

'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase					 =	logging.get_logger(__name__)

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		=YolosConfig()

     # size of the architecture
     if "yolos_ti" in yolos_name:
          __lowercase      		=1_92
          __lowercase      		=7_68
          __lowercase      		=12
          __lowercase      		=3
          __lowercase      		=[8_00, 13_33]
          __lowercase      		=False
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=3_30
          __lowercase      		=14
          __lowercase      		=6
          __lowercase      		=13_20
     elif "yolos_s" in yolos_name:
          __lowercase      		=3_84
          __lowercase      		=15_36
          __lowercase      		=12
          __lowercase      		=6
     elif "yolos_b" in yolos_name:
          __lowercase      		=[8_00, 13_44]

     __lowercase      		=91
     __lowercase      		='huggingface/label-files'
     __lowercase      		='coco-detection-id2label.json'
     __lowercase      		=json.load(open(hf_hub_download(lowercase__,       lowercase__,       repo_type='dataset'			),       'r'			)			)
     __lowercase      		={int(lowercase__			): v for k, v in idalabel.items()}
     __lowercase      		=idalabel
     __lowercase      		={v: k for k, v in idalabel.items()}

     return config

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosConfig,       lowercase__    :	bool = False			):






     '''simple docstring'''





     for i in range(config.num_hidden_layers			):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''			)
          __lowercase      		=state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''			)
          # next, add query, keys and values (in that order) to the state dict
          __lowercase      		=in_proj_weight[: config.hidden_size, :]
          __lowercase      		=in_proj_bias[: config.hidden_size]
          __lowercase      		=in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          __lowercase      		=in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          __lowercase      		=in_proj_weight[-config.hidden_size :, :]
          __lowercase      		=in_proj_bias[-config.hidden_size :]

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     if "backbone" in name:
          __lowercase      		=name.replace('backbone',       'vit'			)
     if "cls_token" in name:
          __lowercase      		=name.replace('cls_token',       'embeddings.cls_token'			)
     if "det_token" in name:
          __lowercase      		=name.replace('det_token',       'embeddings.detection_tokens'			)
     if "mid_pos_embed" in name:
          __lowercase      		=name.replace('mid_pos_embed',       'encoder.mid_position_embeddings'			)
     if "pos_embed" in name:
          __lowercase      		=name.replace('pos_embed',       'embeddings.position_embeddings'			)
     if "patch_embed.proj" in name:
          __lowercase      		=name.replace('patch_embed.proj',       'embeddings.patch_embeddings.projection'			)
     if "blocks" in name:
          __lowercase      		=name.replace('blocks',       'encoder.layer'			)
     if "attn.proj" in name:
          __lowercase      		=name.replace('attn.proj',       'attention.output.dense'			)
     if "attn" in name:
          __lowercase      		=name.replace('attn',       'attention.self'			)
     if "norm1" in name:
          __lowercase      		=name.replace('norm1',       'layernorm_before'			)
     if "norm2" in name:
          __lowercase      		=name.replace('norm2',       'layernorm_after'			)
     if "mlp.fc1" in name:
          __lowercase      		=name.replace('mlp.fc1',       'intermediate.dense'			)
     if "mlp.fc2" in name:
          __lowercase      		=name.replace('mlp.fc2',       'output.dense'			)
     if "class_embed" in name:
          __lowercase      		=name.replace('class_embed',       'class_labels_classifier'			)
     if "bbox_embed" in name:
          __lowercase      		=name.replace('bbox_embed',       'bbox_predictor'			)
     if "vit.norm" in name:
          __lowercase      		=name.replace('vit.norm',       'vit.layernorm'			)

     return name

def 					__UpperCamelCase				(						lowercase__    :	dict,       lowercase__    :	YolosForObjectDetection			):






     '''simple docstring'''





     for key in orig_state_dict.copy().keys():
          __lowercase      		=orig_state_dict.pop(lowercase__			)

          if "qkv" in key:
               __lowercase      		=key.split('.'			)
               __lowercase      		=int(key_split[2]			)
               __lowercase      		=model.vit.encoder.layer[layer_num].attention.attention.all_head_size
               if "weight" in key:
                    __lowercase      		=val[:dim, :]
                    __lowercase      		=val[
                        dim : dim * 2, :
                    ]
                    __lowercase      		=val[-dim:, :]
               else:
                    __lowercase      		=val[:dim]
                    __lowercase      		=val[dim : dim * 2]
                    __lowercase      		=val[-dim:]
          else:
               __lowercase      		=val

     return orig_state_dict

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		='http://images.cocodataset.org/val2017/000000039769.jpg'
     __lowercase      		=Image.open(requests.get(lowercase__,       stream=lowercase__			).raw			)
     return im

@torch.no_grad()
def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	str,       lowercase__    :	bool = False			):






     '''simple docstring'''





     __lowercase      		=get_yolos_config(lowercase__			)

     # load original state_dict
     __lowercase      		=torch.load(lowercase__,       map_location='cpu'			)['model']

     # load 🤗 model
     __lowercase      		=YolosForObjectDetection(lowercase__			)
     model.eval()
     __lowercase      		=convert_state_dict(lowercase__,       lowercase__			)
     model.load_state_dict(lowercase__			)

     # Check outputs on an image, prepared by YolosImageProcessor
     __lowercase      		=8_00 if yolos_name != 'yolos_ti' else 5_12
     __lowercase      		=YolosImageProcessor(format='coco_detection',       size=lowercase__			)
     __lowercase      		=image_processor(images=prepare_img(),       return_tensors='pt'			)
     __lowercase      		=model(**lowercase__			)
     __lowercase		,  __lowercase      		=outputs.logits, outputs.pred_boxes

