





'''simple docstring'''

from __future__ import annotations

from typing import TypedDict
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	str
       _snake_case      :	int







def    a__							(							lowercase   :	str )						->						list[str]:





     """simple docstring"""

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

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







def    a__							(							lowercase   :	str )						->						BWTTransformDict:





     """simple docstring"""

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

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







def    a__							(							lowercase   :	str,   lowercase   :	int )						->						str:





     """simple docstring"""

     if not isinstance(lowercase,   lowercase ):
          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:
          _UpperCamelCase							       =			int(lowercase )
     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(lowercase ):
          raise ValueError(
              '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).''' )

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


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







'''simple docstring'''






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
 from PIL import Image
else:






 class    __magic_name__				:






        @staticmethod
        def      lowerCAmelCase						(     *snake_case    ,				**snake_case)		->    str:




           '''simple docstring'''
           pass





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  Optional[Any]				       =hashlib.mda(image.tobytes()				)
   return m.hexdigest()[:1_0]





def lowerCamelCase__     (		__lowerCamelCase			:				Image				):







   '''simple docstring'''




   _UpperCAmelCase  :  List[str]				       =np.array(__lowerCamelCase				)
   _UpperCAmelCase  :  List[str]				       =npimg.shape
   return {"hash": hashimage(__lowerCamelCase				), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class    __magic_name__				(						unittest.TestCase							):
       UpperCAmelCase		 =dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       UpperCAmelCase		 =dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =MaskGenerationPipeline(model=snake_case    ,				image_processor=snake_case)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          pass






       @require_tf
       @unittest.skip('Image segmentation not implemented in TF')
       def      lowerCAmelCase						(     self)		->    Union[str, Any]:




          '''simple docstring'''
          pass






       @slow
       @require_torch
       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =pipeline('mask-generation'    ,				model='facebook/sam-vit-huge')

          _UpperCAmelCase  :  Union[str, Any]				       =image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg'    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  str				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.88_71}
              ]    ,				)
          # fmt: on






       @require_torch
       @slow
       def      lowerCAmelCase						(     self)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       ='facebook/sam-vit-huge'
          _UpperCAmelCase  :  Optional[int]				       =pipeline('mask-generation'    ,				model=snake_case)

          _UpperCAmelCase  :  str				       =image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg'    ,				pred_iou_thresh=1    ,				points_per_batch=2_5_6)

          # Shortening by hashing
          _UpperCAmelCase  :  Tuple				       =[]
          for i, o in enumerate(outputs['masks']):
             new_outupt += [{"mask": mask_to_test_readable(snake_case), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(snake_case    ,				decimals=4)    ,				[
                  {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.00_53},
              ]    ,				)

