





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


if is_vision_available():
    import PIL


A_				     =							logging.get_logger(__name__)


class 		UpperCamelCase__     (       a							):
      '''simple docstring'''






      _snake_case           =				['''pixel_values''']




      def __init__(     self     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = PIL.Image.NEAREST     , SCREAMING_SNAKE_CASE = False     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = 1 / 2_55     , SCREAMING_SNAKE_CASE = False     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = True     , **SCREAMING_SNAKE_CASE     , )				->  None:
        super().__init__(**SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  size if size is not None else {'height': 3_46, 'width': 3_46}
        __lowerCAmelCase		:    str			     =  get_size_dict(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    Tuple			     =  crop_size if crop_size is not None else {'height': 2_89, 'width': 2_89}
        __lowerCAmelCase		:    Tuple			     =  get_size_dict(SCREAMING_SNAKE_CASE     , param_name='crop_size' )

        __lowerCAmelCase		:    List[Any]			     =  do_resize
        __lowerCAmelCase		:    Optional[int]			     =  size
        __lowerCAmelCase		:    Any			     =  resample
        __lowerCAmelCase		:    Union[str, Any]			     =  do_center_crop
        __lowerCAmelCase		:    Tuple			     =  crop_size
        __lowerCAmelCase		:    Optional[int]			     =  do_rescale
        __lowerCAmelCase		:    List[Any]			     =  rescale_factor
        __lowerCAmelCase		:    int			     =  rescale_offset
        __lowerCAmelCase		:    Tuple			     =  do_normalize
        __lowerCAmelCase		:    Dict			     =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __lowerCAmelCase		:    Tuple			     =  image_std if image_std is not None else IMAGENET_STANDARD_STD
        __lowerCAmelCase		:    Optional[int]			     =  include_top




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = PIL.Image.NEAREST     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  np.ndarray:
        __lowerCAmelCase		:    Optional[int]			     =  get_size_dict(SCREAMING_SNAKE_CASE )
        if "height" not in size or "width" not in size:
          raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
        return resize(
            SCREAMING_SNAKE_CASE     , size=(size['height'], size['width'])     , resample=SCREAMING_SNAKE_CASE     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  np.ndarray:
        __lowerCAmelCase		:    Union[str, Any]			     =  get_size_dict(SCREAMING_SNAKE_CASE )
        if "height" not in size or "width" not in size:
          raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}""" )
        return center_crop(SCREAMING_SNAKE_CASE     , size=(size['height'], size['width'])     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = True     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  Any:
        if offset:
          __lowerCAmelCase		:    Optional[int]			     =  (image - 1_2_7.5) * scale
          if data_format is not None:
            __lowerCAmelCase		:    Tuple			     =  to_channel_dimension_format(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE )
          __lowerCAmelCase		:    Tuple			     =  rescaled_image.astype(np.floataa )
        else:
          __lowerCAmelCase		:    List[str]			     =  rescale(SCREAMING_SNAKE_CASE     , scale=SCREAMING_SNAKE_CASE     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )
        return rescaled_image




      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , **SCREAMING_SNAKE_CASE     , )				->  np.ndarray:
        return normalize(SCREAMING_SNAKE_CASE     , mean=SCREAMING_SNAKE_CASE     , std=SCREAMING_SNAKE_CASE     , data_format=SCREAMING_SNAKE_CASE     , **SCREAMING_SNAKE_CASE )







      def 	snake_case  (     self     , SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE=None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = None     , SCREAMING_SNAKE_CASE = ChannelDimension.FIRST     , **SCREAMING_SNAKE_CASE     , )				->  PIL.Image.Image:
        __lowerCAmelCase		:    List[str]			     =  do_resize if do_resize is not None else self.do_resize
        __lowerCAmelCase		:    int			     =  resample if resample is not None else self.resample
        __lowerCAmelCase		:    Union[str, Any]			     =  do_center_crop if do_center_crop is not None else self.do_center_crop
        __lowerCAmelCase		:    Optional[int]			     =  do_rescale if do_rescale is not None else self.do_rescale
        __lowerCAmelCase		:    Tuple			     =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __lowerCAmelCase		:    Optional[int]			     =  rescale_offset if rescale_offset is not None else self.rescale_offset
        __lowerCAmelCase		:    int			     =  do_normalize if do_normalize is not None else self.do_normalize
        __lowerCAmelCase		:    Optional[Any]			     =  image_mean if image_mean is not None else self.image_mean
        __lowerCAmelCase		:    Optional[int]			     =  image_std if image_std is not None else self.image_std
        __lowerCAmelCase		:    int			     =  include_top if include_top is not None else self.include_top

        __lowerCAmelCase		:    List[str]			     =  size if size is not None else self.size
        __lowerCAmelCase		:    int			     =  get_size_dict(SCREAMING_SNAKE_CASE )
        __lowerCAmelCase		:    int			     =  crop_size if crop_size is not None else self.crop_size
        __lowerCAmelCase		:    List[Any]			     =  get_size_dict(SCREAMING_SNAKE_CASE     , param_name='crop_size' )

        __lowerCAmelCase		:    Optional[Any]			     =  make_list_of_images(SCREAMING_SNAKE_CASE )

        if not valid_images(SCREAMING_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 or resample is None:
          raise ValueError('Size and resample 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.' )

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

        if do_resize:
          __lowerCAmelCase		:    Dict			     =  [self.resize(image=SCREAMING_SNAKE_CASE     , size=SCREAMING_SNAKE_CASE     , resample=SCREAMING_SNAKE_CASE ) for image in images]

        if do_center_crop:
          __lowerCAmelCase		:    str			     =  [self.center_crop(image=SCREAMING_SNAKE_CASE     , size=SCREAMING_SNAKE_CASE ) for image in images]

        if do_rescale:
          __lowerCAmelCase		:    List[Any]			     =  [self.rescale(image=SCREAMING_SNAKE_CASE     , scale=SCREAMING_SNAKE_CASE     , offset=SCREAMING_SNAKE_CASE ) for image in images]

        if do_normalize:
          __lowerCAmelCase		:    Tuple			     =  [self.normalize(image=SCREAMING_SNAKE_CASE     , mean=SCREAMING_SNAKE_CASE     , std=SCREAMING_SNAKE_CASE ) for image in images]

        if include_top:
          __lowerCAmelCase		:    Any			     =  [self.normalize(image=SCREAMING_SNAKE_CASE     , mean=[0, 0, 0]     , std=SCREAMING_SNAKE_CASE ) for image in images]

        __lowerCAmelCase		:    List[str]			     =  [to_channel_dimension_format(SCREAMING_SNAKE_CASE     , SCREAMING_SNAKE_CASE ) for image in images]

        __lowerCAmelCase		:    Tuple			     =  {'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE     , tensor_type=SCREAMING_SNAKE_CASE )












'''simple docstring'''


from __future__ import annotations




def       A							(      _UpperCAmelCase   :				list					)  ->				int | float:


  '''simple docstring'''




  __lowerCAmelCase		:    List[Any]			     =  sorted(_UpperCAmelCase					)
  __lowerCAmelCase		:    List[str]			     =  len(_UpperCAmelCase					)
  __lowerCAmelCase		:    Any			     =  length >> 1
  return (
      (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
      if length % 2 == 0
      else sorted_list[mid_index]
  )




def       A							(      )  ->				Union[str, Any]:


  '''simple docstring'''




  import doctest

  doctest.testmod()


if __name__ == "__main__":
    main()