     __lowercase		,  __lowercase      		=None, None
     if yolos_name == "yolos_ti":
          __lowercase      		=torch.tensor(
              [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]			)
          __lowercase      		=torch.tensor(
              [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]			)
     elif yolos_name == "yolos_s_200_pre":
          __lowercase      		=torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]			)
          __lowercase      		=torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]			)
     elif yolos_name == "yolos_s_300_pre":
          __lowercase      		=torch.tensor(
              [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]			)
          __lowercase      		=torch.tensor(
              [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]			)
     elif yolos_name == "yolos_s_dWr":
          __lowercase      		=torch.tensor(
              [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]			)
          __lowercase      		=torch.tensor(
              [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]			)
     elif yolos_name == "yolos_base":
          __lowercase      		=torch.tensor(
              [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]			)
          __lowercase      		=torch.tensor(
              [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]			)
     else:
          raise ValueError(F'''Unknown yolos_name: {yolos_name}'''			)

     assert torch.allclose(logits[0, :3, :3],       lowercase__,       atol=1E-4			)
     assert torch.allclose(pred_boxes[0, :3, :3],       lowercase__,       atol=1E-4			)

     Path(lowercase__			).mkdir(exist_ok=lowercase__			)
     print(F'''Saving model {yolos_name} to {pytorch_dump_folder_path}'''			)
     model.save_pretrained(lowercase__			)
     print(F'''Saving image processor to {pytorch_dump_folder_path}'''			)
     image_processor.save_pretrained(lowercase__			)

     if push_to_hub:
          __lowercase      		={
              'yolos_ti': 'yolos-tiny',
              'yolos_s_200_pre': 'yolos-small',
              'yolos_s_300_pre': 'yolos-small-300',
              'yolos_s_dWr': 'yolos-small-dwr',
              'yolos_base': 'yolos-base',
          }

          print('Pushing to the hub...'			)
          __lowercase      		=model_mapping[yolos_name]
          image_processor.push_to_hub(lowercase__,       organization='hustvl'			)
          model.push_to_hub(lowercase__,       organization='hustvl'			)


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--yolos_name''',
           default='''yolos_s_200_pre''',
           type=str,
           help=(
               '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
               ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
           ),
       )
       parser.add_argument(
           '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
       )
       parser.add_argument(
           '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
       )

       UpperCAmelCase					 =	parser.parse_args()
       convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





'''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


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''': 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 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			["input_ids", "attention_mask"]

       lowerCAmelCase_												=			[]
       lowerCAmelCase_												=			[]







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







            """simple docstring"""







            __lowercase      		=AddedToken(__lowercase    ,   lstrip=__lowercase    ,   rstrip=__lowercase   ) if isinstance(__lowercase    ,   __lowercase   ) else mask_token

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

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

            __lowercase      		=spm.SentencePieceProcessor(**self.sp_model_kwargs   )
            self.sp_model.Load(str(__lowercase   )   )
            __lowercase      		=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
            __lowercase      		={'<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
            __lowercase      		=1

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

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

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







       def __getstate__(				self      :			Tuple   ):







            """simple docstring"""







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







       def __setstate__(				self      :			List[Any]    ,   __lowercase      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=d

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

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







       @property
       def       snake_case (				self      :			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       snake_case (				self      :			Tuple   ):







            """simple docstring"""







            return self._src_lang







       @src_lang.setter
       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str   ):







            """simple docstring"""







            __lowercase      		=new_src_lang
            self.set_src_lang_special_tokens(self._src_lang   )







       def       snake_case (				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   )

            __lowercase      		=[1] * len(self.prefix_tokens   )
            __lowercase      		=[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       snake_case (				self      :			Optional[Any]    ,   __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       snake_case (				self      :			int    ,   __lowercase      :			List[int]    ,   __lowercase      :			Optional[List[int]] = None   ):







            """simple docstring"""







            __lowercase      		=[self.sep_token_id]
            __lowercase      		=[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       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			Dict    ,   __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'   )
            __lowercase      		=src_lang
            __lowercase      		=self(__lowercase    ,   add_special_tokens=__lowercase    ,   return_tensors=__lowercase    ,   **__lowercase   )
            __lowercase      		=self.convert_tokens_to_ids(__lowercase   )
            __lowercase      		=tgt_lang_id
            return inputs







       def       snake_case (				self      :			Any   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Any    ,   __lowercase      :			str   ):







            """simple docstring"""







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







       def       snake_case (				self      :			int    ,   __lowercase      :			int   ):







            """simple docstring"""







            if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
            __lowercase      		=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       snake_case (				self      :			int    ,   __lowercase      :			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       snake_case (				self      :			str    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







            __lowercase      		=''.join(__lowercase   ).replace(__lowercase    ,   ' '   ).strip()
            return out_string







       def       snake_case (				self      :			Optional[Any]    ,   __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
            __lowercase      		=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:
                      __lowercase      		=self.sp_model.serialized_model_proto()
                      fi.write(__lowercase   )

            return (out_vocab_file,)







       def       snake_case (				self      :			List[str]    ,   __lowercase      :			List[str]    ,   __lowercase      :			str = "eng_Latn"    ,   __lowercase      :			Optional[List[str]] = None    ,   __lowercase      :			str = "fra_Latn"    ,   **__lowercase      :			List[str]    ,   ):







            """simple docstring"""







            __lowercase      		=src_lang
            __lowercase      		=tgt_lang
            return super().prepare_seqaseq_batch(__lowercase    ,   __lowercase    ,   **__lowercase   )







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            return self.set_src_lang_special_tokens(self.src_lang   )







       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            return self.set_tgt_lang_special_tokens(self.tgt_lang   )







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			List[str]   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Union[str, Any]    ,   __lowercase      :			str   ):







            """simple docstring"""







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


