

"""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


__lowerCamelCase										=		logging.get_logger(__name__)


if is_vision_available():
 import PIL
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''pixel_values''']







     def __init__(      self ,						UpperCamelCase_ = True ,						UpperCamelCase_ = None ,						UpperCamelCase_ = PILImageResampling.BICUBIC ,						UpperCamelCase_ = True ,						UpperCamelCase_ = None ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 1 / 255 ,						UpperCamelCase_ = True ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = True ,						**UpperCamelCase_ ,						):
      super().__init__(**UpperCamelCase_       )
      __magic_name__     =      size if size is not None else {'''shortest_edge''': 224}
      __magic_name__     =      get_size_dict(UpperCamelCase_ ,						default_to_square=UpperCamelCase_       )
      __magic_name__     =      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      __magic_name__     =      get_size_dict(UpperCamelCase_ ,						default_to_square=UpperCamelCase_ ,						param_name='''crop_size'''       )

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







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = PILImageResampling.BICUBIC ,						UpperCamelCase_ = None ,						**UpperCamelCase_ ,						):
      __magic_name__     =      get_size_dict(UpperCamelCase_ ,						default_to_square=UpperCamelCase_       )
      if "shortest_edge" not in size:
       raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''       )
      __magic_name__     =      get_resize_output_image_size(UpperCamelCase_ ,						size=size['''shortest_edge'''] ,						default_to_square=UpperCamelCase_       )
      return resize(UpperCamelCase_ ,						size=UpperCamelCase_ ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_ ,						):
      __magic_name__     =      get_size_dict(UpperCamelCase_       )
      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(UpperCamelCase_ ,						size=(size['''height'''], size['''width''']) ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_ ,						):
      return rescale(UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_ ,						):
      return normalize(UpperCamelCase_ ,						mean=UpperCamelCase_ ,						std=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = ChannelDimension.FIRST ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize if do_resize is not None else self.do_resize
      __magic_name__     =      size if size is not None else self.size
      __magic_name__     =      get_size_dict(UpperCamelCase_ ,						param_name='''size''' ,						default_to_square=UpperCamelCase_       )
      __magic_name__     =      resample if resample is not None else self.resample
      __magic_name__     =      do_center_crop if do_center_crop is not None else self.do_center_crop
      __magic_name__     =      crop_size if crop_size is not None else self.crop_size
      __magic_name__     =      get_size_dict(UpperCamelCase_ ,						param_name='''crop_size''' ,						default_to_square=UpperCamelCase_       )
      __magic_name__     =      do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__     =      rescale_factor if rescale_factor is not None else self.rescale_factor
      __magic_name__     =      do_normalize if do_normalize is not None else self.do_normalize
      __magic_name__     =      image_mean if image_mean is not None else self.image_mean
      __magic_name__     =      image_std if image_std is not None else self.image_std
      __magic_name__     =      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

      __magic_name__     =      make_list_of_images(UpperCamelCase_       )

      if not valid_images(UpperCamelCase_       ):
       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:
       __magic_name__     =      [convert_to_rgb(UpperCamelCase_       ) for image in images]

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

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

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

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

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

      __magic_name__     =      [to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_       ) for image in images]

      __magic_name__     =      {'''pixel_values''': images}
      return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )




"""simple docstring"""






class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_       ):
      __magic_name__     =      size
      __magic_name__     =      [0] * size
      __magic_name__     =      [0] * size







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return index | (index + 1)







     @staticmethod
     def 	lowerCAmelCase__    (      UpperCamelCase_       ):
      return (index & (index + 1)) - 1







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      value
      while index < self.size:
       __magic_name__     =      self.get_prev(UpperCamelCase_       ) + 1
       if current_left_border == index:
        __magic_name__     =      value
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       )
       __magic_name__     =      self.get_next(UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      right -= 1  # Because of right is exclusive
      __magic_name__     =      0
      while left <= right:
       __magic_name__     =      self.get_prev(UpperCamelCase_       )
       if left <= current_left:
        __magic_name__     =      max(UpperCamelCase_ ,						self.tree[right]       )
        __magic_name__     =      current_left
       else:
        __magic_name__     =      max(UpperCamelCase_ ,						self.arr[right]       )
        right -= 1
      return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()

