





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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__lowercase													=			logging.get_logger(__name__)

class   __snake_case				(			_a   ):






    '''simple docstring'''
    _lowerCAmelCase     =	["""pixel_values"""]





    def __init__(   self						:		List[Any]			,						snake_case						:		bool = True			,						snake_case						:		Dict[str, int] = None			,						snake_case						:		bool = True			,						snake_case						:		Dict[str, int] = None			,						snake_case						:		PILImageResampling = PILImageResampling.BICUBIC			,						snake_case						:		bool = True			,						snake_case						:		Union[int, float] = 1 / 255			,						snake_case						:		bool = True			,						snake_case						:		Optional[Union[float, List[float]]] = None			,						snake_case						:		Optional[Union[float, List[float]]] = None			,						**snake_case						:		Optional[Any]			,						):

      """simple docstring"""





      super().__init__(**snake_case							)
      __lowerCAmelCase			=				crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
      __lowerCAmelCase			=				get_size_dict(snake_case			,						param_name='''crop_size'''							)
      __lowerCAmelCase			=				size if size is not None else {'''height''': 224, '''width''': 224}
      __lowerCAmelCase			=				get_size_dict(snake_case							)

      __lowerCAmelCase			=				do_center_crop
      __lowerCAmelCase			=				crop_size
      __lowerCAmelCase			=				do_resize
      __lowerCAmelCase			=				size
      __lowerCAmelCase			=				resample
      __lowerCAmelCase			=				do_rescale
      __lowerCAmelCase			=				rescale_factor
      __lowerCAmelCase			=				do_normalize
      __lowerCAmelCase			=				image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      __lowerCAmelCase			=				image_std if image_std is not None else IMAGENET_DEFAULT_STD





    def 	A		(   self						:		Optional[Any]			,						snake_case						:		np.ndarray			,						snake_case						:		Dict[str, int]			,						snake_case						:		Optional[int] = None			,						snake_case						:		Optional[Union[str, ChannelDimension]] = None			,						**snake_case						:		str			,						):

      """simple docstring"""





      __lowerCAmelCase			=				self.size if size is None else size
      __lowerCAmelCase			=				get_size_dict(snake_case							)
      __lowerCAmelCase			=				get_size_dict(snake_case			,						param_name='''crop_size'''							)

      __lowerCAmelCase	,						__lowerCAmelCase			=				get_image_size(snake_case							)
      __lowerCAmelCase			=				min(snake_case			,						snake_case							)
      __lowerCAmelCase			=				(size['''height'''] / crop_size['''height''']) * min_dim
      __lowerCAmelCase			=				(size['''width'''] / crop_size['''width''']) * min_dim
      return center_crop(snake_case			,						size=(cropped_height, cropped_width)			,						data_format=snake_case			,						**snake_case							)





    def 	A		(   self						:		List[str]			,						snake_case						:		np.ndarray			,						snake_case						:		Dict[str, int]			,						snake_case						:		PILImageResampling = PIL.Image.BICUBIC			,						snake_case						:		Optional[Union[str, ChannelDimension]] = None			,						**snake_case						:		List[str]			,						):

      """simple docstring"""





      __lowerCAmelCase			=				get_size_dict(snake_case							)
      if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''							)
      return resize(
          snake_case			,						size=(size['''height'''], size['''width'''])			,						resample=snake_case			,						data_format=snake_case			,						**snake_case							)





    def 	A		(   self						:		List[Any]			,						snake_case						:		np.ndarray			,						snake_case						:		Union[int, float]			,						snake_case						:		Optional[Union[str, ChannelDimension]] = None			,						**snake_case						:		str			,						):

      """simple docstring"""





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





    def 	A		(   self						:		int			,						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]			,						):

      """simple docstring"""





