

from __future__ import annotations

from typing import TypedDict







class 			lowercase    (	lowercase_      ):
				__SCREAMING_SNAKE_CASE :			str
				__SCREAMING_SNAKE_CASE :			int





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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

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





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






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

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





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






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

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


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







import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
					import torch

					from transformers import (
					    XLMForMultipleChoice,
					    XLMForQuestionAnswering,
					    XLMForQuestionAnsweringSimple,
					    XLMForSequenceClassification,
					    XLMForTokenClassification,
					    XLMModel,
					    XLMWithLMHeadModel,
					)
					from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST







class 			lowercase    :




				def __init__( self				,       snake_case				,       snake_case=13				,       snake_case=7				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=True				,       snake_case=False				,       snake_case=False				,       snake_case=False				,       snake_case=2				,       snake_case=99				,       snake_case=0				,       snake_case=32				,       snake_case=5				,       snake_case=4				,       snake_case=0.1				,       snake_case=0.1				,       snake_case=512				,       snake_case=2				,       snake_case=0.02				,       snake_case=2				,       snake_case=4				,       snake_case="last"				,       snake_case=True				,       snake_case=None				,       snake_case=0				,       ):
											snake_case_          =   parent
											snake_case_          =   batch_size
											snake_case_          =   seq_length
											snake_case_          =   is_training
											snake_case_          =   use_input_lengths
											snake_case_          =   use_token_type_ids
											snake_case_          =   use_labels
											snake_case_          =   gelu_activation
											snake_case_          =   sinusoidal_embeddings
											snake_case_          =   causal
											snake_case_          =   asm
											snake_case_          =   n_langs
											snake_case_          =   vocab_size
											snake_case_          =   n_special
											snake_case_          =   hidden_size
											snake_case_          =   num_hidden_layers
											snake_case_          =   num_attention_heads
											snake_case_          =   hidden_dropout_prob
											snake_case_          =   attention_probs_dropout_prob
											snake_case_          =   max_position_embeddings
											snake_case_          =   type_sequence_label_size
											snake_case_          =   initializer_range
											snake_case_          =   num_labels
											snake_case_          =   num_choices
											snake_case_          =   summary_type
											snake_case_          =   use_proj
											snake_case_          =   scope
											snake_case_          =   bos_token_id




				def    a							( self	):
											snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size	)
											snake_case_          =   random_attention_mask([self.batch_size, self.seq_length]	)

											snake_case_          =   None
											if self.use_input_lengths:
																		snake_case_          =   (
																		    ids_tensor([self.batch_size]				,       vocab_size=2	) + self.seq_length - 2
																		)  # small variation of seq_length

											snake_case_          =   None
											if self.use_token_type_ids:
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.n_langs	)

											snake_case_          =   None
											snake_case_          =   None
											snake_case_          =   None
											if self.use_labels:
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.type_sequence_label_size	)
																		snake_case_          =   ids_tensor([self.batch_size, self.seq_length]				,       self.num_labels	)
																		snake_case_          =   ids_tensor([self.batch_size]				,       2	).float()
																		snake_case_          =   ids_tensor([self.batch_size]				,       self.num_choices	)

											snake_case_          =   self.get_config()

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    input_lengths,
											    sequence_labels,
											    token_labels,
											    is_impossible_labels,
											    choice_labels,
											    input_mask,
											)




				def    a							( self	):
											return XLMConfig(
											    vocab_size=self.vocab_size				,       n_special=self.n_special				,       emb_dim=self.hidden_size				,       n_layers=self.num_hidden_layers				,       n_heads=self.num_attention_heads				,       dropout=self.hidden_dropout_prob				,       attention_dropout=self.attention_probs_dropout_prob				,       gelu_activation=self.gelu_activation				,       sinusoidal_embeddings=self.sinusoidal_embeddings				,       asm=self.asm				,       causal=self.causal				,       n_langs=self.n_langs				,       max_position_embeddings=self.max_position_embeddings				,       initializer_range=self.initializer_range				,       summary_type=self.summary_type				,       use_proj=self.use_proj				,       num_labels=self.num_labels				,       bos_token_id=self.bos_token_id				,       )




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMModel(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   model(snake_case				,       lengths=snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case				,       langs=snake_case	)
											snake_case_          =   model(snake_case	)
											self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMWithLMHeadModel(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       token_type_ids=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.vocab_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnsweringSimple(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)
											snake_case_          =   outputs
											self.parent.assertEqual(result.start_logits.shape				,       (self.batch_size, self.seq_length)	)
											self.parent.assertEqual(result.end_logits.shape				,       (self.batch_size, self.seq_length)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForQuestionAnswering(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       p_mask=snake_case				,       )

											snake_case_          =   model(
											    snake_case				,       start_positions=snake_case				,       end_positions=snake_case				,       cls_index=snake_case				,       is_impossible=snake_case				,       )

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											snake_case_          =   model(snake_case				,       start_positions=snake_case				,       end_positions=snake_case	)

