



'''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
  lowercase_			=  "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 lowerCAmelCase  (__A  ,     __A  ,     __A=None  ,     __A=None  ,     __A=None  ,     __A=None  ,     __A=None  ,     __A=None  ,     ):



     """simple docstring"""



     if attention_mask is None:
          _a     =  np.where(input_ids != config.pad_token_id  ,     1  ,     0)
     if decoder_attention_mask is None:
          _a     =  np.where(decoder_input_ids != config.pad_token_id  ,     1  ,     0)
     if head_mask is None:
          _a     =  np.ones((config.encoder_layers, config.encoder_attention_heads))
     if decoder_head_mask is None:
          _a     =  np.ones((config.decoder_layers, config.decoder_attention_heads))
     if cross_attn_head_mask is None:
          _a     =  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 __A      :



 '''simple docstring'''







 def __init__(self			,       A			,       A=13			,       A=7			,       A=True			,       A=False			,       A=99			,       A=16			,       A=2			,       A=4			,       A=4			,       A="gelu"			,       A=0.1			,       A=0.1			,       A=32			,       A=2			,       A=1			,       A=0			,       A=0.02			,       )   ->					Union[str, Any]:


      """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_act
      _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
      _a     =  initializer_range



 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,       self.vocab_size	)			,       3			,       self.vocab_size	)
      _a     =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,       dtype=np.intaa	))			,       -1	)

      _a     =  shift_tokens_right(A			,       1			,       2	)

      _a     =  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=A			,       )
      _a     =  prepare_marian_inputs_dict(A			,       A			,       A	)
      return config, inputs_dict



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _a  ,   _a     =  self.prepare_config_and_inputs()
      return config, inputs_dict



 def  a__  (self			,       A			,       A			,       A	)   ->					str:


      """simple docstring"""



      _a     =  20
      _a     =  model_class_name(A	)

      _a     =  model.encode(inputs_dict['''input_ids''']	)

      _a  ,   _a     =  (
          inputs_dict['''decoder_input_ids'''],
          inputs_dict['''decoder_attention_mask'''],
      )

      _a     =  model.init_cache(decoder_input_ids.shape[0]			,       A			,       A	)
      _a     =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,       dtype='''i4'''	)

      _a     =  jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]			,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,       )
      _a     =  model.decode(
          decoder_input_ids[:, :-1]			,       A			,       decoder_attention_mask=A			,       past_key_values=A			,       decoder_position_ids=A			,       )

      _a     =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,       dtype='''i4'''	)
      _a     =  model.decode(
          decoder_input_ids[:, -1:]			,       A			,       decoder_attention_mask=A			,       past_key_values=outputs_cache.past_key_values			,       decoder_position_ids=A			,       )

      _a     =  model.decode(A			,       A	)

      _a     =  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  a__  (self			,       A			,       A			,       A	)   ->					Optional[int]:


      """simple docstring"""



      _a     =  20
      _a     =  model_class_name(A	)

      _a     =  model.encode(inputs_dict['''input_ids''']	)

      _a  ,   _a     =  (
          inputs_dict['''decoder_input_ids'''],
          inputs_dict['''decoder_attention_mask'''],
      )

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

      _a     =  model.init_cache(decoder_input_ids.shape[0]			,       A			,       A	)
      _a     =  jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]			,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,       )

      _a     =  model.decode(
          decoder_input_ids[:, :-1]			,       A			,       decoder_attention_mask=A			,       past_key_values=A			,       decoder_position_ids=A			,       )
      _a     =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,       dtype='''i4'''	)
      _a     =  model.decode(
          decoder_input_ids[:, -1:]			,       A			,       past_key_values=outputs_cache.past_key_values			,       decoder_attention_mask=A			,       decoder_position_ids=A			,       )

      _a     =  model.decode(A			,       A			,       decoder_attention_mask=A	)

      _a     =  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 __A      (  A       ,       unittest.TestCase       ,       A						):



 '''simple docstring'''







 __lowerCamelCase   :	Any										=	True
 __lowerCamelCase   :	List[Any]										=	(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
 __lowerCamelCase   :	Dict										=	(FlaxMarianMTModel,) if is_flax_available() else ()



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


      """simple docstring"""



      _a     =  FlaxMarianModelTester(self	)



 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a  ,   _a     =  self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(A			,       A			,       A	)



