




'''simple docstring'''



import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
      from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor






class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A,							A=13,							A=32,							A=3,							A=4,							A=[10, 20, 30, 40],							A=[2, 2, 3, 2],							A=True,							A=True,							A=37,							A="gelu",							A=10,							A=0.02,							A=["stage2", "stage3", "stage4"],							A=[2, 3, 4],							A=None,							):







             '''simple docstring'''







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







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.get_config()

             return config, pixel_values, labels







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return ConvNextVaConfig(
                 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=A,							initializer_range=self.initializer_range,							out_features=self.out_features,							out_indices=self.out_indices,							num_labels=self.num_labels,							)







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						ConvNextVaModel(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      str    					=						model(A  )
             # 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        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						ConvNextVaForImageClassification(A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						model(A,							labels=A  )
             self.parent.assertEqual(result.logits.shape,							(self.batch_size, self.num_labels)  )







       def        UpperCamelCase_							(      self,							A,							A,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						ConvNextVaBackbone(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      int    					=						model(A  )

             # 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
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						ConvNextVaBackbone(config=A  )
             model.to(A  )
             model.eval()
             SCREAMING_SNAKE_CASE   :      List[str]    					=						model(A  )

             # 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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.prepare_config_and_inputs()
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						config_and_inputs
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						{'pixel_values': pixel_values}
             return config, inputs_dict







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						self.prepare_config_and_inputs()
             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Dict    					=						config_and_inputs
             SCREAMING_SNAKE_CASE   :      List[str]    					=						{'pixel_values': pixel_values, 'labels': labels}
             return config, inputs_dict






@require_torch
class 							_a				(			SCREAMING_SNAKE_CASE ,      SCREAMING_SNAKE_CASE ,      unittest.TestCase ):





       '''simple docstring'''






       A						:				Any        =						(
           (
               ConvNextVaModel,
               ConvNextVaForImageClassification,
               ConvNextVaBackbone,
           )
           if is_torch_available()
           else ()
       )
       A						:				List[str]        =						(
           {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
           if is_torch_available()
           else {}
       )

       A						:				Any        =						False
       A						:				Tuple        =						False
       A						:				Union[str, Any]        =						False
       A						:				Any        =						False
       A						:				List[Any]        =						False







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      int    					=						ConvNextVaModelTester(self  )
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ConfigTester(self,							config_class=A,							has_text_modality=A,							hidden_size=37  )







       def        UpperCamelCase_							(      self  ):







             '''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        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return







       @unittest.skip(reason='ConvNextV2 does not use inputs_embeds'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       @unittest.skip(reason='ConvNextV2 does not support input and output embeddings'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       @unittest.skip(reason='ConvNextV2 does not use feedforward chunking'  )
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             pass







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not self.model_tester.is_training:
                   return

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Any    					=						self.model_tester.prepare_config_and_inputs_with_labels()
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						True

                   if model_class.__name__ in [
                       *get_values(A  ),
                       *get_values(A  ),
                   ]:
                         continue

                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model_class(A  )
                   model.to(A  )
                   model.train()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self._prepare_for_class(A,							A,							return_labels=A  )
                   SCREAMING_SNAKE_CASE   :      int    					=						model(**A  ).loss
                   loss.backward()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if not self.model_tester.is_training:
                   return

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      List[Any]    					=						self.model_tester.prepare_config_and_inputs_with_labels()
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						False
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						True

                   if (
                       model_class.__name__
                       in [*get_values(A  ), *get_values(A  )]
                       or not model_class.supports_gradient_checkpointing
                   ):
                         continue

                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model_class(A  )
                   model.to(A  )
                   model.gradient_checkpointing_enable()
                   model.train()
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						self._prepare_for_class(A,							A,							return_labels=A  )
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						model(**A  ).loss
                   loss.backward()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE   :      int    					=						model_class(A  )
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						inspect.signature(model.forward  )
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   SCREAMING_SNAKE_CASE   :      Any    					=						[*signature.parameters.keys()]

                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						['pixel_values']
                   self.assertListEqual(arg_names[:1],							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             def check_hidden_states_output(A,							A,							A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						model_class(A  )
                   model.to(A  )
                   model.eval()

                   with torch.no_grad():
                         SCREAMING_SNAKE_CASE   :      List[Any]    					=						model(**self._prepare_for_class(A,							A  )  )

