


'''simple docstring'''



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A__		: Dict												=  """platform"""

      import jax
      import jax.numpy as jnp

      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right



def       UpperCAmelCase__ (     UpperCAmelCase_   :    Optional[Any]				,   UpperCAmelCase_   :    Optional[int]				,   UpperCAmelCase_   :    str=None				,   UpperCAmelCase_   :    Any=None				,   UpperCAmelCase_   :    Any=None				,   UpperCAmelCase_   :    str=None				,   UpperCAmelCase_   :    Tuple=None				,   UpperCAmelCase_   :    int=None				,   )					->		Union[str, Any]:
     if attention_mask is None:
          __lowerCamelCase       :     int        =		np.where(input_ids != config.pad_token_id				,   1				,   0							)
     if decoder_attention_mask is None:
          __lowerCamelCase       :     List[str]        =		np.where(decoder_input_ids != config.pad_token_id				,   1				,   0							)
     if head_mask is None:
          __lowerCamelCase       :     Any        =		np.ones((config.encoder_layers, config.encoder_attention_heads)							)
     if decoder_head_mask is None:
          __lowerCamelCase       :     Tuple        =		np.ones((config.decoder_layers, config.decoder_attention_heads)							)
     if cross_attn_head_mask is None:
          __lowerCamelCase       :     Tuple        =		np.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": attention_mask,
     }





class        UpperCAmelCase_  :





    """simple docstring"""






    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_=16      ,					SCREAMING_SNAKE_CASE_=2      ,					SCREAMING_SNAKE_CASE_=4      ,					SCREAMING_SNAKE_CASE_=4      ,					SCREAMING_SNAKE_CASE_="gelu"      ,					SCREAMING_SNAKE_CASE_=0.1      ,					SCREAMING_SNAKE_CASE_=0.1      ,					SCREAMING_SNAKE_CASE_=32      ,					SCREAMING_SNAKE_CASE_=2      ,					SCREAMING_SNAKE_CASE_=1      ,					SCREAMING_SNAKE_CASE_=0      ,					SCREAMING_SNAKE_CASE_=0.0_2      ,					)    ->					Optional[Any]:
         __lowerCamelCase       :     List[Any]        =		parent
         __lowerCamelCase       :     Tuple        =		batch_size
         __lowerCamelCase       :     Dict        =		seq_length
         __lowerCamelCase       :     Optional[Any]        =		is_training
         __lowerCamelCase       :     Optional[Any]        =		use_labels
         __lowerCamelCase       :     Tuple        =		vocab_size
         __lowerCamelCase       :     List[Any]        =		hidden_size
         __lowerCamelCase       :     Tuple        =		num_hidden_layers
         __lowerCamelCase       :     List[Any]        =		num_attention_heads
         __lowerCamelCase       :     List[str]        =		intermediate_size
         __lowerCamelCase       :     int        =		hidden_act
         __lowerCamelCase       :     Any        =		hidden_dropout_prob
         __lowerCamelCase       :     Tuple        =		attention_probs_dropout_prob
         __lowerCamelCase       :     int        =		max_position_embeddings
         __lowerCamelCase       :     Any        =		eos_token_id
         __lowerCamelCase       :     str        =		pad_token_id
         __lowerCamelCase       :     Dict        =		bos_token_id
         __lowerCamelCase       :     Optional[int]        =		initializer_range







    def 				lowercase_    (							self     )    ->					Dict:
         __lowerCamelCase       :     Union[str, Any]        =		np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,					self.vocab_size     )      ,					3      ,					self.vocab_size     )
         __lowerCamelCase       :     Optional[Any]        =		np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,					dtype=np.intaa     ))      ,					-1     )

