





from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_UpperCAmelCase        =				logging.get_logger(__name__)



class 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					List[Any]         =   ['''pixel_values''']

       def __init__(		self: Any					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Optional[Dict[str, int]] = None					,   _SCREAMING_SNAKE_CASE: PILImageResampling = PILImageResampling.BILINEAR					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Dict[str, int] = None					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Union[int, float] = 1 / 255					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   **_SCREAMING_SNAKE_CASE: Any					,   )       -> None:




              """simple docstring"""


              super().__init__(**_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  size if size is not None else {"shortest_edge": 256}
              UpperCamelCase_          =  get_size_dict(_SCREAMING_SNAKE_CASE					,   default_to_square=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  crop_size if crop_size is not None else {"height": 224, "width": 224}
              UpperCamelCase_          =  get_size_dict(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  do_resize
              UpperCamelCase_          =  size
              UpperCamelCase_          =  resample
              UpperCamelCase_          =  do_center_crop
              UpperCamelCase_          =  crop_size
              UpperCamelCase_          =  do_rescale
              UpperCamelCase_          =  rescale_factor
              UpperCamelCase_          =  do_normalize
              UpperCamelCase_          =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              UpperCamelCase_          =  image_std if image_std is not None else IMAGENET_STANDARD_STD

       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: Dict[str, int]					,   _SCREAMING_SNAKE_CASE: PILImageResampling = PILImageResampling.BICUBIC					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: List[str]					,   )       -> np.ndarray:




              """simple docstring"""


              UpperCamelCase_          =  get_size_dict(_SCREAMING_SNAKE_CASE					,   default_to_square=_SCREAMING_SNAKE_CASE		)
              if "shortest_edge" not in size:
                     raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
              UpperCamelCase_          =  get_resize_output_image_size(_SCREAMING_SNAKE_CASE					,   size=size["shortest_edge"]					,   default_to_square=_SCREAMING_SNAKE_CASE		)
              return resize(_SCREAMING_SNAKE_CASE					,   size=_SCREAMING_SNAKE_CASE					,   resample=_SCREAMING_SNAKE_CASE					,   data_format=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Optional[int]					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: Dict[str, int]					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: List[Any]					,   )       -> np.ndarray:




              """simple docstring"""


              UpperCamelCase_          =  get_size_dict(_SCREAMING_SNAKE_CASE		)
              return center_crop(_SCREAMING_SNAKE_CASE					,   size=(size["height"], size["width"])					,   data_format=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: float					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: str		)       -> np.ndarray:




              """simple docstring"""


              return rescale(_SCREAMING_SNAKE_CASE					,   scale=_SCREAMING_SNAKE_CASE					,   data_format=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: np.ndarray					,   _SCREAMING_SNAKE_CASE: Union[float, List[float]]					,   _SCREAMING_SNAKE_CASE: Union[float, List[float]]					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, ChannelDimension]] = None					,   **_SCREAMING_SNAKE_CASE: Any					,   )       -> np.ndarray:




              """simple docstring"""


              return normalize(_SCREAMING_SNAKE_CASE					,   mean=_SCREAMING_SNAKE_CASE					,   std=_SCREAMING_SNAKE_CASE					,   data_format=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: ImageInput					,   _SCREAMING_SNAKE_CASE: Optional[bool] = None					,   _SCREAMING_SNAKE_CASE: Dict[str, int] = None					,   _SCREAMING_SNAKE_CASE: PILImageResampling = None					,   _SCREAMING_SNAKE_CASE: bool = None					,   _SCREAMING_SNAKE_CASE: Dict[str, int] = None					,   _SCREAMING_SNAKE_CASE: Optional[bool] = None					,   _SCREAMING_SNAKE_CASE: Optional[float] = None					,   _SCREAMING_SNAKE_CASE: Optional[bool] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[float, List[float]]] = None					,   _SCREAMING_SNAKE_CASE: Optional[Union[str, TensorType]] = None					,   _SCREAMING_SNAKE_CASE: Union[str, ChannelDimension] = ChannelDimension.FIRST					,   **_SCREAMING_SNAKE_CASE: List[str]					,   )       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  do_resize if do_resize is not None else self.do_resize
              UpperCamelCase_          =  size if size is not None else self.size
              UpperCamelCase_          =  get_size_dict(_SCREAMING_SNAKE_CASE					,   default_to_square=_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  resample if resample is not None else self.resample
              UpperCamelCase_          =  do_center_crop if do_center_crop is not None else self.do_center_crop
              UpperCamelCase_          =  crop_size if crop_size is not None else self.crop_size
              UpperCamelCase_          =  get_size_dict(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  do_rescale if do_rescale is not None else self.do_rescale
              UpperCamelCase_          =  rescale_factor if rescale_factor is not None else self.rescale_factor
              UpperCamelCase_          =  do_normalize if do_normalize is not None else self.do_normalize
              UpperCamelCase_          =  image_mean if image_mean is not None else self.image_mean
              UpperCamelCase_          =  image_std if image_std is not None else self.image_std

