:
'''simple docstring'''
print("Generating primitive root of p... | 461 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_tor... | 715 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import Pretraine... | 165 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = ["""image_processor""", """feature_extractor"""]
lowerCAmelCase_ = """TvltImageProcessor"""
lowerCAmelCase_ = """... | 3 |
from __future__ import annotations
from collections import Counter
from random import random
class a :
'''simple docstring'''
def __init__( self : Optional[Any] ):
UpperCAmelCase_ = {}
def lowerCamelCase_ ( self : i... | 144 | 0 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required ... | 586 |
from __future__ import annotations
from fractions import Fraction
def _snake_case( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10)... | 586 | 1 |
"""simple docstring"""
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.ser... | 88 |
"""simple docstring"""
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimu... | 535 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def __UpperCAmelCase ( UpperCAmelCase )-> str:
"""simple docstring"""
def decorator(UpperCAmelCase ):
lowercase = getattr(UpperCAmelCase, '''handle_key''', [] ... | 479 | from __future__ import annotations
from collections import deque
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : list[str] ) -> List[str]:
'''simple docstring'''
lowercase = []
self.adlis... | 479 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmelCase = {
"""YituTech/conv-bert-base""": """https://huggi... | 462 |
import coval # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator
import datasets
lowerCAmelCase = datasets.logging.get_logger(__name__)
lowerCAmelCase = """\
@InProceedings{moosavi2019minimum,
author = ... | 462 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise Optio... | 52 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def _a ( self : Any , _snake_case : str=None , _snake_case : Dict=None , _snake_case... | 52 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=A ):
lowerCAmelCase_ = ["torch", "scipy"]
def __init__( self : Any , *__lowercase : Optional[Any] , **__lowercase : Dict ):
... | 119 |
'''simple docstring'''
def __UpperCamelCase ( lowercase__ : list[int] ):
'''simple docstring'''
if not nums: # Makes sure that the list is not empty
raise ValueError('List is empty' )
__lowercase =sum(lowercase__ ) / len(lowercase__ ) # Calculate ... | 119 | 1 |
"""simple docstring"""
def A( snake_case_ ):
"""simple docstring"""
return str(snake_case_ ) == str(snake_case_ )[::-1]
def A( snake_case_ ):
"""simple docstring"""
return int(snake_case_ ) + int(str(snake_case_ )[::-1] )
def A... | 120 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class ... | 120 | 1 |
"""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,... | 255 |
import pytest
_lowerCamelCase ="""__dummy_dataset1__"""
_lowerCamelCase ="""
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn... | 681 | 0 |
'''simple docstring'''
import re
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class UpperCAmelCase__ ( lowercase__ ):
"""simple docstring"""
__UpperCAmelCase : Any = ['''image_processor''', '''tokenizer'''... | 319 |
'''simple docstring'''
import math
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self : List[str] ,_a : Tuple=0 ): # a graph with Node 0,1,...,N-1
'''simple docstring'''
_a : List[Any] = n
_a : int = [
... | 319 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger... | 419 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_... | 419 | 1 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __versio... | 714 | from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class __UpperCAmelCase( A__ ):
"""simple docstring"""
def __init__( self ... | 236 | 0 |
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( A_... | 32 |
"""simple docstring"""
def A__ ( __lowerCamelCase ):
"""simple docstring"""
if not head:
return True
# split the list to two parts
_lowerCAmelCase , _lowerCAmelCase = head.next, head
while fast and fast.next:
_lowerCAmelCase = fast.next.next... | 589 | 0 |
'''simple docstring'''
lowerCamelCase_ : Tuple = """Alexander Joslin"""
import operator as op
from .stack import Stack
def lowerCAmelCase( __lowerCamelCase ):
__a = {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}
__a = Stack()
__a ... | 714 | lowerCamelCase_ : Optional[Any] = {
"""Pillow""": """Pillow<10.0.0""",
"""accelerate""": """accelerate>=0.20.3""",
"""av""": """av==9.2.0""",
"""beautifulsoup4""": """beautifulsoup4""",
"""black""": """black~=23.1""",
"""codecarbon""": """codecarbon==1.2.0""",
"""cookiecutter"... | 246 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__A = {
"""configuration_layoutlmv3""": [