											((snake_case_)    ,   )          =   result_with_labels.to_tuple()

											self.parent.assertEqual(result_with_labels.loss.shape				,       ()	)
											self.parent.assertEqual(result.start_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(result.start_top_index.shape				,       (self.batch_size, model.config.start_n_top)	)
											self.parent.assertEqual(
											    result.end_top_log_probs.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(
											    result.end_top_index.shape				,       (self.batch_size, model.config.start_n_top * model.config.end_n_top)	)
											self.parent.assertEqual(result.cls_logits.shape				,       (self.batch_size,)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   XLMForSequenceClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case	)
											snake_case_          =   model(snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.loss.shape				,       ()	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_labels
											snake_case_          =   XLMForTokenClassification(snake_case	)
											model.to(snake_case	)
											model.eval()

											snake_case_          =   model(snake_case				,       attention_mask=snake_case				,       labels=snake_case	)
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length, self.num_labels)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       ):
											snake_case_          =   self.num_choices
											snake_case_          =   XLMForMultipleChoice(config=snake_case	)
											model.to(snake_case	)
											model.eval()
											snake_case_          =   input_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   token_type_ids.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   input_mask.unsqueeze(1	).expand(-1				,       self.num_choices				,       -1	).contiguous()
											snake_case_          =   model(
											    snake_case				,       attention_mask=snake_case				,       token_type_ids=snake_case				,       labels=snake_case				,       )
											self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.num_choices)	)




				def    a							( self	):
											snake_case_          =   self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   (
											    snake_case_
											)    ,   
											)          =   config_and_inputs
											snake_case_          =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
											return config, inputs_dict







@require_torch
class 			lowercase    (	lowercase_     ,  lowercase_     ,  lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      (
				    (
				        XLMModel,
				        XLMWithLMHeadModel,
				        XLMForQuestionAnswering,
				        XLMForSequenceClassification,
				        XLMForQuestionAnsweringSimple,
				        XLMForTokenClassification,
				        XLMForMultipleChoice,
				    )
				    if is_torch_available()
				    else ()
				)
				__SCREAMING_SNAKE_CASE :			Tuple	      =      (
				    (XLMWithLMHeadModel,) if is_torch_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				__SCREAMING_SNAKE_CASE :			int	      =      (
				    {
				        '''feature-extraction''': XLMModel,
				        '''fill-mask''': XLMWithLMHeadModel,
				        '''question-answering''': XLMForQuestionAnsweringSimple,
				        '''text-classification''': XLMForSequenceClassification,
				        '''text-generation''': XLMWithLMHeadModel,
				        '''token-classification''': XLMForTokenClassification,
				        '''zero-shot''': XLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case	):
											if (
											    pipeline_test_casse_name == "QAPipelineTests"
											    and tokenizer_name is not None
											    and not tokenizer_name.endswith('Fast'	)
											):
																		# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
																		# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
																		# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
																		return True

											return False




				def    a							( self				,       snake_case				,       snake_case				,       snake_case=False	):
											snake_case_          =   super()._prepare_for_class(snake_case				,       snake_case				,       return_labels=snake_case	)

											if return_labels:
																		if model_class.__name__ == "XLMForQuestionAnswering":
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)
																									snake_case_          =   torch.zeros(
																									    self.model_tester.batch_size				,       dtype=torch.long				,       device=snake_case	)

											return inputs_dict




				def    a							( self	):
											snake_case_          =   XLMModelTester(self	)
											snake_case_          =   ConfigTester(self				,       config_class=snake_case				,       emb_dim=37	)




				def    a							( self	):
											self.config_tester.run_common_tests()




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_model(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_lm_head(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_simple_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_qa(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_sequence_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_token_classif(*snake_case	)




				def    a							( self	):
											snake_case_          =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_attentions in attentions]				,       [True] * len(snake_case	)	)
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_attentions in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   min_length + idx + 1

																		snake_case_          =   (
																		    batch_size * num_beam_groups,
																		    config.num_attention_heads,
																		    tgt_len,
																		    src_len,
																		)
																		# check attn size
																		self.assertListEqual(
																		    [layer_attention.shape for layer_attention in iter_attentions]				,       [expected_shape] * len(snake_case	)	)




				def    a							( self				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case				,       snake_case=False				,       snake_case=1	):
											self.assertIsInstance(snake_case				,       snake_case	)
											self.assertListEqual(
											    [isinstance(snake_case				,       snake_case	) for iter_hidden_states in hidden_states]				,       [True] * len(snake_case	)				,       )
											self.assertEqual(len(snake_case	)				,       (max_length - min_length) * num_beam_groups	)

											for idx, iter_hidden_states in enumerate(snake_case	):
																		# adds PAD dummy token
																		snake_case_          =   min_length + idx + 1
																		snake_case_          =   (batch_size * num_beam_groups, seq_len, config.hidden_size)
																		# check hidden size
																		self.assertListEqual(
																		    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,       [expected_shape] * len(snake_case	)				,       )
											pass




				@slow
				def    a							( self	):
											for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_          =   XLMModel.from_pretrained(snake_case	)
																		self.assertIsNotNone(snake_case	)







@require_torch
class 			lowercase    (	unittest.TestCase      ):




				@slow
				def    a							( self	):
											snake_case_          =   XLMWithLMHeadModel.from_pretrained('xlm-mlm-en-2048'	)
											model.to(snake_case	)
											snake_case_          =   torch.tensor([[14, 447]]				,       dtype=torch.long				,       device=snake_case	)  # the president
											snake_case_          =   [
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											    14,
											    447,
											]  # the president the president the president the president the president the president the president the president the president the president
											# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
											snake_case_          =   model.generate(snake_case				,       do_sample=snake_case	)
											self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,       snake_case	)




