


'''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


A__:    List[str]					    =       logging.get_logger(__name__)


if is_vision_available():
   import PIL







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str	       =							["pixel_values"]






      def __init__( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = PILImageResampling.BICUBIC     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Union[int, float] = 1 / 2_5_5     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Optional[Union[float, List[float]]] = None     ,						SCREAMING_SNAKE_CASE			:Optional[Union[float, List[float]]] = None     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						**SCREAMING_SNAKE_CASE			:Optional[int]     ,						)       ->					None:




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE	)
             _a    : Union[str, Any]     					=size if size is not None else {"""shortest_edge""": 2_2_4}
             _a    : Dict     					=get_size_dict(SCREAMING_SNAKE_CASE     ,						default_to_square=SCREAMING_SNAKE_CASE	)
             _a    : int     					=crop_size if crop_size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
             _a    : Any     					=get_size_dict(SCREAMING_SNAKE_CASE     ,						default_to_square=SCREAMING_SNAKE_CASE     ,						param_name="""crop_size"""	)

             _a    : Any     					=do_resize
             _a    : Union[str, Any]     					=size
             _a    : Union[str, Any]     					=resample
             _a    : Any     					=do_center_crop
             _a    : Dict     					=crop_size
             _a    : List[Any]     					=do_rescale
             _a    : Union[str, Any]     					=rescale_factor
             _a    : Optional[int]     					=do_normalize
             _a    : Tuple     					=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
             _a    : Any     					=image_std if image_std is not None else OPENAI_CLIP_STD
             _a    : Dict     					=do_convert_rgb






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:np.ndarray     ,						SCREAMING_SNAKE_CASE			:Dict[str, int]     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = PILImageResampling.BICUBIC     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, ChannelDimension]] = None     ,						**SCREAMING_SNAKE_CASE			:List[str]     ,						)       ->					np.ndarray:




             '''simple docstring'''
             _a    : List[Any]     					=get_size_dict(SCREAMING_SNAKE_CASE     ,						default_to_square=SCREAMING_SNAKE_CASE	)
             if "shortest_edge" not in size:
                    raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"	)
             _a    : Optional[int]     					=get_resize_output_image_size(SCREAMING_SNAKE_CASE     ,						size=size["""shortest_edge"""]     ,						default_to_square=SCREAMING_SNAKE_CASE	)
             return resize(SCREAMING_SNAKE_CASE     ,						size=SCREAMING_SNAKE_CASE     ,						resample=SCREAMING_SNAKE_CASE     ,						data_format=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:List[Any]     ,						SCREAMING_SNAKE_CASE			:np.ndarray     ,						SCREAMING_SNAKE_CASE			:Dict[str, int]     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, ChannelDimension]] = None     ,						**SCREAMING_SNAKE_CASE			:Dict     ,						)       ->					np.ndarray:




             '''simple docstring'''
             _a    : int     					=get_size_dict(SCREAMING_SNAKE_CASE	)
             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(SCREAMING_SNAKE_CASE     ,						size=(size["""height"""], size["""width"""])     ,						data_format=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Dict     ,						SCREAMING_SNAKE_CASE			:np.ndarray     ,						SCREAMING_SNAKE_CASE			:Union[int, float]     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, ChannelDimension]] = None     ,						**SCREAMING_SNAKE_CASE			:Optional[Any]     ,						)       ->					List[Any]:




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






      def __UpperCAmelCase       ( self			:Any     ,						SCREAMING_SNAKE_CASE			:np.ndarray     ,						SCREAMING_SNAKE_CASE			:Union[float, List[float]]     ,						SCREAMING_SNAKE_CASE			:Union[float, List[float]]     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, ChannelDimension]] = None     ,						**SCREAMING_SNAKE_CASE			:List[Any]     ,						)       ->					np.ndarray:




             '''simple docstring'''
             return normalize(SCREAMING_SNAKE_CASE     ,						mean=SCREAMING_SNAKE_CASE     ,						std=SCREAMING_SNAKE_CASE     ,						data_format=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Union[str, Any]     ,						SCREAMING_SNAKE_CASE			:ImageInput     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = None     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:int = None     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:float = None     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Optional[Union[float, List[float]]] = None     ,						SCREAMING_SNAKE_CASE			:Optional[Union[float, List[float]]] = None     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, TensorType]] = None     ,						SCREAMING_SNAKE_CASE			:Optional[ChannelDimension] = ChannelDimension.FIRST     ,						**SCREAMING_SNAKE_CASE			:int     ,						)       ->					PIL.Image.Image:




