import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          FlaubertForMultipleChoice,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertModel,
          FlaubertWithLMHeadModel,
      )
      from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class    snake_case       (   UpperCamelCase_    ):




  def __init__(   self  :						str  ,			a_  :						Optional[Any]  ,			a_  :						Optional[Any]=13  ,			a_  :						List[Any]=7  ,			a_  :						Optional[int]=True  ,			a_  :						Any=True  ,			a_  :						Dict=True  ,			a_  :						Tuple=True  ,			a_  :						str=True  ,			a_  :						Tuple=False  ,			a_  :						Optional[int]=False  ,			a_  :						Union[str, Any]=False  ,			a_  :						int=2  ,			a_  :						Dict=99  ,			a_  :						Union[str, Any]=0  ,			a_  :						str=32  ,			a_  :						Union[str, Any]=5  ,			a_  :						Any=4  ,			a_  :						int=0.1  ,			a_  :						Any=0.1  ,			a_  :						List[str]=512  ,			a_  :						Optional[int]=12  ,			a_  :						Union[str, Any]=2  ,			a_  :						List[Any]=0.02  ,			a_  :						Tuple=3  ,			a_  :						int=4  ,			a_  :						Optional[int]="last"  ,			a_  :						List[str]=None  ,			a_  :						List[Any]=None  ,			)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		parent
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		batch_size
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		seq_length
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		is_training
       SCREAMING_SNAKE_CASE__					:					str      =		use_input_lengths
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		use_token_type_ids
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		use_labels
       SCREAMING_SNAKE_CASE__					:					List[str]      =		gelu_activation
       SCREAMING_SNAKE_CASE__					:					str      =		sinusoidal_embeddings
       SCREAMING_SNAKE_CASE__					:					Any      =		causal
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		asm
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		n_langs
       SCREAMING_SNAKE_CASE__					:					Dict      =		vocab_size
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		n_special
       SCREAMING_SNAKE_CASE__					:					Any      =		hidden_size
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		num_hidden_layers
       SCREAMING_SNAKE_CASE__					:					Dict      =		num_attention_heads
       SCREAMING_SNAKE_CASE__					:					List[str]      =		hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					:					int      =		max_position_embeddings
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		type_vocab_size
       SCREAMING_SNAKE_CASE__					:					str      =		type_sequence_label_size
       SCREAMING_SNAKE_CASE__					:					Dict      =		initializer_range
       SCREAMING_SNAKE_CASE__					:					Dict      =		num_labels
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		num_choices
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		summary_type
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		use_proj
       SCREAMING_SNAKE_CASE__					:					List[str]      =		scope




  def 						__lowercase(   self  :						Optional[int]					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.vocab_size					)
       SCREAMING_SNAKE_CASE__					:					str      =		random_attention_mask([self.batch_size, self.seq_length]					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		None
       if self.use_input_lengths:
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		(
                ids_tensor([self.batch_size]  ,			vocab_size=2					) + self.seq_length - 2
            )  # small variation of seq_length

       SCREAMING_SNAKE_CASE__					:					Tuple      =		None
       if self.use_token_type_ids:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.n_langs					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		None
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		None
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__					:					Any      =		ids_tensor([self.batch_size]  ,			self.type_sequence_label_size					)
            SCREAMING_SNAKE_CASE__					:					Dict      =		ids_tensor([self.batch_size, self.seq_length]  ,			self.num_labels					)
            SCREAMING_SNAKE_CASE__					:					List[str]      =		ids_tensor([self.batch_size]  ,			2					).float()
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		ids_tensor([self.batch_size]  ,			self.num_choices					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		self.get_config()

       return (
           config,
           input_ids,
           token_type_ids,
           input_lengths,
           sequence_labels,
           token_labels,
           is_impossible_labels,
           choice_labels,
           input_mask,
       )




  def 						__lowercase(   self  :						int					)->							Any:




       """simple docstring"""
       return FlaubertConfig(
           vocab_size=self.vocab_size  ,			n_special=self.n_special  ,			emb_dim=self.hidden_size  ,			n_layers=self.num_hidden_layers  ,			n_heads=self.num_attention_heads  ,			dropout=self.hidden_dropout_prob  ,			attention_dropout=self.attention_probs_dropout_prob  ,			gelu_activation=self.gelu_activation  ,			sinusoidal_embeddings=self.sinusoidal_embeddings  ,			asm=self.asm  ,			causal=self.causal  ,			n_langs=self.n_langs  ,			max_position_embeddings=self.max_position_embeddings  ,			initializer_range=self.initializer_range  ,			summary_type=self.summary_type  ,			use_proj=self.use_proj  ,			)




  def 						__lowercase(   self  :						List[Any]  ,			a_  :						Union[str, Any]  ,			a_  :						Any  ,			a_  :						Union[str, Any]  ,			a_  :						Any  ,			a_  :						Union[str, Any]  ,			a_  :						List[str]  ,			a_  :						List[Any]  ,			a_  :						int  ,			a_  :						Any  ,			)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					str      =		FlaubertModel(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		model(a_  ,			lengths=a_  ,			langs=a_					)
       SCREAMING_SNAKE_CASE__					:					int      =		model(a_  ,			langs=a_					)
       SCREAMING_SNAKE_CASE__					:					Tuple      =		model(a_					)
       self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.hidden_size)					)




  def 						__lowercase(   self  :						str  ,			a_  :						str  ,			a_  :						int  ,			a_  :						Union[str, Any]  ,			a_  :						List[Any]  ,			a_  :						Union[str, Any]  ,			a_  :						Any  ,			a_  :						int  ,			a_  :						Tuple  ,			a_  :						str  ,			)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		FlaubertWithLMHeadModel(a_					)
       model.to(a_					)
       model.eval()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			token_type_ids=a_  ,			labels=a_					)
       self.parent.assertEqual(result.loss.shape  ,			()					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.vocab_size)					)




  def 						__lowercase(   self  :						Any  ,			a_  :						Optional[Any]  ,			a_  :						Union[str, Any]  ,			a_  :						str  ,			a_  :						int  ,			a_  :						Union[str, Any]  ,			a_  :						List[Any]  ,			a_  :						Any  ,			a_  :						Any  ,			a_  :						Union[str, Any]  ,			)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		FlaubertForQuestionAnsweringSimple(a_					)
       model.to(a_					)
       model.eval()

       SCREAMING_SNAKE_CASE__					:					Dict      =		model(a_					)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		model(a_  ,			start_positions=a_  ,			end_positions=a_					)
       self.parent.assertEqual(result.start_logits.shape  ,			(self.batch_size, self.seq_length)					)
       self.parent.assertEqual(result.end_logits.shape  ,			(self.batch_size, self.seq_length)					)




  def 						__lowercase(   self  :						Tuple  ,			a_  :						Tuple  ,			a_  :						int  ,			a_  :						Optional[Any]  ,			a_  :						Optional[int]  ,			a_  :						Tuple  ,			a_  :						List[Any]  ,			a_  :						int  ,			a_  :						Optional[int]  ,			a_  :						int  ,			)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		FlaubertForQuestionAnswering(a_					)
       model.to(a_					)
       model.eval()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		model(
           a_  ,			start_positions=a_  ,			end_positions=a_  ,			cls_index=a_  ,			is_impossible=a_  ,			p_mask=a_  ,			)

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		model(
           a_  ,			start_positions=a_  ,			end_positions=a_  ,			cls_index=a_  ,			is_impossible=a_  ,			)

       ((SCREAMING_SNAKE_CASE__)      ,				)					:					Optional[Any]      =		result_with_labels.to_tuple()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_  ,			start_positions=a_  ,			end_positions=a_					)

