





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase       =				logging.get_logger(__name__)

_UpperCAmelCase       =				{
    'uw-madison/mra-base-512-4': 'https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json',
}






class   snake_case_							(						_UpperCamelCase		):
  A_     				=       'mra'






  def __init__(				self					:	Union[str, Any]  ,    _snake_case					:	int=50265  ,    _snake_case					:	List[Any]=768  ,    _snake_case					:	str=12  ,    _snake_case					:	int=12  ,    _snake_case					:	List[str]=3072  ,    _snake_case					:	Union[str, Any]="gelu"  ,    _snake_case					:	str=0.1  ,    _snake_case					:	Optional[Any]=0.1  ,    _snake_case					:	str=512  ,    _snake_case					:	Any=1  ,    _snake_case					:	Any=0.02  ,    _snake_case					:	Optional[int]=1E-5  ,    _snake_case					:	int="absolute"  ,    _snake_case					:	Any=4  ,    _snake_case					:	Any="full"  ,    _snake_case					:	Optional[Any]=0  ,    _snake_case					:	Optional[Any]=0  ,    _snake_case					:	Dict=1  ,    _snake_case					:	List[Any]=0  ,    _snake_case					:	Dict=2  ,    **_snake_case					:	Union[str, Any]  ,    )->int:






   '''simple docstring'''

   super().__init__(pad_token_id=_UpperCAmelCase  ,    bos_token_id=_UpperCAmelCase  ,    eos_token_id=_UpperCAmelCase  ,    **_UpperCAmelCase     )

   __lowerCAmelCase	:			str         =  vocab_size
   __lowerCAmelCase	:			Optional[int]         =  max_position_embeddings
   __lowerCAmelCase	:			str         =  hidden_size
   __lowerCAmelCase	:			Optional[Any]         =  num_hidden_layers
   __lowerCAmelCase	:			Dict         =  num_attention_heads
   __lowerCAmelCase	:			int         =  intermediate_size
   __lowerCAmelCase	:			List[str]         =  hidden_act
   __lowerCAmelCase	:			Dict         =  hidden_dropout_prob
   __lowerCAmelCase	:			Tuple         =  attention_probs_dropout_prob
   __lowerCAmelCase	:			Union[str, Any]         =  initializer_range
   __lowerCAmelCase	:			Optional[int]         =  type_vocab_size
   __lowerCAmelCase	:			Any         =  layer_norm_eps
   __lowerCAmelCase	:			str         =  position_embedding_type
   __lowerCAmelCase	:			List[Any]         =  block_per_row
   __lowerCAmelCase	:			Union[str, Any]         =  approx_mode
   __lowerCAmelCase	:			Union[str, Any]         =  initial_prior_first_n_blocks
   __lowerCAmelCase	:			int         =  initial_prior_diagonal_n_blocks




'''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       __magic_name__		:
							lowerCAmelCase			:       Optional[Any]  							=   MBartConfig
							lowerCAmelCase			:       str  							=   {}
							lowerCAmelCase			:       Dict  							=   'gelu'







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







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

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

									_a	: List[str]								=				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	: Optional[int]								=				prepare_mbart_inputs_dict(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)
									return config, inputs_dict







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Optional[int]						):
									_a	: str								=				TFMBartModel(config=_UpperCAmelCase						).get_decoder()
									_a	: List[Any]								=				inputs_dict['input_ids']

									_a	: Optional[Any]								=				input_ids[:1, :]
									_a	: Tuple								=				inputs_dict['attention_mask'][:1, :]
									_a	: List[Any]								=				inputs_dict['head_mask']
									_a	: str								=				1

									# first forward pass
									_a	: Union[str, Any]								=				model(_UpperCAmelCase      ,attention_mask=_UpperCAmelCase      ,head_mask=_UpperCAmelCase      ,use_cache=_UpperCAmelCase						)

									_a			,   _a	: Optional[int]								=				outputs.to_tuple()
									_a	: Union[str, Any]								=				past_key_values[1]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     )     -> Union[str, Any]:
		if attention_mask is None:
				_a	: List[Any]								=				tf.cast(tf.math.not_equal(lowerCAmelCase_		,     config.pad_token_id    )		,     tf.inta    )
		if decoder_attention_mask is None:
				_a	: Any								=				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	: int								=				tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
		if decoder_head_mask is None:
				_a	: Any								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		if cross_attn_head_mask is None:
				_a	: int								=				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       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       List[str]  							=   (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							lowerCAmelCase			:       Optional[Any]  							=   (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							lowerCAmelCase			:       Dict  							=   (
							    {
							        'conversational': TFMBartForConditionalGeneration,
							        'feature-extraction': TFMBartModel,
							        'summarization': TFMBartForConditionalGeneration,
							        'text2text-generation': TFMBartForConditionalGeneration,
							        'translation': TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCAmelCase			:       Dict  							=   True
							lowerCAmelCase			:       List[str]  							=   False
							lowerCAmelCase			:       str  							=   False







							def 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			int						):
									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

									return False







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				TFMBartModelTester(self						)
									_a	: Dict								=				ConfigTester(self      ,config_class=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[Any]								=				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       __magic_name__		(       unittest.TestCase  ):
							lowerCAmelCase			:       List[Any]  							=   [
							    ' UN Chief Says There Is No Military Solution in Syria',
							]
							lowerCAmelCase			:       Dict  							=   [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							]
							lowerCAmelCase			:       Tuple  							=   'facebook/mbart-large-en-ro'







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									return AutoTokenizer.from_pretrained(self.model_name						)







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Any								=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model







							def 				__lowercase     (				self   :			Optional[int]      ,**_UpperCAmelCase   :			Dict						):
									_a	: List[Any]								=				self.translate_src_text(**_UpperCAmelCase						)
									self.assertListEqual(self.expected_text      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Tuple      ,**_UpperCAmelCase   :			str						):
									_a	: List[Any]								=				self.tokenizer(self.src_text      ,**_UpperCAmelCase      ,return_tensors='tf'						)
									_a	: Any								=				self.model.generate(
									    model_inputs.input_ids      ,attention_mask=model_inputs.attention_mask      ,num_beams=2						)
									_a	: Tuple								=				self.tokenizer.batch_decode(_UpperCAmelCase      ,skip_special_tokens=_UpperCAmelCase						)
									return generated_words







							@slow
							def 				__lowercase     (				self   :			Tuple						):
									self._assert_generated_batch_equal_expected()




