


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       A    (   unittest.TestCase ):


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase=1_3      ,				lowerCAmelCase=7      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=True      ,				lowerCAmelCase=9_9      ,				lowerCAmelCase=3_2      ,				lowerCAmelCase=5      ,				lowerCAmelCase=4      ,				lowerCAmelCase=3_7      ,				lowerCAmelCase="gelu"      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=0.1      ,				lowerCAmelCase=5_1_2      ,				lowerCAmelCase=1_6      ,				lowerCAmelCase=2      ,				lowerCAmelCase=0.02      ,				lowerCAmelCase=4      ,				):
									__lowercase=			parent
									__lowercase=			batch_size
									__lowercase=			seq_length
									__lowercase=			is_training
									__lowercase=			use_attention_mask
									__lowercase=			use_token_type_ids
									__lowercase=			use_labels
									__lowercase=			vocab_size
									__lowercase=			hidden_size
									__lowercase=			num_hidden_layers
									__lowercase=			num_attention_heads
									__lowercase=			intermediate_size
									__lowercase=			hidden_act
									__lowercase=			hidden_dropout_prob
									__lowercase=			attention_probs_dropout_prob
									__lowercase=			max_position_embeddings
									__lowercase=			type_vocab_size
									__lowercase=			type_sequence_label_size
									__lowercase=			initializer_range
									__lowercase=			num_choices


							def 			_A       (self					):
									__lowercase=			ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size					)

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

									__lowercase=			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_=lowerCAmelCase      ,				)

									return config, input_ids, attention_mask







							def 			_A       (self					):
									__lowercase=			self.prepare_config_and_inputs()
									__lowercase,							__lowercase,							__lowercase=			config_and_inputs
									__lowercase=			{'input_ids': input_ids, 'attention_mask': attention_mask}
									return config, inputs_dict





@require_flax
class       A    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Optional[int]       =(
							    (
							        FlaxDistilBertModel,
							        FlaxDistilBertForMaskedLM,
							        FlaxDistilBertForMultipleChoice,
							        FlaxDistilBertForQuestionAnswering,
							        FlaxDistilBertForSequenceClassification,
							        FlaxDistilBertForTokenClassification,
							        FlaxDistilBertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)


							def 			_A       (self					):
									__lowercase=			FlaxDistilBertModelTester(self					)







							@slow
							def 			_A       (self					):
									for model_class_name in self.all_model_classes:
											__lowercase=			model_class_name.from_pretrained('distilbert-base-uncased'					)
											__lowercase=			model(np.ones((1, 1)					)					)
											self.assertIsNotNone(lowerCAmelCase					)





@require_flax
class       A    (   unittest.TestCase ):


							@slow
							def 			_A       (self					):
									__lowercase=			FlaxDistilBertModel.from_pretrained('distilbert-base-uncased'					)
									__lowercase=			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]]					)
									__lowercase=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
									__lowercase=			model(lowerCAmelCase      ,				attention_mask=lowerCAmelCase					)[0]
									__lowercase=			(1, 1_1, 7_6_8)
									self.assertEqual(output.shape      ,				lowerCAmelCase					)
									__lowercase=			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]      ,				lowerCAmelCase      ,				atol=1E-4					)					)










def        _lowerCamelCase(	lowercase__   , lowercase__ = " "	)						->	list:

		'''simple docstring'''







		__lowercase=			[]

		__lowercase=			0
		for index, char in enumerate(lowercase__	):
				if char == separator:
						split_words.append(string[last_index:index]	)
						__lowercase=			index + 1
				elif index + 1 == len(lowercase__	):
						split_words.append(string[last_index : index + 1]	)
		return split_words


if __name__ == "__main__":
	from doctest import testmod

	testmod()






