




"""simple docstring"""



import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 torch import nn

      from transformers import (
          MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
          MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
          MODEL_MAPPING,
          DeiTForImageClassification,
          DeiTForImageClassificationWithTeacher,
          DeiTForMaskedImageModeling,
          DeiTModel,
      )
      from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor




class      UpperCAmelCase_  :




    def __init__(   self						:	Dict     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	List[str]=13     ,			snake_case_						:	List[Any]=30     ,			snake_case_						:	Dict=2     ,			snake_case_						:	int=3     ,			snake_case_						:	Dict=True     ,			snake_case_						:	Any=True     ,			snake_case_						:	Any=32     ,			snake_case_						:	Optional[Any]=5     ,			snake_case_						:	List[Any]=4     ,			snake_case_						:	int=37     ,			snake_case_						:	Any="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	int=0.1     ,			snake_case_						:	Tuple=10     ,			snake_case_						:	Optional[int]=0.02     ,			snake_case_						:	Dict=3     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=2     ,			)				->							Any:




     '''simple docstring'''

     A__	=    parent
     A__	=    batch_size
     A__	=    image_size
     A__	=    patch_size
     A__	=    num_channels
     A__	=    is_training
     A__	=    use_labels
     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__	=    type_sequence_label_size
     A__	=    initializer_range
     A__	=    scope
     A__	=    encoder_stride

     # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
     A__	=    (image_size // patch_size) ** 2
     A__	=    num_patches + 2




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

     A__	=    None
     if self.use_labels:
      A__	=    ids_tensor([self.batch_size]     ,			self.type_sequence_label_size				)

     A__	=    self.get_config()

     return config, pixel_values, labels




    def 							__magic_name__							(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     return DeiTConfig(
         image_size=self.image_size     ,			patch_size=self.patch_size     ,			num_channels=self.num_channels     ,			hidden_size=self.hidden_size     ,			num_hidden_layers=self.num_hidden_layers     ,			num_attention_heads=self.num_attention_heads     ,			intermediate_size=self.intermediate_size     ,			hidden_act=self.hidden_act     ,			hidden_dropout_prob=self.hidden_dropout_prob     ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,			is_decoder=snake_case_     ,			initializer_range=self.initializer_range     ,			encoder_stride=self.encoder_stride     ,			)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Dict     ,			snake_case_						:	Dict				)				->							Any:




     '''simple docstring'''

     A__	=    DeiTModel(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_				)
     self.parent.assertEqual(result.last_hidden_state.shape     ,			(self.batch_size, self.seq_length, self.hidden_size)				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	int     ,			snake_case_						:	Dict     ,			snake_case_						:	str				)				->							Tuple:




     '''simple docstring'''

     A__	=    DeiTForMaskedImageModeling(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_				)
     self.parent.assertEqual(
         result.reconstruction.shape     ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)				)

     # test greyscale images
     A__	=    1
     A__	=    DeiTForMaskedImageModeling(snake_case_				)
     model.to(snake_case_				)
     model.eval()

     A__	=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
     A__	=    model(snake_case_				)
     self.parent.assertEqual(result.reconstruction.shape     ,			(self.batch_size, 1, self.image_size, self.image_size)				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]				)				->							Dict:




     '''simple docstring'''

     A__	=    self.type_sequence_label_size
     A__	=    DeiTForImageClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.type_sequence_label_size)				)

     # test greyscale images
     A__	=    1
     A__	=    DeiTForImageClassification(snake_case_				)
     model.to(snake_case_				)
     model.eval()

     A__	=    floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
     A__	=    model(snake_case_     ,			labels=snake_case_				)
     self.parent.assertEqual(result.logits.shape     ,			(self.batch_size, self.type_sequence_label_size)				)




    def 							__magic_name__							(   self						:	Dict				)				->							int:




     '''simple docstring'''

     A__	=    self.prepare_config_and_inputs()
     (
         (
         A__				
     ),				(
         A__				
     ),				(
         A__				
     ),								
     )	=    config_and_inputs
     A__	=    {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				(
        (
            DeiTModel,
            DeiTForImageClassification,
            DeiTForImageClassificationWithTeacher,
            DeiTForMaskedImageModeling,
        )
        if is_torch_available()
        else ()
    )
    lowercase__						 =				(
        {
            '''feature-extraction''': DeiTModel,
            '''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
        }
        if is_torch_available()
        else {}
    )

    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False




    def 							__magic_name__							(   self						:	str				)				->							Tuple:




     '''simple docstring'''

     A__	=    DeiTModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			has_text_modality=snake_case_     ,			hidden_size=37				)




    def 							__magic_name__							(   self						:	Any				)				->							Optional[int]:




