


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  snake_case_     (unittest.TestCase   ):
					def __init__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Tuple=13    ,__snake_case     :str=7    ,__snake_case     :Tuple=True    ,__snake_case     :str=True    ,__snake_case     :Union[str, Any]=True    ,__snake_case     :List[str]=True    ,__snake_case     :Optional[int]=99    ,__snake_case     :List[str]=32    ,__snake_case     :Union[str, Any]=5    ,__snake_case     :Union[str, Any]=4    ,__snake_case     :int=37    ,__snake_case     :List[Any]="gelu"    ,__snake_case     :Union[str, Any]=0.1    ,__snake_case     :Any=0.1    ,__snake_case     :List[Any]=5_12    ,__snake_case     :Optional[int]=16    ,__snake_case     :int=2    ,__snake_case     :str=0.02    ,__snake_case     :Optional[Any]=4    ,)      ->					List[str]:
												a__    =  parent
												a__    =  batch_size
												a__    =  seq_length
												a__    =  is_training
												a__    =  use_attention_mask
												a__    =  use_token_type_ids
												a__    =  use_labels
												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__    =  type_vocab_size
												a__    =  type_sequence_label_size
												a__    =  initializer_range
												a__    =  num_choices
					def 				lowerCamelCase__(   self     :Optional[Any]   )      ->					List[str]:
												a__    =  ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size   )

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

												a__    =  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_=__snake_case    ,)

												return config, input_ids, attention_mask



					def 				lowerCamelCase__(   self     :Tuple   )      ->					Optional[Any]:
												a__    =  self.prepare_config_and_inputs()
												a__   ,      a__   ,      a__    =  config_and_inputs
												a__    =  {'input_ids': input_ids, 'attention_mask': attention_mask}
												return config, inputs_dict







@require_flax
class  snake_case_     (lowerCamelCase_			,		unittest.TestCase   ):
					UpperCAmelCase__      :					Optional[int]					   =    (
					    (
					        FlaxDistilBertModel,
					        FlaxDistilBertForMaskedLM,
					        FlaxDistilBertForMultipleChoice,
					        FlaxDistilBertForQuestionAnswering,
					        FlaxDistilBertForSequenceClassification,
					        FlaxDistilBertForTokenClassification,
					        FlaxDistilBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					def 				lowerCamelCase__(   self     :int   )      ->					Optional[Any]:
												a__    =  FlaxDistilBertModelTester(self   )



					@slow
					def 				lowerCamelCase__(   self     :int   )      ->					Optional[Any]:
												for model_class_name in self.all_model_classes:
																			a__    =  model_class_name.from_pretrained('distilbert-base-uncased'   )
																			a__    =  model(np.ones((1, 1)   )   )
																			self.assertIsNotNone(__snake_case   )







@require_flax
class  snake_case_     (unittest.TestCase   ):
					@slow
					def 				lowerCamelCase__(   self     :Tuple   )      ->					Optional[Any]:
												a__    =  FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'   )
												a__    =  np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]   )
												a__    =  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
												a__    =  model(__snake_case    ,attention_mask=__snake_case   )[0]
												a__    =  (1, 11, 7_68)
												self.assertEqual(output.shape    ,__snake_case   )
												a__    =  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]    ,__snake_case    ,atol=1E-4   )   )








from __future__ import annotations






def 							__lowercase     (				__lowerCAmelCase							:       list[int]     ):  # This function is recursive
							a__    =  len(__lowerCAmelCase     )
							# If the array contains only one element, we return it (it's the stop condition of
							# recursion)
							if array_length <= 1:
														return array
														# Else
							a__    =  array[0]
							a__    =  False
							a__    =  1
							a__    =  []
							while not is_found and i < array_length:
														if array[i] < pivot:
																					a__    =  True
																					a__    =  [element for element in array[i:] if element >= array[i]]
																					a__    =  longest_subsequence(__lowerCAmelCase     )
																					if len(__lowerCAmelCase     ) > len(__lowerCAmelCase     ):
																												a__    =  temp_array
														else:
																					i += 1

							a__    =  [element for element in array[1:] if element >= pivot]
							a__    =  [pivot, *longest_subsequence(__lowerCAmelCase     )]
							if len(__lowerCAmelCase     ) > len(__lowerCAmelCase     ):
														return temp_array
							else:
														return longest_subseq


if __name__ == "__main__":
						import doctest

						doctest.testmod()




