





"""simple docstring"""





from __future__ import annotations

from typing import TypedDict
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							str
				__snake_case :							int



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				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 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str ):


							'''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.''' )

							__lowerCamelCase   :   Union[str, Any]       =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
							__lowerCamelCase   :   BWTTransformDict       ={
							    "bwt_string": "".join([word[-1] for word in rotations] ),
							    "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE ),
							}
							return response



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				int ):


							'''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:
														__lowerCamelCase   :   str       =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).''' )

							__lowerCamelCase   :   Optional[int]       =[''''''] * len(SCREAMING_SNAKE_CASE )
							for _ in range(len(SCREAMING_SNAKE_CASE ) ):
														for i in range(len(SCREAMING_SNAKE_CASE ) ):
																					__lowerCamelCase   :   List[str]       =bwt_string[i] + ordered_rotations[i]
														ordered_rotations.sort()
							return ordered_rotations[idx_original_string]


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





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import numpy as np
							import tensorflow as tf

							from transformers import TFXLMRobertaModel
@require_tf
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				@slow
				def        __lowercase	(  self	:Union[str, Any]      ):
											__lowerCamelCase   :   List[Any]       =TFXLMRobertaModel.from_pretrained('''jplu/tf-xlm-roberta-base'''      )

											__lowerCamelCase   :   Dict       ={
											    '''input_ids''': tf.convert_to_tensor([[0, 2646, 1_0269, 83, 9_9942, 2]]    ,     dtype=tf.intaa      ),  # "My dog is cute"
											    '''attention_mask''': tf.convert_to_tensor([[1, 1, 1, 1, 1, 1]]    ,     dtype=tf.intaa      ),
											}

											__lowerCamelCase   :   Optional[int]       =model(__lowercase      )['''last_hidden_state''']
											__lowerCamelCase   :   str       =tf.TensorShape((1, 6, 768)      )
											self.assertEqual(output.shape    ,     __lowercase      )
											# compare the actual values for a slice.
											__lowerCamelCase   :   str       =tf.convert_to_tensor(
											    [
											        [
											            [0.0681762, 0.10894451, 0.06772504],
											            [-0.06423668, 0.02366615, 0.04329344],
											            [-0.06057295, 0.09974135, -0.00070584],
											        ]
											    ]    ,     dtype=tf.floataa    ,     )

											self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,     expected_slice.numpy()    ,     atol=1e-4      )      )




