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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


__lowerCAmelCase    :  List[Any]										= logging.get_logger(__name__)


if is_vision_available():
    import PIL
class 				lowerCamelCase			(							__snake_case					):
      __lowerCamelCase							=					['pixel_values']






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


          '''simple docstring'''







          super().__init__(**__lowerCamelCase						)

          snake_case:						str         =   size if size is not None else {"""height""": 25_60, """width""": 19_20}
          if isinstance(__lowerCamelCase  ,		(tuple, list)						):
              # The previous feature extractor size parameter was in (width, height) format
              snake_case:						Any         =   size[::-1]
          snake_case:						Dict         =   get_size_dict(__lowerCamelCase						)

          snake_case:						Optional[Any]         =   do_resize
          snake_case:						int         =   size
          snake_case:						int         =   resample
          snake_case:						List[str]         =   do_thumbnail
          snake_case:						int         =   do_align_long_axis
          snake_case:						Dict         =   do_pad
          snake_case:						Union[str, Any]         =   do_rescale
          snake_case:						str         =   rescale_factor
          snake_case:						Union[str, Any]         =   do_normalize
          snake_case:						List[str]         =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          snake_case:						Union[str, Any]         =   image_std if image_std is not None else IMAGENET_STANDARD_STD






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = None						)     ->				np.ndarray:


          '''simple docstring'''







          snake_case    , snake_case:						Dict         =   get_image_size(__lowerCamelCase						)
          snake_case    , snake_case:						int         =   size["""height"""], size["""width"""]

          if (output_width < output_height and input_width > input_height) or (
              output_width > output_height and input_width < input_height
          ):
              snake_case:						Optional[Any]         =   np.rotaa(__lowerCamelCase  ,		3						)

          if data_format is not None:
              snake_case:						int         =   to_channel_dimension_format(__lowerCamelCase  ,		__lowerCamelCase						)

          return image






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		)     ->				np.ndarray:


          '''simple docstring'''







          snake_case    , snake_case:						Dict         =   size["""height"""], size["""width"""]
          snake_case    , snake_case:						Optional[Any]         =   get_image_size(__lowerCamelCase						)

          snake_case:						int         =   output_width - input_width
          snake_case:						str         =   output_height - input_height

          if random_padding:
              snake_case:						Dict         =   np.random.randint(low=0  ,		high=delta_height + 1						)
              snake_case:						str         =   np.random.randint(low=0  ,		high=delta_width + 1						)
          else:
              snake_case:						str         =   delta_height // 2
              snake_case:						Optional[int]         =   delta_width // 2

          snake_case:						List[Any]         =   delta_height - pad_top
          snake_case:						List[str]         =   delta_width - pad_left

          snake_case:						Optional[int]         =   ((pad_top, pad_bottom), (pad_left, pad_right))
          return pad(__lowerCamelCase  ,		__lowerCamelCase  ,		data_format=__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		*__lowerCamelCase  ,		**__lowerCamelCase						)     ->				str:


          '''simple docstring'''







          logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""						)
          return self.pad_image(*__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = PILImageResampling.BICUBIC  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				np.ndarray:


          '''simple docstring'''







          snake_case    , snake_case:						List[Any]         =   get_image_size(__lowerCamelCase						)
          snake_case    , snake_case:						Any         =   size["""height"""], size["""width"""]

          # We always resize to the smallest of either the input or output size.
          snake_case:						str         =   min(__lowerCamelCase  ,		__lowerCamelCase						)
          snake_case:						int         =   min(__lowerCamelCase  ,		__lowerCamelCase						)

          if height == input_height and width == input_width:
              return image

          if input_height > input_width:
              snake_case:						Union[str, Any]         =   int(input_width * height / input_height						)
          elif input_width > input_height:
              snake_case:						List[str]         =   int(input_height * width / input_width						)

          return resize(
              __lowerCamelCase  ,		size=(height, width)  ,		resample=__lowerCamelCase  ,		reducing_gap=2.0  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = PILImageResampling.BICUBIC  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				np.ndarray:


          '''simple docstring'''







          snake_case:						str         =   get_size_dict(__lowerCamelCase						)
          snake_case:						List[str]         =   min(size["""height"""]  ,		size["""width"""]						)
          snake_case:						Dict         =   get_resize_output_image_size(__lowerCamelCase  ,		size=__lowerCamelCase  ,		default_to_square=__lowerCamelCase						)
          snake_case:						Optional[Any]         =   resize(__lowerCamelCase  ,		size=__lowerCamelCase  ,		resample=__lowerCamelCase  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)
          return resized_image






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				Union[str, Any]:


          '''simple docstring'''







          return rescale(__lowerCamelCase  ,		scale=__lowerCamelCase  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		**__lowerCamelCase  ,		)     ->				np.ndarray:


          '''simple docstring'''







          return normalize(__lowerCamelCase  ,		mean=__lowerCamelCase  ,		std=__lowerCamelCase  ,		data_format=__lowerCamelCase  ,		**__lowerCamelCase						)






      def 			lowerCAmelCase_   (      self  ,		__lowerCamelCase  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = False  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = None  ,		__lowerCamelCase = ChannelDimension.FIRST  ,		**__lowerCamelCase  ,		)     ->				PIL.Image.Image:


          '''simple docstring'''







          snake_case:						Dict         =   do_resize if do_resize is not None else self.do_resize
          snake_case:						List[str]         =   size if size is not None else self.size
          if isinstance(__lowerCamelCase  ,		(tuple, list)						):
              # Previous feature extractor had size in (width, height) format
              snake_case:						str         =   size[::-1]
          snake_case:						Optional[Any]         =   get_size_dict(__lowerCamelCase						)
          snake_case:						int         =   resample if resample is not None else self.resample
          snake_case:						str         =   do_thumbnail if do_thumbnail is not None else self.do_thumbnail
          snake_case:						Optional[Any]         =   do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
          snake_case:						Tuple         =   do_pad if do_pad is not None else self.do_pad
          snake_case:						List[Any]         =   do_rescale if do_rescale is not None else self.do_rescale
          snake_case:						str         =   rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case:						Dict         =   do_normalize if do_normalize is not None else self.do_normalize
          snake_case:						List[str]         =   image_mean if image_mean is not None else self.image_mean
          snake_case:						int         =   image_std if image_std is not None else self.image_std

          snake_case:						Any         =   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_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True."""						)

          if do_pad and size is None:
              raise ValueError("""Size must be specified if do_pad 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.
          snake_case:						Optional[Any]         =   [to_numpy_array(__lowerCamelCase						) for image in images]

          if do_align_long_axis:
              snake_case:						int         =   [self.align_long_axis(__lowerCamelCase  ,		size=__lowerCamelCase						) for image in images]

          if do_resize:
              snake_case:						Any         =   [self.resize(image=__lowerCamelCase  ,		size=__lowerCamelCase  ,		resample=__lowerCamelCase						) for image in images]

          if do_thumbnail:
              snake_case:						Dict         =   [self.thumbnail(image=__lowerCamelCase  ,		size=__lowerCamelCase						) for image in images]

          if do_pad:
              snake_case:						Optional[Any]         =   [self.pad_image(image=__lowerCamelCase  ,		size=__lowerCamelCase  ,		random_padding=__lowerCamelCase						) for image in images]

          if do_rescale:
              snake_case:						str         =   [self.rescale(image=__lowerCamelCase  ,		scale=__lowerCamelCase						) for image in images]

          if do_normalize:
              snake_case:						Dict         =   [self.normalize(image=__lowerCamelCase  ,		mean=__lowerCamelCase  ,		std=__lowerCamelCase						) for image in images]

          snake_case:						str         =   [to_channel_dimension_format(__lowerCamelCase  ,		__lowerCamelCase						) for image in images]

          snake_case:						Tuple         =   {"""pixel_values""": images}
          return BatchFeature(data=__lowerCamelCase  ,		tensor_type=__lowerCamelCase						)



def   a_					(_lowerCAmelCase  :   str       )->				int:
    snake_case:						List[str]         =   0
    for letter in data:
        snake_case:						int         =   (hash_ << 4) + ord(_lowerCAmelCase       )
        snake_case:						Optional[int]         =   hash_ & 0xf_0_0_0_0_0_0_0
        if x != 0:
            hash_ ^= x >> 24
        hash_ &= ~x
    return hash_


if __name__ == "__main__":
    import doctest

    doctest.testmod()


