






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        snake_case_					:

						"""simple docstring"""



						snake_case__   					=			MarianConfig
						snake_case__   					=			{}
						snake_case__   					=			'''gelu'''



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




											'''simple docstring'''



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

											__a				:	str			=				hidden_dropout_prob
											__a				:	Tuple			=				attention_probs_dropout_prob
											__a				:	int			=				max_position_embeddings
											__a				:	str			=				eos_token_id
											__a				:	Dict			=				pad_token_id
											__a				:	Dict			=				bos_token_id



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




											'''simple docstring'''



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

											__a				:	Optional[int]			=				ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size      )

											__a				:	Optional[Any]			=				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				:	Optional[Any]			=				prepare_marian_inputs_dict(__UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      )
											return config, inputs_dict



						def 					UpperCAmelCase__				(self:				int   ,				__UpperCAmelCase:				int   ,				__UpperCAmelCase:				int      )						->    Tuple:




											'''simple docstring'''



											__a				:	Dict			=				TFMarianModel(config=__UpperCAmelCase      ).get_decoder()
											__a				:	Union[str, Any]			=				inputs_dict["input_ids"]

											__a				:	str			=				input_ids[:1, :]
											__a				:	str			=				inputs_dict["attention_mask"][:1, :]
											__a				:	str			=				inputs_dict["head_mask"]
											__a				:	Optional[Any]			=				1

											# first forward pass
											__a				:	Any			=				model(__UpperCAmelCase   ,				attention_mask=__UpperCAmelCase   ,				head_mask=__UpperCAmelCase   ,				use_cache=__UpperCAmelCase      )

											__a			, __a				:	List[str]			=				outputs.to_tuple()

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

											# append to next input_ids and
											__a				:	Dict			=				tf.concat([input_ids, next_tokens]   ,				axis=-1      )
											__a				:	List[Any]			=				tf.concat([attention_mask, next_attn_mask]   ,				axis=-1      )

											__a				:	List[Any]			=				model(__UpperCAmelCase   ,				attention_mask=__UpperCAmelCase      )[0]
											__a				:	List[str]			=				model(__UpperCAmelCase   ,				attention_mask=__UpperCAmelCase   ,				past_key_values=__UpperCAmelCase      )[0]

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

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

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







def 						a_      (__A							,					__A							,					__A							,					__A=None							,					__A=None							,					__A=None							,					__A=None							,					__A=None							,					)							->	Any:



					"""simple docstring"""







					if attention_mask is None:
										__a				:	str			=				tf.cast(tf.math.not_equal(lowercase__							,					config.pad_token_id			)							,					tf.inta			)
					if decoder_attention_mask is None:
										__a				:	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:
										__a				:	Optional[int]			=				tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
					if decoder_head_mask is None:
										__a				:	Union[str, Any]			=				tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
					if cross_attn_head_mask is None:
										__a				:	int			=				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_					(						A_		,       A_		,       unittest.TestCase			):

						"""simple docstring"""



						snake_case__   					=			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
						snake_case__   					=			(TFMarianMTModel,) if is_tf_available() else ()
						snake_case__   					=			(
						    {
						        '''conversational''': TFMarianMTModel,
						        '''feature-extraction''': TFMarianModel,
						        '''summarization''': TFMarianMTModel,
						        '''text2text-generation''': TFMarianMTModel,
						        '''translation''': TFMarianMTModel,
						    }
						    if is_tf_available()
						    else {}
						)
						snake_case__   					=			True
						snake_case__   					=			False
						snake_case__   					=			False



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    str:




											'''simple docstring'''



											__a				:	int			=				TFMarianModelTester(self      )
											__a				:	Tuple			=				ConfigTester(self   ,				config_class=__UpperCAmelCase      )



						def 					UpperCAmelCase__				(self:				Optional[Any]      )						->    Any:




											'''simple docstring'''



											self.config_tester.run_common_tests()



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




											'''simple docstring'''



											__a				:	Optional[int]			=				self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.check_decoder_model_past_large_inputs(*__UpperCAmelCase      )




@require_tf
class        snake_case_					(						unittest.TestCase			):

						"""simple docstring"""



						snake_case__   					=			1_000  # show more chars for failing integration tests



						@classmethod
						def 					UpperCAmelCase__				(cls:				int      )						->    Any:




											'''simple docstring'''



											__a				:	Any			=				f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
											return cls



						@cached_property
						def 					UpperCAmelCase__				(self:				Any      )						->    Dict:




											'''simple docstring'''



											return AutoTokenizer.from_pretrained(self.model_name      )



						@property
						def 					UpperCAmelCase__				(self:				Optional[int]      )						->    Tuple:




											'''simple docstring'''



											return self.tokenizer.eos_token_id



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




											'''simple docstring'''



											warnings.simplefilter("error"      )
											__a				:	Any			=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
											assert isinstance(__UpperCAmelCase   ,				__UpperCAmelCase      )
											__a				:	Dict			=				model.config
											self.assertListEqual(c.bad_words_ids   ,				[[c.pad_token_id]]      )
											self.assertEqual(c.max_length   ,				512      )
											self.assertEqual(c.decoder_start_token_id   ,				c.pad_token_id      )
											return model



						def 					UpperCAmelCase__				(self:				Union[str, Any]   ,				**__UpperCAmelCase:				Optional[int]      )						->    List[str]:




											'''simple docstring'''



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



						def 					UpperCAmelCase__				(self:				Union[str, Any]   ,				**__UpperCAmelCase:				Optional[Any]      )						->    int:




											'''simple docstring'''



											__a				:	Optional[Any]			=				self.tokenizer(self.src_text   ,				**__UpperCAmelCase   ,				padding=__UpperCAmelCase   ,				return_tensors="tf"      )
											__a				:	Optional[Any]			=				self.model.generate(
											    model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				max_length=128      )
											__a				:	Optional[Any]			=				self.tokenizer.batch_decode(generated_ids.numpy()   ,				skip_special_tokens=__UpperCAmelCase      )
											return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class        snake_case_					(						A_			):

						"""simple docstring"""



						snake_case__   					=			'''mt'''
						snake_case__   					=			'''en'''
						snake_case__   					=			['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
						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 					UpperCAmelCase__				(self:				Optional[int]      )						->    Any:




											'''simple docstring'''



											self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class        snake_case_					(						A_			):

						"""simple docstring"""



						snake_case__   					=			'''en'''
						snake_case__   					=			'''zh'''
						snake_case__   					=			['''My name is Wolfgang and I live in Berlin''']
						snake_case__   					=			['''我叫沃尔夫冈 我住在柏林''']



						@unittest.skip("Skipping until #12647 is resolved."      )
						@slow
						def 					UpperCAmelCase__				(self:				Any      )						->    Union[str, Any]:




											'''simple docstring'''



											self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class        snake_case_					(						A_			):

						"""simple docstring"""



						snake_case__   					=			'''en'''
						snake_case__   					=			'''ROMANCE'''
						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.''',
						]
						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 					UpperCAmelCase__				(self:				List[Any]      )						->    List[str]:




											'''simple docstring'''



											self._assert_generated_batch_equal_expected()



						@unittest.skip("Skipping until #12647 is resolved."      )
						@slow
						def 					UpperCAmelCase__				(self:				str      )						->    str:




											'''simple docstring'''



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










def 						a_      (__A			)							->	list:



					"""simple docstring"""
					if len(__A			) <= 1:
										return data
					else:
										return [
										    *quick_sort([e for e in data[1:] if e <= data[0]]			),
										    data[0],
										    *quick_sort([e for e in data[1:] if e > data[0]]			),
										]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





