

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


_UpperCamelCase     =    logging.get_logger(__name__)


if is_vision_available():
    import PIL



class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 __SCREAMING_SNAKE_CASE      							=       ["""pixel_values"""]




 def __init__(self , __a = True , __a = None , __a = PILImageResampling.BICUBIC , __a = True , __a = None , __a = True , __a = 1 / 255 , __a = True , __a = None , __a = None , __a = True , **__a , )				->      None:






  """simple docstring"""





  super().__init__(**__a				)
  UpperCAmelCase__           =       size if size is not None else {'''shortest_edge''': 224}
  UpperCAmelCase__           =       get_size_dict(__a , default_to_square=__a				)
  UpperCAmelCase__           =       crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
  UpperCAmelCase__           =       get_size_dict(__a				)

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




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






  """simple docstring"""





  UpperCAmelCase__           =       get_size_dict(__a , default_to_square=__a				)
  UpperCAmelCase__           =       get_resize_output_image_size(
      __a , size=(size['height'], size['width']) , default_to_square=__a				)
  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				)
  return center_crop(__a , size=(size['height'], size['width']) , data_format=__a , **__a				)




 def 						UpperCamelCase__							(self , __a , __a , __a = None , **__a , )				->      Any:






  """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 = 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__           =       size if size is not None else self.size
  UpperCAmelCase__           =       get_size_dict(__a , default_to_square=__a				)
  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__           =       crop_size if crop_size is not None else self.crop_size
  UpperCAmelCase__           =       get_size_dict(__a				)
  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__           =       do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

  UpperCAmelCase__           =       make_list_of_images(__a				)

  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.'				)

  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:
   UpperCAmelCase__           =       [convert_to_rgb(__a				) for image in images]

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

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

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

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

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

  UpperCAmelCase__           =       [to_channel_dimension_format(__a , __a				) for image in images]

  UpperCAmelCase__           =       {'''pixel_values''': images}
  return BatchFeature(data=__a , tensor_type=__a				)






def 		UpperCamelCase_(    snake_case__:						int     ) ->				int:
 if not isinstance(snake_case__						,					snake_case__     ):
  UpperCAmelCase__           =       f"Input value of [number={number}] must be an integer"
  raise TypeError(snake_case__     )
 if number < 1:
  raise ValueError('Input must be a positive integer'     )
 return number * (2 * number - 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



