

'''simple docstring'''




import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
      import jax.numpy as jnp

      from transformers.models.albert.modeling_flax_albert import (
          FlaxAlbertForMaskedLM,
          FlaxAlbertForMultipleChoice,
          FlaxAlbertForPreTraining,
          FlaxAlbertForQuestionAnswering,
          FlaxAlbertForSequenceClassification,
          FlaxAlbertForTokenClassification,
          FlaxAlbertModel,
      )






class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 def __init__(   self		:   str			,				a__		:   Tuple			,				a__		:   Dict=13			,				a__		:   int=7			,				a__		:   int=True			,				a__		:   Dict=True			,				a__		:   Optional[int]=True			,				a__		:   Optional[int]=True			,				a__		:   int=99			,				a__		:   Union[str, Any]=32			,				a__		:   Any=5			,				a__		:   Optional[Any]=4			,				a__		:   Tuple=37			,				a__		:   List[str]="gelu"			,				a__		:   List[str]=0.1			,				a__		:   Optional[Any]=0.1			,				a__		:   Union[str, Any]=512			,				a__		:   Optional[int]=16			,				a__		:   Optional[Any]=2			,				a__		:   Union[str, Any]=0.02			,				a__		:   Any=4			,				):
      __magic_name__						       =    parent
      __magic_name__						       =    batch_size
      __magic_name__						       =    seq_length
      __magic_name__						       =    is_training
      __magic_name__						       =    use_attention_mask
      __magic_name__						       =    use_token_type_ids
      __magic_name__						       =    use_labels
      __magic_name__						       =    vocab_size
      __magic_name__						       =    hidden_size
      __magic_name__						       =    num_hidden_layers
      __magic_name__						       =    num_attention_heads
      __magic_name__						       =    intermediate_size
      __magic_name__						       =    hidden_act
      __magic_name__						       =    hidden_dropout_prob
      __magic_name__						       =    attention_probs_dropout_prob
      __magic_name__						       =    max_position_embeddings
      __magic_name__						       =    type_vocab_size
      __magic_name__						       =    type_sequence_label_size
      __magic_name__						       =    initializer_range
      __magic_name__						       =    num_choices



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size      )

      __magic_name__						       =    None
      if self.use_attention_mask:
           __magic_name__						       =    random_attention_mask([self.batch_size, self.seq_length]      )

      __magic_name__						       =    None
      if self.use_token_type_ids:
           __magic_name__						       =    ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size      )

      __magic_name__						       =    AlbertConfig(
          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			,				type_vocab_size=self.type_vocab_size			,				is_decoder=a__			,				initializer_range=self.initializer_range			,				)

      return config, input_ids, token_type_ids, attention_mask



 def 			snake_case__  (   self		:   Union[str, Any]      ):
      __magic_name__						       =    self.prepare_config_and_inputs()
      __magic_name__       ,  __magic_name__       ,  __magic_name__       ,  __magic_name__						       =    config_and_inputs
      __magic_name__						       =    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict






@require_flax
class   _SCREAMING_SNAKE_CASE   (   __a		,unittest.TestCase     ):
 __SCREAMING_SNAKE_CASE					:Optional[int]      	=					(
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )



 def 			snake_case__  (   self		:   Dict      ):
      __magic_name__						       =    FlaxAlbertModelTester(self      )



 @slow
 def 			snake_case__  (   self		:   List[Any]      ):
      for model_class_name in self.all_model_classes:
           __magic_name__						       =    model_class_name.from_pretrained('''albert-base-v2'''      )
           __magic_name__						       =    model(np.ones((1, 1)      )      )
           self.assertIsNotNone(a__      )






@require_flax
class   _SCREAMING_SNAKE_CASE   (   unittest.TestCase     ):



 @slow
 def 			snake_case__  (   self		:   Tuple      ):
      __magic_name__						       =    FlaxAlbertModel.from_pretrained('''albert-base-v2'''      )
      __magic_name__						       =    np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]      )
      __magic_name__						       =    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
      __magic_name__						       =    model(a__			,				attention_mask=a__      )[0]
      __magic_name__						       =    (1, 11, 768)
      self.assertEqual(output.shape			,				a__      )
      __magic_name__						       =    np.array(
          [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]      )

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]			,				a__			,				atol=1E-4      )      )









'''simple docstring'''




import functools




def 						UpperCamelCase	(			a		,						a			)				->     int:






     '''simple docstring'''
     __magic_name__						       =    len(a			)
     __magic_name__						       =    len(a			)

     @functools.cache
     def min_distance(a		,						a			) -> int:
          # if first word index is overflow - delete all from the second word
          if indexa >= len_worda:
               return len_worda - indexa
          # if second word index is overflow - delete all from the first word
          if indexa >= len_worda:
               return len_worda - indexa
          __magic_name__						       =    int(worda[indexa] != worda[indexa]			)  # current letters not identical
          return min(
              1 + min_distance(indexa + 1		,						a			)		,						1 + min_distance(a		,						indexa + 1			)		,						diff + min_distance(indexa + 1		,						indexa + 1			)		,						)

     return min_distance(0		,						0			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()






