
"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


_a								=						logging.get_logger(__name__)



def  lowerCamelCase__						(     __snake_case      )				->						List[List[ImageInput]]:


					"""simple docstring"""





					if isinstance(__snake_case,	(list, tuple)      ) and isinstance(videos[0],	(list, tuple)      ) and is_valid_image(videos[0][0]      ):
										return videos

					elif isinstance(__snake_case,	(list, tuple)      ) and is_valid_image(videos[0]      ):
										return [videos]

					elif is_valid_image(__snake_case      ):
										return [[videos]]

					raise ValueError(F'''Could not make batched video from {videos}'''      )






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = ['pixel_values']



		def __init__(   self							,						__a = True							,						__a = None							,						__a = PILImageResampling.BILINEAR							,						__a = True							,						__a = None							,						__a = True							,						__a = 1 / 2_55							,						__a = True							,						__a = None							,						__a = None							,						**__a							,						)			->			None:






							'''simple docstring'''
							super().__init__(**__a)
							_UpperCamelCase									=    size if size is not None else {'''shortest_edge''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
							_UpperCamelCase									=    get_size_dict(__a							,						param_name='''crop_size''')

							_UpperCamelCase									=    do_resize
							_UpperCamelCase									=    size
							_UpperCamelCase									=    do_center_crop
							_UpperCamelCase									=    crop_size
							_UpperCamelCase									=    resample
							_UpperCamelCase									=    do_rescale
							_UpperCamelCase									=    rescale_factor
							_UpperCamelCase									=    do_normalize
							_UpperCamelCase									=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							_UpperCamelCase									=    image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = PILImageResampling.BILINEAR							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a)
							if "shortest_edge" in size:
												_UpperCamelCase									=    get_resize_output_image_size(__a							,						size['''shortest_edge''']							,						default_to_square=__a)
							elif "height" in size and "width" in size:
												_UpperCamelCase									=    (size['''height'''], size['''width'''])
							else:
												raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''')
							return resize(__a							,						size=__a							,						resample=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							_UpperCamelCase									=    get_size_dict(__a)
							if "height" not in size or "width" not in size:
												raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''')
							return center_crop(__a							,						size=(size['''height'''], size['''width'''])							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			Dict:






							'''simple docstring'''
							return rescale(__a							,						scale=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a							,						__a							,						__a = None							,						**__a							,						)			->			np.ndarray:






							'''simple docstring'''
							return normalize(__a							,						mean=__a							,						std=__a							,						data_format=__a							,						**__a)



		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						)			->			np.ndarray:






							'''simple docstring'''
							if do_resize and size is None or resample is None:
												raise ValueError('''Size and resample 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.''')

							# All transformations expect numpy arrays.
							_UpperCamelCase									=    to_numpy_array(__a)

							if do_resize:
												_UpperCamelCase									=    self.resize(image=__a							,						size=__a							,						resample=__a)

							if do_center_crop:
												_UpperCamelCase									=    self.center_crop(__a							,						size=__a)

							if do_rescale:
												_UpperCamelCase									=    self.rescale(image=__a							,						scale=__a)

							if do_normalize:
												_UpperCamelCase									=    self.normalize(image=__a							,						mean=__a							,						std=__a)

							_UpperCamelCase									=    to_channel_dimension_format(__a							,						__a)
							return image




		def 		UpperCAmelCase				(   self							,						__a							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = None							,						__a = ChannelDimension.FIRST							,						**__a							,						)			->			PIL.Image.Image:






							'''simple docstring'''
							_UpperCamelCase									=    do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase									=    resample if resample is not None else self.resample
							_UpperCamelCase									=    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCamelCase									=    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCamelCase									=    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCamelCase									=    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCamelCase									=    image_mean if image_mean is not None else self.image_mean
							_UpperCamelCase									=    image_std if image_std is not None else self.image_std

							_UpperCamelCase									=    size if size is not None else self.size
							_UpperCamelCase									=    get_size_dict(__a							,						default_to_square=__a)
							_UpperCamelCase									=    crop_size if crop_size is not None else self.crop_size
							_UpperCamelCase									=    get_size_dict(__a							,						param_name='''crop_size''')

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

							_UpperCamelCase									=    make_batched(__a)

							_UpperCamelCase									=    [
							    [
							        self._preprocess_image(
							            image=__a							,						do_resize=__a							,						size=__a							,						resample=__a							,						do_center_crop=__a							,						crop_size=__a							,						do_rescale=__a							,						rescale_factor=__a							,						do_normalize=__a							,						image_mean=__a							,						image_std=__a							,						data_format=__a							,						)
							        for img in video
							    ]
							    for video in videos
							]

							_UpperCamelCase									=    {'''pixel_values''': videos}
							return BatchFeature(data=__a							,						tensor_type=__a)










from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""simple docstring"""







	A             =					HfArgumentParser(UpperCamelCase__    )
	A             =					parser.parse_args_into_dataclasses()[0]
	A             =					TensorFlowBenchmark(args=UpperCamelCase__    )
	try:
		A             =					parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		A             =					"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
		A             =					""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
		A             =					""""""
		A             =					eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
		A             =					[]
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]    )
			else:
				wrong_args.append(UpperCamelCase__    )
		if len(UpperCamelCase__    ) > 0:
			A             =					full_error_msg + begin_error_msg + str(UpperCamelCase__    )
		raise ValueError(UpperCamelCase__    )
	benchmark.run()


if __name__ == "__main__":
				main()



