code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
lowercase__ :Optional[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
lowercase__ :List[str] = [file for file in fi... | 101 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {
"configuration_blenderbot": [
"BLENDERBOT... | 57 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCAmelCase ( unit... | 102 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 57 | 0 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformers.utils impor... | 103 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __... | 57 | 0 |
'''simple docstring'''
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__ = {
'''xlm-mlm-en-204... | 104 |
"""simple docstring"""
A : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingf... | 57 | 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/lic... | 105 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring''... | 57 | 0 |
"""simple docstring"""
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__UpperCamelCase : Optional[int] = {
'''n_samples''': 6_4,
'''horizon''': 3_2,
'''num_inference_steps''': 2_0,
'''n_guide_steps''': 2, # can set to 0 fo... | 106 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : 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),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 | 0 |
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.'
)
| 107 |
"""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... | 57 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class SCREAMING_SNAKE_CASE__ (... | 108 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( _UpperCamelCase = 4 ):
'''simple docstring'''
__lowerCAmelCase = abs(_UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(_UpperCamelCase )] for y in range(_UpperCamelCase )]... | 57 | 0 |
"""simple docstring"""
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
__lowerCAmelCase : int
__lowerCAmelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple doc... | 109 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 57 | 0 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class _a ( UpperCamelCase__ ):
def __init__( self: Tuple , UpperCamelCase_: Callable , U... | 110 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
... | 57 | 0 |
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 import (
center_crop,
get_resiz... | 343 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json",
}
class A ( lowerCA... | 199 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =["""image_processor""", """t... | 57 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simpli... | 181 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _UpperCamelCase :
'''simple docstring'''
pass
| 57 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __lowerCAmelCase ) -> str:
SCREAMING_SNAKE_CASE__ : Dict = 0.00
SCREAMING_SNAKE_CASE__ : Any = 0
for resistor in resistors:
if resistor <= 0:
... | 132 |
"""simple docstring"""
import sys
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = []
def snake_case ( self , __a ):
return self.node_position[vertex]
d... | 57 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
_snake_case = "2020.9.26"
_snake_case = "xcodz-dot, cclaus, dhruvmanila"
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCas... | 294 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 57 | 0 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configur... | 96 |
"""simple docstring"""
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ... | 57 | 0 |
_snake_case = "Input must be a string of 8 numbers plus letter"
_snake_case = "TRWAGMYFPDXBNJZSQVHLCKE"
def lowerCAmelCase_ ( snake_case_ ):
if not isinstance(_UpperCamelCase,_UpperCamelCase ):
_A : Tuple = f'''Expected str... | 26 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCamelCase , _UpperCamelCas... | 57 | 0 |
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, req... | 140 |
"""simple docstring"""
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = []
__lowerCAmelCase = 1
while len(_UpperCamelCase ) < 1e6:
constant.append(str(_UpperCamelCase ) )
i += 1
__lowerCAmelCase = "".join(_UpperCamelCase... | 57 | 0 |
class _UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
pass
class _UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
pass
class _UpperCamelCase :
"""simple docstring"""
def __init__( self ) -> int:
... | 210 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp... | 57 | 0 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_s... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Optional[int] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
... | 57 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffuse... | 55 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : Dict = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
... | 57 | 0 |
def lowercase( UpperCamelCase_ , UpperCamelCase_ ) -> List[Any]:
'''simple docstring'''
while second != 0:
UpperCamelCase = first & second
first ^= second
UpperCamelCase = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.t... | 343 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A : Any = "examples/"
A : Optional[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__versio... | 57 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def a_ ( SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE__ : str , SCREAMING_SNAKE_CASE_... | 199 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {
"configuration_blenderbot": [
"BLENDERBOT... | 57 | 0 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class lowerCamelCase_ ( pl.LightningModule ):
def __init__( self : int , ... | 181 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 57 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import Lea... | 132 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __... | 57 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_pegasus_x": ["PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP", "PegasusXConfig"],
}
try:
if not is_tor... | 294 |
"""simple docstring"""
A : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingf... | 57 | 0 |
"""simple docstring"""
def _snake_case ( lowercase__ ):
return "".join(chr(ord(_UpperCamelCase ) - 32 ) if 'a' <= char <= 'z' else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod() | 96 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring''... | 57 | 0 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_snake_case = logging.getLogger()
