
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


__A				=logging.get_logger(__name__)


if is_vision_available():
			import PIL




class     _SCREAMING_SNAKE_CASE     (      lowerCamelCase_						):
	lowerCAmelCase__         = ["""pixel_values"""]



	def __init__(    self						,  lowercase = True						,  lowercase = None						,  lowercase = PILImageResampling.BICUBIC						,  lowercase = True						,  lowercase = None						,  lowercase = True						,  lowercase = 1 / 255						,  lowercase = True						,  lowercase = None						,  lowercase = None						,  lowercase = True						,  **lowercase						,  )				->       None:
					super().__init__(**lowerCAmelCase__ )
					lowerCamelCase_						=			size if size is not None else {"shortest_edge": 224}
					lowerCamelCase_						=			get_size_dict(lowerCAmelCase__						,  default_to_square=lowerCAmelCase__ )
					lowerCamelCase_						=			crop_size if crop_size is not None else {"height": 224, "width": 224}
					lowerCamelCase_						=			get_size_dict(lowerCAmelCase__						,  default_to_square=lowerCAmelCase__						,  param_name="crop_size" )

					lowerCamelCase_						=			do_resize
					lowerCamelCase_						=			size
					lowerCamelCase_						=			resample
					lowerCamelCase_						=			do_center_crop
					lowerCamelCase_						=			crop_size
					lowerCamelCase_						=			do_rescale
					lowerCamelCase_						=			rescale_factor
					lowerCamelCase_						=			do_normalize
					lowerCamelCase_						=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					lowerCamelCase_						=			image_std if image_std is not None else OPENAI_CLIP_STD
					lowerCamelCase_						=			do_convert_rgb



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = PILImageResampling.BICUBIC						,  lowercase = None						,  **lowercase						,  )				->       np.ndarray:
					lowerCamelCase_						=			get_size_dict(lowerCAmelCase__						,  default_to_square=lowerCAmelCase__ )
					if "shortest_edge" not in size:
									raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
					lowerCamelCase_						=			get_resize_output_image_size(lowerCAmelCase__						,  size=size["shortest_edge"]						,  default_to_square=lowerCAmelCase__ )
					return resize(lowerCAmelCase__						,  size=lowerCAmelCase__						,  resample=lowerCAmelCase__						,  data_format=lowerCAmelCase__						,  **lowerCAmelCase__ )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = None						,  **lowercase						,  )				->       np.ndarray:
					lowerCamelCase_						=			get_size_dict(lowerCAmelCase__ )
					if "height" not in size or "width" not in size:
									raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
					return center_crop(lowerCAmelCase__						,  size=(size["height"], size["width"])						,  data_format=lowerCAmelCase__						,  **lowerCAmelCase__ )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase = None						,  **lowercase						,  )				->       Optional[Any]:
					return rescale(lowerCAmelCase__						,  scale=lowerCAmelCase__						,  data_format=lowerCAmelCase__						,  **lowerCAmelCase__ )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase = None						,  **lowercase						,  )				->       np.ndarray:
					return normalize(lowerCAmelCase__						,  mean=lowerCAmelCase__						,  std=lowerCAmelCase__						,  data_format=lowerCAmelCase__						,  **lowerCAmelCase__ )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = ChannelDimension.FIRST						,  **lowercase						,  )				->       PIL.Image.Image:
					lowerCamelCase_						=			do_resize if do_resize is not None else self.do_resize
					lowerCamelCase_						=			size if size is not None else self.size
					lowerCamelCase_						=			get_size_dict(lowerCAmelCase__						,  param_name="size"						,  default_to_square=lowerCAmelCase__ )
					lowerCamelCase_						=			resample if resample is not None else self.resample
					lowerCamelCase_						=			do_center_crop if do_center_crop is not None else self.do_center_crop
					lowerCamelCase_						=			crop_size if crop_size is not None else self.crop_size
					lowerCamelCase_						=			get_size_dict(lowerCAmelCase__						,  param_name="crop_size"						,  default_to_square=lowerCAmelCase__ )
					lowerCamelCase_						=			do_rescale if do_rescale is not None else self.do_rescale
					lowerCamelCase_						=			rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCamelCase_						=			do_normalize if do_normalize is not None else self.do_normalize
					lowerCamelCase_						=			image_mean if image_mean is not None else self.image_mean
					lowerCamelCase_						=			image_std if image_std is not None else self.image_std
					lowerCamelCase_						=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					lowerCamelCase_						=			make_list_of_images(lowerCAmelCase__ )

					if not valid_images(lowerCAmelCase__ ):
									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:
									lowerCamelCase_						=			[convert_to_rgb(lowerCAmelCase__ ) for image in images]

					# All transformations expect numpy arrays.
					lowerCamelCase_						=			[to_numpy_array(lowerCAmelCase__ ) for image in images]

					if do_resize:
									lowerCamelCase_						=			[self.resize(image=lowerCAmelCase__						,  size=lowerCAmelCase__						,  resample=lowerCAmelCase__ ) for image in images]

					if do_center_crop:
									lowerCamelCase_						=			[self.center_crop(image=lowerCAmelCase__						,  size=lowerCAmelCase__ ) for image in images]

					if do_rescale:
									lowerCamelCase_						=			[self.rescale(image=lowerCAmelCase__						,  scale=lowerCAmelCase__ ) for image in images]

					if do_normalize:
									lowerCamelCase_						=			[self.normalize(image=lowerCAmelCase__						,  mean=lowerCAmelCase__						,  std=lowerCAmelCase__ ) for image in images]

					lowerCamelCase_						=			[to_channel_dimension_format(lowerCAmelCase__						,  lowerCAmelCase__ ) for image in images]

					lowerCamelCase_						=			{"pixel_values": images}
					return BatchFeature(data=lowerCAmelCase__						,  tensor_type=lowerCAmelCase__ )






"""simple docstring"""




from __future__ import annotations


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						list[int]:
						return [ord(SCREAMING_SNAKE_CASE_  ) - 96 for elem in plain]


def     lowercase			(SCREAMING_SNAKE_CASE_		:				list[int]  )   ->						str:
						return "".join(chr(elem + 96  ) for elem in encoded  )


def     lowercase			()   ->						None:
						SCREAMING_SNAKE_CASE													=					encode(input('-> '  ).strip().lower()  )
						print('Encoded: ' ,			SCREAMING_SNAKE_CASE_  )
						print('Decoded:' ,			decode(SCREAMING_SNAKE_CASE_  )  )


if __name__ == "__main__":
	main()