     '''simple docstring'''

     self.config_tester.run_common_tests()




    @unittest.skip(reason="DeiT does not use inputs_embeds"				)
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Tuple:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	Dict				)				->							Dict:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      A__	=    model_class(snake_case_				)
      self.assertIsInstance(model.get_input_embeddings()     ,			(nn.Module)				)
      A__	=    model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(snake_case_     ,			nn.Linear				)				)




    def 							__magic_name__							(   self						:	Tuple				)				->							List[Any]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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

      A__	=    ["pixel_values"]
      self.assertListEqual(arg_names[:1]     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Any				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Tuple:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_				)




    def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Any     ,			snake_case_						:	int     ,			snake_case_						:	str=False				)				->							Dict:




     '''simple docstring'''

     A__	=    super()._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)

     if return_labels:
      if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
       del inputs_dict["labels"]

     return inputs_dict




    def 							__magic_name__							(   self						:	str				)				->							Tuple:




     '''simple docstring'''

     if not self.model_tester.is_training:
      return

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()
     A__	=    True

     for model_class in self.all_model_classes:
      # DeiTForImageClassificationWithTeacher supports inference-only
      if (
          model_class in get_values(snake_case_				)
          or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
      ):
       continue
      A__	=    model_class(snake_case_				)
      model.to(snake_case_				)
      model.train()
      A__	=    self._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)
      A__	=    model(**snake_case_				).loss
      loss.backward()




    def 							__magic_name__							(   self						:	Any				)				->							str:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
      return

     A__	=    False
     A__	=    True

     for model_class in self.all_model_classes:
      if model_class in get_values(snake_case_				) or not model_class.supports_gradient_checkpointing:
       continue
      # DeiTForImageClassificationWithTeacher supports inference-only
      if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
       continue
      A__	=    model_class(snake_case_				)
      model.gradient_checkpointing_enable()
      model.to(snake_case_				)
      model.train()
      A__	=    self._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)
      A__	=    model(**snake_case_				).loss
      loss.backward()




    def 							__magic_name__							(   self						:	str				)				->							Optional[int]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     A__	=    [
         {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
         {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
         {"title": "regression", "num_labels": 1, "dtype": torch.float},
     ]

     for model_class in self.all_model_classes:
      if (
          model_class
          not in [
              *get_values(snake_case_				),
              *get_values(snake_case_				),
          ]
          or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
      ):
       continue

      for problem_type in problem_types:
       with self.subTest(msg=F"""Testing {model_class} with {problem_type["title"]}"""				):
        A__	=    problem_type["title"]
        A__	=    problem_type["num_labels"]

        A__	=    model_class(snake_case_				)
        model.to(snake_case_				)
        model.train()

        A__	=    self._prepare_for_class(snake_case_     ,			snake_case_     ,			return_labels=snake_case_				)

        if problem_type["num_labels"] > 1:
         A__	=    inputs["labels"].unsqueeze(1				).repeat(1     ,			problem_type["num_labels"]				)

        A__	=    inputs["labels"].to(problem_type["dtype"]				)

        # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
        # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
        # they have the same size." which is a symptom something in wrong for the regression problem.
        # See https://github.com/huggingface/transformers/issues/11780
        with warnings.catch_warnings(record=snake_case_				) as warning_list:
         A__	=    model(**snake_case_				).loss
        for w in warning_list:
         if "Using a target size that is different to the input size" in str(w.message				):
          raise ValueError(
              F"""Something is going wrong in the regression problem: intercepted {w.message}"""				)

        loss.backward()




    @slow
    def 							__magic_name__							(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      A__	=    DeiTModel.from_pretrained(snake_case_				)
      self.assertIsNotNone(snake_case_				)







def   _SCREAMING_SNAKE_CASE       (							)						->		Tuple:
 A__	=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
 return image






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




    @cached_property
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Optional[int]:




     '''simple docstring'''

     return (
         DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"				)
         if is_vision_available()
         else None
     )




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




     '''simple docstring'''

     A__	=    DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"				).to(
         snake_case_				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=snake_case_     ,			return_tensors="pt"				).to(snake_case_				)

     # forward pass
     with torch.no_grad():
      A__	=    model(**snake_case_				)

     # verify the logits
     A__	=    torch.Size((1, 1_000)				)
     self.assertEqual(outputs.logits.shape     ,			snake_case_				)

     A__	=    torch.tensor([-1.0266, 0.1912, -1.2861]				).to(snake_case_				)

