





'''simple docstring'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
					]

					for test_file in test_files:
										snake_case_  :							Any            =	get_tester_classes(test_file)
										for tester_class in tester_classes:
															# A few tester classes don't have `parent` parameter in `__init__`.
															# TODO: deal this better
															try:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	5
																														if target is not None and v > target:
																																			failures.append(
																																			    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																			    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																			    f''' than {target}.'''
																																			)

					if len(failures) > 0:
										raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))












'''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,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case_  :							Any            =	logging.get_logger(__name__)


if is_vision_available():
					import PIL


class        __a   (lowerCamelCase  ):
							__a       : Any          =							["pixel_values"]

							def __init__(				self				:  Optional[int]							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  bool = True							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  bool = True							,  __magic_name__				:  Union[int, float] = 1 / 2_55							,  __magic_name__				:  bool = True							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = True							,  **__magic_name__				:  Union[str, Any]							,  )       ->							None:







													"""simple docstring"""
													super().__init__(**__magic_name__ )
													UpperCAmelCase_      :   List[Any]      =	size if size is not None else {'''shortest_edge''': 2_24}
													UpperCAmelCase_      :   List[str]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   int      =	crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__ )

													UpperCAmelCase_      :   str      =	do_resize
													UpperCAmelCase_      :   Optional[Any]      =	size
													UpperCAmelCase_      :   int      =	resample
													UpperCAmelCase_      :   List[str]      =	do_center_crop
													UpperCAmelCase_      :   Optional[Any]      =	crop_size
													UpperCAmelCase_      :   List[str]      =	do_rescale
													UpperCAmelCase_      :   Union[str, Any]      =	rescale_factor
													UpperCAmelCase_      :   int      =	do_normalize
													UpperCAmelCase_      :   Any      =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
													UpperCAmelCase_      :   Optional[Any]      =	image_std if image_std is not None else OPENAI_CLIP_STD
													UpperCAmelCase_      :   int      =	do_convert_rgb

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  PILImageResampling = PILImageResampling.BICUBIC							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   Any      =	get_resize_output_image_size(
													    __magic_name__							,  size=(size['''height'''], size['''width'''])							,  default_to_square=__magic_name__ )
													return resize(__magic_name__							,  size=__magic_name__							,  resample=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  List[Any]							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Dict[str, int]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							np.ndarray:







													"""simple docstring"""
													UpperCAmelCase_      :   List[Any]      =	get_size_dict(__magic_name__ )
													return center_crop(__magic_name__							,  size=(size['''height'''], size['''width'''])							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[int, float]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Any							,  )       ->							Union[str, Any]:







													"""simple docstring"""
													return rescale(__magic_name__							,  scale=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )

							def      UpperCAmelCase__						(				self				:  str							,  __magic_name__				:  np.ndarray							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Union[float, List[float]]							,  __magic_name__				:  Optional[Union[str, ChannelDimension]] = None							,  **__magic_name__				:  Optional[int]							,  )       ->							np.ndarray:







													"""simple docstring"""
													return normalize(__magic_name__							,  mean=__magic_name__							,  std=__magic_name__							,  data_format=__magic_name__							,  **__magic_name__ )



							def      UpperCAmelCase__						(				self				:  Dict							,  __magic_name__				:  ImageInput							,  __magic_name__				:  bool = None							,  __magic_name__				:  Dict[str, int] = None							,  __magic_name__				:  PILImageResampling = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  int = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  float = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  Optional[Union[float, List[float]]] = None							,  __magic_name__				:  bool = None							,  __magic_name__				:  Optional[Union[str, TensorType]] = None							,  __magic_name__				:  Optional[ChannelDimension] = ChannelDimension.FIRST							,  **__magic_name__				:  Union[str, Any]							,  )       ->							PIL.Image.Image:







													"""simple docstring"""
													UpperCAmelCase_      :   int      =	do_resize if do_resize is not None else self.do_resize
													UpperCAmelCase_      :   Dict      =	size if size is not None else self.size
													UpperCAmelCase_      :   Union[str, Any]      =	get_size_dict(__magic_name__							,  default_to_square=__magic_name__ )
													UpperCAmelCase_      :   str      =	resample if resample is not None else self.resample
													UpperCAmelCase_      :   Tuple      =	do_center_crop if do_center_crop is not None else self.do_center_crop
													UpperCAmelCase_      :   List[str]      =	crop_size if crop_size is not None else self.crop_size
													UpperCAmelCase_      :   Optional[Any]      =	get_size_dict(__magic_name__ )
													UpperCAmelCase_      :   str      =	do_rescale if do_rescale is not None else self.do_rescale
													UpperCAmelCase_      :   Tuple      =	rescale_factor if rescale_factor is not None else self.rescale_factor
													UpperCAmelCase_      :   List[Any]      =	do_normalize if do_normalize is not None else self.do_normalize
													UpperCAmelCase_      :   Tuple      =	image_mean if image_mean is not None else self.image_mean
													UpperCAmelCase_      :   int      =	image_std if image_std is not None else self.image_std
													UpperCAmelCase_      :   Optional[Any]      =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

													UpperCAmelCase_      :   Any      =	make_list_of_images(__magic_name__ )

													if not valid_images(__magic_name__ ):
																			raise ValueError(
																			    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																			    '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

													if do_resize and size is None:
																			raise ValueError('''Size must be specified if do_resize is True.''' )

													if do_center_crop and crop_size is None:
																			raise ValueError('''Crop size must be specified if do_center_crop is True.''' )

													if do_rescale and rescale_factor is None:
																			raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

													if do_normalize and (image_mean is None or image_std is None):
																			raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

													# PIL RGBA images are converted to RGB
													if do_convert_rgb:
																			UpperCAmelCase_      :   Optional[int]      =	[convert_to_rgb(__magic_name__ ) for image in images]

													# All transformations expect numpy arrays.
													UpperCAmelCase_      :   Optional[Any]      =	[to_numpy_array(__magic_name__ ) for image in images]

													if do_resize:
																			UpperCAmelCase_      :   Dict      =	[self.resize(image=__magic_name__							,  size=__magic_name__							,  resample=__magic_name__ ) for image in images]

													if do_center_crop:
																			UpperCAmelCase_      :   Union[str, Any]      =	[self.center_crop(image=__magic_name__							,  size=__magic_name__ ) for image in images]

													if do_rescale:
																			UpperCAmelCase_      :   Dict      =	[self.rescale(image=__magic_name__							,  scale=__magic_name__ ) for image in images]

													if do_normalize:
																			UpperCAmelCase_      :   Optional[int]      =	[self.normalize(image=__magic_name__							,  mean=__magic_name__							,  std=__magic_name__ ) for image in images]

													UpperCAmelCase_      :   Optional[Any]      =	[to_channel_dimension_format(__magic_name__							,  __magic_name__ ) for image in images]

													UpperCAmelCase_      :   int      =	{'''pixel_values''': images}
													return BatchFeature(data=__magic_name__							,  tensor_type=__magic_name__ )