             '''simple docstring'''
             _a    : Optional[int]     					=do_resize if do_resize is not None else self.do_resize
             _a    : Optional[int]     					=size if size is not None else self.size
             _a    : List[Any]     					=get_size_dict(SCREAMING_SNAKE_CASE     ,						param_name="""size"""     ,						default_to_square=SCREAMING_SNAKE_CASE	)
             _a    : Any     					=resample if resample is not None else self.resample
             _a    : Any     					=do_center_crop if do_center_crop is not None else self.do_center_crop
             _a    : Optional[Any]     					=crop_size if crop_size is not None else self.crop_size
             _a    : Optional[Any]     					=get_size_dict(SCREAMING_SNAKE_CASE     ,						param_name="""crop_size"""     ,						default_to_square=SCREAMING_SNAKE_CASE	)
             _a    : Any     					=do_rescale if do_rescale is not None else self.do_rescale
             _a    : Union[str, Any]     					=rescale_factor if rescale_factor is not None else self.rescale_factor
             _a    : List[Any]     					=do_normalize if do_normalize is not None else self.do_normalize
             _a    : Optional[int]     					=image_mean if image_mean is not None else self.image_mean
             _a    : Tuple     					=image_std if image_std is not None else self.image_std
             _a    : int     					=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

             _a    : 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:
                    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:
                    _a    : Tuple     					=[convert_to_rgb(SCREAMING_SNAKE_CASE	) for image in images]

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

             if do_resize:
                    _a    : Union[str, Any]     					=[self.resize(image=SCREAMING_SNAKE_CASE     ,						size=SCREAMING_SNAKE_CASE     ,						resample=SCREAMING_SNAKE_CASE	) for image in images]

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

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

             if do_normalize:
                    _a    : Union[str, Any]     					=[self.normalize(image=SCREAMING_SNAKE_CASE     ,						mean=SCREAMING_SNAKE_CASE     ,						std=SCREAMING_SNAKE_CASE	) for image in images]

             _a    : List[Any]     					=[to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	) for image in images]

             _a    : Tuple     					={"""pixel_values""": images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE     ,						tensor_type=SCREAMING_SNAKE_CASE	)






'''simple docstring'''







from __future__ import annotations

from typing import TypedDict







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str
      __UpperCamelCase       :  int


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			list[str]:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )

       return [s[i:] + s[:i] for i in range(len(_UpperCAmelCase  )  )]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str  )  ->			BWTTransformDict:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter s type must be str."""  )
       if not s:
              raise ValueError("""The parameter s must not be empty."""  )

       _a    : List[Any]     					=all_rotations(_UpperCAmelCase  )
       rotations.sort()  # sort the list of rotations in alphabetically order
       # make a string composed of the last char of each rotation
       _a    : BWTTransformDict     					={
           "bwt_string": "".join([word[-1] for word in rotations]  ),
           "idx_original_string": rotations.index(_UpperCAmelCase  ),
       }
       return response


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	int  )  ->			str:
       if not isinstance(_UpperCAmelCase    ,_UpperCAmelCase  ):
              raise TypeError("""The parameter bwt_string type must be str."""  )
       if not bwt_string:
              raise ValueError("""The parameter bwt_string must not be empty."""  )
       try:
              _a    : List[str]     					=int(_UpperCAmelCase  )
       except ValueError:
              raise TypeError(
                  """The parameter idx_original_string type must be int or passive"""
                  """ of cast to int."""  )
       if idx_original_string < 0:
              raise ValueError("""The parameter idx_original_string must not be lower than 0."""  )
       if idx_original_string >= len(_UpperCAmelCase  ):
              raise ValueError(
                  """The parameter idx_original_string must be lower than""" """ len(bwt_string)."""  )

       _a    : Optional[int]     					=[""""""] * len(_UpperCAmelCase  )
       for _ in range(len(_UpperCAmelCase  )  ):
              for i in range(len(_UpperCAmelCase  )  ):
                     _a    : int     					=bwt_string[i] + ordered_rotations[i]
              ordered_rotations.sort()
       return ordered_rotations[idx_original_string]


if __name__ == "__main__":
   A__:    Any					    =       '''Provide a string that I will generate its BWT transform: '''
   A__:    Union[str, Any]					    =       input(entry_msg).strip()
   A__:    Optional[int]					    =       bwt_transform(s)
   print(
       F"Burrows Wheeler transform for string '{s}' results "
       F"in '{result['bwt_string']}'"
   )
   A__:    Union[str, Any]					    =       reverse_bwt(result['''bwt_string'''], result['''idx_original_string'''])
   print(
       F"Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' "
       F"we get original string '{original_string}'"
   )


