




'''simple docstring'''


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
 from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor







class      SCREAMING_SNAKE_CASE    :






      def __init__( self   :  str						,						lowercase__   :  Dict						,						lowercase__   :  Union[str, Any]=13						,						lowercase__   :  List[Any]=32						,						lowercase__   :  int=3						,						lowercase__   :  int=4						,						lowercase__   :  int=[10, 20, 30, 40]						,						lowercase__   :  Optional[Any]=[2, 2, 3, 2]						,						lowercase__   :  str=True						,						lowercase__   :  Any=True						,						lowercase__   :  Optional[int]=37						,						lowercase__   :  Optional[Any]="gelu"						,						lowercase__   :  List[Any]=10						,						lowercase__   :  Tuple=0.02						,						lowercase__   :  List[Any]=["stage2", "stage3", "stage4"]						,						lowercase__   :  List[str]=[2, 3, 4]						,						lowercase__   :  List[Any]=None						,						):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  parent
           a_     :							int     						=  batch_size
           a_     :							int     						=  image_size
           a_     :							List[str]     						=  num_channels
           a_     :							Optional[Any]     						=  num_stages
           a_     :							Optional[Any]     						=  hidden_sizes
           a_     :							Dict     						=  depths
           a_     :							int     						=  is_training
           a_     :							Tuple     						=  use_labels
           a_     :							Optional[int]     						=  intermediate_size
           a_     :							Union[str, Any]     						=  hidden_act
           a_     :							str     						=  num_labels
           a_     :							Any     						=  initializer_range
           a_     :							List[str]     						=  out_features
           a_     :							List[Any]     						=  out_indices
           a_     :							Any     						=  scope






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Any     						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

           a_     :							Optional[Any]     						=  None
           if self.use_labels:
                a_     :							int     						=  ids_tensor([self.batch_size]						,						self.num_labels       )

           a_     :							Any     						=  self.get_config()
           return config, pixel_values, labels






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return ConvNextConfig(
               num_channels=self.num_channels						,						hidden_sizes=self.hidden_sizes						,						depths=self.depths						,						num_stages=self.num_stages						,						hidden_act=self.hidden_act						,						is_decoder=lowercase__						,						initializer_range=self.initializer_range						,						out_features=self.out_features						,						out_indices=self.out_indices						,						num_labels=self.num_labels						,						)






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  List[str]						,						lowercase__   :  Dict						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							Dict     						=  ConvNextModel(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							Any     						=  model(lowercase__       )
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape						,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,						)






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  int						,						lowercase__   :  str						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  ConvNextForImageClassification(lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							str     						=  model(lowercase__						,						labels=lowercase__       )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)       )






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  str						,						lowercase__   :  Tuple						,						lowercase__   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  ConvNextBackbone(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							List[Any]     						=  model(lowercase__       )

           # verify hidden states
           self.parent.assertEqual(len(result.feature_maps       )						,						len(config.out_features       )       )
           self.parent.assertListEqual(list(result.feature_maps[0].shape       )						,						[self.batch_size, self.hidden_sizes[1], 4, 4]       )

           # verify channels
           self.parent.assertEqual(len(model.channels       )						,						len(config.out_features       )       )
           self.parent.assertListEqual(model.channels						,						config.hidden_sizes[1:]       )

           # verify backbone works with out_features=None
           a_     :							Tuple     						=  None
           a_     :							str     						=  ConvNextBackbone(config=lowercase__       )
           model.to(lowercase__       )
           model.eval()
           a_     :							str     						=  model(lowercase__       )

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps       )						,						1       )
           self.parent.assertListEqual(list(result.feature_maps[0].shape       )						,						[self.batch_size, self.hidden_sizes[-1], 1, 1]       )

           # verify channels
           self.parent.assertEqual(len(model.channels       )						,						1       )
           self.parent.assertListEqual(model.channels						,						[config.hidden_sizes[-1]]       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							int     						=  self.prepare_config_and_inputs()
           a_						,     a_						,     a_     :							str     						=  config_and_inputs
           a_     :							Dict     						=  {"""pixel_values""": pixel_values}
           return config, inputs_dict







@require_torch
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Optional[int]   							=					(
          (
              ConvNextModel,
              ConvNextForImageClassification,
              ConvNextBackbone,
          )
          if is_torch_available()
          else ()
      )
      __magic_name__  :       Optional[int]   							=					(
          {'''feature-extraction''': ConvNextModel, '''image-classification''': ConvNextForImageClassification}
          if is_torch_available()
          else {}
      )

      __magic_name__  :       Dict   							=					True
      __magic_name__  :       str   							=					False
      __magic_name__  :       Tuple   							=					False
      __magic_name__  :       Dict   							=					False
      __magic_name__  :       Union[str, Any]   							=					False






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  ConvNextModelTester(self       )
           a_     :							int     						=  ConfigTester(self						,						config_class=lowercase__						,						has_text_modality=lowercase__						,						hidden_size=37       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return






      @unittest.skip(reason="""ConvNext does not use inputs_embeds"""       )
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           pass






