






'''simple docstring'''







import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
		from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def 		_UpperCAmelCase (				__A				: Dict    ,		__A				: List[Any]    ,		__A				: List[Any]    ,		__A				: str=None    ,		__A				: List[str]=None    ,		__A				: str=None    ,		__A				: str=None    ,		__A				: int=None    ,		):
				if attention_mask is None:
								a_		:	int									=   input_ids.ne(config.pad_token_id   )
				if decoder_attention_mask is None:
								a_		:	Optional[int]									=   decoder_input_ids.ne(config.pad_token_id   )
				if head_mask is None:
								a_		:	Optional[int]									=   torch.ones(config.encoder_layers    ,		config.encoder_attention_heads    ,		device=__A   )
				if decoder_head_mask is None:
								a_		:	Union[str, Any]									=   torch.ones(config.decoder_layers    ,		config.decoder_attention_heads    ,		device=__A   )
				if cross_attn_head_mask is None:
								a_		:	Optional[Any]									=   torch.ones(config.decoder_layers    ,		config.decoder_attention_heads    ,		device=__A   )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}





class 						SCREAMING_SNAKE_CASE       :






			def __init__(			self   :      int	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      Tuple=13	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=7	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=True	,    __SCREAMING_SNAKE_CASE   :      int=False	,    __SCREAMING_SNAKE_CASE   :      Optional[int]=99	,    __SCREAMING_SNAKE_CASE   :      int=16	,    __SCREAMING_SNAKE_CASE   :      Any=2	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=4	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]=4	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]="relu"	,    __SCREAMING_SNAKE_CASE   :      Any=0.1	,    __SCREAMING_SNAKE_CASE   :      str=0.1	,    __SCREAMING_SNAKE_CASE   :      Any=0.0	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=0.0	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]=20	,    __SCREAMING_SNAKE_CASE   :      Any=2	,    __SCREAMING_SNAKE_CASE   :      Tuple=1	,    __SCREAMING_SNAKE_CASE   :      List[Any]=0	,    )      ->      List[str]:
							a_		:	Optional[Any]									=   parent
							a_		:	Union[str, Any]									=   batch_size
							a_		:	Any									=   seq_length
							a_		:	Dict									=   is_training
							a_		:	List[Any]									=   use_labels
							a_		:	str									=   vocab_size
							a_		:	List[str]									=   hidden_size
							a_		:	Optional[int]									=   num_hidden_layers
							a_		:	Union[str, Any]									=   num_attention_heads
							a_		:	Dict									=   intermediate_size
							a_		:	Optional[Any]									=   hidden_act
							a_		:	Optional[int]									=   hidden_dropout_prob
							a_		:	Optional[int]									=   attention_probs_dropout_prob
							a_		:	Union[str, Any]									=   encoder_layerdrop
							a_		:	List[Any]									=   decoder_layerdrop
							a_		:	List[Any]									=   max_position_embeddings
							a_		:	List[str]									=   eos_token_id
							a_		:	str									=   pad_token_id
							a_		:	List[Any]									=   bos_token_id






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Union[str, Any]:
							a_		:	Any									=   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size       )
							a_		:	Dict									=   self.eos_token_id  # Eos Token
							a_		:	Optional[Any]									=   ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size       )

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for M2M100 the position_ids are prepared such that
							# all pad tokens have pos id = 2 and rest are between 2..seq_length
							# and the seq_length here is seq_length - num_pad_tokens
							# but when using past, there is no way of knowing if the past input ids had
							# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
							# position_ids being off by num_pad_tokens in past input
							a_		:	Union[str, Any]									=   input_ids.clamp(self.pad_token_id + 1       )
							a_		:	List[str]									=   decoder_input_ids.clamp(self.pad_token_id + 1       )

							a_		:	List[str]									=   self.get_config()
							a_		:	List[str]									=   prepare_mam_aaa_inputs_dict(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return config, inputs_dict






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      Optional[int]:
							return MaMaaaConfig(
							    vocab_size=self.vocab_size	,    d_model=self.hidden_size	,    encoder_layers=self.num_hidden_layers	,    decoder_layers=self.num_hidden_layers	,    encoder_attention_heads=self.num_attention_heads	,    decoder_attention_heads=self.num_attention_heads	,    encoder_ffn_dim=self.intermediate_size	,    decoder_ffn_dim=self.intermediate_size	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    encoder_layerdrop=self.encoder_layerdrop	,    decoder_layerdrop=self.decoder_layerdrop	,    max_position_embeddings=self.max_position_embeddings	,    eos_token_id=self.eos_token_id	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Any:
							a_					,      a_		:	Optional[Any]									=   self.prepare_config_and_inputs()
							return config, inputs_dict






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   MaMaaaModel(config=__SCREAMING_SNAKE_CASE       ).get_decoder().to(__SCREAMING_SNAKE_CASE       ).eval()
							a_		:	List[str]									=   inputs_dict['''input_ids''']
							a_		:	int									=   inputs_dict['''attention_mask''']
							a_		:	Any									=   inputs_dict['''head_mask''']

