





'''simple docstring'''






from __future__ import annotations

from typing import TypedDict







class 			lowerCamelCase					(						a__  ):
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    42





def       lowercase_      (		lowercase__						)   ->list[str]:
						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       lowercase_      (		lowercase__						)   ->BWTTransformDict:
						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.'						)

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





def       lowercase_      (		lowercase__				,  lowercase__						)   ->str:
						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:
												_snake_case:   List[str]  					=		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).'						)

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


if __name__ == "__main__":
		A   : List[str]        =			'Provide a string that I will generate its BWT transform: '
		A   : int        =			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   : Optional[int]        =			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 unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
		import tensorflow as tf

if is_tensorflow_text_available():
		from transformers.models.bert import TFBertTokenizer


A   : Tuple             =			['bert-base-uncased', 'bert-base-cased']
A   : str             =			'hf-internal-testing/tiny-bert-tf-only'

if is_tf_available():







		class 			lowerCamelCase					(						tf.keras.Model  ):


							def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  List[Any]       ):





													'''simple docstring'''





													super().__init__()
													_snake_case:   List[Any]  					=		tokenizer
													_snake_case:   str  					=		AutoConfig.from_pretrained(__snake_case       )
													_snake_case:   List[Any]  					=		TFAutoModel.from_config(__snake_case       )




							def    SCREAMING_SNAKE_CASE_		(					self	:  str       ,     __snake_case	:  List[str]       ):





													'''simple docstring'''





													_snake_case:   Optional[int]  					=		self.tokenizer(__snake_case       )
													_snake_case:   Tuple  					=		self.bert(**__snake_case       )
													return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 			lowerCamelCase					(						unittest.TestCase  ):


					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											super().setUp()

											_snake_case:   List[Any]  					=		[
											    BertTokenizer.from_pretrained(__snake_case       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
											]  # repeat for when fast_bert_tokenizer=false
											_snake_case:   List[Any]  					=		[TFBertTokenizer.from_pretrained(__snake_case       ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
											    TFBertTokenizer.from_pretrained(__snake_case       ,     use_fast_bert_tokenizer=__snake_case       )
											    for checkpoint in TOKENIZER_CHECKPOINTS
											]
											assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

											_snake_case:   int  					=		[
											    'This is a straightforward English test sentence.',
											    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
											    'Now we\'re going to add some Chinese: 一 二 三 一二三',
											    'And some much more rare Chinese: 齉 堃 齉堃',
											    'Je vais aussi écrire en français pour tester les accents',
											    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
											]
											_snake_case:   str  					=		list(zip(self.test_sentences       ,     self.test_sentences[::-1]       )       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Union[str, Any]       ):





											'''simple docstring'''





											for tokenizer, tf_tokenizer in zip(self.tokenizers       ,     self.tf_tokenizers       ):
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   List[Any]  					=		tokenizer(__snake_case       ,     return_tensors='tf'       ,     padding='longest'       )
																							_snake_case:   Optional[Any]  					=		tf_tokenizer(__snake_case       )

																							for key in python_outputs.keys():
																													self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape       )       )
																													self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]       ,     tf.intaa       ) == tf_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   str  					=		tf_tokenizer(self.paired_sentences       )
																	_snake_case:   Any  					=		tf_tokenizer(
																	    text=[sentence[0] for sentence in self.paired_sentences]       ,     text_pair=[sentence[1] for sentence in self.paired_sentences]       ,     )
																	for key in merged_outputs.keys():
																							self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]       ,     tf.intaa       ) == separated_outputs[key]       )       )


					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  Tuple       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   List[str]  					=		tf.function(__snake_case       )
																	for test_inputs in (self.test_sentences, self.paired_sentences):
																							_snake_case:   Union[str, Any]  					=		tf.constant(__snake_case       )
																							_snake_case:   Any  					=		compiled_tokenizer(__snake_case       )
																							_snake_case:   Union[str, Any]  					=		tf_tokenizer(__snake_case       )

																							for key in eager_outputs.keys():
																													self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )




					@slow
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											for tf_tokenizer in self.tf_tokenizers:
																	_snake_case:   Optional[Any]  					=		ModelToSave(tokenizer=__snake_case       )
																	_snake_case:   Tuple  					=		tf.convert_to_tensor(self.test_sentences       )
																	_snake_case:   List[str]  					=		model(__snake_case       )  # Build model with some sample inputs
																	with TemporaryDirectory() as tempdir:
																							_snake_case:   List[str]  					=		Path(__snake_case       ) / 'saved.model'
																							model.save(__snake_case       )
																							_snake_case:   int  					=		tf.keras.models.load_model(__snake_case       )
																	_snake_case:   int  					=		loaded_model(__snake_case       )
																	# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																	self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output       )       )       ,     1e-5       )





