





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor

class 					A     (unittest.TestCase					):



      def __init__(       self  ,	lowercase_  ,	lowercase_=7  ,	lowercase_=3  ,	lowercase_=30  ,	lowercase_=400  ,	lowercase_=True  ,	lowercase_=None  ,	lowercase_=0.9  ,	lowercase_=None  ,	lowercase_=True  ,	lowercase_=[0.5, 0.5, 0.5]  ,	lowercase_=[0.5, 0.5, 0.5]  ,	)   -> List[Any]:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    size if size is not None else {'''shortest_edge''': 30}
             _snake_case   :				Optional[int]  					=    crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
             _snake_case   :				Optional[Any]  					=    parent
             _snake_case   :				List[Any]  					=    batch_size
             _snake_case   :				Dict  					=    num_channels
             _snake_case   :				List[str]  					=    min_resolution
             _snake_case   :				List[str]  					=    max_resolution
             _snake_case   :				List[str]  					=    do_resize_and_center_crop
             _snake_case   :				Optional[Any]  					=    size
             _snake_case   :				Tuple  					=    crop_pct
             _snake_case   :				Dict  					=    crop_size
             _snake_case   :				Tuple  					=    do_normalize
             _snake_case   :				List[str]  					=    image_mean
             _snake_case   :				Any  					=    image_std



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             return {
                 "size": self.size,
                 "do_resize_and_center_crop": self.do_resize_and_center_crop,
                 "crop_pct": self.crop_pct,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
             }


@require_torch
@require_vision
class 					A     (SCREAMING_SNAKE_CASE_							,unittest.TestCase					):
      _SCREAMING_SNAKE_CASE       =				PoolFormerImageProcessor if is_vision_available() else None



      def  __a			(       self       )   -> int:



             '''simple docstring'''

             _snake_case   :				Any  					=    PoolFormerImageProcessingTester(self       )



      @property
      def  __a			(       self       )   -> int:



             '''simple docstring'''

             return self.image_processor_tester.prepare_image_processor_dict()



      def  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             _snake_case   :				Optional[int]  					=    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_resize_and_center_crop'''       )       )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''size'''       )       )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''crop_pct'''       )       )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''do_normalize'''       )       )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_mean'''       )       )
             self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE  ,	'''image_std'''       )       )



      def  __a			(       self       )   -> Dict:



             '''simple docstring'''

             _snake_case   :				Dict  					=    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size  ,	{'''shortest_edge''': 30}       )
             self.assertEqual(image_processor.crop_size  ,	{'''height''': 30, '''width''': 30}       )

             _snake_case   :				Dict  					=    self.image_processing_class.from_dict(self.image_processor_dict  ,	size=42  ,	crop_size=84       )
             self.assertEqual(image_processor.size  ,	{'''shortest_edge''': 42}       )
             self.assertEqual(image_processor.crop_size  ,	{'''height''': 84, '''width''': 84}       )



      def  __a			(       self       )   -> Dict:



             '''simple docstring'''

             pass



      def  __a			(       self       )   -> Union[str, Any]:



             '''simple docstring'''

             _snake_case   :				Optional[Any]  					=    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL images
             _snake_case   :				str  					=    prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE       )
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	Image.Image       )

             # Test not batched input
             _snake_case   :				List[str]  					=    image_processing(image_inputs[0]  ,	return_tensors='''pt'''       ).pixel_values

             self.assertEqual(
                 encoded_images.shape  ,	(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )  ,	)

             # Test batched
             _snake_case   :				Tuple  					=    image_processing(__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt'''       ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,	(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )  ,	)



      def  __a			(       self       )   -> List[Any]:



             '''simple docstring'''

             _snake_case   :				Union[str, Any]  					=    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _snake_case   :				Union[str, Any]  					=    prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	numpify=__SCREAMING_SNAKE_CASE       )
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	np.ndarray       )

             # Test not batched input
             _snake_case   :				Any  					=    image_processing(image_inputs[0]  ,	return_tensors='''pt'''       ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,	(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )  ,	)

             # Test batched
             _snake_case   :				Any  					=    image_processing(__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt'''       ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,	(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )  ,	)



      def  __a			(       self       )   -> List[str]:



             '''simple docstring'''

             _snake_case   :				Optional[Any]  					=    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _snake_case   :				Optional[int]  					=    prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=__SCREAMING_SNAKE_CASE  ,	torchify=__SCREAMING_SNAKE_CASE       )
             for image in image_inputs:
                    self.assertIsInstance(__SCREAMING_SNAKE_CASE  ,	torch.Tensor       )

             # Test not batched input
             _snake_case   :				str  					=    image_processing(image_inputs[0]  ,	return_tensors='''pt'''       ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,	(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )  ,	)

             # Test batched
             _snake_case   :				str  					=    image_processing(__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt'''       ).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,	(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )  ,	)














'''simple docstring'''







from __future__ import annotations

from typing import TypedDict





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					42
			snake_case__	=					42





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )

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





def 		_UpperCAmelCase (				__A				: str   ):
				if not isinstance(__A    ,		__A   ):
								raise TypeError('''The parameter s type must be str.'''   )
				if not s:
								raise ValueError('''The parameter s must not be empty.'''   )

				a_		:	str									=   all_rotations(__A   )
				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(__A   ),
				}
				return response





def 		_UpperCAmelCase (				__A				: str    ,		__A				: int   ):
				if not isinstance(__A    ,		__A   ):
								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_		:	Union[str, Any]									=   int(__A   )
				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(__A   ):
								raise ValueError(
								    '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).'''   )

				a_		:	Union[str, Any]									=   [''''''] * len(__A   )
				for _ in range(len(__A   )   ):
								for i in range(len(__A   )   ):
												a_		:	int									=   bwt_string[i] + ordered_rotations[i]
								ordered_rotations.sort()
				return ordered_rotations[idx_original_string]


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




