'''simple docstring'''


import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
     FlaxDistilBertForMaskedLM,
     FlaxDistilBertForMultipleChoice,
     FlaxDistilBertForQuestionAnswering,
     FlaxDistilBertForSequenceClassification,
     FlaxDistilBertForTokenClassification,
     FlaxDistilBertModel,
 )






class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  def __init__(			self				,		snake_case_				,		snake_case_=1_3				,		snake_case_=7				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=True				,		snake_case_=9_9				,		snake_case_=3_2				,		snake_case_=5				,		snake_case_=4				,		snake_case_=3_7				,		snake_case_="gelu"				,		snake_case_=0.1				,		snake_case_=0.1				,		snake_case_=5_1_2				,		snake_case_=1_6				,		snake_case_=2				,		snake_case_=0.02				,		snake_case_=4				,		):




       '''simple docstring'''

       UpperCAmelCase_							:       Optional[Any]       =				parent
       UpperCAmelCase_							:       Any       =				batch_size
       UpperCAmelCase_							:       Optional[Any]       =				seq_length
       UpperCAmelCase_							:       Optional[int]       =				is_training
       UpperCAmelCase_							:       Optional[Any]       =				use_attention_mask
       UpperCAmelCase_							:       Optional[int]       =				use_token_type_ids
       UpperCAmelCase_							:       int       =				use_labels
       UpperCAmelCase_							:       Union[str, Any]       =				vocab_size
       UpperCAmelCase_							:       Optional[int]       =				hidden_size
       UpperCAmelCase_							:       Tuple       =				num_hidden_layers
       UpperCAmelCase_							:       Tuple       =				num_attention_heads
       UpperCAmelCase_							:       Dict       =				intermediate_size
       UpperCAmelCase_							:       Tuple       =				hidden_act
       UpperCAmelCase_							:       Optional[Any]       =				hidden_dropout_prob
       UpperCAmelCase_							:       Optional[Any]       =				attention_probs_dropout_prob
       UpperCAmelCase_							:       Dict       =				max_position_embeddings
       UpperCAmelCase_							:       Optional[Any]       =				type_vocab_size
       UpperCAmelCase_							:       str       =				type_sequence_label_size
       UpperCAmelCase_							:       List[str]       =				initializer_range
       UpperCAmelCase_							:       int       =				num_choices


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       int       =				ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size						)

       UpperCAmelCase_							:       Dict       =				None
       if self.use_attention_mask:
            UpperCAmelCase_							:       Any       =				random_attention_mask([self.batch_size, self.seq_length]						)

       UpperCAmelCase_							:       str       =				DistilBertConfig(
           vocab_size=self.vocab_size				,		dim=self.hidden_size				,		n_layers=self.num_hidden_layers				,		n_heads=self.num_attention_heads				,		hidden_dim=self.intermediate_size				,		hidden_act=self.hidden_act				,		dropout=self.hidden_dropout_prob				,		attention_dropout=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		initializer_range=self.initializer_range				,		tie_weights_=__A				,		)

       return config, input_ids, attention_mask






  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       Tuple       =				self.prepare_config_and_inputs()
       UpperCAmelCase_							:       List[str]       =				config_and_inputs
       UpperCAmelCase_							:       Optional[Any]       =				{"""input_ids""": input_ids, """attention_mask""": attention_mask}
       return config, inputs_dict







@require_flax
class 					__SCREAMING_SNAKE_CASE						(	A__						,     unittest.TestCase	):







  '''simple docstring'''






  lowerCamelCase_		:Tuple						   =		(
      (
          FlaxDistilBertModel,
          FlaxDistilBertForMaskedLM,
          FlaxDistilBertForMultipleChoice,
          FlaxDistilBertForQuestionAnswering,
          FlaxDistilBertForSequenceClassification,
          FlaxDistilBertForTokenClassification,
          FlaxDistilBertForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )


  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       str       =				FlaxDistilBertModelTester(self						)






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       for model_class_name in self.all_model_classes:
            UpperCAmelCase_							:       Dict       =				model_class_name.from_pretrained('distilbert-base-uncased'						)
            UpperCAmelCase_							:       str       =				model(np.ones((1, 1)						)						)
            self.assertIsNotNone(__A						)







@require_flax
class 					__SCREAMING_SNAKE_CASE						(	unittest.TestCase	):







  '''simple docstring'''






  @slow
  def _UpperCamelCase					(			self						):




       '''simple docstring'''

       UpperCAmelCase_							:       List[str]       =				FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'						)
       UpperCAmelCase_							:       List[Any]       =				np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]						)
       UpperCAmelCase_							:       List[str]       =				np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
       UpperCAmelCase_							:       List[Any]       =				model(__A				,		attention_mask=__A						)[0]
       UpperCAmelCase_							:       List[Any]       =				(1, 1_1, 7_6_8)
       self.assertEqual(output.shape				,		__A						)
       UpperCAmelCase_							:       Tuple       =				np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]						)

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







'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     UpperCAmelCase_							:       Optional[int]       =				[0] * len(lowerCamelCase_       )

     for i in range(1     ,					len(lowerCamelCase_       )       ):
          # use last results for better performance - dynamic programming
          UpperCAmelCase_							:       List[Any]       =				prefix_result[i - 1]
          while j > 0 and input_string[i] != input_string[j]:
               UpperCAmelCase_							:       str       =				prefix_result[j - 1]

          if input_string[i] == input_string[j]:
               j += 1
          UpperCAmelCase_							:       Any       =				j

     return prefix_result


def  _lowerCamelCase   (				lowerCamelCase_ :							str       ):



     """simple docstring"""





     return max(prefix_function(lowerCamelCase_       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