							# first forward pass
							a_		:	Any									=   model(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE	,    head_mask=__SCREAMING_SNAKE_CASE	,    use_cache=__SCREAMING_SNAKE_CASE       )

							a_					,      a_		:	Any									=   outputs.to_tuple()

							# create hypothetical multiple next token and extent to next_input_ids
							a_		:	int									=   ids_tensor((self.batch_size, 3)	,    config.vocab_size       )
							a_		:	Tuple									=   ids_tensor((self.batch_size, 3)	,    2       )

							# append to next input_ids and
							a_		:	List[str]									=   torch.cat([input_ids, next_tokens]	,    dim=-1       )
							a_		:	Tuple									=   torch.cat([attention_mask, next_attn_mask]	,    dim=-1       )

							a_		:	List[str]									=   model(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE       )['''last_hidden_state''']
							a_		:	List[str]									=   model(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE	,    past_key_values=__SCREAMING_SNAKE_CASE       )[
							    '''last_hidden_state'''
							]

							# select random slice
							a_		:	Optional[int]									=   ids_tensor((1,)	,    output_from_past.shape[-1]       ).item()
							a_		:	Tuple									=   output_from_no_past[:, -3:, random_slice_idx].detach()
							a_		:	Optional[int]									=   output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]       )

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    atol=1e-2       )       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      List[str]:
							a_		:	List[str]									=   MaMaaaModel(config=__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       ).eval()
							a_		:	List[str]									=   model(**__SCREAMING_SNAKE_CASE       )

							a_		:	List[Any]									=   outputs.encoder_last_hidden_state
							a_		:	Optional[int]									=   outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
											a_		:	Optional[Any]									=   model.get_encoder()
											encoder.save_pretrained(__SCREAMING_SNAKE_CASE       )
											a_		:	str									=   MaMaaaEncoder.from_pretrained(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   encoder(inputs_dict['''input_ids''']	,    attention_mask=inputs_dict['''attention_mask''']       )[
							    0
							]

							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3       )

							with tempfile.TemporaryDirectory() as tmpdirname:
											a_		:	List[Any]									=   model.get_decoder()
											decoder.save_pretrained(__SCREAMING_SNAKE_CASE       )
											a_		:	List[str]									=   MaMaaaDecoder.from_pretrained(__SCREAMING_SNAKE_CASE       ).to(__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   decoder(
							    input_ids=inputs_dict['''decoder_input_ids''']	,    attention_mask=inputs_dict['''decoder_attention_mask''']	,    encoder_hidden_states=__SCREAMING_SNAKE_CASE	,    encoder_attention_mask=inputs_dict['''attention_mask''']	,    )[0]

							self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3       )





@require_torch
class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					(
			    (
			        MaMaaaModel,
			        MaMaaaForConditionalGeneration,
			    )
			    if is_torch_available()
			    else ()
			)
			snake_case__	=					(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
			snake_case__	=					(
			    {
			        "conversational": MaMaaaForConditionalGeneration,
			        "feature-extraction": MaMaaaModel,
			        "summarization": MaMaaaForConditionalGeneration,
			        "text2text-generation": MaMaaaForConditionalGeneration,
			        "translation": MaMaaaForConditionalGeneration,
			    }
			    if is_torch_available()
			    else {}
			)
			snake_case__	=					True
			snake_case__	=					True
			snake_case__	=					False
			snake_case__	=					False






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      str       )      ->      List[Any]:
							if pipeline_test_casse_name == "TranslationPipelineTests":
											# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
											# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
											return True

							return False






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      int:
							a_		:	Dict									=   MaMaaaModelTester(self       )
							a_		:	Optional[Any]									=   ConfigTester(self	,    config_class=__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							self.config_tester.run_common_tests()






			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      Any:
							a_					,      a_		:	Any									=   self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
											a_		:	Dict									=   model_class(__SCREAMING_SNAKE_CASE       )

											with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__SCREAMING_SNAKE_CASE       )
															a_					,      a_		:	Tuple									=   model_class.from_pretrained(__SCREAMING_SNAKE_CASE	,    output_loading_info=__SCREAMING_SNAKE_CASE       )
											self.assertEqual(info['''missing_keys''']	,    []       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	Any									=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]       )      ->      List[str]:
							a_		:	Dict									=   self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_					,      a_		:	str									=   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
											a_		:	Union[str, Any]									=   model_class(__SCREAMING_SNAKE_CASE       )
											model.to(__SCREAMING_SNAKE_CASE       )
											model.eval()

											a_		:	Optional[Any]									=   copy.deepcopy(self._prepare_for_class(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )       )

