
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_ :
			a	:			Any					   =							MBartConfig
			a	:			Any					   =							{}
			a	:			str					   =							'''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				,			):
									_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    UpperCamelCase_				(      self  ):
									_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(__snake_case				,			__snake_case				,			__snake_case  )
									return config, inputs_dict






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					TFMBartModel(config=__snake_case  ).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(__snake_case				,			attention_mask=__snake_case				,			head_mask=__snake_case				,			use_cache=__snake_case  )

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







def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	int		,   SCREAMING_SNAKE_CASE_	:	Tuple=None		,   SCREAMING_SNAKE_CASE_	:	Tuple=None		,   SCREAMING_SNAKE_CASE_	:	List[Any]=None		,   SCREAMING_SNAKE_CASE_	:	Optional[Any]=None		,   SCREAMING_SNAKE_CASE_	:	Union[str, Any]=None		,   )  ->     Dict:


						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_ (			snake_case_					,			snake_case_					,			unittest.TestCase    ):
			a	:			Dict					   =							(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
			a	:			Optional[Any]					   =							(TFMBartForConditionalGeneration,) if is_tf_available() else ()
			a	:			List[str]					   =							(
			    {
			        '''conversational''': TFMBartForConditionalGeneration,
			        '''feature-extraction''': TFMBartModel,
			        '''summarization''': TFMBartForConditionalGeneration,
			        '''text2text-generation''': TFMBartForConditionalGeneration,
			        '''translation''': TFMBartForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			a	:			Optional[int]					   =							True
			a	:			List[Any]					   =							False
			a	:			str					   =							False






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
															# Exception encountered when calling layer '...'
															return True

									return False






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					TFMBartModelTester(self  )
									_lowercase					       =					ConfigTester(self				,			config_class=__snake_case  )






			def    UpperCamelCase_				(      self  ):
									self.config_tester.run_common_tests()






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*__snake_case  )







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






			@cached_property
			def    UpperCamelCase_				(      self  ):
									return AutoTokenizer.from_pretrained(self.model_name  )






			@cached_property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
									return model






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									_lowercase					       =					self.translate_src_text(**__snake_case  )
									self.assertListEqual(self.expected_text				,			__snake_case  )






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									_lowercase					       =					self.tokenizer(self.src_text				,			**__snake_case				,			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(__snake_case				,			skip_special_tokens=__snake_case  )
									return generated_words






			@slow
			def    UpperCamelCase_				(      self  ):
									self._assert_generated_batch_equal_expected()






'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					Union[str, Any]     ,     __snake_case     :					Callable     ,     __snake_case     :					Optional[Features] = None     ,     __snake_case     :					str = None     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional[dict] = None     ,     __snake_case     :					Optional[int] = None     ,     **__snake_case     :					Optional[Any]     ,     )							-> Union[str, Any]:
            super().__init__(
                features=__snake_case     ,     cache_dir=__snake_case     ,     keep_in_memory=__snake_case     ,     streaming=__snake_case     ,     num_proc=__snake_case     ,     **__snake_case     ,     )
            _lowerCAmelCase					=				Generator(
                cache_dir=__snake_case     ,     features=__snake_case     ,     generator=__snake_case     ,     gen_kwargs=__snake_case     ,     **__snake_case     ,     )




      def 							lowercase__       (     self     :					str    )							-> Dict:
            # Build iterable dataset
            if self.streaming:
                  _lowerCAmelCase					=				self.builder.as_streaming_dataset(split="""train"""    )
            # Build regular (map-style) dataset
            else:
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None

                  self.builder.download_and_prepare(
                      download_config=__snake_case     ,     download_mode=__snake_case     ,     verification_mode=__snake_case     ,     base_path=__snake_case     ,     num_proc=self.num_proc     ,     )
                  _lowerCAmelCase					=				self.builder.as_dataset(
                      split="""train"""     ,     verification_mode=__snake_case     ,     in_memory=self.keep_in_memory    )
            return dataset