              UpperCamelCase_          =  make_list_of_images(_SCREAMING_SNAKE_CASE		)

              if not valid_images(_SCREAMING_SNAKE_CASE		):
                     raise ValueError(
                         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                         "torch.Tensor, tf.Tensor or jax.ndarray."		)

              if do_resize and size is None:
                     raise ValueError("Size must be specified if do_resize is True."		)

              if do_center_crop and crop_size is None:
                     raise ValueError("Crop size must be specified if do_center_crop is True."		)

              if do_rescale and rescale_factor is None:
                     raise ValueError("Rescale factor must be specified if do_rescale is True."		)

              if do_normalize and (image_mean is None or image_std is None):
                     raise ValueError("Image mean and std must be specified if do_normalize is True."		)

              # All transformations expect numpy arrays.
              UpperCamelCase_          =  [to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

              if do_resize:
                     UpperCamelCase_          =  [self.resize(image=_SCREAMING_SNAKE_CASE					,   size=_SCREAMING_SNAKE_CASE					,   resample=_SCREAMING_SNAKE_CASE		) for image in images]

              if do_center_crop:
                     UpperCamelCase_          =  [self.center_crop(image=_SCREAMING_SNAKE_CASE					,   size=_SCREAMING_SNAKE_CASE		) for image in images]

              if do_rescale:
                     UpperCamelCase_          =  [self.rescale(image=_SCREAMING_SNAKE_CASE					,   scale=_SCREAMING_SNAKE_CASE		) for image in images]

              if do_normalize:
                     UpperCamelCase_          =  [self.normalize(image=_SCREAMING_SNAKE_CASE					,   mean=_SCREAMING_SNAKE_CASE					,   std=_SCREAMING_SNAKE_CASE		) for image in images]

              UpperCamelCase_          =  [to_channel_dimension_format(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) for image in images]

              UpperCamelCase_          =  {"pixel_values": images}
              return BatchFeature(data=_SCREAMING_SNAKE_CASE					,   tensor_type=_SCREAMING_SNAKE_CASE		)








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


_UpperCAmelCase        =				logging.get_logger(__name__)

_UpperCAmelCase        =				'▁'

_UpperCAmelCase        =				{'vocab_file': 'sentencepiece.bpe.model'}

_UpperCAmelCase        =				{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

_UpperCAmelCase        =				{
    'facebook/nllb-200-distilled-600M': 1_0_2_4,
}

# 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 							_UpperCamelCase					(	lowerCAmelCase_     ):
       _UpperCamelCase				:					Dict         =   VOCAB_FILES_NAMES
       _UpperCamelCase				:					str         =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _UpperCamelCase				:					Tuple         =   PRETRAINED_VOCAB_FILES_MAP
       _UpperCamelCase				:					Union[str, Any]         =   ['''input_ids''', '''attention_mask''']

       _UpperCamelCase				:					List[int]         =   []
       _UpperCamelCase				:					List[int]         =   []

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




              """simple docstring"""


              UpperCamelCase_          =  AddedToken(_SCREAMING_SNAKE_CASE					,   lstrip=_SCREAMING_SNAKE_CASE					,   rstrip=_SCREAMING_SNAKE_CASE		) if isinstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		) else mask_token

              UpperCamelCase_          =  {} if sp_model_kwargs is None else sp_model_kwargs
              UpperCamelCase_          =  legacy_behaviour

              super().__init__(
                  bos_token=_SCREAMING_SNAKE_CASE					,   eos_token=_SCREAMING_SNAKE_CASE					,   unk_token=_SCREAMING_SNAKE_CASE					,   sep_token=_SCREAMING_SNAKE_CASE					,   cls_token=_SCREAMING_SNAKE_CASE					,   pad_token=_SCREAMING_SNAKE_CASE					,   mask_token=_SCREAMING_SNAKE_CASE					,   tokenizer_file=_SCREAMING_SNAKE_CASE					,   src_lang=_SCREAMING_SNAKE_CASE					,   tgt_lang=_SCREAMING_SNAKE_CASE					,   additional_special_tokens=_SCREAMING_SNAKE_CASE					,   sp_model_kwargs=self.sp_model_kwargs					,   legacy_behaviour=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE					,   )

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.Load(str(_SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  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>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
              # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

              # Mimic fairseq token-to-id alignment for the first 4 token
              UpperCamelCase_          =  {"<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
              UpperCamelCase_          =  1

