






from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel







@require_tf
class 				lowerCamelCase_     :
							SCREAMING_SNAKE_CASE_						     =			MarianConfig
							SCREAMING_SNAKE_CASE_						     =			{}
							SCREAMING_SNAKE_CASE_						     =			'gelu'
							def __init__(						self		:					Optional[Any]							,__lowerCamelCase		:					Optional[int]							,__lowerCamelCase		:					Tuple=13							,__lowerCamelCase		:					str=7							,__lowerCamelCase		:					Tuple=True							,__lowerCamelCase		:					int=False							,__lowerCamelCase		:					List[Any]=99							,__lowerCamelCase		:					Union[str, Any]=32							,__lowerCamelCase		:					Optional[Any]=2							,__lowerCamelCase		:					Optional[Any]=4							,__lowerCamelCase		:					int=37							,__lowerCamelCase		:					Tuple=0.1							,__lowerCamelCase		:					Tuple=0.1							,__lowerCamelCase		:					Any=20							,__lowerCamelCase		:					Optional[int]=2							,__lowerCamelCase		:					Optional[int]=1							,__lowerCamelCase		:					Dict=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  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''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_marian_inputs_dict(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
								return config, inputs_dict




							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]							,__lowerCamelCase		:					int							,__lowerCamelCase		:					List[Any]					):





								'''simple docstring'''



								a		=			TFMarianModel(config=__lowerCamelCase					).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(__lowerCamelCase							,attention_mask=__lowerCamelCase							,head_mask=__lowerCamelCase							,use_cache=__lowerCamelCase					)

								a	, a		=			outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								a		=			ids_tensor((self.batch_size, 3)							,config.vocab_size					)
								a		=			tf.cast(ids_tensor((self.batch_size, 3)							,2					)							,tf.inta					)

								# append to next input_ids and
								a		=			tf.concat([input_ids, next_tokens]							,axis=-1					)
								a		=			tf.concat([attention_mask, next_attn_mask]							,axis=-1					)

								a		=			model(__lowerCamelCase							,attention_mask=__lowerCamelCase					)[0]
								a		=			model(__lowerCamelCase							,attention_mask=__lowerCamelCase							,past_key_values=__lowerCamelCase					)[0]

								self.parent.assertEqual(next_tokens.shape[1]							,output_from_past.shape[1]					)

								# select random slice
								a		=			int(ids_tensor((1,)							,output_from_past.shape[-1]					)					)
								a		=			output_from_no_past[:, -3:, random_slice_idx]
								a		=			output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__lowerCamelCase							,__lowerCamelCase							,rtol=1e-3					)





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_,  snake_case_,  snake_case_=None,  snake_case_=None,  snake_case_=None,  snake_case_=None,  snake_case_=None,  )   ->  int:


	"""simple docstring"""



	if attention_mask is None:
		a		=			tf.cast(tf.math.not_equal(snake_case_,  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 				lowerCamelCase_     (	a_ ,						a_ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
							SCREAMING_SNAKE_CASE_						     =			(TFMarianMTModel,) if is_tf_available() else ()
							SCREAMING_SNAKE_CASE_						     =			(
							    {
							        'conversational': TFMarianMTModel,
							        'feature-extraction': TFMarianModel,
							        'summarization': TFMarianMTModel,
							        'text2text-generation': TFMarianMTModel,
							        'translation': TFMarianMTModel,
							    }
							    if is_tf_available()
							    else {}
							)
							SCREAMING_SNAKE_CASE_						     =			True
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			TFMarianModelTester(self					)
								a		=			ConfigTester(self							,config_class=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								self.config_tester.run_common_tests()




							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase					)







@require_tf
class 				lowerCamelCase_     (	unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			10_00  # show more chars for failing integration tests
							@classmethod
							def  SCREAMING_SNAKE_CASE_ (						cls		:					str					):





								'''simple docstring'''



								a		=			F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls
							@cached_property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								return AutoTokenizer.from_pretrained(self.model_name					)
							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								return self.tokenizer.eos_token_id
							@cached_property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								warnings.simplefilter('''error'''					)
								a		=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
								assert isinstance(__lowerCamelCase							,__lowerCamelCase					)
								a		=			model.config
								self.assertListEqual(c.bad_words_ids							,[[c.pad_token_id]]					)
								self.assertEqual(c.max_length							,5_12					)
								self.assertEqual(c.decoder_start_token_id							,c.pad_token_id					)
								return model
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]							,**__lowerCamelCase		:					Dict					):





								'''simple docstring'''



								a		=			self.translate_src_text(**__lowerCamelCase					)
								self.assertListEqual(self.expected_text							,__lowerCamelCase					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,**__lowerCamelCase		:					List[Any]					):





								'''simple docstring'''



								a		=			self.tokenizer(self.src_text							,**__lowerCamelCase							,padding=__lowerCamelCase							,return_tensors='''tf'''					)
								a		=			self.model.generate(
								    model_inputs.input_ids							,attention_mask=model_inputs.attention_mask							,num_beams=2							,max_length=1_28					)
								a		=			self.tokenizer.batch_decode(generated_ids.numpy()							,skip_special_tokens=__lowerCamelCase					)
								return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			'mt'
							SCREAMING_SNAKE_CASE_						     =			'en'
							SCREAMING_SNAKE_CASE_						     =			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
							SCREAMING_SNAKE_CASE_						     =			['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
							@unittest.skip('''Skipping until #12647 is resolved.'''					)
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			'en'
							SCREAMING_SNAKE_CASE_						     =			'zh'
							SCREAMING_SNAKE_CASE_						     =			['My name is Wolfgang and I live in Berlin']
							SCREAMING_SNAKE_CASE_						     =			['我叫沃尔夫冈 我住在柏林']
							@unittest.skip('''Skipping until #12647 is resolved.'''					)
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			'en'
							SCREAMING_SNAKE_CASE_						     =			'ROMANCE'
							SCREAMING_SNAKE_CASE_						     =			[
							    '>>fr<< Don\'t spend so much time watching TV.',
							    '>>pt<< Your message has been sent.',
							    '>>es<< He\'s two years older than me.',
							]
							SCREAMING_SNAKE_CASE_						     =			[
							    'Ne passez pas autant de temps à regarder la télé.',
							    'A sua mensagem foi enviada.',
							    'Es dos años más viejo que yo.',
							]
							@unittest.skip('''Skipping until #12647 is resolved.'''					)
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								self._assert_generated_batch_equal_expected()




							@unittest.skip('''Skipping until #12647 is resolved.'''					)
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								a		=			TranslationPipeline(self.model							,self.tokenizer							,framework='''tf'''					)
								a		=			pipeline(self.src_text					)
								self.assertEqual(self.expected_text							,[x['''translation_text'''] for x in output]					)













def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  int:


	"""simple docstring"""



	return n * (2 * n - 1)





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  bool:


	"""simple docstring"""



	a		=			(1 + 2_4 * n) ** 0.5
	return ((1 + root) / 6) % 1 == 0





def   SCREAMING_SNAKE_CASE__						(   snake_case_ = 1_4_4  )   ->  int:


	"""simple docstring"""



	a		=			start
	a		=			hexagonal_num(snake_case_  )
	while not is_pentagonal(snake_case_  ):
		n += 1
		a		=			hexagonal_num(snake_case_  )
	return num


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





