



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


a_								=       logging.get_logger(__name__)


if is_vision_available():
  import PIL



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 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						,					):



    '''simple docstring'''
    super().__init__(**__UpperCAmelCase					)
    __lowerCamelCase    				=     size if size is not None else {'''shortest_edge''': 224}
    __lowerCamelCase    				=     get_size_dict(__UpperCAmelCase						,					default_to_square=__UpperCAmelCase					)
    __lowerCamelCase    				=     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
    __lowerCamelCase    				=     get_size_dict(__UpperCAmelCase						,					default_to_square=__UpperCAmelCase						,					param_name='''crop_size'''					)

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




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = PILImageResampling.BICUBIC						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     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()}"""					)
    __lowerCamelCase    				=     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						,					):



    '''simple docstring'''
    __lowerCamelCase    				=     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						,					):



    '''simple docstring'''
    return rescale(__UpperCAmelCase						,					scale=__UpperCAmelCase						,					data_format=__UpperCAmelCase						,					**__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    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						,					):



    '''simple docstring'''
    __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(__UpperCAmelCase						,					param_name='''size'''						,					default_to_square=__UpperCAmelCase					)
    __lowerCamelCase    				=     resample if resample is not None else self.resample
    __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(__UpperCAmelCase						,					param_name='''crop_size'''						,					default_to_square=__UpperCAmelCase					)
    __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    				=     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

    __lowerCamelCase    				=     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:
       __lowerCamelCase    				=     [convert_to_rgb(__UpperCAmelCase					) for image in images]

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

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

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

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

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

    __lowerCamelCase    				=     [to_channel_dimension_format(__UpperCAmelCase						,					__UpperCAmelCase					) for image in images]

    __lowerCamelCase    				=     {'''pixel_values''': images}
    return BatchFeature(data=__UpperCAmelCase						,					tensor_type=__UpperCAmelCase					)











def 		a__      (		_UpperCamelCase      :			int				):
   __lowerCamelCase    				=     int(_UpperCamelCase				)
   if decimal in (0, 1):  # Exit cases for the recursion
      return str(_UpperCamelCase				)
   __lowerCamelCase				,__lowerCamelCase    				=     divmod(_UpperCamelCase			,2				)
   return binary_recursive(_UpperCamelCase				) + str(_UpperCamelCase				)





def 		a__      (		_UpperCamelCase      :			str				):
   __lowerCamelCase    				=     str(_UpperCamelCase				).strip()
   if not number:
      raise ValueError('''No input value was provided'''				)
   __lowerCamelCase    				=     '''-''' if number.startswith('''-'''				) else ''''''
   __lowerCamelCase    				=     number.lstrip('''-'''				)
   if not number.isnumeric():
      raise ValueError('''Input value is not an integer'''				)
   return F"""{negative}0b{binary_recursive(int(_UpperCamelCase				)				)}"""


if __name__ == "__main__":
  from doctest import testmod

  testmod()






