
'''simple docstring'''







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__  :
					lowerCAmelCase_	  =	MarianConfig
					lowerCAmelCase_	  =	{}
					lowerCAmelCase_	  =	"""gelu"""






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


											"""simple docstring"""





											lowercase_      :	List[Any]						=			parent
											lowercase_      :	List[Any]						=			batch_size
											lowercase_      :	Optional[int]						=			seq_length
											lowercase_      :	str						=			is_training
											lowercase_      :	str						=			use_labels
											lowercase_      :	Optional[Any]						=			vocab_size
											lowercase_      :	Dict						=			hidden_size
											lowercase_      :	Tuple						=			num_hidden_layers
											lowercase_      :	Optional[int]						=			num_attention_heads
											lowercase_      :	Any						=			intermediate_size

											lowercase_      :	List[str]						=			hidden_dropout_prob
											lowercase_      :	Optional[int]						=			attention_probs_dropout_prob
											lowercase_      :	str						=			max_position_embeddings
											lowercase_      :	List[Any]						=			eos_token_id
											lowercase_      :	int						=			pad_token_id
											lowercase_      :	str						=			bos_token_id






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											lowercase_      :	List[str]						=			ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size						)

											lowercase_      :	int						=			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_      :	Optional[int]						=			prepare_marian_inputs_dict(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
											return config, inputs_dict






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			TFMarianModel(config=__SCREAMING_SNAKE_CASE						).get_decoder()
											lowercase_      :	List[str]						=			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_      :	int						=			1

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

											lowercase_		,					lowercase_      :	Optional[Any]						=			outputs.to_tuple()

											# create hypothetical next token and extent to next_input_ids
											lowercase_      :	Optional[Any]						=			ids_tensor((self.batch_size, 3)							,				config.vocab_size						)
											lowercase_      :	int						=			tf.cast(ids_tensor((self.batch_size, 3)							,				2						)							,				tf.inta						)

											# append to next input_ids and
											lowercase_      :	int						=			tf.concat([input_ids, next_tokens]							,				axis=-1						)
											lowercase_      :	List[str]						=			tf.concat([attention_mask, next_attn_mask]							,				axis=-1						)

											lowercase_      :	Union[str, Any]						=			model(__SCREAMING_SNAKE_CASE							,				attention_mask=__SCREAMING_SNAKE_CASE						)[0]
											lowercase_      :	Optional[int]						=			model(__SCREAMING_SNAKE_CASE							,				attention_mask=__SCREAMING_SNAKE_CASE							,				past_key_values=__SCREAMING_SNAKE_CASE						)[0]

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

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

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







def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Optional[Any] , __SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					int=None , __SCREAMING_SNAKE_CASE   :					List[Any]=None , __SCREAMING_SNAKE_CASE   :					Dict=None , __SCREAMING_SNAKE_CASE   :					List[str]=None , __SCREAMING_SNAKE_CASE   :					str=None , ):



						"""simple docstring"""





						if attention_mask is None:
												lowercase_      :	List[Any]						=			tf.cast(tf.math.not_equal(lowerCamelCase__ , config.pad_token_id				) , tf.inta				)
						if decoder_attention_mask is None:
												lowercase_      :	Tuple						=			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_      :	Union[str, Any]						=			tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
						if decoder_head_mask is None:
												lowercase_      :	Tuple						=			tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
						if cross_attn_head_mask is None:
												lowercase_      :	List[str]						=			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__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
					lowerCAmelCase_	  =	(TFMarianMTModel,) if is_tf_available() else ()
					lowerCAmelCase_	  =	(
					    {
					        """conversational""": TFMarianMTModel,
					        """feature-extraction""": TFMarianModel,
					        """summarization""": TFMarianMTModel,
					        """text2text-generation""": TFMarianMTModel,
					        """translation""": TFMarianMTModel,
					    }
					    if is_tf_available()
					    else {}
					)
					lowerCAmelCase_	  =	True
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			TFMarianModelTester(self						)
											lowercase_      :	Optional[int]						=			ConfigTester(self							,				config_class=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											self.config_tester.run_common_tests()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE						)







@require_tf
class 					lowerCAmelCase__  (    unittest.TestCase	):
					lowerCAmelCase_	  =	1_0_0_0  # show more chars for failing integration tests






					@classmethod
					def       _snake_case       (  cls						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
											return cls






					@cached_property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return AutoTokenizer.from_pretrained(self.model_name						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return self.tokenizer.eos_token_id






					@cached_property
					def       _snake_case       (  self						):


											"""simple docstring"""





											warnings.simplefilter('''error'''						)
											lowercase_      :	int						=			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
											assert isinstance(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)
											lowercase_      :	Optional[int]						=			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       _snake_case       (  self							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Any						=			self.translate_src_text(**__SCREAMING_SNAKE_CASE						)
											self.assertListEqual(self.expected_text							,				__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self							,				**__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.tokenizer(self.src_text							,				**__SCREAMING_SNAKE_CASE							,				padding=__SCREAMING_SNAKE_CASE							,				return_tensors='''tf'''						)
											lowercase_      :	int						=			self.model.generate(
											    model_inputs.input_ids							,				attention_mask=model_inputs.attention_mask							,				num_beams=2							,				max_length=1_28						)
											lowercase_      :	Optional[Any]						=			self.tokenizer.batch_decode(generated_ids.numpy()							,				skip_special_tokens=__SCREAMING_SNAKE_CASE						)
											return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	"""mt"""
					lowerCAmelCase_	  =	"""en"""
					lowerCAmelCase_	  =	["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
					lowerCAmelCase_	  =	["""Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."""]






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


											"""simple docstring"""





											self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	"""en"""
					lowerCAmelCase_	  =	"""zh"""
					lowerCAmelCase_	  =	["""My name is Wolfgang and I live in Berlin"""]
					lowerCAmelCase_	  =	["""我叫沃尔夫冈 我住在柏林"""]






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


											"""simple docstring"""





											self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 					lowerCAmelCase__  (    lowerCamelCase_	):
					lowerCAmelCase_	  =	"""en"""
					lowerCAmelCase_	  =	"""ROMANCE"""
					lowerCAmelCase_	  =	[
					    """>>fr<< Don't spend so much time watching TV.""",
					    """>>pt<< Your message has been sent.""",
					    """>>es<< He's two years older than me.""",
					]
					lowerCAmelCase_	  =	[
					    """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       _snake_case       (  self						):


											"""simple docstring"""





											self._assert_generated_batch_equal_expected()






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


											"""simple docstring"""





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








"""simple docstring"""







def 	lowerCAmelCase__      (  lowerCamelCase__      )			->				list:
							for i, _ in enumerate(lowerCamelCase__      ):
														if (i % 2 == 1) == (nums[i - 1] > nums[i]):
																					A   ,	A    =       nums[i], nums[i - 1]

							return nums


if __name__ == "__main__":
							print('Enter the array elements:')
							A     =      list(map(int, input().split()))
							print('The unsorted array is:')
							print(array)
							print('Array after Wiggle sort:')
							print(wiggle_sort(array))