         __lowerCamelCase       :     Dict        =		shift_tokens_right(SCREAMING_SNAKE_CASE_      ,					1      ,					2     )

         __lowerCamelCase       :     List[Any]        =		MarianConfig(
             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_id=self.eos_token_id      ,					bos_token_id=self.bos_token_id      ,					pad_token_id=self.pad_token_id      ,					initializer_range=self.initializer_range      ,					use_cache=SCREAMING_SNAKE_CASE_      ,					)
         __lowerCamelCase       :     int        =		prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )
         return config, inputs_dict







    def 				lowercase_    (							self     )    ->					Any:
         __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		self.prepare_config_and_inputs()
         return config, inputs_dict







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					Dict:
         __lowerCamelCase       :     Union[str, Any]        =		20
         __lowerCamelCase       :     str        =		model_class_name(SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Any        =		model.encode(inputs_dict['input_ids']     )

         __lowerCamelCase    ,	__lowerCamelCase       :     int        =		(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         __lowerCamelCase       :     Optional[Any]        =		model.init_cache(decoder_input_ids.shape[0]      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Tuple        =		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,					dtype='i4'     )

         __lowerCamelCase       :     str        =		jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]      ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,					)
         __lowerCamelCase       :     Dict        =		model.decode(
             decoder_input_ids[:, :-1]      ,					SCREAMING_SNAKE_CASE_      ,					decoder_attention_mask=SCREAMING_SNAKE_CASE_      ,					past_key_values=SCREAMING_SNAKE_CASE_      ,					decoder_position_ids=SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     List[Any]        =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,					dtype='i4'     )
         __lowerCamelCase       :     str        =		model.decode(
             decoder_input_ids[:, -1:]      ,					SCREAMING_SNAKE_CASE_      ,					decoder_attention_mask=SCREAMING_SNAKE_CASE_      ,					past_key_values=outputs_cache.past_key_values      ,					decoder_position_ids=SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     Union[str, Any]        =		model.decode(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     str        =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
         self.parent.assertTrue(diff < 1E-3      ,					msg=f'Max diff is {diff}'     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Optional[int]        =		20
         __lowerCamelCase       :     List[Any]        =		model_class_name(SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     List[Any]        =		model.encode(inputs_dict['input_ids']     )

         __lowerCamelCase    ,	__lowerCamelCase       :     Tuple        =		(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         __lowerCamelCase       :     Any        =		jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
             ]      ,					axis=-1      ,					)

         __lowerCamelCase       :     List[Any]        =		model.init_cache(decoder_input_ids.shape[0]      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Tuple        =		jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]      ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,					)

         __lowerCamelCase       :     Tuple        =		model.decode(
             decoder_input_ids[:, :-1]      ,					SCREAMING_SNAKE_CASE_      ,					decoder_attention_mask=SCREAMING_SNAKE_CASE_      ,					past_key_values=SCREAMING_SNAKE_CASE_      ,					decoder_position_ids=SCREAMING_SNAKE_CASE_      ,					)
         __lowerCamelCase       :     Any        =		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,					dtype='i4'     )
         __lowerCamelCase       :     Optional[Any]        =		model.decode(
             decoder_input_ids[:, -1:]      ,					SCREAMING_SNAKE_CASE_      ,					past_key_values=outputs_cache.past_key_values      ,					decoder_attention_mask=SCREAMING_SNAKE_CASE_      ,					decoder_position_ids=SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     Any        =		model.decode(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					decoder_attention_mask=SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     List[str]        =		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
         self.parent.assertTrue(diff < 1E-3      ,					msg=f'Max diff is {diff}'     )







@require_flax
class        UpperCAmelCase_  (_UpperCAmelCase ,		unittest.TestCase ,		_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : int												=      True
    lowerCamelCase  : Any												=      (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
    lowerCamelCase  : List[str]												=      (FlaxMarianMTModel,) if is_flax_available() else ()







    def 				lowercase_    (							self     )    ->					Tuple:
         __lowerCamelCase       :     Any        =		FlaxMarianModelTester(self     )







    def 				lowercase_    (							self     )    ->					str:
         __lowerCamelCase    ,	__lowerCamelCase       :     Any        =		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					int:
         __lowerCamelCase    ,	__lowerCamelCase       :     Optional[Any]        =		self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self     )    ->					Optional[int]:
         __lowerCamelCase    ,	__lowerCamelCase       :     Dict        =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__     ):
                   __lowerCamelCase       :     str        =		self._prepare_for_class(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )
                   __lowerCamelCase       :     Any        =		model_class(SCREAMING_SNAKE_CASE_     )