              UpperCamelCase_          =  len(self.sp_model		)
              UpperCamelCase_          =  {
                  code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE		)
              }
              UpperCamelCase_          =  {v: k for k, v in self.lang_code_to_id.items()}
              UpperCamelCase_          =  len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

              self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
              UpperCamelCase_          =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
              UpperCamelCase_          =  list(self.lang_code_to_id.keys()		)

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

              UpperCamelCase_          =  src_lang if src_lang is not None else "eng_Latn"
              UpperCamelCase_          =  self.lang_code_to_id[self._src_lang]
              UpperCamelCase_          =  tgt_lang
              self.set_src_lang_special_tokens(self._src_lang		)

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




              """simple docstring"""


              UpperCamelCase_          =  self.__dict__.copy()
              UpperCamelCase_          =  None
              UpperCamelCase_          =  self.sp_model.serialized_model_proto()
              return state

       def __setstate__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  d

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

              UpperCamelCase_          =  spm.SentencePieceProcessor(**self.sp_model_kwargs		)
              self.sp_model.LoadFromSerializedProto(self.sp_model_proto		)

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




              """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: Union[str, Any]		)       -> str:




              """simple docstring"""


              return self._src_lang

       @src_lang.setter
       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: str		)       -> None:




              """simple docstring"""


              UpperCamelCase_          =  new_src_lang
              self.set_src_lang_special_tokens(self._src_lang		)

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None					,   _SCREAMING_SNAKE_CASE: bool = False		)       -> List[int]:




              """simple docstring"""


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

              UpperCamelCase_          =  [1] * len(self.prefix_tokens		)
              UpperCamelCase_          =  [1] * len(self.suffix_tokens		)
              if token_ids_a is None:
                     return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones
              return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE		)) + ([0] * len(_SCREAMING_SNAKE_CASE		)) + suffix_ones

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_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 lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: List[int]					,   _SCREAMING_SNAKE_CASE: Optional[List[int]] = None		)       -> List[int]:




              """simple docstring"""


              UpperCamelCase_          =  [self.sep_token_id]
              UpperCamelCase_          =  [self.cls_token_id]

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

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: Optional[int]					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str]					,   _SCREAMING_SNAKE_CASE: Optional[str]					,   **_SCREAMING_SNAKE_CASE: Tuple		)       -> 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"		)
              UpperCamelCase_          =  src_lang
              UpperCamelCase_          =  self(_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tgt_lang_id
              return inputs

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




              """simple docstring"""


              UpperCamelCase_          =  {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
              vocab.update(self.added_tokens_encoder		)
              return vocab

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: str		)       -> List[str]:




              """simple docstring"""


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

       def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: str		)       -> Optional[int]:




              """simple docstring"""


              if token in self.fairseq_tokens_to_ids:
                     return self.fairseq_tokens_to_ids[token]
              UpperCamelCase_          =  self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE		)

              # 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: int					,   _SCREAMING_SNAKE_CASE: Union[str, Any]		)       -> Any:




              """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: int					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  "".join(_SCREAMING_SNAKE_CASE		).replace(_SCREAMING_SNAKE_CASE					,   " "		).strip()
              return out_string

       def lowercase (		self: str					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[str] = None		)       -> Tuple[str]:




              """simple docstring"""


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

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

              return (out_vocab_file,)

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: str = "eng_Latn"					,   _SCREAMING_SNAKE_CASE: Optional[List[str]] = None					,   _SCREAMING_SNAKE_CASE: str = "fra_Latn"					,   **_SCREAMING_SNAKE_CASE: List[str]					,   )       -> BatchEncoding:




              """simple docstring"""


              UpperCamelCase_          =  src_lang
              UpperCamelCase_          =  tgt_lang
              return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   **_SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              return self.set_src_lang_special_tokens(self.src_lang		)

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              return self.set_tgt_lang_special_tokens(self.tgt_lang		)

       def lowercase (		self: List[str]					,   _SCREAMING_SNAKE_CASE: Any		)       -> None:




              """simple docstring"""


              UpperCamelCase_          =  self.lang_code_to_id[src_lang]
              if self.legacy_behaviour:
                     UpperCamelCase_          =  []
                     UpperCamelCase_          =  [self.eos_token_id, self.cur_lang_code]
              else:
                     UpperCamelCase_          =  [self.cur_lang_code]
                     UpperCamelCase_          =  [self.eos_token_id]



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: str		)       -> None:




              """simple docstring"""


              UpperCamelCase_          =  self.lang_code_to_id[lang]
              if self.legacy_behaviour:
                     UpperCamelCase_          =  []
                     UpperCamelCase_          =  [self.eos_token_id, self.cur_lang_code]
              else:
                     UpperCamelCase_          =  [self.cur_lang_code]
                     UpperCamelCase_          =  [self.eos_token_id]