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

                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.model_tester.num_stages
                   self.assertEqual(len(A  ),							expected_num_stages + 1  )

                   # ConvNextV2'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],							)

             SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   SCREAMING_SNAKE_CASE   :      Any    					=						True
                   check_hidden_states_output(A,							A,							A  )

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

                   check_hidden_states_output(A,							A,							A  )







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*A  )







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						ConvNextVaModel.from_pretrained(A  )
                   self.assertIsNotNone(A  )




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
      return image






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





       '''simple docstring'''






       @cached_property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return AutoImageProcessor.from_pretrained('facebook/convnextv2-tiny-1k-224'  ) if is_vision_available() else None







       @slow
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						ConvNextVaForImageClassification.from_pretrained('facebook/convnextv2-tiny-1k-224'  ).to(A  )

             SCREAMING_SNAKE_CASE   :      int    					=						self.default_image_processor
             SCREAMING_SNAKE_CASE   :      Any    					=						prepare_img()
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						preprocessor(images=A,							return_tensors='pt'  ).to(A  )

             # forward pass
             with torch.no_grad():
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						model(**A  )

             # verify the logits
             SCREAMING_SNAKE_CASE   :      int    					=						torch.Size((1, 1_000)  )
             self.assertEqual(outputs.logits.shape,							A  )

             SCREAMING_SNAKE_CASE   :      str    					=						torch.tensor([0.99_96, 0.19_66, -0.43_86]  ).to(A  )
             self.assertTrue(torch.allclose(outputs.logits[0, :3],							A,							atol=1E-4  )  )










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_nllb import NllbTokenizer
else:
			UpperCAmelCase__      			=						None


UpperCAmelCase__      			=						logging.get_logger(__name__)


UpperCAmelCase__      			=						{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__      			=						{
    '''vocab_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''facebook/nllb-200-distilled-600M''': (
            '''https://huggingface.co/facebook/nllb-200-distilled-600M/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCAmelCase__      			=						{
    '''facebook/nllb-large-en-ro''': 1024,
    '''facebook/nllb-200-distilled-600M''': 1024,
}