                   @jax.jit
                   def encode_jitted(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=None      ,					**SCREAMING_SNAKE_CASE_     ):
                        return model.encode(input_ids=SCREAMING_SNAKE_CASE_      ,					attention_mask=SCREAMING_SNAKE_CASE_     )

                   with self.subTest('JIT Enabled'     ):
                        __lowerCamelCase       :     int        =		encode_jitted(**SCREAMING_SNAKE_CASE_     ).to_tuple()

                   with self.subTest('JIT Disabled'     ):
                        with jax.disable_jit():
                             __lowerCamelCase       :     Any        =		encode_jitted(**SCREAMING_SNAKE_CASE_     ).to_tuple()

                   self.assertEqual(len(SCREAMING_SNAKE_CASE_     )      ,					len(SCREAMING_SNAKE_CASE_     )     )
                   for jitted_output, output in zip(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ):
                        self.assertEqual(jitted_output.shape      ,					output.shape     )







    def 				lowercase_    (							self     )    ->					List[Any]:
         __lowerCamelCase    ,	__lowerCamelCase       :     Dict        =		self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__     ):
                   __lowerCamelCase       :     Union[str, Any]        =		model_class(SCREAMING_SNAKE_CASE_     )
                   __lowerCamelCase       :     Union[str, Any]        =		model.encode(inputs_dict['input_ids']      ,					inputs_dict['attention_mask']     )

                   __lowerCamelCase       :     List[Any]        =		{
                       'decoder_input_ids': inputs_dict['decoder_input_ids'],
                       'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                       'encoder_outputs': encoder_outputs,
                   }

                   @jax.jit
                   def decode_jitted(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ):
                        return model.decode(
                            decoder_input_ids=SCREAMING_SNAKE_CASE_      ,					decoder_attention_mask=SCREAMING_SNAKE_CASE_      ,					encoder_outputs=SCREAMING_SNAKE_CASE_      ,					)

                   with self.subTest('JIT Enabled'     ):
                        __lowerCamelCase       :     Dict        =		decode_jitted(**SCREAMING_SNAKE_CASE_     ).to_tuple()

                   with self.subTest('JIT Disabled'     ):
                        with jax.disable_jit():
                             __lowerCamelCase       :     Optional[int]        =		decode_jitted(**SCREAMING_SNAKE_CASE_     ).to_tuple()

                   self.assertEqual(len(SCREAMING_SNAKE_CASE_     )      ,					len(SCREAMING_SNAKE_CASE_     )     )
                   for jitted_output, output in zip(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ):
                        self.assertEqual(jitted_output.shape      ,					output.shape     )







    @slow
    def 				lowercase_    (							self     )    ->					Tuple:
         for model_class_name in self.all_model_classes:
              __lowerCamelCase       :     Any        =		model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'     )
              # FlaxMarianForSequenceClassification expects eos token in input_ids
              __lowerCamelCase       :     int        =		np.ones((1, 1)     ) * model.config.eos_token_id
              __lowerCamelCase       :     List[Any]        =		model(SCREAMING_SNAKE_CASE_     )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_     )







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    lowerCamelCase  : List[str]												=      None
    lowerCamelCase  : Union[str, Any]												=      None







    @classmethod
    def 				lowercase_    (							cls     )    ->					None:
         __lowerCamelCase       :     Optional[Any]        =		f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
         return cls







    @cached_property
    def 				lowercase_    (							self     )    ->					List[Any]:
         return MarianTokenizer.from_pretrained(self.model_name     )







