





'''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


A__    :       List[str]              =logging.get_logger(__name__)


if is_vision_available():
      import PIL




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Any											=       ['''pixel_values''']




      def __init__(      self      :			Any      ,      __snake_case      :			bool = True      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			PILImageResampling = PILImageResampling.BICUBIC      ,      __snake_case      :			bool = True      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			bool = True      ,      __snake_case      :			Union[int, float] = 1 / 2_55      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			bool = True      ,      **__snake_case      :			int      ,      )    ->				None:
            super().__init__(**__snake_case  )
            _lowerCAmelCase												=							size if size is not None else {"""shortest_edge""": 2_24}
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      default_to_square=__snake_case  )
            _lowerCAmelCase												=							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      default_to_square=__snake_case      ,      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     lowercase__   (      self      :			Optional[Any]      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Dict[str, int]      ,      __snake_case      :			PILImageResampling = PILImageResampling.BICUBIC      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			Tuple      ,      )    ->				np.ndarray:
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      default_to_square=__snake_case  )
            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(__snake_case      ,      size=size["""shortest_edge"""]      ,      default_to_square=__snake_case  )
            return resize(__snake_case      ,      size=__snake_case      ,      resample=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Dict[str, int]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			Any      ,      )    ->				np.ndarray:
            _lowerCAmelCase												=							get_size_dict(__snake_case  )
            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(__snake_case      ,      size=(size["""height"""], size["""width"""])      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[str]      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Union[int, float]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			str      ,      )    ->				List[str]:
            return rescale(__snake_case      ,      scale=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Tuple      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Union[float, List[float]]      ,      __snake_case      :			Union[float, List[float]]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			List[str]      ,      )    ->				np.ndarray:
            return normalize(__snake_case      ,      mean=__snake_case      ,      std=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Optional[int]      ,      __snake_case      :			ImageInput      ,      __snake_case      :			bool = None      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			PILImageResampling = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			int = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			float = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			Optional[Union[str, TensorType]] = None      ,      __snake_case      :			Optional[ChannelDimension] = ChannelDimension.FIRST      ,      **__snake_case      :			Tuple      ,      )    ->				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(__snake_case      ,      param_name="""size"""      ,      default_to_square=__snake_case  )
            _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(__snake_case      ,      param_name="""crop_size"""      ,      default_to_square=__snake_case  )
            _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(__snake_case  )

            if not valid_images(__snake_case  ):
                  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(__snake_case  ) for image in images]

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

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

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

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

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

            _lowerCAmelCase												=							[to_channel_dimension_format(__snake_case      ,      __snake_case  ) for image in images]

            _lowerCAmelCase												=							{"""pixel_values""": images}
            return BatchFeature(data=__snake_case      ,      tensor_type=__snake_case  )













'''simple docstring'''






def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      if len(lowerCAmelCase  ) != len(lowerCAmelCase  ):
            raise ValueError("""String lengths must match!"""  )

      _lowerCAmelCase												=							0

      for chara, chara in zip(lowerCAmelCase							,     lowerCAmelCase  ):
            if chara != chara:
                  count += 1

      return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()






