






"""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       snake_case_       :



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					MBartConfig
	SCREAMING_SNAKE_CASE_:							Dict   			=					{}
	SCREAMING_SNAKE_CASE_:							Tuple   			=					"""gelu"""


	def __init__(      self  ,					__a  ,					__a=13  ,					__a=7  ,					__a=True  ,					__a=False  ,					__a=99  ,					__a=32  ,					__a=2  ,					__a=4  ,					__a=37  ,					__a=0.1  ,					__a=0.1  ,					__a=20  ,					__a=2  ,					__a=1  ,					__a=0  ,					):


					"""simple docstring"""

					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						):


					"""simple docstring"""

					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(__a  ,					__a  ,					__a						)
					return config, inputs_dict






	def       _UpperCAmelCase						(      self  ,					__a  ,					__a						):


					"""simple docstring"""

					A__							 =       TFMBartModel(config=__a						).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(__a  ,					attention_mask=__a  ,					head_mask=__a  ,					use_cache=__a						)

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





def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,lowerCAmelCase__=None       ,):
				if attention_mask is None:
								A__							 =       tf.cast(tf.math.not_equal(lowerCAmelCase__       ,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       snake_case_       (    _lowerCamelCase    ,							_lowerCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a						):


					"""simple docstring"""

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

					return False


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       TFMBartModelTester(self						)
					A__							 =       ConfigTester(self  ,					config_class=__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					self.config_tester.run_common_tests()






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       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       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

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


	@cached_property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					return AutoTokenizer.from_pretrained(self.model_name						)


	@cached_property
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					A__							 =       self.translate_src_text(**__a						)
					self.assertListEqual(self.expected_text  ,					__a						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					A__							 =       self.tokenizer(self.src_text  ,					**__a  ,					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(__a  ,					skip_special_tokens=__a						)
					return generated_words






	@slow
	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					self._assert_generated_batch_equal_expected()










"""simple docstring"""






from maths.prime_check import is_prime



def  __lowerCamelCase					(					lowerCAmelCase__				):
				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				):
								A__							 =       f'''Input value of [number={number}] must be an integer'''
								raise TypeError(lowerCAmelCase__				)
				if is_prime(lowerCAmelCase__				) and is_prime(number + 2				):
								return number + 2
				else:
								return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()