"""L... | 93 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 126 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 ImageProc... | 343 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchv... | 343 | 1 |
'''simple docstring'''
import functools
def _UpperCamelCase (_lowerCamelCase : str , _lowerCamelCase : str )-> str:
'''simple docstring'''
__snake_case = len(__snake_case )
__snake_case = len(__snake_case )
@functools... | 24 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTest... | 88 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__snake_case = {
"""configuration_rag""": ["""RagConfig"""],
"""retrieval_rag""": ["""RagRetriever"""],
"""tokenization_rag""": ["""RagTokenizer"""],
}
tr... | 708 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_b... | 400 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase = 100 ):
SCREAMING_SNAKE_CASE_: List[str] =(n * (n + 1) // 2) ** 2
SCREAMING_SNAKE_CASE_: Optional[int] =n * (n + 1) * (2 * n + 1) // 6
return sum_cubes - sum_squares
if __name__ == "__main__":
print(f"""{so... | 409 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testin... | 409 | 1 |
from PIL import Image
def _UpperCamelCase (a__ :Image , a__ :int ):
"""simple docstring"""
UpperCamelCase__ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(a__ :int ) -> int:
return int(128 + factor * (c - 128... | 706 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, ... | 548 | 0 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : int ) -> bool:
_lowercase = len(SCREAMING_SNAKE_CASE_ )
_lowercase = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a su... | 287 |
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 torch
i... | 287 | 1 |
import re
def _a ( UpperCamelCase_ : str ) -> list:
"""simple docstring"""
return [char.split() for char in re.split(R"[^ a-z A-Z 0-9 \s]" , str_ )]
def _a ( UpperCamelCase_ : str ) -> str:
"""simple docstring"""
lowerCA... | 719 |
def _a ( UpperCamelCase_ : list , UpperCamelCase_ : list ) -> float:
"""simple docstring"""
_validate_point(UpperCamelCase_ )
_validate_point(UpperCamelCase_ )
if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
raise ValueError... | 115 | 0 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def __lowercase ( _a ):
snake_case_ : Dict = test_file.split(os.path.sep ... | 123 |
"""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 truly tiny -
# all file... | 123 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if... | 454 |
'''simple docstring'''
# Copyright 2021 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
#
... | 454 | 1 |
from __future__ import annotations
def __lowerCAmelCase ( __lowerCamelCase : float , __lowerCamelCase : float , __lowerCamelCase : float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and o... | 354 |
from manim import *
class __a ( SCREAMING_SNAKE_CASE ):
def UpperCamelCase ( self : Tuple)-> Dict:
__lowerCAmelCase =Rectangle(height=0.5 , width=0.5)
__lowerCAmelCase =Rectangle(height=0.4_6 , width=0.4_6).set_stroke(width=0)
_... | 354 | 1 |
"""simple docstring"""
def UpperCAmelCase ( a_ ):
'''simple docstring'''
lowerCamelCase : Dict = int(a_ )
if n_element < 1:
lowerCamelCase : str = ValueError('a should be a positive number' )
raise my_error
lowerCamelCase : Dict ... | 700 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
... | 133 | 0 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__snake_case = logging.get_logger(__name__)
__s... | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import i... | 462 | 0 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_p... | 718 |
def _A (UpperCamelCase : int , UpperCamelCase : int ) ->int:
'''simple docstring'''
while b:
lowerCamelCase__ ,lowerCamelCase__ : int = b, a % b
return a
def _A (UpperCamelCase : int , UpperCamelCase : int ) ->... | 96 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
'''configuration_jukebox''': [
'''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''JukeboxConfig''',
'''JukeboxPriorConfig''',
'''Juke... | 282 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def __lowerCamelCase ( _lowercase ) -> str:
if not isinstance(_lowercase , _lowercase ):
raise TypeError('Undefined for non-integers' )
elif precision < 1:
raise ValueError('... | 282 | 1 |
_lowercase: Dict = {str(digit): digit**5 for digit in range(1_0)}
def _lowerCamelCase ( snake_case ):
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(snake_case ) )
def _lowerCamelCase ( ):
return sum(
number
for number in range(1_000 ... | 715 | import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase__ ( UpperCAmelCase ):
UpperCamelCase__ ... | 225 | 0 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class _lowercase ( __lowercase ):
_SCREAMING_SNAKE_CASE : str = "SpeechT5FeatureExtractor"
_SCREAMING_SNAKE_CASE : int = "SpeechT5Tokenizer"
