





'''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 					__a      (unittest.TestCase     ):

							def __init__(							self							:     List[str]       ,     __magic_name__							:     Tuple       ,     __magic_name__							:     List[str]=13       ,     __magic_name__							:     Tuple=7       ,     __magic_name__							:     str=True       ,     __magic_name__							:     Dict=True       ,     __magic_name__							:     List[Any]=True       ,     __magic_name__							:     Optional[Any]=True       ,     __magic_name__							:     Optional[Any]=99       ,     __magic_name__							:     Optional[int]=32       ,     __magic_name__							:     List[str]=5       ,     __magic_name__							:     Optional[int]=4       ,     __magic_name__							:     Tuple=37       ,     __magic_name__							:     int="gelu"       ,     __magic_name__							:     Tuple=0.1       ,     __magic_name__							:     Optional[int]=0.1       ,     __magic_name__							:     Any=5_12       ,     __magic_name__							:     List[str]=16       ,     __magic_name__							:     str=2       ,     __magic_name__							:     List[str]=0.0_2       ,     __magic_name__							:     Optional[int]=4       ,     )					->       int:







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

							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Optional[int]     						=  ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size   )

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

													UpperCAmelCase_			:      List[Any]     						=  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_=__magic_name__       ,     )

													return config, input_ids, attention_mask



							def     UpperCAmelCase__	(							self							:     Dict   )					->       int:







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





@require_flax
class 					__a      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   Union[str, Any]									=       (
							    (
							        FlaxDistilBertModel,
							        FlaxDistilBertForMaskedLM,
							        FlaxDistilBertForMultipleChoice,
							        FlaxDistilBertForQuestionAnswering,
							        FlaxDistilBertForSequenceClassification,
							        FlaxDistilBertForTokenClassification,
							        FlaxDistilBertForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)

							def     UpperCAmelCase__	(							self							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  FlaxDistilBertModelTester(self   )



							@slow
							def     UpperCAmelCase__	(							self							:     List[str]   )					->       Tuple:







													"""simple docstring"""
													for model_class_name in self.all_model_classes:
																			UpperCAmelCase_			:      Optional[int]     						=  model_class_name.from_pretrained('''distilbert-base-uncased'''   )
																			UpperCAmelCase_			:      List[str]     						=  model(np.ones((1, 1)   )   )
																			self.assertIsNotNone(__magic_name__   )





@require_flax
class 					__a      (unittest.TestCase     ):

							@slow
							def     UpperCAmelCase__	(							self							:     Tuple   )					->       Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_			:      Tuple     						=  FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''   )
													UpperCAmelCase_			:      List[Any]     						=  np.array([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]   )
													UpperCAmelCase_			:      Optional[Any]     						=  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
													UpperCAmelCase_			:      Tuple     						=  model(__magic_name__       ,     attention_mask=__magic_name__   )[0]
													UpperCAmelCase_			:      Tuple     						=  (1, 11, 7_68)
													self.assertEqual(output.shape       ,     __magic_name__   )
													UpperCAmelCase_			:      List[Any]     						=  np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]   )

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












'''simple docstring'''







def        lowerCamelCase_      (  SCREAMING_SNAKE_CASE__      :			int   ) ->	str:
						if number > 0:
												raise ValueError('''input must be a negative integer'''   )
						UpperCAmelCase_			:      Union[str, Any]     						=  len(bin(SCREAMING_SNAKE_CASE__   )[3:]   )
						UpperCAmelCase_			:      Union[str, Any]     						=  bin(abs(SCREAMING_SNAKE_CASE__   ) - (1 << binary_number_length)   )[3:]
						UpperCAmelCase_			:      Optional[Any]     						=  (
						    (
						        '''1'''
						        + '''0''' * (binary_number_length - len(SCREAMING_SNAKE_CASE__   ))
						        + twos_complement_number
						    )
						    if number < 0
						    else '''0'''
						)
						return "0b" + twos_complement_number


if __name__ == "__main__":
					import doctest

					doctest.testmod()