def lower... | 26 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : 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),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 | 0 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
... | 140 |
"""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... | 57 | 0 |
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__a : List[str] = {
"sample_size": 3_2,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1_0_0_0,
"bl... | 210 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( _UpperCamelCase = 4 ):
'''simple docstring'''
__lowerCAmelCase = abs(_UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(_UpperCamelCase )] for y in range(_UpperCamelCase )]... | 57 | 0 |
from ..utils import DummyObject, requires_backends
class a (metaclass=lowerCAmelCase__ ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = ["""torch"""]
def __init__( self : Dict , *lowerCamelCase : Union[str, Any] , **lowerCam... | 123 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 57 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict ):
_validate_point(_UpperCamelCase )
_validate_point(_UpperCamelCase )
if len(_UpperCamelCase ) != len(_UpperCamelCase ):
raise ValueError("Both points must be in... | 55 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
... | 57 | 0 |
def lowercase( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , ) -> Tuple:
'''simple docstring'''
UpperCamelCase = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise Val... | 343 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils impor... | 199 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =["""image_processor""", """t... | 57 | 0 |
'''simple docstring'''
import operator as op
def a__ ( lowerCAmelCase__ ) -> Any:
UpperCAmelCase__ : Optional[int] = []
UpperCAmelCase__ : Any = lambda lowerCAmelCase__ , lowerCAmelCase__ : int(x / y ) # noqa: E731 in... | 181 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _UpperCamelCase :
'''simple docstring'''
pass
| 57 | 0 |
"""simple docstring"""
import os
import string
import sys
a :Union[str, Any] = 1 << 8
a :Optional[Any] = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_... | 132 |
"""simple docstring"""
import sys
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = []
def snake_case ( self , __a ):
return self.node_position[vertex]
d... | 57 | 0 |
"""simple docstring"""
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu... | 294 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 57 | 0 |
"""simple docstring"""
from __future__ import annotations
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase , lowercase ):
_lowerCamelCase, _lowerCamelCase : int = text, pattern
_lowerCam... | 96 |
"""simple docstring"""
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ... | 57 | 0 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowerCAmelCase_ ( snake_case_ = True,*snake_case_,**snake_case_ ):
if not is_tqdm_available():
raise ImportError("""Accelerate's `tqd... | 26 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCamelCase , _UpperCamelCas... | 57 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {
"configuration_clipseg": [
"CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP",
"CLIPSegConfig",
"CLIPSegTextConfig",
"CLIPSegVisionConfig"... | 140 |
"""simple docstring"""
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = []
__lowerCAmelCase = 1
while len(_UpperCamelCase ) < 1e6:
constant.append(str(_UpperCamelCase ) )
i += 1
__lowerCAmelCase = "".join(_UpperCamelCase... | 57 | 0 |
from ....utils import logging
__a : Optional[int] = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=None , lowerCAmelCase__=20_48 ) -> ... | 210 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp... | 57 | 0 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Effici... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Optional[int] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
... | 57 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case ( UpperCAmelCase_ : Tuple ):
lowerCamelCase_ = len(_UpperCamelCase )
# We need to create solution object to save path.
lowerCamelCase_ = [[0 for _ in range(_UpperCamelCase )] for _ in r... | 55 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : Dict = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
... | 57 | 0 |
def lowercase( UpperCamelCase_ ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def lowercase( UpperCamelCase_ ) -> An... | 343 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A : Any = "examples/"
A : Optional[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__versio... | 57 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers ... | 199 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {
"configuration_blenderbot": [
"BLENDERBOT... | 57 | 0 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase__ = 0B1011_0011_1110_1100_1001_00... | 181 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 57 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
... | 132 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __... | 57 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokeni... | 294 |
"""simple docstring"""
A : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingf... | 57 | 0 |
"""simple docstring"""
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
... | 96 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring''... | 57 | 0 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class lowercase ( lowerCAmelCase__ ):
def __init__( self , _a="" , _a="train" ) -> Tuple:
assert os.path.isdir(__a )
_A : List[str] =... | 26 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : 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),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 | 0 |
def UpperCamelCase ( __lowercase : Dict = 60_08_51_47_51_43 ):
'''simple docstring'''
try:
A_ : List[str] = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError('Parameter n must be int or castable to int.' )
if n <= 0:
raise ValueErro... | 140 |
"""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... | 57 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class _UpperCamelCase ( lo... | 210 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( _UpperCamelCase = 4 ):
'''simple docstring'''
__lowerCAmelCase = abs(_UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(_UpperCamelCase )] for y in range(_UpperCamelCase )]... | 57 | 0 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
_snake_case :... | 123 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 57 | 0 |