											if not self.is_encoder_decoder:
															a_		:	int									=   inputs['''input_ids''']
															del inputs["input_ids"]
											else:
															a_		:	Tuple									=   inputs['''input_ids''']
															a_		:	Union[str, Any]									=   inputs.get('''decoder_input_ids'''	,    __SCREAMING_SNAKE_CASE       )
															del inputs["input_ids"]
															inputs.pop('''decoder_input_ids'''	,    __SCREAMING_SNAKE_CASE       )

											a_		:	int									=   model.get_input_embeddings()
											if not self.is_encoder_decoder:
															a_		:	Optional[int]									=   wte(__SCREAMING_SNAKE_CASE       )
											else:
															a_		:	Union[str, Any]									=   wte(__SCREAMING_SNAKE_CASE       )
															a_		:	Tuple									=   wte(__SCREAMING_SNAKE_CASE       )

											with torch.no_grad():
															model(**__SCREAMING_SNAKE_CASE       )[0]







			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      Optional[Any]:
							a_					,      a_		:	Optional[Any]									=   self.model_tester.prepare_config_and_inputs()
							a_		:	Any									=   input_dict['''input_ids''']
							a_		:	Dict									=   input_ids.ne(1       ).to(__SCREAMING_SNAKE_CASE       )
							a_		:	List[Any]									=   MaMaaaForConditionalGeneration(__SCREAMING_SNAKE_CASE       ).eval().to(__SCREAMING_SNAKE_CASE       )
							if torch_device == "cuda":
											model.half()
							model.generate(__SCREAMING_SNAKE_CASE	,    attention_mask=__SCREAMING_SNAKE_CASE       )
							model.generate(num_beams=4	,    do_sample=__SCREAMING_SNAKE_CASE	,    early_stopping=__SCREAMING_SNAKE_CASE	,    num_return_sequences=3       )





def 		_UpperCAmelCase (				__A				: Any   ):
				return torch.tensor(__A    ,		dtype=torch.long    ,		device=__A   )


__lowerCAmelCase									=   1E-4





@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):






			@cached_property
			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Optional[Any]:
							return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[Any]:
							a_		:	int									=   MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''       ).to(__SCREAMING_SNAKE_CASE       )
							a_		:	int									=   _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
							a_		:	List[str]									=   _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
							a_		:	Tuple									=   prepare_mam_aaa_inputs_dict(model.config	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							with torch.no_grad():
											a_		:	int									=   model(**__SCREAMING_SNAKE_CASE       )[0]
							a_		:	Any									=   torch.Size((1, 11, 1024)       )
							self.assertEqual(output.shape	,    __SCREAMING_SNAKE_CASE       )
							# change to expected output here
							a_		:	Dict									=   torch.tensor(
							    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]	,    device=__SCREAMING_SNAKE_CASE       )
							self.assertTrue(torch.allclose(output[:, :3, :3]	,    __SCREAMING_SNAKE_CASE	,    atol=__SCREAMING_SNAKE_CASE       )       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Optional[Any]:
							a_		:	List[Any]									=   MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''       ).to(__SCREAMING_SNAKE_CASE       )

							# change to intended input
							a_		:	Optional[int]									=   _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
							a_		:	Union[str, Any]									=   _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
							a_		:	str									=   prepare_mam_aaa_inputs_dict(model.config	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							with torch.no_grad():
											a_		:	Union[str, Any]									=   model(**__SCREAMING_SNAKE_CASE       )[0]
							a_		:	List[str]									=   torch.Size((1, 11, model.config.vocab_size)       )
							self.assertEqual(output.shape	,    __SCREAMING_SNAKE_CASE       )
							# change to expected output here
							a_		:	List[str]									=   torch.tensor(
							    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]	,    device=__SCREAMING_SNAKE_CASE       )
							self.assertTrue(torch.allclose(output[:, :3, :3]	,    __SCREAMING_SNAKE_CASE	,    atol=__SCREAMING_SNAKE_CASE       )       )







			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							a_		:	Union[str, Any]									=   MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''       ).to(__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''	,    src_lang='''fr'''	,    tgt_lang='''en'''       )

							a_		:	Optional[int]									=   [
							    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
							    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
							    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
							    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
							    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
							]

							# The below article tests that we don't add any hypotheses outside of the top n_beams
							a_		:	Any									=   tokenizer(__SCREAMING_SNAKE_CASE	,    padding=__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       )

							a_		:	Optional[int]									=   model.generate(
							    input_ids=dct['''input_ids'''].to(__SCREAMING_SNAKE_CASE       )	,    attention_mask=dct['''attention_mask'''].to(__SCREAMING_SNAKE_CASE       )	,    num_beams=5	,    forced_bos_token_id=tokenizer.get_lang_id('''en'''       )	,    )

							a_		:	Tuple									=   [
							    '''The NSA case highlights the total absence of intelligence debate''',
							    '''I think there are two levels of response from the French government.''',
							    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
							    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
							    ''' communications in France.''',
							]

							a_		:	str									=   tokenizer.batch_decode(
							    hypotheses_batch.tolist()	,    clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )
							assert generated == expected_en












'''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}'"""
		)




