



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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


UpperCAmelCase_		 =					logging.get_logger(__name__)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Any	,   _snake_case	:Dict				)  ->     Any:
   _A      =       b.T
   _A      =       np.sum(np.square(_snake_case				)	,   axis=1				)
   _A      =       np.sum(np.square(_snake_case				)	,   axis=0				)
   _A      =       np.matmul(_snake_case	,   _snake_case				)
   _A      =       aa[:, None] - 2 * ab + ba[None, :]
   return d

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]	,   _snake_case	:Optional[Any]				)  ->     Optional[Any]:
   _A      =       x.reshape(-1	,   3				)
   _A      =       squared_euclidean_distance(_snake_case	,   _snake_case				)
   return np.argmin(_snake_case	,   axis=1				)


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 a__					: Union[str, Any] =  ["pixel_values"]

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Optional[Union[List[List[int]], np.ndarray]] = None			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					Dict[str, int] = None			,						__lowerCAmelCase					:					PILImageResampling = PILImageResampling.BILINEAR			,						__lowerCAmelCase					:					bool = True			,						__lowerCAmelCase					:					bool = True			,						**__lowerCAmelCase					:					Any			,						)   ->	None:
    super().__init__(**__lowerCAmelCase      )
    _A      =       size if size is not None else {'''height''': 2_56, '''width''': 2_56}
    _A      =       get_size_dict(__lowerCAmelCase      )
    _A      =       np.array(__lowerCAmelCase      ) if clusters is not None else None
    _A      =       do_resize
    _A      =       size
    _A      =       resample
    _A      =       do_normalize
    _A      =       do_color_quantize

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					np.ndarray			,						__lowerCAmelCase					:					Dict[str, int]			,						__lowerCAmelCase					:					PILImageResampling = PILImageResampling.BILINEAR			,						__lowerCAmelCase					:					Optional[Union[str, ChannelDimension]] = None			,						**__lowerCAmelCase					:					List[Any]			,						)   ->	np.ndarray:
    _A      =       get_size_dict(__lowerCAmelCase      )
    if "height" not in size or "width" not in size:
       raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''      )
    return resize(
        __lowerCAmelCase			,						size=(size['''height'''], size['''width'''])			,						resample=__lowerCAmelCase			,						data_format=__lowerCAmelCase			,						**__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					np.ndarray			,						__lowerCAmelCase					:					Optional[Union[str, ChannelDimension]] = None			,						)   ->	np.ndarray:
    _A      =       rescale(image=__lowerCAmelCase			,						scale=1 / 127.5			,						data_format=__lowerCAmelCase      )
    _A      =       image - 1
    return image

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					ImageInput			,						__lowerCAmelCase					:					bool = None			,						__lowerCAmelCase					:					Dict[str, int] = None			,						__lowerCAmelCase					:					PILImageResampling = None			,						__lowerCAmelCase					:					bool = None			,						__lowerCAmelCase					:					Optional[bool] = None			,						__lowerCAmelCase					:					Optional[Union[List[List[int]], np.ndarray]] = None			,						__lowerCAmelCase					:					Optional[Union[str, TensorType]] = None			,						__lowerCAmelCase					:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST			,						**__lowerCAmelCase					:					int			,						)   ->	PIL.Image.Image:
    _A      =       do_resize if do_resize is not None else self.do_resize
    _A      =       size if size is not None else self.size
    _A      =       get_size_dict(__lowerCAmelCase      )
    _A      =       resample if resample is not None else self.resample
    _A      =       do_normalize if do_normalize is not None else self.do_normalize
    _A      =       do_color_quantize if do_color_quantize is not None else self.do_color_quantize
    _A      =       clusters if clusters is not None else self.clusters
    _A      =       np.array(__lowerCAmelCase      )

    _A      =       make_list_of_images(__lowerCAmelCase      )

    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 or resample is None:
       raise ValueError('''Size and resample must be specified if do_resize is True.'''      )

    if do_color_quantize and clusters is None:
       raise ValueError('''Clusters must be specified if do_color_quantize is True.'''      )

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

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

    if do_normalize:
       _A      =       [self.normalize(image=__lowerCAmelCase      ) for image in images]

    if do_color_quantize:
       _A      =       [to_channel_dimension_format(__lowerCAmelCase			,						ChannelDimension.LAST      ) for image in images]
       # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
       _A      =       np.array(__lowerCAmelCase      )
       _A      =       color_quantize(__lowerCAmelCase			,						__lowerCAmelCase      ).reshape(images.shape[:-1]      )

       # flatten to (batch_size, height*width)
       _A      =       images.shape[0]
       _A      =       images.reshape(__lowerCAmelCase			,						-1      )

       # We need to convert back to a list of images to keep consistent behaviour across processors.
       _A      =       list(__lowerCAmelCase      )
    else:
       _A      =       [to_channel_dimension_format(__lowerCAmelCase			,						__lowerCAmelCase      ) for image in images]

    _A      =       {'''input_ids''': images}
    return BatchFeature(data=__lowerCAmelCase			,						tensor_type=__lowerCAmelCase      )











'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

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



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      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					  =     int(SCREAMING_SNAKE_CASE__     )
   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(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   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}'"""
 )