'''simple docstring'''
import re
import subprocess
import sys
a_ : Tuple = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
a_ : str = (
subprocess.check_output(f'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-... | 55 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
... | 57 | 0 |
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, PLBartTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import... | 343 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 0 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A ( lowerCAmelCase__ ):
UpperCamelCase__ : Any =(DDPMParallelScheduler,)
def lowerCamelCase ( self : Any , **lowercase_ : L... | 199 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =["""image_processor""", """t... | 57 | 0 |
'''simple docstring'''
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
UpperCamelCase__ = logging.... | 181 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _UpperCamelCase :
'''simple docstring'''
pass
| 57 | 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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_o... | 132 |
"""simple docstring"""
import sys
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = []
def snake_case ( self , __a ):
return self.node_position[vertex]
d... | 57 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from... | 294 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 57 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
... | 96 |
"""simple docstring"""
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ... | 57 | 0 |
_snake_case = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_snake_case = [
999,
... | 26 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCamelCase , _UpperCamelCas... | 57 | 0 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class UpperCAmelCase ( lowerCAmelCase__ ):
... | 140 |
"""simple docstring"""
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = []
__lowerCAmelCase = 1
while len(_UpperCamelCase ) < 1e6:
constant.append(str(_UpperCamelCase ) )
i += 1
__lowerCAmelCase = "".join(_UpperCamelCase... | 57 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__a : List[Any] = {
"configuration_convnext": ["CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvNextConfig", "ConvNextOnnx... | 210 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp... | 57 | 0 |
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
_snake_case : Union[str, Any] = datasets.utils.log... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Optional[int] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
... | 57 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int ):
if len(_UpperCamelCase ) < 2:
return collection
def circle_sort_util(UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[Any] ... | 55 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : Dict = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
... | 57 | 0 |
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.test... | 343 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A : Any = "examples/"
A : Optional[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__versio... | 57 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase = {
"configuration_poolformer": [
"POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"PoolFormerConfig",
"PoolFormerOnnxCon... | 199 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {
"configuration_blenderbot": [
"BLENDERBOT... | 57 | 0 |
'''simple docstring'''
from sklearn.metrics import fa_score
import datasets
UpperCamelCase__ = "\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"
UpperCamelCase__ = ... | 181 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 57 | 0 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
a :Tuple = logging.get_logger(__name__)
a :Optional[Any] = {
"vocab_file": "vocab.json",
"tokenizer_config... | 132 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __... | 57 | 0 |
"""simple docstring"""
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
... | 294 |
"""simple docstring"""
A : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingf... | 57 | 0 |
"""simple docstring"""
import baseaa
def _snake_case ( lowercase__ ):
return baseaa.aaaencode(string.encode('utf-8' ) )
def _snake_case ( lowercase__ ):
return baseaa.aaadecode(_UpperCamelCase ).decode('utf-8' )
if __name__ ==... | 96 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring''... | 57 | 0 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_snake_case = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32a... | 26 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : 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),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {"vocab_file"... | 140 |
"""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... | 57 | 0 |
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
__lowercase = [0] * len(_UpperCamelCase )
for i in range(1 , len(_UpperCamelCase ) ):
# use last results for better performance - dynamic programming
__lowercase = ... | 210 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( _UpperCamelCase = 4 ):
'''simple docstring'''
__lowerCAmelCase = abs(_UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(_UpperCamelCase )] for y in range(_UpperCamelCase )]... | 57 | 0 |
from copy import deepcopy
class a :
"""simple docstring"""
def __init__( self : int , lowerCamelCase : Tuple = None , lowerCamelCase : Union[str, Any] = None ) -> List[str]:
if arr is None and size is not None:
__sna... | 123 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 57 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class snake_case :
"""simple docstring"""
_lowerCamelCase = 42... | 55 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
... | 57 | 0 |
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
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"goog... | 343 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 0 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class A ( lowerCAmelCase__ ):
def __init__( self : Optional[Any] , lowercase_ : Optional[Any] , lowercase_ ... | 199 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =["""image_processor""", """t... | 57 | 0 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokeni... | 181 |
"""simple docstring"""
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class _UpperCamelCase :