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







    @cached_property
    def 				lowercase_    (							self     )    ->					List[str]:
         __lowerCamelCase       :     FlaxMarianMTModel        =		FlaxMarianMTModel.from_pretrained(self.model_name     )
         self.assertEqual(model.config.decoder_start_token_id      ,					model.config.pad_token_id     )
         return model







    def 				lowercase_    (							self      ,					**SCREAMING_SNAKE_CASE_     )    ->					List[Any]:
         __lowerCamelCase       :     Optional[Any]        =		self.translate_src_text(**SCREAMING_SNAKE_CASE_     )
         self.assertListEqual(self.expected_text      ,					SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					**SCREAMING_SNAKE_CASE_     )    ->					Any:
         __lowerCamelCase       :     Dict        =		self.tokenizer(self.src_text      ,					padding=SCREAMING_SNAKE_CASE_      ,					return_tensors='np'      ,					**SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     List[Any]        =		self.model.generate(
             model_inputs.input_ids      ,					attention_mask=model_inputs.attention_mask      ,					num_beams=2      ,					max_length=1_28      ,					).sequences
         __lowerCamelCase       :     List[Any]        =		self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      ,					skip_special_tokens=SCREAMING_SNAKE_CASE_     )
         return generated_words







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : List[Any]												=      'en'
    lowerCamelCase  : Optional[Any]												=      'fr'
    lowerCamelCase  : List[str]												=      [
        'I am a small frog.',
        'Now I can forget the 100 words of german that I know.',
    ]
    lowerCamelCase  : Any												=      [
        'Je suis une petite grenouille.',
        'Maintenant, je peux oublier les 100 mots d\'allemand que je connais.',
    ]







    @slow
    def 				lowercase_    (							self     )    ->					str:
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Any												=      'fr'
    lowerCamelCase  : Union[str, Any]												=      'en'
    lowerCamelCase  : Any												=      [
        'Donnez moi le micro.',
        'Tom et Mary étaient assis à une table.',  # Accents
    ]
    lowerCamelCase  : Dict												=      [
        'Give me the microphone.',
        'Tom and Mary were sitting at a table.',
    ]







    @slow
    def 				lowercase_    (							self     )    ->					Dict:
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Optional[int]												=      'mt'
    lowerCamelCase  : Tuple												=      'en'
    lowerCamelCase  : Dict												=      ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
    lowerCamelCase  : Optional[Any]												=      ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']







    @slow
    def 				lowercase_    (							self     )    ->					Union[str, Any]:
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : List[str]												=      'en'
    lowerCamelCase  : str												=      'de'
    lowerCamelCase  : str												=      [
        'I am a small frog.',
        'Now I can forget the 100 words of german that I know.',
        'Tom asked his teacher for advice.',
        'That\'s how I would do it.',
        'Tom really admired Mary\'s courage.',
        'Turn around and close your eyes.',
    ]
    lowerCamelCase  : Any												=      [
        'Ich bin ein kleiner Frosch.',
        'Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.',
        'Tom bat seinen Lehrer um Rat.',
        'So würde ich das machen.',
        'Tom bewunderte Marias Mut wirklich.',
        'Drehen Sie sich um und schließen Sie die Augen.',
    ]







    @slow
    def 				lowercase_    (							self     )    ->					Any:
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






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







    @slow
    def 				lowercase_    (							self     )    ->					Optional[int]:
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Any												=      'ru'
    lowerCamelCase  : int												=      'fr'
    lowerCamelCase  : List[str]												=      ['Он показал мне рукопись своей новой пьесы.']
    lowerCamelCase  : Optional[Any]												=      ['Il m\'a montré le manuscrit de sa nouvelle pièce.']