def __init__( self : Optional[int] ,... | 56 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase , UpperCamelCase :List[Any] = position
UpperCamelCase :Any = [
(y + 1, x + 2),
(y - 1, x + 2)... | 658 | 0 |
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
_A = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( ... | 721 |
from math import factorial
def __SCREAMING_SNAKE_CASE ( UpperCamelCase : int , UpperCamelCase : int ) -> int:
"""simple docstring"""
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(UpperCamelCase ) // (factorial... | 403 | 0 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Dict, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lower... | 82 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_... | 71 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A__ : List[Any] = datase... | 272 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
A__ : int = datasets.logging.get_logger(__name__)
A__ : Optional[Any] = '\\n@inproceedings{rei-EtAl:2020:WMT,\n author = {Rei, Ricardo and Stewart, Craig and Farinha, Ana C and Lavie, ... | 272 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : str = '''▁'''
_UpperCAmelCase : Union[str, Any] = {'''vocab_fil... | 72 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | 591 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCAmelCase_ : Tuple = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
... | 718 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
UpperCAmelCase_ : int = get_logger(__name__)
class lowercase__ ( enum.Enum ):
__UpperCamelCase = """all_checks"""
__UpperCamelCase ... | 440 | 0 |
"""simple docstring"""
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
_enforce_args(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
if n == 0:
return 0
UpperCamelCase : List[str] = float("""-inf""" ... | 102 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
Autoenco... | 102 | 1 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 10**-10 ) -> float:
... | 716 |
import os
from datetime import datetime as dt
from github import Github
SCREAMING_SNAKE_CASE_ = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def __lowercase ( ) -> Optional[int]:
'''simple docstring'''
SCREAMING_S... | 116 | 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
from... | 538 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE__ : Dict = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
imp... | 538 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_... | 648 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 648 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizer... | 54 |
def a__ ( lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight mu... | 54 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
... | 307 |
def UpperCamelCase__ ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> int:
"""simple docstring"""
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
_a ... | 307 | 1 |
from __future__ import annotations
_lowerCAmelCase = 8.988E9 # units = N * m^s * C^-2
def _snake_case ( __snake_case , __snake_case , __snake_case , __snake_case ):
_UpperCamelCase = abs(chargea * chargea )
if (force, chargea, chargea, distance).count(0 ... | 10 |
'''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,
)
lowerCAmelCase : List[Any] = {
... | 372 | 0 |
import numpy as np
__A : Optional[int] =[
['''a''', '''b''', '''c''', '''d''', '''e'''],
['''f''', '''g''', '''h''', '''i''', '''k'''],
['''l''', '''m''', '''n''', '''o''', '''p'''],
['''q''', '''r''', '''s''', '''t''', '''u'''],
['''v''', '''w''', '''x''', '''y''', '''z'''],
]
cla... | 706 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self ) -> None:
lowerCamelCase_ = Vector([1, 2, 3] ... | 313 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __UpperCAmelCase ( lowerCamelCase_ : str , lowerCamelCase_ : complex , lowerCamelCase_ : str = "x" , lowerCamelCase_ : float = 10**-10 , lowerCamelCase_ : ... | 105 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class lowerCAmelCase_ ( lowerCamelCase_ ):
def __init__( self ,*snake_case__ ,**s... | 105 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
)
| 717 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def UpperCamelCase__ ( lowerCAmelCase__ ):
lowercase = args.pruning_method
lowercase = args.threshold
lowercase = args.model_na... | 72 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL... | 670 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Union[str, Any] = {'c... | 701 |
'''simple docstring'''
from random import randint
from tempfile import TemporaryFile
import numpy as np
def _a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case : List[Any] = 0
if start < end:
... | 47 | 0 |
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