



"""simple docstring"""

import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	from transformers.models.roberta.modeling_flax_roberta import (
	    FlaxRobertaForCausalLM,
	    FlaxRobertaForMaskedLM,
	    FlaxRobertaForMultipleChoice,
	    FlaxRobertaForQuestionAnswering,
	    FlaxRobertaForSequenceClassification,
	    FlaxRobertaForTokenClassification,
	    FlaxRobertaModel,
	)




class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def __init__(self						,     _lowercase						,     _lowercase=13						,     _lowercase=7						,     _lowercase=True						,     _lowercase=True						,     _lowercase=True						,     _lowercase=True						,     _lowercase=99						,     _lowercase=32						,     _lowercase=5						,     _lowercase=4						,     _lowercase=37						,     _lowercase="gelu"						,     _lowercase=0.1						,     _lowercase=0.1						,     _lowercase=512						,     _lowercase=16						,     _lowercase=2						,     _lowercase=0.02						,     _lowercase=4						,     ):





													'''simple docstring'''

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



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size							)

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

													__a		:		Optional[int]									=       None
													if self.use_token_type_ids:
																				__a		:		List[str]									=       ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size							)

													__a		:		Tuple									=       RobertaConfig(
													    vocab_size=self.vocab_size						,     hidden_size=self.hidden_size						,     num_hidden_layers=self.num_hidden_layers						,     num_attention_heads=self.num_attention_heads						,     intermediate_size=self.intermediate_size						,     hidden_act=self.hidden_act						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     type_vocab_size=self.type_vocab_size						,     is_decoder=_lowercase						,     initializer_range=self.initializer_range						,     )

													return config, input_ids, token_type_ids, attention_mask



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       self.prepare_config_and_inputs()
													__a							,     __a							,     __a							,     __a		:		List[Any]									=       config_and_inputs
													__a		:		str									=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
													return config, inputs_dict





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[Any]									=       self.prepare_config_and_inputs()
													__a							,     __a							,     __a							,     __a		:		Tuple									=       config_and_inputs

													__a		:		Any									=       True
													__a		:		Any									=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
													__a		:		List[str]									=       ids_tensor([self.batch_size, self.seq_length]						,     vocab_size=2							)

													return (
													    config,
													    input_ids,
													    token_type_ids,
													    encoder_hidden_states,
													    encoder_attention_mask,
													)







@require_flax
class SCREAMING_SNAKE_CASE__					(		__snake_case     ,					unittest.TestCase      ):
						_lowerCAmelCase							=					True

						_lowerCAmelCase							=					(
						    (
						        FlaxRobertaModel,
						        FlaxRobertaForCausalLM,
						        FlaxRobertaForMaskedLM,
						        FlaxRobertaForSequenceClassification,
						        FlaxRobertaForTokenClassification,
						        FlaxRobertaForMultipleChoice,
						        FlaxRobertaForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Optional[int]									=       FlaxRobertaModelTester(self							)





						@slow
						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													for model_class_name in self.all_model_classes:
																				__a		:		Optional[int]									=       model_class_name.from_pretrained("""roberta-base"""						,     from_pt=_lowercase							)
																				__a		:		Optional[int]									=       model(np.ones((1, 1)							)							)
																				self.assertIsNotNone(_lowercase							)






"""simple docstring"""

def       __magic_name__    (   _lowerCamelCase				: list[int]					):
							if not nums:  # Makes sure that the list is not empty
														raise ValueError("""List is empty"""					)

							__a		:		Any									=       sum(_lowerCamelCase					) / len(_lowerCamelCase					)  # Calculate the average
							return sum(abs(x - average					) for x in nums					) / len(_lowerCamelCase					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