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


      """simple docstring"""



      _a  ,   _a     =  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(A			,       A			,       A	)



 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      _a  ,   _a     =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__	):
                _a     =  self._prepare_for_class(A			,       A	)
                _a     =  model_class(A	)

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

                with self.subTest('''JIT Enabled'''	):
                     _a     =  encode_jitted(**A	).to_tuple()

                with self.subTest('''JIT Disabled'''	):
                     with jax.disable_jit():
                          _a     =  encode_jitted(**A	).to_tuple()

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



 def  a__  (self	)   ->					str:


      """simple docstring"""



      _a  ,   _a     =  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__	):
                _a     =  model_class(A	)
                _a     =  model.encode(inputs_dict['''input_ids''']			,       inputs_dict['''attention_mask''']	)

                _a     =  {
                    '''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(A			,       A			,       A	):
                     return model.decode(
                         decoder_input_ids=A			,       decoder_attention_mask=A			,       encoder_outputs=A			,       )

                with self.subTest('''JIT Enabled'''	):
                     _a     =  decode_jitted(**A	).to_tuple()

                with self.subTest('''JIT Disabled'''	):
                     with jax.disable_jit():
                          _a     =  decode_jitted(**A	).to_tuple()

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



 @slow
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      for model_class_name in self.all_model_classes:
           _a     =  model_class_name.from_pretrained('''Helsinki-NLP/opus-mt-en-de'''	)
           # FlaxMarianForSequenceClassification expects eos token in input_ids
           _a     =  np.ones((1, 1)	) * model.config.eos_token_id
           _a     =  model(A	)
           self.assertIsNotNone(A	)






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



 '''simple docstring'''







 __lowerCamelCase   :	Tuple										=	None
 __lowerCamelCase   :	int										=	None



 @classmethod
 def  a__  (cls	)   ->					None:


      """simple docstring"""



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



 @cached_property
 def  a__  (self	)   ->					int:


      """simple docstring"""



      return MarianTokenizer.from_pretrained(self.model_name	)



 @property
 def  a__  (self	)   ->					int:


      """simple docstring"""



      return self.tokenizer.eos_token_id



 @cached_property
 def  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _a     =  FlaxMarianMTModel.from_pretrained(self.model_name	)
      self.assertEqual(model.config.decoder_start_token_id			,       model.config.pad_token_id	)
      return model



 def  a__  (self			,       **A	)   ->					Tuple:


      """simple docstring"""



      _a     =  self.translate_src_text(**A	)
      self.assertListEqual(self.expected_text			,       A	)



 def  a__  (self			,       **A	)   ->					Dict:


      """simple docstring"""



      _a     =  self.tokenizer(self.src_text			,       padding=A			,       return_tensors='''np'''			,       **A	)
      _a     =  self.model.generate(
          model_inputs.input_ids			,       attention_mask=model_inputs.attention_mask			,       num_beams=2			,       max_length=128			,       ).sequences
      _a     =  self.tokenizer.batch_decode(A			,       skip_special_tokens=A	)
      return generated_words






@require_flax
@require_sentencepiece
@require_tokenizers
class __A      (  A						):



 '''simple docstring'''







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



 @slow
 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class __A      (  A						):



 '''simple docstring'''







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



 @slow
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class __A      (  A						):



 '''simple docstring'''







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



 @slow
 def  a__  (self	)   ->					Any:


      """simple docstring"""



      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	Optional[int]										=	'en'
 __lowerCamelCase   :	Union[str, Any]										=	'de'
 __lowerCamelCase   :	Optional[Any]										=	[
     '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   :	Optional[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  a__  (self	)   ->					Dict:


      """simple docstring"""



      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	str										=	'en'
 __lowerCamelCase   :	List[Any]										=	'zh'
 __lowerCamelCase   :	Union[str, Any]										=	['My name is Wolfgang and I live in Berlin']
 __lowerCamelCase   :	Optional[int]										=	['我叫沃尔夫冈 我住在柏林']



 @slow
 def  a__  (self	)   ->					Dict:


      """simple docstring"""



      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class __A      (  A						):



 '''simple docstring'''







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



 @slow
 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      self._assert_generated_batch_equal_expected()






