


"""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     A__     :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			MBartConfig
	SCREAMING_SNAKE_CASE					   =			{}
	SCREAMING_SNAKE_CASE					   =			'gelu'

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

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= parent
				__lowerCAmelCase   :						Optional[int]				= batch_size
				__lowerCAmelCase   :						int				= seq_length
				__lowerCAmelCase   :						Union[str, Any]				= is_training
				__lowerCAmelCase   :						Optional[Any]				= use_labels
				__lowerCAmelCase   :						Optional[int]				= vocab_size
				__lowerCAmelCase   :						Any				= hidden_size
				__lowerCAmelCase   :						List[str]				= num_hidden_layers
				__lowerCAmelCase   :						List[Any]				= num_attention_heads
				__lowerCAmelCase   :						Tuple				= intermediate_size
				__lowerCAmelCase   :						Union[str, Any]				= hidden_dropout_prob
				__lowerCAmelCase   :						str				= attention_probs_dropout_prob
				__lowerCAmelCase   :						Any				= max_position_embeddings
				__lowerCAmelCase   :						Optional[int]				= eos_token_id
				__lowerCAmelCase   :						int				= pad_token_id
				__lowerCAmelCase   :						Any				= bos_token_id

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= ids_tensor([self.batch_size, self.seq_length - 1] ,    self.vocab_size)
				__lowerCAmelCase   :						Tuple				= tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size) ,    1)
				__lowerCAmelCase   :						Tuple				= tf.concat([input_ids, eos_tensor] ,    axis=1)

				__lowerCAmelCase   :						Any				= ids_tensor([self.batch_size, self.seq_length] ,    self.vocab_size)

				__lowerCAmelCase   :						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 ,    )
				__lowerCAmelCase   :						List[Any]				= prepare_mbart_inputs_dict(__a ,    __a ,    __a)
				return config, inputs_dict







	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Any)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= TFMBartModel(config=__a).get_decoder()
				__lowerCAmelCase   :						List[Any]				= inputs_dict["input_ids"]

				__lowerCAmelCase   :						Union[str, Any]				= input_ids[:1, :]
				__lowerCAmelCase   :						Union[str, Any]				= inputs_dict["attention_mask"][:1, :]
				__lowerCAmelCase   :						Optional[int]				= inputs_dict["head_mask"]
				__lowerCAmelCase   :						Optional[Any]				= 1

				# first forward pass
				__lowerCAmelCase   :						int				= model(__a ,    attention_mask=__a ,    head_mask=__a ,    use_cache=__a)

				__lowerCAmelCase		,			__lowerCAmelCase   :						int				= outputs.to_tuple()
				__lowerCAmelCase   :						List[Any]				= past_key_values[1]






def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case      ,__snake_case=None      ,__snake_case=None      ,__snake_case=None      ,__snake_case=None      ,__snake_case=None      ,)		->     Optional[int]:





			if attention_mask is None:
						__lowerCAmelCase   :						List[Any]				= tf.cast(tf.math.not_equal(__snake_case      ,config.pad_token_id			)      ,tf.inta			)
			if decoder_attention_mask is None:
						__lowerCAmelCase   :						int				= 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   :						int				= tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
			if decoder_head_mask is None:
						__lowerCAmelCase   :						str				= tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
			if cross_attn_head_mask is None:
						__lowerCAmelCase   :						Tuple				= 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__     (						__SCREAMING_SNAKE_CASE						,	__SCREAMING_SNAKE_CASE						,	unittest.TestCase							):




	'''simple docstring'''





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

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Optional[int])       -> Dict:

				"""simple docstring"""

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

				return False

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= TFMBartModelTester(self)
				__lowerCAmelCase   :						int				= ConfigTester(self ,    config_class=__a)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

				"""simple docstring"""

				self.config_tester.run_common_tests()







	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*__a)






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




	'''simple docstring'''





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

	@cached_property
	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> List[Any]:

				"""simple docstring"""

				return AutoTokenizer.from_pretrained(self.model_name)

	@cached_property
	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name)
				return model

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    **_SCREAMING_SNAKE_CASE:	Union[str, Any])       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.translate_src_text(**__a)
				self.assertListEqual(self.expected_text ,    __a)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    **_SCREAMING_SNAKE_CASE:	int)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.tokenizer(self.src_text ,    **__a ,    return_tensors="tf")
				__lowerCAmelCase   :						Tuple				= self.model.generate(
				    model_inputs.input_ids ,    attention_mask=model_inputs.attention_mask ,    num_beams=2)
				__lowerCAmelCase   :						Optional[int]				= self.tokenizer.batch_decode(__a ,    skip_special_tokens=__a)
				return generated_words







	@slow
	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				self._assert_generated_batch_equal_expected()




"""simple docstring"""






import re

from filelock import FileLock


try:
	import nltk

	__snake_case							:     Any   =					True
except (ImportError, ModuleNotFoundError):
	__snake_case							:     Optional[int]   =					False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)


def     _lowercase       (						__snake_case			)		->     str:
			re.sub("<n>"      ,""      ,__snake_case			)  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(__snake_case			)			)