      @unittest.skip(reason="""ConvNext does not support input and output embeddings"""       )
      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           pass






      @unittest.skip(reason="""ConvNext does not use feedforward chunking"""       )
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           pass






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_						,     a_     :							int     						=  self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                a_     :							Dict     						=  model_class(lowercase__       )
                a_     :							Any     						=  inspect.signature(model.forward       )
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                a_     :							Tuple     						=  [*signature.parameters.keys()]

                a_     :							List[Any]     						=  ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]						,						lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*lowercase__       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           def check_hidden_states_output(lowercase__   :  Union[str, Any]						,						lowercase__   :  Any						,						lowercase__   :  List[str]       ):
                a_     :							List[str]     						=  model_class(lowercase__       )
                model.to(lowercase__       )
                model.eval()

                with torch.no_grad():
                     a_     :							Union[str, Any]     						=  model(**self._prepare_for_class(lowercase__						,						lowercase__       )       )

                a_     :							List[Any]     						=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                a_     :							List[str]     						=  self.model_tester.num_stages
                self.assertEqual(len(lowercase__       )						,						expected_num_stages + 1       )

                # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]       )						,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,						)

           a_						,     a_     :							Union[str, Any]     						=  self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                a_     :							List[Any]     						=  True
                check_hidden_states_output(lowercase__						,						lowercase__						,						lowercase__       )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                a_     :							Any     						=  True

                check_hidden_states_output(lowercase__						,						lowercase__						,						lowercase__       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase__       )






      @slow
      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_     :							Optional[Any]     						=  ConvNextModel.from_pretrained(lowercase__       )
                self.assertIsNotNone(lowercase__       )



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Union[str, Any]     						=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
     return image







@require_torch
@require_vision
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      @cached_property
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224"""       ) if is_vision_available() else None






      @slow
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224"""       ).to(lowercase__       )

           a_     :							List[Any]     						=  self.default_image_processor
           a_     :							List[str]     						=  prepare_img()
           a_     :							List[Any]     						=  image_processor(images=lowercase__						,						return_tensors="""pt"""       ).to(lowercase__       )

           # forward pass
           with torch.no_grad():
                a_     :							Tuple     						=  model(**lowercase__       )

           # verify the logits
           a_     :							Dict     						=  torch.Size((1, 1000)       )
           self.assertEqual(outputs.logits.shape						,						lowercase__       )

           a_     :							List[str]     						=  torch.tensor([-0.0260, -0.4739, 0.1911]       ).to(lowercase__       )

           self.assertTrue(torch.allclose(outputs.logits[0, :3]						,						lowercase__						,						atol=1e-4       )       )







@require_torch
class      SCREAMING_SNAKE_CASE    (						unittest.TestCase							,      snake_case_				):
      __magic_name__  :       List[str]   							=					(ConvNextBackbone,) if is_torch_available() else ()
      __magic_name__  :       Optional[Any]   							=					ConvNextConfig

      __magic_name__  :       Union[str, Any]   							=					False






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  ConvNextModelTester(self       )






'''simple docstring'''


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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_      :  Optional[int]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  str				     =  '▁'

lowerCAmelCase_      :  List[str]				     =  {'vocab_file': 'sentencepiece.bpe.model'}

lowerCAmelCase_      :  Optional[Any]				     =  {
    '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'
        ),
    }
}

lowerCAmelCase_      :  List[str]				     =  {
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
lowerCAmelCase_      :  Optional[Any]				     =  ['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      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Any   							=					VOCAB_FILES_NAMES
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       Optional[Any]   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       Union[str, Any]   							=					['''input_ids''', '''attention_mask''']

      __magic_name__  :       List[int]   							=					[]
      __magic_name__  :       List[int]   							=					[]






      def __init__( self   :  int						,						lowercase__   :  Tuple						,						lowercase__   :  Optional[int]=None						,						lowercase__   :  str=None						,						lowercase__   :  Dict="</s>"						,						lowercase__   :  Tuple="</s>"						,						lowercase__   :  Any="<s>"						,						lowercase__   :  Tuple="<unk>"						,						lowercase__   :  List[Any]="<pad>"						,						lowercase__   :  Dict="<mask>"						,						lowercase__   :  Optional[Dict[str, Any]] = None						,						**lowercase__   :  Tuple						,						):


           '''simple docstring'''

           a_     :							Any     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else mask_token

           a_     :							Union[str, Any]     						=  {} if sp_model_kwargs is None else sp_model_kwargs

           a_     :							int     						=  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__(
               src_lang=lowercase__						,						tgt_lang=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

           a_     :							Union[str, Any]     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  vocab_file

           # Original fairseq vocab and spm vocab must be "aligned":
           # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
           # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
           # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
           # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

           # Mimic fairseq token-to-id alignment for the first 4 token
           a_     :							Tuple     						=  {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

           # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
           a_     :							List[str]     						=  1

           a_     :							List[Any]     						=  len(self.sp_model       )
           a_     :							List[str]     						=  {
               code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase__       )
           }
           a_     :							str     						=  {v: k for k, v in self.lang_code_to_id.items()}
           a_     :							Any     						=  len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

           self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
           a_     :							int     						=  {v: k for k, v in self.fairseq_tokens_to_ids.items()}

           a_     :							List[Any]     						=  src_lang if src_lang is not None else """en_XX"""
           a_     :							Any     						=  self.lang_code_to_id[self._src_lang]
           a_     :							Union[str, Any]     						=  tgt_lang
           self.set_src_lang_special_tokens(self._src_lang       )






      @property
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token






      @property
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           return self._src_lang






      @src_lang.setter
      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Tuple     						=  new_src_lang
           self.set_src_lang_special_tokens(self._src_lang       )






      def __getstate__( self   :  Any       ):


           '''simple docstring'''

           a_     :							str     						=  self.__dict__.copy()
           a_     :							Dict     						=  None
           return state






      def __setstate__( self   :  Optional[Any]						,						lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  d

           # for backward compatibility
           if not hasattr(self						,						"""sp_model_kwargs"""       ):
                a_     :							Tuple     						=  {}

           a_     :							int     						=  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
           self.sp_model.Load(self.vocab_file       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Dict     						=  {self.convert_ids_to_tokens(lowercase__       ): i for i in range(self.vocab_size       )}
           vocab.update(self.added_tokens_encoder       )
           return vocab






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           return self.sp_model.encode(lowercase__						,						out_type=lowercase__       )






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str       ):


           '''simple docstring'''

           if token in self.fairseq_tokens_to_ids:
                return self.fairseq_tokens_to_ids[token]
           a_     :							List[str]     						=  self.sp_model.PieceToId(lowercase__       )

           # Need to return unknown token if the SP model returned 0
           return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  int       ):


           '''simple docstring'''

           if index in self.fairseq_ids_to_tokens:
                return self.fairseq_ids_to_tokens[index]
           return self.sp_model.IdToPiece(index - self.fairseq_offset       )






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  []
           a_     :							List[str]     						=  """"""
           a_     :							Dict     						=  False
           for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                     if not prev_is_special:
                          out_string += " "
                     out_string += self.sp_model.decode(lowercase__       ) + token
                     a_     :							str     						=  True
                     a_     :							Optional[Any]     						=  []
                else:
                     current_sub_tokens.append(lowercase__       )
                     a_     :							Union[str, Any]     						=  False
           out_string += self.sp_model.decode(lowercase__       )
           return out_string.strip()






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           if not os.path.isdir(lowercase__       ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
                return
           a_     :							Dict     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )

           if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase__       ) and os.path.isfile(self.vocab_file       ):
                copyfile(self.vocab_file						,						lowercase__       )
           elif not os.path.isfile(self.vocab_file       ):
                with open(lowercase__						,						"""wb"""       ) as fi:
                     a_     :							Dict     						=  self.sp_model.serialized_model_proto()
                     fi.write(lowercase__       )

           return (out_vocab_file,)






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None						,						lowercase__   :  bool = False       ):


           '''simple docstring'''

           if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=lowercase__						,						token_ids_a=lowercase__						,						already_has_special_tokens=lowercase__       )

           a_     :							Union[str, Any]     						=  [1] * len(self.prefix_tokens       )
           a_     :							List[Any]     						=  [1] * len(self.suffix_tokens       )
           if token_ids_a is None:
                return prefix_ones + ([0] * len(lowercase__       )) + suffix_ones
           return prefix_ones + ([0] * len(lowercase__       )) + ([0] * len(lowercase__       )) + suffix_ones






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''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 							lowercase_						( self   :  Any						,						lowercase__   :  Optional[int]						,						lowercase__   :  str						,						lowercase__   :  Optional[str]						,						lowercase__   :  Optional[str]						,						**lowercase__   :  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_     :							Union[str, Any]     						=  src_lang
           a_     :							Dict     						=  self(lowercase__						,						add_special_tokens=lowercase__						,						return_tensors=lowercase__						,						**lowercase__       )
           a_     :							Tuple     						=  self.convert_tokens_to_ids(lowercase__       )
           a_     :							Optional[Any]     						=  tgt_lang_id
           return inputs






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  List[str]						,						lowercase__   :  str = "en_XX"						,						lowercase__   :  Optional[List[str]] = None						,						lowercase__   :  str = "ro_RO"						,						**lowercase__   :  Union[str, Any]						,						):


           '''simple docstring'''

           a_     :							str     						=  src_lang
           a_     :							Dict     						=  tgt_lang
           return super().prepare_seqaseq_batch(lowercase__						,						lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang       )






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Any     						=  self.lang_code_to_id[src_lang]
           a_     :							Dict     						=  [self.cur_lang_code_id]
           a_     :							Any     						=  [self.eos_token_id]






      def 							lowercase_						( self   :  Any						,						lowercase__   :  str       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.lang_code_to_id[tgt_lang]
           a_     :							str     						=  [self.cur_lang_code_id]
           a_     :							Union[str, Any]     						=  [self.eos_token_id]
