



'''simple docstring'''





import unittest

from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import BertGenerationDecoder, BertGenerationEncoder






class __A			:



 '''simple docstring'''







 def __init__(self ,   A ,   A=13 ,   A=7 ,   A=True ,   A=True ,   A=99 ,   A=32 ,   A=5 ,   A=4 ,   A=37 ,   A="gelu" ,   A=0.1 ,   A=0.1 ,   A=50 ,   A=0.02 ,   A=True ,   A=None ,   )    ->     int:


      """simple docstring"""



      _a      =  parent
      _a      =  batch_size
      _a      =  seq_length
      _a      =  is_training
      _a      =  use_input_mask
      _a      =  vocab_size
      _a      =  hidden_size
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  intermediate_size
      _a      =  hidden_act
      _a      =  hidden_dropout_prob
      _a      =  attention_probs_dropout_prob
      _a      =  max_position_embeddings
      _a      =  initializer_range
      _a      =  use_labels
      _a      =  scope



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size       )

      _a      =  None
      if self.use_input_mask:
           _a      =  random_attention_mask([self.batch_size, self.seq_length]       )

      if self.use_labels:
           _a      =  ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size       )

      _a      =  self.get_config()

      return config, input_ids, input_mask, token_labels



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      return BertGenerationConfig(
          vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   hidden_act=self.hidden_act ,   hidden_dropout_prob=self.hidden_dropout_prob ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob ,   max_position_embeddings=self.max_position_embeddings ,   is_decoder=A ,   initializer_range=self.initializer_range ,   )



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      (
          (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    
      )      =  self.prepare_config_and_inputs()

      _a      =  True
      _a      =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
      _a      =  ids_tensor([self.batch_size, self.seq_length] ,   vocab_size=2       )

      return (
          config,
          input_ids,
          input_mask,
          token_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   **A ,   )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  BertGenerationEncoder(config=A       )
      model.to(A       )
      model.eval()
      _a      =  model(A ,   attention_mask=A       )
      _a      =  model(A       )
      self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A ,   A ,   **A ,   )    ->     List[Any]:


      """simple docstring"""



      _a      =  True
      _a      =  BertGenerationEncoder(config=A       )
      model.to(A       )
      model.eval()
      _a      =  model(
          A ,   attention_mask=A ,   encoder_hidden_states=A ,   encoder_attention_mask=A ,   )
      _a      =  model(
          A ,   attention_mask=A ,   encoder_hidden_states=A ,   )
      self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   A ,   A ,   **A ,   )    ->     Tuple:


      """simple docstring"""



      _a      =  True
      _a      =  True
      _a      =  BertGenerationDecoder(config=A       ).to(A       ).eval()

      # first forward pass
      _a      =  model(
          A ,   attention_mask=A ,   encoder_hidden_states=A ,   encoder_attention_mask=A ,   use_cache=A ,   )
      _a      =  outputs.past_key_values

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

      # append to next input_ids and
      _a      =  torch.cat([input_ids, next_tokens] ,   dim=-1       )
      _a      =  torch.cat([input_mask, next_mask] ,   dim=-1       )

      _a      =  model(
          A ,   attention_mask=A ,   encoder_hidden_states=A ,   encoder_attention_mask=A ,   output_hidden_states=A ,   )['''hidden_states'''][0]
      _a      =  model(
          A ,   attention_mask=A ,   encoder_hidden_states=A ,   encoder_attention_mask=A ,   past_key_values=A ,   output_hidden_states=A ,   )['''hidden_states'''][0]

      # select random slice
      _a      =  ids_tensor((1,) ,   output_from_past.shape[-1]       ).item()
      _a      =  output_from_no_past[:, -3:, random_slice_idx].detach()
      _a      =  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(A ,   A ,   atol=1E-3       )       )



 def 					a__  (self ,   A ,   A ,   A ,   A ,   *A ,   )    ->     Dict:


      """simple docstring"""



      _a      =  BertGenerationDecoder(A       )
      model.to(A       )
      model.eval()
      _a      =  model(A ,   attention_mask=A ,   labels=A       )
      self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)       )



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a  ,    _a  ,    _a  ,    _a      =  self.prepare_config_and_inputs()
      _a      =  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
      return config, inputs_dict






@require_torch
class __A			(  A   ,   A   ,   A   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       str          =       (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
 __lowerCamelCase    :       Any          =       (BertGenerationDecoder,) if is_torch_available() else ()
 __lowerCamelCase    :       Tuple          =       (
     {'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
     if is_torch_available()
     else {}
 )



 def 					a__  (self       )    ->     List[Any]:


      """simple docstring"""



      _a      =  BertGenerationEncoderTester(self       )
      _a      =  ConfigTester(self ,   config_class=A ,   hidden_size=37       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      self.config_tester.run_common_tests()



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*A       )



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      _a  ,    _a  ,    _a  ,    _a      =  self.model_tester.prepare_config_and_inputs()
      _a      =  '''bert'''
      self.model_tester.create_and_check_model(A ,   A ,   A ,   A       )



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*A       )



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_decoder_model_past_large_inputs(*A       )



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      (
          (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    (
          _a
      )  ,    
      )      =  self.model_tester.prepare_config_and_inputs_for_decoder()

      _a      =  None

      self.model_tester.create_and_check_model_as_decoder(
          A ,   A ,   A ,   A ,   A ,   A ,   )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_for_causal_lm(*A       )



 @slow
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      self.assertIsNotNone(A       )






@require_torch
class __A			(  unittest.TestCase						):



 '''simple docstring'''







 @slow
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  BertGenerationEncoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      _a      =  torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]       )
      with torch.no_grad():
           _a      =  model(A       )[0]
      _a      =  torch.Size([1, 8, 1_024]       )
      self.assertEqual(output.shape ,   A       )
      _a      =  torch.tensor(
          [[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3] ,   A ,   atol=1E-4       )       )






@require_torch
class __A			(  unittest.TestCase						):



 '''simple docstring'''







 @slow
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  BertGenerationDecoder.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder'''       )
      _a      =  torch.tensor([[101, 7_592, 1_010, 2_026, 3_899, 2_003, 10_140, 102]]       )
      with torch.no_grad():
           _a      =  model(A       )[0]
      _a      =  torch.Size([1, 8, 50_358]       )
      self.assertEqual(output.shape ,   A       )
      _a      =  torch.tensor(
          [[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]]       )
      self.assertTrue(torch.allclose(output[:, :3, :3] ,   A ,   atol=1E-4       )       )








'''simple docstring'''





from __future__ import annotations

from typing import TypedDict






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str
 __lowerCamelCase    :       int





def lowerCAmelCase  (__A):



     """simple docstring"""



     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 lowerCAmelCase  (__A):



     """simple docstring"""



     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      =  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      =  {
         "bwt_string": "".join([word[-1] for word in rotations]),
         "idx_original_string": rotations.index(__A),
     }
     return response




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



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


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