# fmt: off
UpperCAmelCase__      			=						['''ace_Arab''', '''ace_Latn''', '''acm_Arab''', '''acq_Arab''', '''aeb_Arab''', '''afr_Latn''', '''ajp_Arab''', '''aka_Latn''', '''amh_Ethi''', '''apc_Arab''', '''arb_Arab''', '''ars_Arab''', '''ary_Arab''', '''arz_Arab''', '''asm_Beng''', '''ast_Latn''', '''awa_Deva''', '''ayr_Latn''', '''azb_Arab''', '''azj_Latn''', '''bak_Cyrl''', '''bam_Latn''', '''ban_Latn''', '''bel_Cyrl''', '''bem_Latn''', '''ben_Beng''', '''bho_Deva''', '''bjn_Arab''', '''bjn_Latn''', '''bod_Tibt''', '''bos_Latn''', '''bug_Latn''', '''bul_Cyrl''', '''cat_Latn''', '''ceb_Latn''', '''ces_Latn''', '''cjk_Latn''', '''ckb_Arab''', '''crh_Latn''', '''cym_Latn''', '''dan_Latn''', '''deu_Latn''', '''dik_Latn''', '''dyu_Latn''', '''dzo_Tibt''', '''ell_Grek''', '''eng_Latn''', '''epo_Latn''', '''est_Latn''', '''eus_Latn''', '''ewe_Latn''', '''fao_Latn''', '''pes_Arab''', '''fij_Latn''', '''fin_Latn''', '''fon_Latn''', '''fra_Latn''', '''fur_Latn''', '''fuv_Latn''', '''gla_Latn''', '''gle_Latn''', '''glg_Latn''', '''grn_Latn''', '''guj_Gujr''', '''hat_Latn''', '''hau_Latn''', '''heb_Hebr''', '''hin_Deva''', '''hne_Deva''', '''hrv_Latn''', '''hun_Latn''', '''hye_Armn''', '''ibo_Latn''', '''ilo_Latn''', '''ind_Latn''', '''isl_Latn''', '''ita_Latn''', '''jav_Latn''', '''jpn_Jpan''', '''kab_Latn''', '''kac_Latn''', '''kam_Latn''', '''kan_Knda''', '''kas_Arab''', '''kas_Deva''', '''kat_Geor''', '''knc_Arab''', '''knc_Latn''', '''kaz_Cyrl''', '''kbp_Latn''', '''kea_Latn''', '''khm_Khmr''', '''kik_Latn''', '''kin_Latn''', '''kir_Cyrl''', '''kmb_Latn''', '''kon_Latn''', '''kor_Hang''', '''kmr_Latn''', '''lao_Laoo''', '''lvs_Latn''', '''lij_Latn''', '''lim_Latn''', '''lin_Latn''', '''lit_Latn''', '''lmo_Latn''', '''ltg_Latn''', '''ltz_Latn''', '''lua_Latn''', '''lug_Latn''', '''luo_Latn''', '''lus_Latn''', '''mag_Deva''', '''mai_Deva''', '''mal_Mlym''', '''mar_Deva''', '''min_Latn''', '''mkd_Cyrl''', '''plt_Latn''', '''mlt_Latn''', '''mni_Beng''', '''khk_Cyrl''', '''mos_Latn''', '''mri_Latn''', '''zsm_Latn''', '''mya_Mymr''', '''nld_Latn''', '''nno_Latn''', '''nob_Latn''', '''npi_Deva''', '''nso_Latn''', '''nus_Latn''', '''nya_Latn''', '''oci_Latn''', '''gaz_Latn''', '''ory_Orya''', '''pag_Latn''', '''pan_Guru''', '''pap_Latn''', '''pol_Latn''', '''por_Latn''', '''prs_Arab''', '''pbt_Arab''', '''quy_Latn''', '''ron_Latn''', '''run_Latn''', '''rus_Cyrl''', '''sag_Latn''', '''san_Deva''', '''sat_Beng''', '''scn_Latn''', '''shn_Mymr''', '''sin_Sinh''', '''slk_Latn''', '''slv_Latn''', '''smo_Latn''', '''sna_Latn''', '''snd_Arab''', '''som_Latn''', '''sot_Latn''', '''spa_Latn''', '''als_Latn''', '''srd_Latn''', '''srp_Cyrl''', '''ssw_Latn''', '''sun_Latn''', '''swe_Latn''', '''swh_Latn''', '''szl_Latn''', '''tam_Taml''', '''tat_Cyrl''', '''tel_Telu''', '''tgk_Cyrl''', '''tgl_Latn''', '''tha_Thai''', '''tir_Ethi''', '''taq_Latn''', '''taq_Tfng''', '''tpi_Latn''', '''tsn_Latn''', '''tso_Latn''', '''tuk_Latn''', '''tum_Latn''', '''tur_Latn''', '''twi_Latn''', '''tzm_Tfng''', '''uig_Arab''', '''ukr_Cyrl''', '''umb_Latn''', '''urd_Arab''', '''uzn_Latn''', '''vec_Latn''', '''vie_Latn''', '''war_Latn''', '''wol_Latn''', '''xho_Latn''', '''ydd_Hebr''', '''yor_Latn''', '''yue_Hant''', '''zho_Hans''', '''zho_Hant''', '''zul_Latn''']




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      VOCAB_FILES_NAMES
						snake_case__        =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						snake_case__        =      PRETRAINED_VOCAB_FILES_MAP
						snake_case__        =      ["""input_ids""", """attention_mask"""]
						snake_case__        =      NllbTokenizer

						snake_case__        =      []
						snake_case__        =      []



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




											'''simple docstring'''



											__a					:						Dict      =					AddedToken(__UpperCAmelCase ,					lstrip=__UpperCAmelCase ,					rstrip=__UpperCAmelCase   ) if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ) else mask_token
											__a					:						List[str]      =					legacy_behaviour
											super().__init__(
											    vocab_file=__UpperCAmelCase ,					tokenizer_file=__UpperCAmelCase ,					bos_token=__UpperCAmelCase ,					eos_token=__UpperCAmelCase ,					sep_token=__UpperCAmelCase ,					cls_token=__UpperCAmelCase ,					unk_token=__UpperCAmelCase ,					pad_token=__UpperCAmelCase ,					mask_token=__UpperCAmelCase ,					src_lang=__UpperCAmelCase ,					tgt_lang=__UpperCAmelCase ,					additional_special_tokens=__UpperCAmelCase ,					legacy_behaviour=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

											__a					:						Union[str, Any]      =					vocab_file
											__a					:						str      =					False if not self.vocab_file else True

