
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 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	def __init__(      self	:Tuple  ,     a	:Optional[int]  ,     a	:Any=1_3  ,     a	:str=7  ,     a	:Optional[Any]=True  ,     a	:Tuple=True  ,     a	:List[str]=True  ,     a	:str=True  ,     a	:Dict=9_9  ,     a	:Optional[Any]=3_2  ,     a	:Optional[Any]=5  ,     a	:Tuple=4  ,     a	:List[str]=3_7  ,     a	:Union[str, Any]="gelu"  ,     a	:Union[str, Any]=0.1  ,     a	:Tuple=0.1  ,     a	:Union[str, Any]=5_1_2  ,     a	:Optional[Any]=1_6  ,     a	:Optional[Any]=2  ,     a	:int=0.02  ,     a	:Optional[int]=4  ,     )	->				List[Any]:
								__UpperCamelCase			:     int         =      parent
								__UpperCamelCase			:     Union[str, Any]         =      batch_size
								__UpperCamelCase			:     Union[str, Any]         =      seq_length
								__UpperCamelCase			:     Any         =      is_training
								__UpperCamelCase			:     Union[str, Any]         =      use_attention_mask
								__UpperCamelCase			:     Union[str, Any]         =      use_token_type_ids
								__UpperCamelCase			:     List[str]         =      use_labels
								__UpperCamelCase			:     List[str]         =      vocab_size
								__UpperCamelCase			:     str         =      hidden_size
								__UpperCamelCase			:     Dict         =      num_hidden_layers
								__UpperCamelCase			:     List[str]         =      num_attention_heads
								__UpperCamelCase			:     Optional[Any]         =      intermediate_size
								__UpperCamelCase			:     Union[str, Any]         =      hidden_act
								__UpperCamelCase			:     int         =      hidden_dropout_prob
								__UpperCamelCase			:     int         =      attention_probs_dropout_prob
								__UpperCamelCase			:     str         =      max_position_embeddings
								__UpperCamelCase			:     Optional[int]         =      type_vocab_size
								__UpperCamelCase			:     Union[str, Any]         =      type_sequence_label_size
								__UpperCamelCase			:     Tuple         =      initializer_range
								__UpperCamelCase			:     Optional[Any]         =      num_choices







	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     List[Any]         =      ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size		)

								__UpperCamelCase			:     Any         =      None
								if self.use_attention_mask:
															__UpperCamelCase			:     Union[str, Any]         =      random_attention_mask([self.batch_size, self.seq_length]		)

								__UpperCamelCase			:     Dict         =      None
								if self.use_token_type_ids:
															__UpperCamelCase			:     Tuple         =      ids_tensor([self.batch_size, self.seq_length]  ,     self.type_vocab_size		)

								__UpperCamelCase			:     int         =      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	:List[str]		)	->				Optional[int]:
								__UpperCamelCase			:     Tuple         =      self.prepare_config_and_inputs()
								__UpperCamelCase			:     str         =      config_and_inputs
								__UpperCamelCase			:     Tuple         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict







	def      _lowerCamelCase       (      self	:int		)	->				List[str]:
								__UpperCamelCase			:     List[Any]         =      self.prepare_config_and_inputs()
								__UpperCamelCase			:     Dict         =      config_and_inputs

								__UpperCamelCase			:     Tuple         =      True
								__UpperCamelCase			:     Any         =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
								__UpperCamelCase			:     Optional[Any]         =      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 				lowerCamelCase__							(       snake_case__  ,		unittest.TestCase):




	'''simple docstring'''




	lowerCAmelCase_			=  True

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







	def      _lowerCamelCase       (      self	:Tuple		)	->				Tuple:
								__UpperCamelCase			:     List[str]         =      FlaxRobertaModelTester(self		)







	@slow
	def      _lowerCamelCase       (      self	:List[Any]		)	->				Optional[int]:
								for model_class_name in self.all_model_classes:
															__UpperCamelCase			:     Union[str, Any]         =      model_class_name.from_pretrained("roberta-base"  ,     from_pt=lowercase_		)
															__UpperCamelCase			:     Union[str, Any]         =      model(np.ones((1, 1)		)		)
															self.assertIsNotNone(lowercase_		)

def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    int = 200)      ->					int:






							'''simple docstring'''





							__UpperCamelCase			:     Any         =      [1, 2, 5, 10, 20, 50, 100, 200]
							__UpperCamelCase			:     Any         =      [0] * (pence + 1)
							__UpperCamelCase			:     Union[str, Any]         =      1  # base case: 1 way to make 0 pence

							for coin in coins:
														for i in range(_lowerCamelCase		,						pence + 1		,						1):
																					number_of_ways[i] += number_of_ways[i - coin]
							return number_of_ways[pence]


if __name__ == "__main__":
		assert solution(200) == 73682