





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     UpperCamelCase__					:






		'''simple docstring'''






		__a       :       Union[str, Any]       =	MBartConfig
		__a       :       str       =	{}
		__a       :       str       =	"""gelu"""






		def __init__(							self,   snake_case__,   snake_case__=13,   snake_case__=7,   snake_case__=True,   snake_case__=False,   snake_case__=99,   snake_case__=32,   snake_case__=2,   snake_case__=4,   snake_case__=37,   snake_case__=0.1,   snake_case__=0.1,   snake_case__=20,   snake_case__=2,   snake_case__=1,   snake_case__=0,   )       -> Optional[int]:






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






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									lowercase_	:   Union[str, Any]  				=     ids_tensor([self.batch_size, self.seq_length - 1],   self.vocab_size  )
									lowercase_	:   List[str]  				=     tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ),   1  )
									lowercase_	:   Dict  				=     tf.concat([input_ids, eos_tensor],   axis=1  )

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

									lowercase_	:   Dict  				=     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_	:   Union[str, Any]  				=     prepare_mbart_inputs_dict(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_  )
									return config, inputs_dict






		def 						snake_case__				(							self,   snake_case__,   snake_case__  )       -> int:






									"""simple docstring"""
									lowercase_	:   Any  				=     TFMBartModel(config=SCREAMING_SNAKE_CASE_  ).get_decoder()
									lowercase_	:   Tuple  				=     inputs_dict["""input_ids"""]

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

									# first forward pass
									lowercase_	:   Optional[Any]  				=     model(SCREAMING_SNAKE_CASE_,   attention_mask=SCREAMING_SNAKE_CASE_,   head_mask=SCREAMING_SNAKE_CASE_,   use_cache=SCREAMING_SNAKE_CASE_  )

									lowercase_				,		lowercase_	:   List[Any]  				=     outputs.to_tuple()
									lowercase_	:   str  				=     past_key_values[1]





def  __magic_name__  (				lowercase				,       lowercase				,       lowercase				,       lowercase=None				,       lowercase=None				,       lowercase=None				,       lowercase=None				,       lowercase=None				,       )				-> List[Any]:

							"""simple docstring"""



							if attention_mask is None:
														lowercase_	:   Any  				=     tf.cast(tf.math.not_equal(__a				,       config.pad_token_id				)				,       tf.inta				)
							if decoder_attention_mask is None:
														lowercase_	:   Dict  				=     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_	:   str  				=     tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
							if decoder_head_mask is None:
														lowercase_	:   Any  				=     tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
							if cross_attn_head_mask is None:
														lowercase_	:   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     UpperCamelCase__					(     __lowercase				, __lowercase				, unittest.TestCase				):






		'''simple docstring'''






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






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__,   snake_case__  )       -> int:






									"""simple docstring"""







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

									return False






		def 						snake_case__				(							self  )       -> List[Any]:






									"""simple docstring"""
									lowercase_	:   Optional[int]  				=     TFMBartModelTester(self  )
									lowercase_	:   Union[str, Any]  				=     ConfigTester(self,   config_class=SCREAMING_SNAKE_CASE_  )






		def 						snake_case__				(							self  )       -> str:






									"""simple docstring"""
									self.config_tester.run_common_tests()






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






									"""simple docstring"""
									lowercase_	:   Any  				=     self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_  )




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






		'''simple docstring'''






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






		@cached_property
		def 						snake_case__				(							self  )       -> List[str]:






									"""simple docstring"""
									return AutoTokenizer.from_pretrained(self.model_name  )






		@cached_property
		def 						snake_case__				(							self  )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   str  				=     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
									return model






		def 						snake_case__				(							self,   **snake_case__  )       -> Optional[Any]:






									"""simple docstring"""
									lowercase_	:   str  				=     self.translate_src_text(**SCREAMING_SNAKE_CASE_  )
									self.assertListEqual(self.expected_text,   SCREAMING_SNAKE_CASE_  )






		def 						snake_case__				(							self,   **snake_case__  )       -> Dict:






									"""simple docstring"""
									lowercase_	:   Optional[Any]  				=     self.tokenizer(self.src_text,   **SCREAMING_SNAKE_CASE_,   return_tensors="""tf"""  )
									lowercase_	:   int  				=     self.model.generate(
									    model_inputs.input_ids,   attention_mask=model_inputs.attention_mask,   num_beams=2  )
									lowercase_	:   Optional[Any]  				=     self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_,   skip_special_tokens=SCREAMING_SNAKE_CASE_  )
									return generated_words






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






									"""simple docstring"""
									self._assert_generated_batch_equal_expected()

"""simple docstring"""




def 							_lowerCamelCase			(			__a     ):
		if divisor % 5 == 0 or divisor % 2 == 0:
				return 0
		SCREAMING_SNAKE_CASE_    			=			1
		SCREAMING_SNAKE_CASE_    			=			1
		while repunit:
				SCREAMING_SNAKE_CASE_    			=			(10 * repunit + 1) % divisor
				repunit_index += 1
		return repunit_index



def 							_lowerCamelCase			(			__a = 1_000_000     ):
		SCREAMING_SNAKE_CASE_    			=			limit - 1
		if divisor % 2 == 0:
				divisor += 1
		while least_divisible_repunit(__a     ) <= limit:
				divisor += 2
		return divisor


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