    @slow
    def 				lowercase_    (							self     )    ->					Optional[Any]:
         self._assert_generated_batch_equal_expected()







@require_flax
@require_sentencepiece
@require_tokenizers
class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : Any												=      'en'
    lowerCamelCase  : List[Any]												=      'ROMANCE'
    lowerCamelCase  : Union[str, Any]												=      [
        '>>fr<< Don\'t spend so much time watching TV.',
        '>>pt<< Your message has been sent.',
        '>>es<< He\'s two years older than me.',
    ]
    lowerCamelCase  : Optional[Any]												=      [
        'Ne passez pas autant de temps à regarder la télé.',
        'A sua mensagem foi enviada.',
        'Es dos años más viejo que yo.',
    ]







    @slow
    def 				lowercase_    (							self     )    ->					Optional[Any]:
         self._assert_generated_batch_equal_expected()










'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


A__		: int												=  logging.get_logger(__name__)

A__		: Union[str, Any]												=  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

A__		: int												=  {
    """vocab_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json""",
    },
}

A__		: int												=  {
    """microsoft/mpnet-base""": 512,
}

A__		: Optional[Any]												=  {
    """microsoft/mpnet-base""": {"""do_lower_case""": True},
}





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : int												=      VOCAB_FILES_NAMES
    lowerCamelCase  : int												=      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  : List[Any]												=      PRETRAINED_INIT_CONFIGURATION
    lowerCamelCase  : int												=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  : Tuple												=      MPNetTokenizer
    lowerCamelCase  : List[Any]												=      ['input_ids', 'attention_mask']







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="</s>"      ,					SCREAMING_SNAKE_CASE_="<s>"      ,					SCREAMING_SNAKE_CASE_="[UNK]"      ,					SCREAMING_SNAKE_CASE_="<pad>"      ,					SCREAMING_SNAKE_CASE_="<mask>"      ,					SCREAMING_SNAKE_CASE_=True      ,					SCREAMING_SNAKE_CASE_=None      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Any:
         super().__init__(
             SCREAMING_SNAKE_CASE_      ,					tokenizer_file=SCREAMING_SNAKE_CASE_      ,					do_lower_case=SCREAMING_SNAKE_CASE_      ,					bos_token=SCREAMING_SNAKE_CASE_      ,					eos_token=SCREAMING_SNAKE_CASE_      ,					sep_token=SCREAMING_SNAKE_CASE_      ,					cls_token=SCREAMING_SNAKE_CASE_      ,					unk_token=SCREAMING_SNAKE_CASE_      ,					pad_token=SCREAMING_SNAKE_CASE_      ,					mask_token=SCREAMING_SNAKE_CASE_      ,					tokenize_chinese_chars=SCREAMING_SNAKE_CASE_      ,					strip_accents=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)

         __lowerCamelCase       :     Union[str, Any]        =		json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
         if (
             pre_tok_state.get('lowercase'      ,					SCREAMING_SNAKE_CASE_     ) != do_lower_case
             or pre_tok_state.get('strip_accents'      ,					SCREAMING_SNAKE_CASE_     ) != strip_accents
         ):
              __lowerCamelCase       :     List[Any]        =		getattr(SCREAMING_SNAKE_CASE_      ,					pre_tok_state.pop('type'     )     )
              __lowerCamelCase       :     List[str]        =		do_lower_case
              __lowerCamelCase       :     str        =		strip_accents
              __lowerCamelCase       :     Union[str, Any]        =		pre_tok_class(**SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     Tuple        =		do_lower_case







    @property
    def 				lowercase_    (							self     )    ->					str:
         if self._mask_token is None:
              if self.verbose:
                   logger.error('Using mask_token, but it is not set yet.'     )
              return None
         return str(self._mask_token     )







    @mask_token.setter
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Any        =		AddedToken(SCREAMING_SNAKE_CASE_      ,					lstrip=SCREAMING_SNAKE_CASE_      ,					rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) else value
         __lowerCamelCase       :     int        =		value







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=None     )    ->					Union[str, Any]:
         __lowerCamelCase       :     Dict        =		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
              return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     List[Any]        =		[self.sep_token_id]
         __lowerCamelCase       :     List[Any]        =		[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep     ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					Tuple[str]:
         __lowerCamelCase       :     Optional[Any]        =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_     )
         return tuple(SCREAMING_SNAKE_CASE_     )