'''simple docstring'''
pass
| 57 | 0 |
"""simple docstring"""
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=() , __lowerCAmelCase=None , ... | 132 |
"""simple docstring"""
import sys
from collections import defaultdict
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ):
__lowerCAmelCase = []
def snake_case ( self , __a ):
return self.node_position[vertex]
d... | 57 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import Lf... | 294 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformer... | 57 | 0 |
"""simple docstring"""
import numpy
# List of input, output pairs
lowercase__ = (
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
lowercase__ = (((515, 22, 13), 555), ((61, 35, 49), 150))
low... | 96 |
"""simple docstring"""
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
pass
class _UpperCamelCase :
'''simple docstring'''
def __init__( self ... | 57 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor... | 26 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError("iterations must be defined as integers" )
if not isinstance(_UpperCamelCase , _UpperCamelCas... | 57 | 0 |
_UpperCAmelCase = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
_UpperCAmelCase = [{"type": "code... | 140 |
"""simple docstring"""
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = []
__lowerCAmelCase = 1
while len(_UpperCamelCase ) < 1e6:
constant.append(str(_UpperCamelCase ) )
i += 1
__lowerCAmelCase = "".join(_UpperCamelCase... | 57 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : Optional[int] = logging.get_logger(__name__)
__a : List[str] = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https... | 210 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp... | 57 | 0 |
def lowerCAmelCase_ ( __lowerCamelCase ):
if num < 0:
return False
__snake_case : int = num
__snake_case : int = 0
while num > 0:
__snake_case : Optional[Any] = rev_num * 1_0 + (num % 1_0)
num /... | 123 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A : Optional[int] = {
"configuration_altclip": [
"ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"AltCLIPConfig",
... | 57 | 0 |
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __snake_case ( UpperCAmelC... | 55 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
A : Dict = logging.getLogger(__name__)
@dataclass
class _UpperCamelCase ( lowerCAmelCase__ ):
... | 57 | 0 |
from ... import PretrainedConfig
_SCREAMING_SNAKE_CASE = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCase__ ):
__lowerCAmelCase = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__lowerCAmel... | 343 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
A : Any = "examples/"
A : Optional[Any] = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__versio... | 57 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import Ble... | 199 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Tuple = {
"configuration_blenderbot": [
"BLENDERBOT... | 57 | 0 |
'''simple docstring'''
import argparse
import json
import os
import torch
from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def a__ ( lowerCAmelCase__ , lowerCAmelCa... | 181 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase = 6008_5147_5143 ):
'''simple docstring'''
try:
__lowerCAmelCase = int(_UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("Parameter n must be int or castable to int." )
if n <= 0:
... | 57 | 0 |
"""simple docstring"""
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
f... | 132 |
"""simple docstring"""
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def __init__( self , __a , __... | 57 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipel... | 294 |
"""simple docstring"""
A : int = {
"Pillow": "Pillow",
"accelerate": "accelerate>=0.11.0",
"compel": "compel==0.1.8",
"black": "black~=23.1",
"datasets": "datasets",
"filelock": "filelock",
"flax": "flax>=0.4.1",
"hf-doc-builder": "hf-doc-builder>=0.3.0",
"huggingf... | 57 | 0 |
"""simple docstring"""
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class lowerCAmelCase__ :
'''simple docstring'''
lowerCamelCase__ = None
def A_ ( self ):
... | 96 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring''... | 57 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import l... | 26 |
"""simple docstring"""
import numpy
# List of input, output pairs
A : 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),
)
A : str = (((5_1_5, 2_2, 1_3), 5_5_5), ((6_1, 3_5, 4_9), 1_5_0))
A : Union[str, ... | 57 | 0 |
from manim import *
class UpperCAmelCase ( lowerCAmelCase__ ):
'''simple docstring'''
def lowerCAmelCase_ ( self ):
"""simple docstring"""
A_ : int = Rectangle(height=0.5 , width=0.5 )
A_ : str = ... | 140 |
"""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... | 57 | 0 |
import os
def UpperCAmelCase ( ):
"""simple docstring"""
with open(os.path.dirname(_UpperCamelCase ) + '''/p022_names.txt''' ) as file:
__lowercase = str(file.readlines()[0] )
__lowercase = names.replace('''\"''' , '''''' ... | 210 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( _UpperCamelCase = 4 ):
'''simple docstring'''
__lowerCAmelCase = abs(_UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(_UpperCamelCase )] for y in range(_UpperCamelCase )]... | 57 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case : Dict = {"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}
try:
if not is_vision_available():
raise OptionalDepen... | 123 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils i... | 57 | 0 |
'''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 snake_case ( unittest.TestCase... | 55 |
"""simple docstring"""
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _UpperCamelCase ( pl.LightningModule ):
'''simple docstring'''
def __init__( self , __a ):
... | 57 | 0 |
from __future__ import annotations
def lowercase( UpperCamelCase_ = 4 ) -> Dict:
'''simple docstring'''
UpperCamelCase = abs(_UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(_UpperCamelCase )] for y in range(_UpperCamelCase )]
def lo... | 343 |
"""simple docstring"""
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = False
while is_sorted is False: # Until all the indices are traversed keep looping
__lowerCAmelCase = True
for i in range(0 , len(_UpperCamelCase ) - ... | 57 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_availabl... | 199 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] =["""image_processor""", """t... | 57 | 0 |
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