


"""simple docstring"""






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 			__lowerCamelCase     :

     '''simple docstring'''
     A_				:		Optional[int]			=						MBartConfig
     A_				:		Union[str, Any]			=						{}
     A_				:		Optional[Any]			=						'gelu'


     def __init__(					self			, __UpperCAmelCase			, __UpperCAmelCase=13			, __UpperCAmelCase=7			, __UpperCAmelCase=True			, __UpperCAmelCase=False			, __UpperCAmelCase=99			, __UpperCAmelCase=32			, __UpperCAmelCase=2			, __UpperCAmelCase=4			, __UpperCAmelCase=37			, __UpperCAmelCase=0.1			, __UpperCAmelCase=0.1			, __UpperCAmelCase=20			, __UpperCAmelCase=2			, __UpperCAmelCase=1			, __UpperCAmelCase=0			, )				->							Optional[Any]:
          _a					= parent
          _a					= batch_size
          _a					= seq_length
          _a					= is_training
          _a					= use_labels
          _a					= vocab_size
          _a					= hidden_size
          _a					= num_hidden_layers
          _a					= num_attention_heads
          _a					= intermediate_size
          _a					= hidden_dropout_prob
          _a					= attention_probs_dropout_prob
          _a					= max_position_embeddings
          _a					= eos_token_id
          _a					= pad_token_id
          _a					= bos_token_id


     def 							_UpperCAmelCase					(					self							)				->							Optional[Any]:
          _a					= ids_tensor([self.batch_size, self.seq_length - 1]			, self.vocab_size							)
          _a					= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)			, 1							)
          _a					= tf.concat([input_ids, eos_tensor]			, axis=1							)

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

          _a					= 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			, )
          _a					= prepare_mbart_inputs_dict(lowercase_			, lowercase_			, lowercase_							)
          return config, inputs_dict





     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Any:
          _a					= TFMBartModel(config=lowercase_							).get_decoder()
          _a					= inputs_dict['''input_ids''']

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

          # first forward pass
          _a					= model(lowercase_			, attention_mask=lowercase_			, head_mask=lowercase_			, use_cache=lowercase_							)

          _a					= outputs.to_tuple()
          _a					= past_key_values[1]







def 					A_      (      _lowerCAmelCase   :    Tuple,   _lowerCAmelCase   :    List[Any],   _lowerCAmelCase   :    str,   _lowerCAmelCase   :    Any=None,   _lowerCAmelCase   :    List[Any]=None,   _lowerCAmelCase   :    Optional[Any]=None,   _lowerCAmelCase   :    Optional[Any]=None,   _lowerCAmelCase   :    int=None,   ):






     """simple docstring"""

     if attention_mask is None:
          _a					= tf.cast(tf.math.not_equal(A_,   config.pad_token_id				),   tf.inta				)
     if decoder_attention_mask is None:
          _a					= 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:
          _a					= tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
     if decoder_head_mask is None:
          _a					= tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
     if cross_attn_head_mask is None:
          _a					= 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 			__lowerCamelCase     (    a_     ,    a_     ,    unittest.TestCase ):

     '''simple docstring'''
     A_				:		List[Any]			=						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
     A_				:		int			=						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
     A_				:		Any			=						(
         {
             'conversational': TFMBartForConditionalGeneration,
             'feature-extraction': TFMBartModel,
             'summarization': TFMBartForConditionalGeneration,
             'text2text-generation': TFMBartForConditionalGeneration,
             'translation': TFMBartForConditionalGeneration,
         }
         if is_tf_available()
         else {}
     )
     A_				:		List[str]			=						True
     A_				:		Optional[int]			=						False
     A_				:		int			=						False


     def 							_UpperCAmelCase					(					self			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase			, __UpperCAmelCase							)				->							Dict:
          if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
               # Exception encountered when calling layer '...'
               return True

          return False


     def 							_UpperCAmelCase					(					self							)				->							Tuple:
          _a					= TFMBartModelTester(self							)
          _a					= ConfigTester(self			, config_class=lowercase_							)


     def 							_UpperCAmelCase					(					self							)				->							List[str]:
          self.config_tester.run_common_tests()





     def 							_UpperCAmelCase					(					self							)				->							Optional[int]:
          _a					= self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_decoder_model_past_large_inputs(*lowercase_							)







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

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


     @cached_property
     def 							_UpperCAmelCase					(					self							)				->							Dict:
          return AutoTokenizer.from_pretrained(self.model_name							)


     @cached_property
     def 							_UpperCAmelCase					(					self							)				->							int:
          _a					= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
          return model


     def 							_UpperCAmelCase					(					self			, **__UpperCAmelCase							)				->							List[str]:
          _a					= self.translate_src_text(**lowercase_							)
          self.assertListEqual(self.expected_text			, lowercase_							)


     def 							_UpperCAmelCase					(					self			, **__UpperCAmelCase							)				->							Union[str, Any]:
          _a					= self.tokenizer(self.src_text			, **lowercase_			, return_tensors='''tf'''							)
          _a					= self.model.generate(
              model_inputs.input_ids			, attention_mask=model_inputs.attention_mask			, num_beams=2							)
          _a					= self.tokenizer.batch_decode(lowercase_			, skip_special_tokens=lowercase_							)
          return generated_words





     @slow
     def 							_UpperCAmelCase					(					self							)				->							Union[str, Any]:
          self._assert_generated_batch_equal_expected()


"""simple docstring"""





def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ ):
	return numa ^ numa < 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()




