






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


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Optional[Any]:


												"""simple docstring"""

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

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

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

												__lowercase      :							Union[str, Any]				=     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=__a		,     initializer_range=self.initializer_range		,     )

												return config, input_ids, token_type_ids, attention_mask



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


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.prepare_config_and_inputs()
												__lowercase			,			__lowercase			,			__lowercase			,			__lowercase      :							List[Any]				=     config_and_inputs

												__lowercase      :							Union[str, Any]				=     True
												__lowercase      :							str				=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
												__lowercase      :							Tuple				=     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     ( __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			str									=      True

					_A						:			Optional[int]									=      (
					    (
					        FlaxRobertaModel,
					        FlaxRobertaForCausalLM,
					        FlaxRobertaForMaskedLM,
					        FlaxRobertaForSequenceClassification,
					        FlaxRobertaForTokenClassification,
					        FlaxRobertaForMultipleChoice,
					        FlaxRobertaForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Any:


												"""simple docstring"""

												__lowercase      :							str				=     FlaxRobertaModelTester(self							)



					@slow
					def       lowerCAmelCase				(  self				:	Dict							)			->  str:


												"""simple docstring"""

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







import fire

from utils import calculate_rouge, save_json
def       snake_case_		(     lowerCAmelCase_       :						Any       ,       lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						str=None       ,       **lowerCAmelCase_       :						str      ):
							__lowercase      :							Tuple				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()]
							__lowercase      :							Dict				=     [x.strip() for x in open(lowerCAmelCase_      ).readlines()][: len(lowerCAmelCase_      )]
							__lowercase      :							Tuple				=     calculate_rouge(lowerCAmelCase_       ,       lowerCAmelCase_       ,       **lowerCAmelCase_      )
							if save_path is not None:
														save_json(lowerCAmelCase_       ,       lowerCAmelCase_       ,       indent=lowerCAmelCase_      )
							return metrics  # these print nicely


if __name__ == "__main__":
	fire.Fire(calculate_rouge_path)