from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel



@require_tf
class  UpperCamelCase				:

					'''simple docstring'''

					A_										=  MBartConfig
					A_										=  {}
					A_										=  'gelu'



					def __init__(					self				,				A_				,				A_=13				,				A_=7				,				A_=True				,				A_=False				,				A_=99				,				A_=32				,				A_=2				,				A_=4				,				A_=37				,				A_=0.1				,				A_=0.1				,				A_=20				,				A_=2				,				A_=1				,				A_=0				,				) ->				Optional[Any]:






											"""simple docstring"""
											_lowerCamelCase			    =				parent
											_lowerCamelCase			    =				batch_size
											_lowerCamelCase			    =				seq_length
											_lowerCamelCase			    =				is_training
											_lowerCamelCase			    =				use_labels
											_lowerCamelCase			    =				vocab_size
											_lowerCamelCase			    =				hidden_size
											_lowerCamelCase			    =				num_hidden_layers
											_lowerCamelCase			    =				num_attention_heads
											_lowerCamelCase			    =				intermediate_size
											_lowerCamelCase			    =				hidden_dropout_prob
											_lowerCamelCase			    =				attention_probs_dropout_prob
											_lowerCamelCase			    =				max_position_embeddings
											_lowerCamelCase			    =				eos_token_id
											_lowerCamelCase			    =				pad_token_id
											_lowerCamelCase			    =				bos_token_id



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






											"""simple docstring"""
											_lowerCamelCase			    =				ids_tensor([self.batch_size, self.seq_length - 1]				,				self.vocab_size						)
											_lowerCamelCase			    =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)				,				1						)
											_lowerCamelCase			    =				tf.concat([input_ids, eos_tensor]				,				axis=1						)

											_lowerCamelCase			    =				ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size						)

											_lowerCamelCase			    =				self.config_cls(
											    vocab_size=self.vocab_size				,				d_model=self.hidden_size				,				encoder_layers=self.num_hidden_layers				,				decoder_layers=self.num_hidden_layers				,				encoder_attention_heads=self.num_attention_heads				,				decoder_attention_heads=self.num_attention_heads				,				encoder_ffn_dim=self.intermediate_size				,				decoder_ffn_dim=self.intermediate_size				,				dropout=self.hidden_dropout_prob				,				attention_dropout=self.attention_probs_dropout_prob				,				max_position_embeddings=self.max_position_embeddings				,				eos_token_ids=[2]				,				bos_token_id=self.bos_token_id				,				pad_token_id=self.pad_token_id				,				decoder_start_token_id=self.pad_token_id				,				**self.config_updates				,				)
											_lowerCamelCase			    =				prepare_mbart_inputs_dict(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_						)
											return config, inputs_dict





					def 		UpperCamelCase_						(					self				,				A_				,				A_						) ->				Optional[Any]:






											"""simple docstring"""
											_lowerCamelCase			    =				TFMBartModel(config=lowerCAmelCase_						).get_decoder()
											_lowerCamelCase			    =				inputs_dict['''input_ids''']

											_lowerCamelCase			    =				input_ids[:1, :]
											_lowerCamelCase			    =				inputs_dict['''attention_mask'''][:1, :]
											_lowerCamelCase			    =				inputs_dict['''head_mask''']
											_lowerCamelCase			    =				1

											# first forward pass
											_lowerCamelCase			    =				model(lowerCAmelCase_				,				attention_mask=lowerCAmelCase_				,				head_mask=lowerCAmelCase_				,				use_cache=lowerCAmelCase_						)

											_lowerCamelCase    ,					_lowerCamelCase			    =				outputs.to_tuple()
											_lowerCamelCase			    =				past_key_values[1]





def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						__UpperCAmelCase=None ,						)						->    List[Any]:



						'''simple docstring'''

						if attention_mask is None:
												_lowerCamelCase			    =				tf.cast(tf.math.not_equal(__lowerCAmelCase ,						config.pad_token_id		) ,						tf.inta		)
						if decoder_attention_mask is None:
												_lowerCamelCase			    =				tf.concat(
												    [
												        tf.ones(decoder_input_ids[:, :1].shape ,						dtype=tf.inta		),
												        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] ,						config.pad_token_id		) ,						tf.inta		),
												    ] ,						axis=-1 ,						)
						if head_mask is None:
												_lowerCamelCase			    =				tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												_lowerCamelCase			    =				tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												_lowerCamelCase			    =				tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						    "head_mask": head_mask,
						    "decoder_head_mask": decoder_head_mask,
						    "cross_attn_head_mask": cross_attn_head_mask,
						}



