


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 	_a							:
							_lowercase     :      Any  				=			MBartConfig
							_lowercase     :      Tuple  				=			{}
							_lowercase     :      Optional[Any]  				=			'''gelu'''

							def __init__(							self:     Optional[Any]   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     Optional[int]=13   ,						UpperCamelCase_:     List[str]=7   ,						UpperCamelCase_:     str=True   ,						UpperCamelCase_:     Optional[Any]=False   ,						UpperCamelCase_:     Union[str, Any]=99   ,						UpperCamelCase_:     Optional[Any]=32   ,						UpperCamelCase_:     Union[str, Any]=2   ,						UpperCamelCase_:     Union[str, Any]=4   ,						UpperCamelCase_:     Union[str, Any]=37   ,						UpperCamelCase_:     List[Any]=0.1   ,						UpperCamelCase_:     str=0.1   ,						UpperCamelCase_:     List[str]=20   ,						UpperCamelCase_:     List[Any]=2   ,						UpperCamelCase_:     int=1   ,						UpperCamelCase_:     Tuple=0   ,						)      ->       List[str]:





									"""simple docstring"""







									lowercase__						=				parent
									lowercase__						=				batch_size
									lowercase__						=				seq_length
									lowercase__						=				is_training
									lowercase__						=				use_labels
									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_hidden_layers
									lowercase__						=				num_attention_heads
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				eos_token_id
									lowercase__						=				pad_token_id
									lowercase__						=				bos_token_id

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Optional[int]:





									"""simple docstring"""







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

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

									lowercase__						=				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   ,						)
									lowercase__						=				prepare_mbart_inputs_dict(UpperCamelCase_   ,						UpperCamelCase_   ,						UpperCamelCase_							)
									return config, inputs_dict


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





									"""simple docstring"""







									lowercase__						=				TFMBartModel(config=UpperCamelCase_							).get_decoder()
									lowercase__						=				inputs_dict['''input_ids''']

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

									# first forward pass
									lowercase__						=				model(UpperCamelCase_   ,						attention_mask=UpperCamelCase_   ,						head_mask=UpperCamelCase_   ,						use_cache=UpperCamelCase_							)

									lowercase__					,		lowercase__						=				outputs.to_tuple()
									lowercase__						=				past_key_values[1]
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    SCREAMING_SNAKE_CASE=None						,    ):


		"""simple docstring"""




		if attention_mask is None:
				lowercase__						=				tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE						,    config.pad_token_id					)						,    tf.inta					)
		if decoder_attention_mask is None:
				lowercase__						=				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:
				lowercase__						=				tf.ones((config.encoder_layers, config.encoder_attention_heads)					)
		if decoder_head_mask is None:
				lowercase__						=				tf.ones((config.decoder_layers, config.decoder_attention_heads)					)
		if cross_attn_head_mask is None:
				lowercase__						=				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 	_a							(      UpperCamelCase__					,     UpperCamelCase__					,     unittest.TestCase      ):
							_lowercase     :      Tuple  				=			(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							_lowercase     :      Dict  				=			(TFMBartForConditionalGeneration,) if is_tf_available() else ()
							_lowercase     :      Optional[int]  				=			(
							    {
							        '''conversational''': TFMBartForConditionalGeneration,
							        '''feature-extraction''': TFMBartModel,
							        '''summarization''': TFMBartForConditionalGeneration,
							        '''text2text-generation''': TFMBartForConditionalGeneration,
							        '''translation''': TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							_lowercase     :      Optional[Any]  				=			True
							_lowercase     :      Tuple  				=			False
							_lowercase     :      Any  				=			False

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						UpperCamelCase_:     Dict   ,						UpperCamelCase_:     Tuple   ,						UpperCamelCase_:     Union[str, Any]   ,						UpperCamelCase_:     str   ,						UpperCamelCase_:     str							)      ->       int:





									"""simple docstring"""







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

									return False

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





									"""simple docstring"""







									lowercase__						=				TFMBartModelTester(self							)
									lowercase__						=				ConfigTester(self   ,						config_class=UpperCamelCase_							)

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





									"""simple docstring"""







									self.config_tester.run_common_tests()


							def 				lowerCamelCase_			(							self:     int							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_							)


@require_sentencepiece
@require_tokenizers
@require_tf
class 	_a							(      unittest.TestCase      ):
							_lowercase     :      Dict  				=			[
							    ''' UN Chief Says There Is No Military Solution in Syria''',
							]
							_lowercase     :      Dict  				=			[
							    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
							]
							_lowercase     :      int  				=			'''facebook/mbart-large-en-ro'''

							@cached_property
							def 				lowerCamelCase_			(							self:     str							)      ->       Optional[Any]:





									"""simple docstring"""







									return AutoTokenizer.from_pretrained(self.model_name							)

							@cached_property
							def 				lowerCamelCase_			(							self:     Any							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
									return model

							def 				lowerCamelCase_			(							self:     List[Any]   ,						**UpperCamelCase_:     Tuple							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				self.translate_src_text(**UpperCamelCase_							)
									self.assertListEqual(self.expected_text   ,						UpperCamelCase_							)

							def 				lowerCamelCase_			(							self:     int   ,						**UpperCamelCase_:     Tuple							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				self.tokenizer(self.src_text   ,						**UpperCamelCase_   ,						return_tensors='''tf'''							)
									lowercase__						=				self.model.generate(
									    model_inputs.input_ids   ,						attention_mask=model_inputs.attention_mask   ,						num_beams=2							)
									lowercase__						=				self.tokenizer.batch_decode(UpperCamelCase_   ,						skip_special_tokens=UpperCamelCase_							)
									return generated_words


							@slow
							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       List[Any]:





									"""simple docstring"""







									self._assert_generated_batch_equal_expected()






def       _a			(				SCREAMING_SNAKE_CASE = 10_00					):


		"""simple docstring"""




		return sum(e for e in range(3						,    SCREAMING_SNAKE_CASE					) if e % 3 == 0 or e % 5 == 0					)


if __name__ == "__main__":
			print(f"""{solution() = }""")