       ((SCREAMING_SNAKE_CASE__)      ,				)					:					int      =		result_with_labels.to_tuple()

       self.parent.assertEqual(result_with_labels.loss.shape  ,			()					)
       self.parent.assertEqual(result.start_top_log_probs.shape  ,			(self.batch_size, model.config.start_n_top)					)
       self.parent.assertEqual(result.start_top_index.shape  ,			(self.batch_size, model.config.start_n_top)					)
       self.parent.assertEqual(
           result.end_top_log_probs.shape  ,			(self.batch_size, model.config.start_n_top * model.config.end_n_top)					)
       self.parent.assertEqual(
           result.end_top_index.shape  ,			(self.batch_size, model.config.start_n_top * model.config.end_n_top)					)
       self.parent.assertEqual(result.cls_logits.shape  ,			(self.batch_size,)					)




  def 						__lowercase(   self  :						Dict  ,			a_  :						int  ,			a_  :						Optional[int]  ,			a_  :						Tuple  ,			a_  :						Dict  ,			a_  :						Any  ,			a_  :						List[str]  ,			a_  :						str  ,			a_  :						List[Any]  ,			a_  :						int  ,			)->							Union[str, Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		FlaubertForSequenceClassification(a_					)
       model.to(a_					)
       model.eval()

       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		model(a_					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		model(a_  ,			labels=a_					)

       self.parent.assertEqual(result.loss.shape  ,			()					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)					)




  def 						__lowercase(   self  :						List[str]  ,			a_  :						Tuple  ,			a_  :						str  ,			a_  :						Optional[Any]  ,			a_  :						List[Any]  ,			a_  :						Tuple  ,			a_  :						List[Any]  ,			a_  :						List[Any]  ,			a_  :						Optional[int]  ,			a_  :						List[str]  ,			)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.num_labels
       SCREAMING_SNAKE_CASE__					:					Any      =		FlaubertForTokenClassification(a_					)
       model.to(a_					)
       model.eval()

       SCREAMING_SNAKE_CASE__					:					List[str]      =		model(a_  ,			attention_mask=a_  ,			labels=a_					)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.num_labels)					)




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						Union[str, Any]  ,			a_  :						str  ,			a_  :						List[str]  ,			a_  :						Any  ,			a_  :						Any  ,			a_  :						Any  ,			a_  :						List[Any]  ,			a_  :						Optional[Any]  ,			a_  :						Any  ,			)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.num_choices
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		FlaubertForMultipleChoice(config=a_					)
       model.to(a_					)
       model.eval()
       SCREAMING_SNAKE_CASE__					:					int      =		input_ids.unsqueeze(1					).expand(-1  ,			self.num_choices  ,			-1					).contiguous()
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		token_type_ids.unsqueeze(1					).expand(-1  ,			self.num_choices  ,			-1					).contiguous()
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		input_mask.unsqueeze(1					).expand(-1  ,			self.num_choices  ,			-1					).contiguous()
       SCREAMING_SNAKE_CASE__					:					Dict      =		model(
           a_  ,			attention_mask=a_  ,			token_type_ids=a_  ,			labels=a_  ,			)
       self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_choices)					)




  def 						__lowercase(   self  :						Optional[Any]					)->							Optional[Any]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Dict      =		self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				(
           SCREAMING_SNAKE_CASE__
       )      ,				
       )					:					Dict      =		config_and_inputs
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		{
           'input_ids': input_ids,
           'token_type_ids': token_type_ids,
           'lengths': input_lengths,
           'attention_mask': input_mask,
       }
       return config, inputs_dict




@require_torch
class    snake_case       (   UpperCamelCase_ ,    UpperCamelCase_ ,    unittest.TestCase    ):
  lowercase_								=  (
      (
          FlaubertModel,
          FlaubertWithLMHeadModel,
          FlaubertForQuestionAnswering,
          FlaubertForQuestionAnsweringSimple,
          FlaubertForSequenceClassification,
          FlaubertForTokenClassification,
          FlaubertForMultipleChoice,
      )
      if is_torch_available()
      else ()
  )
  lowercase_								=  (
      {
          'feature-extraction': FlaubertModel,
          'fill-mask': FlaubertWithLMHeadModel,
          'question-answering': FlaubertForQuestionAnsweringSimple,
          'text-classification': FlaubertForSequenceClassification,
          'token-classification': FlaubertForTokenClassification,
          'zero-shot': FlaubertForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )




  def 						__lowercase(   self  :						Union[str, Any]  ,			a_  :						str  ,			a_  :						List[Any]  ,			a_  :						int  ,			a_  :						str  ,			a_  :						Union[str, Any]					)->							int:




       """simple docstring"""
       if (
           pipeline_test_casse_name == "QAPipelineTests"
           and tokenizer_name is not None
           and not tokenizer_name.endswith('Fast'					)
       ):
            # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
            # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
            # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
            return True

       return False




  def 						__lowercase(   self  :						Optional[Any]  ,			a_  :						List[Any]  ,			a_  :						int  ,			a_  :						List[str]=False					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		super()._prepare_for_class(a_  ,			a_  ,			return_labels=a_					)

       if return_labels:
            if model_class.__name__ == "FlaubertForQuestionAnswering":
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.zeros(
                     self.model_tester.batch_size  ,			dtype=torch.long  ,			device=a_					)
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		torch.zeros(
                     self.model_tester.batch_size  ,			dtype=torch.long  ,			device=a_					)

       return inputs_dict




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		FlaubertModelTester(self					)
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		ConfigTester(self  ,			config_class=a_  ,			emb_dim=37					)




  def 						__lowercase(   self  :						Dict					)->							Any:




       """simple docstring"""
       self.config_tester.run_common_tests()




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Any      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_model(*a_					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_lm_head(*a_					)




  def 						__lowercase(   self  :						Union[str, Any]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_simple_qa(*a_					)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_qa(*a_					)




  def 						__lowercase(   self  :						List[Any]					)->							int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_sequence_classif(*a_					)




  def 						__lowercase(   self  :						Dict					)->							Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Tuple      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_token_classif(*a_					)




  def 						__lowercase(   self  :						Tuple					)->							str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_flaubert_multiple_choice(*a_					)




  @slow
  def 						__lowercase(   self  :						int					)->							List[Any]:




       """simple docstring"""
       for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            SCREAMING_SNAKE_CASE__					:					Tuple      =		FlaubertModel.from_pretrained(a_					)
            self.assertIsNotNone(a_					)




  @slow
  @require_torch_gpu
  def 						__lowercase(   self  :						str					)->							Optional[int]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					int      =		self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
            # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
            if model_class == FlaubertForMultipleChoice:
                 return

            SCREAMING_SNAKE_CASE__					:					Tuple      =		True
            SCREAMING_SNAKE_CASE__					:					Any      =		model_class(config=a_					)

            SCREAMING_SNAKE_CASE__					:					int      =		self._prepare_for_class(a_  ,			a_					)
            SCREAMING_SNAKE_CASE__					:					str      =		torch.jit.trace(
                a_  ,			(inputs_dict['input_ids'].to('cpu'					), inputs_dict['attention_mask'].to('cpu'					))					)

            with tempfile.TemporaryDirectory() as tmp:
                 torch.jit.save(a_  ,			os.path.join(a_  ,			'traced_model.pt'					)					)
                 SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.jit.load(os.path.join(a_  ,			'traced_model.pt'					)  ,			map_location=a_					)
                 loaded(inputs_dict['input_ids'].to(a_					)  ,			inputs_dict['attention_mask'].to(a_					)					)




@require_torch
class    snake_case       (   unittest.TestCase    ):




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					List[str]      =		FlaubertModel.from_pretrained('flaubert/flaubert_base_cased'					)
       SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		model(a_					)[0]
       SCREAMING_SNAKE_CASE__					:					Dict      =		torch.Size((1, 11, 768)					)
       self.assertEqual(output.shape  ,			a_					)
       SCREAMING_SNAKE_CASE__					:					str      =		torch.tensor(
           [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]					)

       self.assertTrue(torch.allclose(output[:, :3, :3]  ,			a_  ,			atol=1e-4					)					)






import fire

from utils import calculate_rouge, save_json
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Tuple			,a__    :   Any=None			,**a__    :   Dict       )					->				Optional[Any]:
	__A :			int		    =			[x.strip() for x in open(a__       ).readlines()]
	__A :			List[str]		    =			[x.strip() for x in open(a__       ).readlines()][: len(a__       )]
	__A :			List[Any]		    =			calculate_rouge(a__			,a__			,**a__       )
	if save_path is not None:
		save_json(a__			,a__			,indent=a__       )
	return metrics  # these print nicely


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





