



'''simple docstring'''





from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
  import tensorflow as tf

  from transformers import TFBlipTextModel
  from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST






class __A			:



 '''simple docstring'''







 def __init__(self ,   A ,   A=12 ,   A=7 ,   A=True ,   A=True ,   A=True ,   A=99 ,   A=32 ,   A=32 ,   A=2 ,   A=4 ,   A=37 ,   A=0.1 ,   A=0.1 ,   A=512 ,   A=0.02 ,   A=0 ,   A=None ,   )    ->     str:


      """simple docstring"""



      _a      =  parent
      _a      =  batch_size
      _a      =  seq_length
      _a      =  is_training
      _a      =  use_input_mask
      _a      =  use_labels
      _a      =  vocab_size
      _a      =  hidden_size
      _a      =  projection_dim
      _a      =  num_hidden_layers
      _a      =  num_attention_heads
      _a      =  intermediate_size
      _a      =  dropout
      _a      =  attention_dropout
      _a      =  max_position_embeddings
      _a      =  initializer_range
      _a      =  scope
      _a      =  bos_token_id



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


      """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 input_mask is not None:
           _a      =  input_mask.numpy()
           _a  ,    _a      =  input_mask.shape
           _a      =  np.random.randint(1 ,   seq_length - 1 ,   size=(batch_size,)       )
           for batch_idx, start_index in enumerate(A       ):
                _a      =  1
                _a      =  0

      _a      =  self.get_config()

      return config, input_ids, tf.convert_to_tensor(A       )



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return BlipTextConfig(
          vocab_size=self.vocab_size ,   hidden_size=self.hidden_size ,   projection_dim=self.projection_dim ,   num_hidden_layers=self.num_hidden_layers ,   num_attention_heads=self.num_attention_heads ,   intermediate_size=self.intermediate_size ,   dropout=self.dropout ,   attention_dropout=self.attention_dropout ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   bos_token_id=self.bos_token_id ,   )



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


      """simple docstring"""



      _a      =  TFBlipTextModel(config=A       )
      _a      =  model(A ,   attention_mask=A ,   training=A       )
      _a      =  model(A ,   training=A       )
      self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)       )
      self.parent.assertEqual(result.pooler_output.shape ,   (self.batch_size, self.hidden_size)       )



 def 					a__  (self       )    ->     int:


      """simple docstring"""



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






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



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       (TFBlipTextModel,) if is_tf_available() else ()
 __lowerCamelCase    :       List[Any]          =       False
 __lowerCamelCase    :       List[Any]          =       False
 __lowerCamelCase    :       List[str]          =       False



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      self.config_tester.run_common_tests()



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


      """simple docstring"""



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



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      pass



 @unittest.skip(reason='''Blip does not use inputs_embeds'''       )
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      pass



 @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''       )
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      pass



 @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''       )
 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      pass



 @slow
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =  TFBlipTextModel.from_pretrained(A       )
           self.assertIsNotNone(A       )



 def 					a__  (self ,   A=True       )    ->     Any:


      """simple docstring"""



      super().test_pt_tf_model_equivalence(allow_missing_keys=A       )








'''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}'"""
  )