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




    @slow
    @require_accelerate
    @require_torch_gpu
    def 							__magic_name__							(   self						:	Tuple				)				->							Optional[int]:




     '''simple docstring'''

     A__	=    DeiTModel.from_pretrained(
         "facebook/deit-base-distilled-patch16-224"     ,			torch_dtype=torch.floataa     ,			device_map="auto"				)
     A__	=    self.default_image_processor

     A__	=    prepare_img()
     A__	=    image_processor(images=snake_case_     ,			return_tensors="pt"				)
     A__	=    inputs.pixel_values.to(snake_case_				)

     # forward pass to make sure inference works in fp16
     with torch.no_grad():
      A__	=    model(snake_case_				)











"""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_nllb import NllbTokenizer
else:
      SCREAMING_SNAKE_CASE    =	None


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE    =	{
    "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"
        ),
    },
}

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

# fmt: off
SCREAMING_SNAKE_CASE    =	["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      UpperCAmelCase_  (						A_							):
    lowercase__						 =				VOCAB_FILES_NAMES
    lowercase__						 =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase__						 =				PRETRAINED_VOCAB_FILES_MAP
    lowercase__						 =				['''input_ids''', '''attention_mask''']
    lowercase__						 =				NllbTokenizer

    lowercase__						 =				[]
    lowercase__						 =				[]




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




     '''simple docstring'''

     A__	=    AddedToken(snake_case_     ,			lstrip=snake_case_     ,			rstrip=snake_case_				) if isinstance(snake_case_     ,			snake_case_				) else mask_token
     A__	=    legacy_behaviour
     super().__init__(
         vocab_file=snake_case_     ,			tokenizer_file=snake_case_     ,			bos_token=snake_case_     ,			eos_token=snake_case_     ,			sep_token=snake_case_     ,			cls_token=snake_case_     ,			unk_token=snake_case_     ,			pad_token=snake_case_     ,			mask_token=snake_case_     ,			src_lang=snake_case_     ,			tgt_lang=snake_case_     ,			additional_special_tokens=snake_case_     ,			legacy_behaviour=snake_case_     ,			**snake_case_     ,			)

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

     A__	=    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__	=    {
         lang_code: self.convert_tokens_to_ids(snake_case_				) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     A__	=    src_lang if src_lang is not None else "eng_Latn"
     A__	=    self.convert_tokens_to_ids(self._src_lang				)
     A__	=    tgt_lang
     self.set_src_lang_special_tokens(self._src_lang				)




    @property
    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							str:




     '''simple docstring'''

     return self._src_lang




    @src_lang.setter
    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

     A__	=    new_src_lang
     self.set_src_lang_special_tokens(self._src_lang				)




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	List[int]     ,			snake_case_						:	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 							__magic_name__							(   self						:	Any     ,			snake_case_						:	List[int]     ,			snake_case_						:	Optional[List[int]] = None				)				->							List[int]:




     '''simple docstring'''

     A__	=    [self.sep_token_id]
     A__	=    [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 							__magic_name__							(   self						:	int     ,			snake_case_						:	Tuple     ,			snake_case_						:	str     ,			snake_case_						:	Optional[str]     ,			snake_case_						:	Optional[str]     ,			**snake_case_						:	Tuple				)				->							List[Any]:




     '''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(snake_case_     ,			add_special_tokens=snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)
     A__	=    self.convert_tokens_to_ids(snake_case_				)
     A__	=    tgt_lang_id
     return inputs




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




     '''simple docstring'''

     A__	=    src_lang
     A__	=    tgt_lang
     return super().prepare_seqaseq_batch(snake_case_     ,			snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     return self.set_src_lang_special_tokens(self.src_lang				)




    def 							__magic_name__							(   self						:	Tuple				)				->							Dict:




     '''simple docstring'''

     return self.set_tgt_lang_special_tokens(self.tgt_lang				)




    def 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	Dict				)				->							None:




     '''simple docstring'''

     A__	=    self.convert_tokens_to_ids(snake_case_				)

     if self.legacy_behaviour:
      A__	=    []
      A__	=    [self.eos_token_id, self.cur_lang_code]
     else:
      A__	=    [self.cur_lang_code]
      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 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	str				)				->							None:




     '''simple docstring'''

     A__	=    self.convert_tokens_to_ids(snake_case_				)
     if self.legacy_behaviour:
      A__	=    []
      A__	=    [self.eos_token_id, self.cur_lang_code]
     else:
      A__	=    [self.cur_lang_code]
      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 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	str     ,			snake_case_						:	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(snake_case_				):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""				)
      return
     A__	=    os.path.join(
         snake_case_     ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

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

     return (out_vocab_file,)