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





    def 	A		(   self						:		int			,						snake_case						:		ImageInput			,						snake_case						:		Optional[bool] = None			,						snake_case						:		Optional[Dict[str, int]] = None			,						snake_case						:		Optional[bool] = None			,						snake_case						:		Optional[Dict[str, int]] = None			,						snake_case						:		PILImageResampling = None			,						snake_case						:		Optional[bool] = None			,						snake_case						:		Optional[float] = None			,						snake_case						:		Optional[bool] = None			,						snake_case						:		Optional[Union[float, List[float]]] = None			,						snake_case						:		Optional[Union[float, List[float]]] = None			,						snake_case						:		Optional[Union[str, TensorType]] = None			,						snake_case						:		ChannelDimension = ChannelDimension.FIRST			,						**snake_case						:		Dict			,						):

      """simple docstring"""





      __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'''							)
      __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							)
      __lowerCAmelCase			=				resample if resample is not None else self.resample
      __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			=				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_center_crop and crop_size is None:
        raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''							)

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

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

      if do_center_crop:
        __lowerCAmelCase			=				[self.center_crop(snake_case			,						snake_case			,						size=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_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							)







class   __snake_case				:






    '''simple docstring'''





    def __init__(   self						:		Any							):

      """simple docstring"""





      __lowerCAmelCase			=				{}
      # indexmap keys are an index and it's values are the number at that index
      __lowerCAmelCase			=				{}





    def 	A		(   self						:		List[Any]			,						snake_case						:		list | str | range			,						snake_case						:		int							):

      """simple docstring"""





      if isinstance(snake_case			,						(range, str)							):
        __lowerCAmelCase			=				list(snake_case							)
      elif not isinstance(snake_case			,						snake_case							):
        raise TypeError(
            '''binary_search_delete() only accepts either a list, range or str'''							)

      __lowerCAmelCase			=				0
      __lowerCAmelCase			=				len(snake_case							) - 1

      while low <= high:
        __lowerCAmelCase			=				(low + high) // 2
        if array[mid] == item:
          array.pop(snake_case							)
          return array
        elif array[mid] < item:
          __lowerCAmelCase			=				mid + 1
        else:
          __lowerCAmelCase			=				mid - 1
      raise ValueError(
          '''Either the item is not in the array or the array was unsorted'''							)





    def 	A		(   self						:		Tuple			,						snake_case						:		list | str | range			,						snake_case						:		int							):

      """simple docstring"""





      if isinstance(snake_case			,						(range, str)							):
        __lowerCAmelCase			=				list(snake_case							)
      elif not isinstance(snake_case			,						snake_case							):
        raise TypeError(
            '''binary_search_insert() only accepts either a list, range or str'''							)

      __lowerCAmelCase			=				0
      __lowerCAmelCase			=				len(snake_case							) - 1

      while low <= high:
        __lowerCAmelCase			=				(low + high) // 2
        if array[mid] == index:
          # If the item already exists in the array,
          # insert it after the existing item
          array.insert(mid + 1			,						snake_case							)
          return array
        elif array[mid] < index:
          __lowerCAmelCase			=				mid + 1
        else:
          __lowerCAmelCase			=				mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
      array.insert(snake_case			,						snake_case							)
      return array





    def 	A		(   self						:		Tuple			,						snake_case						:		int			,						snake_case						:		int							):

      """simple docstring"""





      if index in self.indexmap:
        __lowerCAmelCase			=				self.indexmap[index]
        if len(self.numbermap[n]							) == 1:
          del self.numbermap[n]
        else:
          __lowerCAmelCase			=				self.binary_search_delete(self.numbermap[n]			,						snake_case							)

        # Set new index
      __lowerCAmelCase			=				number

      # Number not seen before or empty so insert number value
      if number not in self.numbermap:
        __lowerCAmelCase			=				[index]

      # Here we need to perform a binary search insertion in order to insert
      # The item in the correct place
      else:
        __lowerCAmelCase			=				self.binary_search_insert(
            self.numbermap[number]			,						snake_case							)





    def 	A		(   self						:		List[str]			,						snake_case						:		int							):

      """simple docstring"""





      return self.numbermap.get(snake_case			,						[-1]							)[0]


if __name__ == "__main__":
      import doctest

      doctest.testmod()
