





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__:






       '''simple docstring'''


       __snake_case       	=			MarianConfig
       __snake_case       	=			{}
       __snake_case       	=			'gelu'




       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase=1_3			,  __lowerCamelCase=7			,  __lowerCamelCase=True			,  __lowerCamelCase=False			,  __lowerCamelCase=9_9			,  __lowerCamelCase=3_2			,  __lowerCamelCase=2			,  __lowerCamelCase=4			,  __lowerCamelCase=3_7			,  __lowerCamelCase=0.1			,  __lowerCamelCase=0.1			,  __lowerCamelCase=2_0			,  __lowerCamelCase=2			,  __lowerCamelCase=1			,  __lowerCamelCase=0			,  )	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							parent
            _SCREAMING_SNAKE_CASE				:		Any  			=							batch_size
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							seq_length
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							is_training
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							use_labels
            _SCREAMING_SNAKE_CASE				:		str  			=							vocab_size
            _SCREAMING_SNAKE_CASE				:		str  			=							hidden_size
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							num_hidden_layers
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							num_attention_heads
            _SCREAMING_SNAKE_CASE				:		int  			=							intermediate_size

            _SCREAMING_SNAKE_CASE				:		Dict  			=							hidden_dropout_prob
            _SCREAMING_SNAKE_CASE				:		Any  			=							attention_probs_dropout_prob
            _SCREAMING_SNAKE_CASE				:		str  			=							max_position_embeddings
            _SCREAMING_SNAKE_CASE				:		Dict  			=							eos_token_id
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							pad_token_id
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							bos_token_id




       def 			UpperCamelCase_	(					self							)	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							ids_tensor([self.batch_size, self.seq_length - 1]			,  self.vocab_size							)
            _SCREAMING_SNAKE_CASE				:		str  			=							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)			,  1							)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							tf.concat([input_ids, eos_tensor]			,  axis=1							)

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

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							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			,  )
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							prepare_marian_inputs_dict(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)
            return config, inputs_dict







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		int  			=							TFMarianModel(config=__lowerCamelCase							).get_decoder()
            _SCREAMING_SNAKE_CASE				:		Any  			=							inputs_dict["input_ids"]

            _SCREAMING_SNAKE_CASE				:		Any  			=							input_ids[:1, :]
            _SCREAMING_SNAKE_CASE				:		str  			=							inputs_dict["attention_mask"][:1, :]
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							inputs_dict["head_mask"]
            _SCREAMING_SNAKE_CASE				:		int  			=							1

            # first forward pass
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							model(__lowerCamelCase			,  attention_mask=__lowerCamelCase			,  head_mask=__lowerCamelCase			,  use_cache=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Optional[int]  			=							outputs.to_tuple()

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

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

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							model(__lowerCamelCase			,  attention_mask=__lowerCamelCase							)[0]
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							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
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							int(ids_tensor((1,)			,  output_from_past.shape[-1]							)							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							output_from_no_past[:, -3:, random_slice_idx]
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							output_from_past[:, :, random_slice_idx]

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






def 			lowerCamelCase__	(__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase,				__lowerCamelCase=None,				__lowerCamelCase=None,				__lowerCamelCase=None,				__lowerCamelCase=None,				__lowerCamelCase=None,				):
     if attention_mask is None:
          _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							tf.cast(tf.math.not_equal(__lowerCamelCase,				config.pad_token_id		),				tf.inta		)
     if decoder_attention_mask is None:
          _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							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:
          _SCREAMING_SNAKE_CASE				:		Dict  			=							tf.ones((config.encoder_layers, config.encoder_attention_heads)		)
     if decoder_head_mask is None:
          _SCREAMING_SNAKE_CASE				:		str  			=							tf.ones((config.decoder_layers, config.decoder_attention_heads)		)
     if cross_attn_head_mask is None:
          _SCREAMING_SNAKE_CASE				:		Any  			=							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__(       __lowercase     ,   __lowercase     ,   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]:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							TFMarianModelTester(self							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							ConfigTester(self			,  config_class=__lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   int:
            self.config_tester.run_common_tests()







       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							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			):






       '''simple docstring'''


       __snake_case       	=			1_0_0_0  # show more chars for failing integration tests




       @classmethod
       def 			UpperCamelCase_	(					cls							)	->   None:
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
            return cls




       @cached_property
       def 			UpperCamelCase_	(					self							)	->   MarianTokenizer:
            return AutoTokenizer.from_pretrained(self.model_name							)




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            return self.tokenizer.eos_token_id




       @cached_property
       def 			UpperCamelCase_	(					self							)	->   str:
            warnings.simplefilter("error"							)
            _SCREAMING_SNAKE_CASE				:		TFMarianMTModel  			=							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
            assert isinstance(__lowerCamelCase			,  __lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							model.config
            self.assertListEqual(c.bad_words_ids			,  [[c.pad_token_id]]							)
            self.assertEqual(c.max_length			,  5_1_2							)
            self.assertEqual(c.decoder_start_token_id			,  c.pad_token_id							)
            return model




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.translate_src_text(**__lowerCamelCase							)
            self.assertListEqual(self.expected_text			,  __lowerCamelCase							)







       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							self.tokenizer(self.src_text			,  **__lowerCamelCase			,  padding=__lowerCamelCase			,  return_tensors="tf"							)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.model.generate(
                model_inputs.input_ids			,  attention_mask=model_inputs.attention_mask			,  num_beams=2			,  max_length=1_2_8							)
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.tokenizer.batch_decode(generated_ids.numpy()			,  skip_special_tokens=__lowerCamelCase							)
            return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 	lowerCAmelCase__(       __lowercase			):






       '''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[Any]:
            self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 	lowerCAmelCase__(       __lowercase			):






       '''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							)	->   Union[str, Any]:
            self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 	lowerCAmelCase__(       __lowercase			):






       '''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							)	->   Dict:
            self._assert_generated_batch_equal_expected()







       @unittest.skip("Skipping until #12647 is resolved."							)
       @slow
       def 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							TranslationPipeline(self.model			,  self.tokenizer			,  framework="tf"							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							pipeline(self.src_text							)
            self.assertEqual(self.expected_text			,  [x["translation_text"] for x in output]							)






from itertools import permutations






def 			lowerCamelCase__	():
     _SCREAMING_SNAKE_CASE				:		Dict  			=							list(map("".join,				permutations("0123456789"		)		)		)
     return result[999999]


if __name__ == "__main__":
       print(solution())