											__a					:						Dict      =					FAIRSEQ_LANGUAGE_CODES.copy()

											if additional_special_tokens is not None:
																# Only add those special tokens if they are not already there.
																_additional_special_tokens.extend(
																    [t for t in additional_special_tokens if t not in _additional_special_tokens]   )

											self.add_special_tokens({"additional_special_tokens": _additional_special_tokens}   )
											__a					:						Optional[Any]      =					{
											    lang_code: self.convert_tokens_to_ids(__UpperCAmelCase   ) for lang_code in FAIRSEQ_LANGUAGE_CODES
											}

											__a					:						Dict      =					src_lang if src_lang is not None else "eng_Latn"
											__a					:						str      =					self.convert_tokens_to_ids(self._src_lang   )
											__a					:						Any      =					tgt_lang
											self.set_src_lang_special_tokens(self._src_lang   )



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




											'''simple docstring'''



											return self._src_lang



						@src_lang.setter
						def        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					str   )    ->     None:




											'''simple docstring'''



											__a					:						Dict      =					new_src_lang
											self.set_src_lang_special_tokens(self._src_lang   )



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					List[int] ,					__UpperCAmelCase:					Optional[List[int]] = 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        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					List[int] ,					__UpperCAmelCase:					Optional[List[int]] = None   )    ->     List[int]:




											'''simple docstring'''



											__a					:						int      =					[self.sep_token_id]
											__a					:						str      =					[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        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					Dict ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					Optional[str] ,					__UpperCAmelCase:					Optional[str] ,					**__UpperCAmelCase:					Optional[int]   )    ->     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					:						Tuple      =					src_lang
											__a					:						Optional[Any]      =					self(__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase   )
											__a					:						List[Any]      =					self.convert_tokens_to_ids(__UpperCAmelCase   )
											__a					:						List[Any]      =					tgt_lang_id
											return inputs



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					str = "eng_Latn" ,					__UpperCAmelCase:					Optional[List[str]] = None ,					__UpperCAmelCase:					str = "fra_Latn" ,					**__UpperCAmelCase:					Dict ,					)    ->     BatchEncoding:




											'''simple docstring'''



											__a					:						Optional[int]      =					src_lang
											__a					:						List[str]      =					tgt_lang
											return super().prepare_seqaseq_batch(__UpperCAmelCase ,					__UpperCAmelCase ,					**__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					List[Any]   )    ->     int:




											'''simple docstring'''



											return self.set_src_lang_special_tokens(self.src_lang   )



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




											'''simple docstring'''



											return self.set_tgt_lang_special_tokens(self.tgt_lang   )



						def        UpperCAmelCase__					(self:					Optional[int] ,					__UpperCAmelCase:					Union[str, Any]   )    ->     None:




											'''simple docstring'''



											__a					:						Optional[Any]      =					self.convert_tokens_to_ids(__UpperCAmelCase   )

											if self.legacy_behaviour:
																__a					:						Dict      =					[]
																__a					:						str      =					[self.eos_token_id, self.cur_lang_code]
											else:
																__a					:						Optional[Any]      =					[self.cur_lang_code]
																__a					:						Optional[int]      =					[self.eos_token_id]

											__a					:						Dict      =					self.convert_ids_to_tokens(self.prefix_tokens   )
											__a					:						List[Any]      =					self.convert_ids_to_tokens(self.suffix_tokens   )

											__a					:						int      =					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        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					str   )    ->     None:




											'''simple docstring'''



											__a					:						List[Any]      =					self.convert_tokens_to_ids(__UpperCAmelCase   )
											if self.legacy_behaviour:
																__a					:						Any      =					[]
																__a					:						Union[str, Any]      =					[self.eos_token_id, self.cur_lang_code]
											else:
																__a					:						Union[str, Any]      =					[self.cur_lang_code]
																__a					:						List[str]      =					[self.eos_token_id]

											__a					:						str      =					self.convert_ids_to_tokens(self.prefix_tokens   )
											__a					:						str      =					self.convert_ids_to_tokens(self.suffix_tokens   )

											__a					:						int      =					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        UpperCAmelCase__					(self:					str ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					Optional[str] = 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(__UpperCAmelCase   ):
																logger.error(f'Vocabulary path ({save_directory}) should be a directory.'   )
																return
											__a					:						Tuple      =					os.path.join(
											    __UpperCAmelCase ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

											return (out_vocab_file,)





