



"""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							):




	'''simple docstring'''





	def __init__(     self:		str       ,			_SCREAMING_SNAKE_CASE:		Tuple       ,			_SCREAMING_SNAKE_CASE:		Any=13       ,			_SCREAMING_SNAKE_CASE:		Dict=7       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=True       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=True       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=99       ,			_SCREAMING_SNAKE_CASE:		str=32       ,			_SCREAMING_SNAKE_CASE:		Dict=5       ,			_SCREAMING_SNAKE_CASE:		str=4       ,			_SCREAMING_SNAKE_CASE:		Tuple=37       ,			_SCREAMING_SNAKE_CASE:		Dict="gelu"       ,			_SCREAMING_SNAKE_CASE:		List[str]=0.1       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.1       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=512       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=16       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=2       ,			_SCREAMING_SNAKE_CASE:		List[Any]=0.02       ,			_SCREAMING_SNAKE_CASE:		List[Any]=4       ,			)				->       str:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size)

				__lowerCAmelCase      :  Optional[int]												=       None
				if self.use_attention_mask:
							__lowerCAmelCase      :  Optional[int]												=       random_attention_mask([self.batch_size, self.seq_length])

				__lowerCAmelCase      :  int												=       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_=_SCREAMING_SNAKE_CASE       ,			)

				return config, input_ids, attention_mask







	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.prepare_config_and_inputs()
				__lowerCAmelCase						,	__lowerCAmelCase						,	__lowerCAmelCase      :  int												=       config_and_inputs
				__lowerCAmelCase      :  Tuple												=       {"input_ids": input_ids, "attention_mask": attention_mask}
				return config, inputs_dict






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




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	(
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       FlaxDistilBertModelTester(self)







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Tuple:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
							__lowerCAmelCase      :  Dict												=       model_class_name.from_pretrained("distilbert-base-uncased")
							__lowerCAmelCase      :  Any												=       model(np.ones((1, 1)))
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)






@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       FlaxDistilBertModel.from_pretrained("distilbert-base-uncased")
				__lowerCAmelCase      :  List[Any]												=       np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
				__lowerCAmelCase      :  Optional[Any]												=       np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
				__lowerCAmelCase      :  Dict												=       model(_SCREAMING_SNAKE_CASE       ,			attention_mask=_SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase      :  Union[str, Any]												=       (1, 11, 768)
				self.assertEqual(output.shape       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  List[Any]												=       np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])

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




"""simple docstring"""






def 			_lowercase				(  __snake_case					,__snake_case	)						->   float:
			if digit_amount > 0:
						return round(number - int(__snake_case	)					,__snake_case	)
			return number - int(__snake_case	)


if __name__ == "__main__":
	print(decimal_isolate(1.53, 0))
	print(decimal_isolate(35.345, 1))
	print(decimal_isolate(35.345, 2))
	print(decimal_isolate(35.345, 3))
	print(decimal_isolate(-14.789, 3))
	print(decimal_isolate(0, 2))
	print(decimal_isolate(-14.123, 1))
	print(decimal_isolate(-14.123, 2))
	print(decimal_isolate(-14.123, 3))