@require_tf
class  UpperCamelCase				( __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase ):

					'''simple docstring'''

					A_										=  (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
					A_										=  (TFMBartForConditionalGeneration,) if is_tf_available() else ()
					A_										=  (
					    {
					        'conversational': TFMBartForConditionalGeneration,
					        'feature-extraction': TFMBartModel,
					        'summarization': TFMBartForConditionalGeneration,
					        'text2text-generation': TFMBartForConditionalGeneration,
					        'translation': TFMBartForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					A_										=  True
					A_										=  False
					A_										=  False



					def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_				,				A_						) ->				str:






											"""simple docstring"""







											if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																	# Exception encountered when calling layer '...'
																	return True

											return False



					def 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											_lowerCamelCase			    =				TFMBartModelTester(self						)
											_lowerCamelCase			    =				ConfigTester(self				,				config_class=lowerCAmelCase_						)



					def 		UpperCamelCase_						(					self						) ->				str:






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





					def 		UpperCamelCase_						(					self						) ->				Any:






											"""simple docstring"""
											_lowerCamelCase			    =				self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_						)





@require_sentencepiece
@require_tokenizers
@require_tf
class  UpperCamelCase				( unittest.TestCase ):

					'''simple docstring'''

					A_										=  [
					    ' UN Chief Says There Is No Military Solution in Syria',
					]
					A_										=  [
					    'Şeful ONU declară că nu există o soluţie militară în Siria',
					]
					A_										=  'facebook/mbart-large-en-ro'



					@cached_property
					def 		UpperCamelCase_						(					self						) ->				List[str]:






											"""simple docstring"""
											return AutoTokenizer.from_pretrained(self.model_name						)



					@cached_property
					def 		UpperCamelCase_						(					self						) ->				Any:






											"""simple docstring"""
											_lowerCamelCase			    =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
											return model



					def 		UpperCamelCase_						(					self				,				**A_						) ->				Union[str, Any]:






											"""simple docstring"""
											_lowerCamelCase			    =				self.translate_src_text(**lowerCAmelCase_						)
											self.assertListEqual(self.expected_text				,				lowerCAmelCase_						)



					def 		UpperCamelCase_						(					self				,				**A_						) ->				Dict:






											"""simple docstring"""
											_lowerCamelCase			    =				self.tokenizer(self.src_text				,				**lowerCAmelCase_				,				return_tensors='''tf'''						)
											_lowerCamelCase			    =				self.model.generate(
											    model_inputs.input_ids				,				attention_mask=model_inputs.attention_mask				,				num_beams=2						)
											_lowerCamelCase			    =				self.tokenizer.batch_decode(lowerCAmelCase_				,				skip_special_tokens=lowerCAmelCase_						)
											return generated_words





					@slow
					def 		UpperCamelCase_						(					self						) ->				str:






											"""simple docstring"""
											self._assert_generated_batch_equal_expected()
import argparse
import json

from tqdm import tqdm
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '''--src_path''' ,						type=__UpperCAmelCase ,						default='''biencoder-nq-dev.json''' ,						help='''Path to raw DPR training data''' ,						)
      parser.add_argument(
          '''--evaluation_set''' ,						type=__UpperCAmelCase ,						help='''where to store parsed evaluation_set file''' ,						)
      parser.add_argument(
          '''--gold_data_path''' ,						type=__UpperCAmelCase ,						help='''where to store parsed gold_data_path file''' ,						)
      _lowerCamelCase			    =				parser.parse_args()

      with open(args.src_path ,						'''r'''		) as src_file, open(args.evaluation_set ,						'''w'''		) as eval_file, open(
          args.gold_data_path ,						'''w'''		) as gold_file:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
            for dpr_record in tqdm(__UpperCAmelCase		):
                  _lowerCamelCase			    =				dpr_record['''question''']
                  _lowerCamelCase			    =				[context['''title'''] for context in dpr_record['''positive_ctxs''']]
                  eval_file.write(question + '''\n'''		)
                  gold_file.write('''\t'''.join(__UpperCAmelCase		) + '''\n'''		)


if __name__ == "__main__":
     main()