@require_flax
@require_sentencepiece
@require_tokenizers
class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	List[str]										=	'en'
 __lowerCamelCase   :	Optional[Any]										=	'ROMANCE'
 __lowerCamelCase   :	int										=	[
     '>>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[int]										=	[
     'Ne passez pas autant de temps à regarder la télé.',
     'A sua mensagem foi enviada.',
     'Es dos años más viejo que yo.',
 ]



 @slow
 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



      self._assert_generated_batch_equal_expected()








'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbartaa import MBartaaTokenizer
else:
  lowercase_			=  None


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase_			=  {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

lowercase_			=  {
    "facebook/mbart-large-50-one-to-many-mmt": 1_024,
}

# fmt: off
lowercase_			=  ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]






class __A      (  A						):



 '''simple docstring'''







 __lowerCamelCase   :	List[str]										=	VOCAB_FILES_NAMES
 __lowerCamelCase   :	int										=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase   :	Optional[int]										=	PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase   :	Any										=	['input_ids', 'attention_mask']
 __lowerCamelCase   :	Optional[Any]										=	MBartaaTokenizer

 __lowerCamelCase   :	List[int]										=	[]
 __lowerCamelCase   :	List[int]										=	[]



 def __init__(self			,       A=None			,       A=None			,       A=None			,       A=None			,       A="</s>"			,       A="</s>"			,       A="<s>"			,       A="<unk>"			,       A="<pad>"			,       A="<mask>"			,       **A			,       )   ->					List[str]:


      """simple docstring"""



      _a     =  AddedToken(A			,       lstrip=A			,       rstrip=A	) if isinstance(A			,       A	) else mask_token

      _a     =  kwargs.get('''additional_special_tokens'''			,       []	)
      kwargs["additional_special_tokens"] += [
          code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          A			,       src_lang=A			,       tgt_lang=A			,       tokenizer_file=A			,       eos_token=A			,       sep_token=A			,       cls_token=A			,       unk_token=A			,       pad_token=A			,       mask_token=A			,       **A			,       )

      _a     =  vocab_file
      _a     =  False if not self.vocab_file else True

      _a     =  {
          lang_code: self.convert_tokens_to_ids(A	) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      _a     =  src_lang if src_lang is not None else '''en_XX'''
      _a     =  tgt_lang
      _a     =  self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang	)



 @property
 def  a__  (self	)   ->					str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang	)



 def  a__  (self			,       A			,       A = None	)   ->					List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def  a__  (self			,       A			,       A = "en_XX"			,       A = None			,       A = "ro_RO"			,       **A			,       )   ->					BatchEncoding:


      """simple docstring"""



      _a     =  src_lang
      _a     =  tgt_lang
      return super().prepare_seqaseq_batch(A			,       A			,       **A	)



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


      """simple docstring"""



      return self.set_src_lang_special_tokens(self.src_lang	)



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


      """simple docstring"""



      return self.set_tgt_lang_special_tokens(self.tgt_lang	)



 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  processors.TemplateProcessing(
          single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str			,       pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str			,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,       self.prefix_tokens + self.suffix_tokens	)	)			,       )



 def  a__  (self			,       A	)   ->					None:


      """simple docstring"""



      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  [self.cur_lang_code_id]
      _a     =  [self.eos_token_id]

      _a     =  self.convert_ids_to_tokens(self.prefix_tokens	)
      _a     =  self.convert_ids_to_tokens(self.suffix_tokens	)

      _a     =  processors.TemplateProcessing(
          single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str			,       pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str			,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str			,       self.prefix_tokens + self.suffix_tokens	)	)			,       )



 def  a__  (self			,       A			,       A			,       A			,       A			,       **A	)   ->					Optional[int]:


      """simple docstring"""



      if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''	)
      _a     =  src_lang
      _a     =  self(A			,       add_special_tokens=A			,       return_tensors=A			,       **A	)
      _a     =  self.convert_tokens_to_ids(A	)
      _a     =  tgt_lang_id
      return inputs



 def  a__  (self			,       A			,       A = None	)   ->					Tuple[str]:


      """simple docstring"""



      if not self.can_save_slow_tokenizer:
           raise ValueError(
               '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
               '''tokenizer.'''	)

      if not os.path.isdir(A	):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
           return
      _a     =  os.path.join(
          A			,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

      if os.path.abspath(self.vocab_file	) != os.path.abspath(A	):
           copyfile(self.vocab_file			,       A	)

      return (out_vocab_file,)



