




'''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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						['''pixel_values''']



   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     Any = True  ,				__lowerCamelCase :     Optional[Any] = None  ,				__lowerCamelCase :     str = PILImageResampling.BICUBIC  ,				__lowerCamelCase :     str = True  ,				__lowerCamelCase :     int = True  ,				__lowerCamelCase :     Any = 1 / 2_5_5  ,				__lowerCamelCase :     List[Any] = None  ,				__lowerCamelCase :     Tuple = True  ,				__lowerCamelCase :     List[Any] = None  ,				__lowerCamelCase :     str = None  ,				**__lowerCamelCase :     List[str]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				default_to_square=__lowerCamelCase  ,				param_name="crop_size"   )

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



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Optional[Any]  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     int = PILImageResampling.BILINEAR  ,				__lowerCamelCase :     Dict = None  ,				**__lowerCamelCase :     Optional[Any]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )

      if "shortest_edge" in size:
         _SCREAMING_SNAKE_CASE	=				get_resize_output_image_size(__lowerCamelCase  ,				size=size["shortest_edge"]  ,				default_to_square=__lowerCamelCase   )
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
         _SCREAMING_SNAKE_CASE	=				(size['height'], size['width'])
      else:
         raise ValueError(F"""Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}"""   )
      return resize(__lowerCamelCase  ,				size=__lowerCamelCase  ,				resample=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     int = None  ,				**__lowerCamelCase :     int  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )
      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(__lowerCamelCase  ,				size=(size["height"], size["width"])  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Any  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Dict  ,				__lowerCamelCase :     Any = None  ,				**__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      return rescale(__lowerCamelCase  ,				scale=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     str  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     Any = None  ,				**__lowerCamelCase :     Dict  ,				):

      """simple docstring"""
      return normalize(__lowerCamelCase  ,				mean=__lowerCamelCase  ,				std=__lowerCamelCase  ,				data_format=__lowerCamelCase  ,				**__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Any  ,				__lowerCamelCase :     Optional[Any] = None  ,				__lowerCamelCase :     List[Any] = None  ,				__lowerCamelCase :     List[str] = None  ,				__lowerCamelCase :     Dict = None  ,				__lowerCamelCase :     str = None  ,				__lowerCamelCase :     List[Any] = None  ,				__lowerCamelCase :     Optional[Any] = None  ,				__lowerCamelCase :     List[str] = None  ,				__lowerCamelCase :     int = None  ,				__lowerCamelCase :     Optional[int] = None  ,				__lowerCamelCase :     Optional[Any] = None  ,				__lowerCamelCase :     str = ChannelDimension.FIRST  ,				**__lowerCamelCase :     Optional[Any]  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				do_resize if do_resize is not None else self.do_resize
      _SCREAMING_SNAKE_CASE	=				do_rescale if do_rescale is not None else self.do_rescale
      _SCREAMING_SNAKE_CASE	=				do_normalize if do_normalize is not None else self.do_normalize
      _SCREAMING_SNAKE_CASE	=				do_center_crop if do_center_crop is not None else self.do_center_crop
      _SCREAMING_SNAKE_CASE	=				crop_size if crop_size is not None else self.crop_size
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase  ,				param_name="crop_size"  ,				default_to_square=__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				resample if resample is not None else self.resample
      _SCREAMING_SNAKE_CASE	=				rescale_factor if rescale_factor is not None else self.rescale_factor
      _SCREAMING_SNAKE_CASE	=				image_mean if image_mean is not None else self.image_mean
      _SCREAMING_SNAKE_CASE	=				image_std if image_std is not None else self.image_std

      _SCREAMING_SNAKE_CASE	=				size if size is not None else self.size
      _SCREAMING_SNAKE_CASE	=				get_size_dict(__lowerCamelCase   )

      if not is_batched(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				[images]

      if not valid_images(__lowerCamelCase   ):
         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."   )

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

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

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

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

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

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

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







'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			str	,					__A    :			str  )			->			str:
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				len(__A  )
   _SCREAMING_SNAKE_CASE	=				(
       first_str_length if first_str_length > second_str_length else second_str_length
   )
   _SCREAMING_SNAKE_CASE	=				[]
   for char_count in range(__A  ):
      if char_count < first_str_length:
         output_list.append(first_str[char_count]  )
      if char_count < second_str_length:
         output_list.append(second_str[char_count]  )
   return "".join(__A  )


if __name__ == "__main__":
 print(alternative_string_arrange('AB', 'XYZ'), end=' ')

