
'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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







def 		A_							(						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      _lowerCamelCase					: List[Any]		    = DPTConfig(embedding_type="hybrid"				)

      if "large" in checkpoint_url:
            _lowerCamelCase					: str		    = 1024
            _lowerCamelCase					: List[str]		    = 4096
            _lowerCamelCase					: int		    = 24
            _lowerCamelCase					: int		    = 16
            _lowerCamelCase					: Optional[Any]		    = [5, 11, 17, 23]
            _lowerCamelCase					: Optional[int]		    = [256, 512, 1024, 1024]
            _lowerCamelCase					: Union[str, Any]		    = (1, 384, 384)

      if "nyu" or "midas" in checkpoint_url:
            _lowerCamelCase					: Dict		    = 768
            _lowerCamelCase					: str		    = [1, 1, 1, 0.5]
            _lowerCamelCase					: Dict		    = [256, 512, 768, 768]
            _lowerCamelCase					: int		    = 150
            _lowerCamelCase					: Any		    = 16
            _lowerCamelCase					: Optional[int]		    = (1, 384, 384)
            _lowerCamelCase					: Optional[Any]		    = False
            _lowerCamelCase					: Dict		    = """project"""

      if "ade" in checkpoint_url:
            _lowerCamelCase					: int		    = True
            _lowerCamelCase					: int		    = 768
            _lowerCamelCase					: Optional[int]		    = [1, 1, 1, 0.5]
            _lowerCamelCase					: Dict		    = 150
            _lowerCamelCase					: Any		    = 16
            _lowerCamelCase					: Optional[int]		    = """huggingface/label-files"""
            _lowerCamelCase					: Optional[int]		    = """ade20k-id2label.json"""
            _lowerCamelCase					: Union[str, Any]		    = json.load(open(cached_download(hf_hub_url(UpperCamelCase__					,						UpperCamelCase__					,						repo_type="dataset"				)				)					,						"r"				)				)
            _lowerCamelCase					: str		    = {int(UpperCamelCase__				): v for k, v in idalabel.items()}
            _lowerCamelCase					: Optional[int]		    = idalabel
            _lowerCamelCase					: Optional[Any]		    = {v: k for k, v in idalabel.items()}
            _lowerCamelCase					: int		    = [1, 150, 480, 480]

      return config, expected_shape







def 		A_							(						_lowerCAmelCase :     Optional[int]				):



      """simple docstring"""




      _lowerCamelCase					: Dict		    = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
      for k in ignore_keys:
            state_dict.pop(UpperCamelCase__					,						UpperCamelCase__				)







def 		A_							(						_lowerCAmelCase :     str				):



      """simple docstring"""




      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            _lowerCamelCase					: List[str]		    = name.replace("pretrained.model"					,						"dpt.encoder"				)
      if "pretrained.model" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("pretrained.model"					,						"dpt.embeddings"				)
      if "patch_embed" in name:
            _lowerCamelCase					: Optional[int]		    = name.replace("patch_embed"					,						""				)
      if "pos_embed" in name:
            _lowerCamelCase					: List[str]		    = name.replace("pos_embed"					,						"position_embeddings"				)
      if "attn.proj" in name:
            _lowerCamelCase					: Any		    = name.replace("attn.proj"					,						"attention.output.dense"				)
      if "proj" in name and "project" not in name:
            _lowerCamelCase					: Dict		    = name.replace("proj"					,						"projection"				)
      if "blocks" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("blocks"					,						"layer"				)
      if "mlp.fc1" in name:
            _lowerCamelCase					: Optional[int]		    = name.replace("mlp.fc1"					,						"intermediate.dense"				)
      if "mlp.fc2" in name:
            _lowerCamelCase					: Any		    = name.replace("mlp.fc2"					,						"output.dense"				)
      if "norm1" in name and "backbone" not in name:
            _lowerCamelCase					: List[Any]		    = name.replace("norm1"					,						"layernorm_before"				)
      if "norm2" in name and "backbone" not in name:
            _lowerCamelCase					: int		    = name.replace("norm2"					,						"layernorm_after"				)
      if "scratch.output_conv" in name:
            _lowerCamelCase					: Tuple		    = name.replace("scratch.output_conv"					,						"head"				)
      if "scratch" in name:
            _lowerCamelCase					: str		    = name.replace("scratch"					,						"neck"				)
      if "layer1_rn" in name:
            _lowerCamelCase					: Optional[Any]		    = name.replace("layer1_rn"					,						"convs.0"				)
      if "layer2_rn" in name:
            _lowerCamelCase					: Dict		    = name.replace("layer2_rn"					,						"convs.1"				)
      if "layer3_rn" in name:
            _lowerCamelCase					: Tuple		    = name.replace("layer3_rn"					,						"convs.2"				)
      if "layer4_rn" in name:
            _lowerCamelCase					: str		    = name.replace("layer4_rn"					,						"convs.3"				)
      if "refinenet" in name:
            _lowerCamelCase					: List[Any]		    = int(name[len("neck.refinenet"				) : len("neck.refinenet"				) + 1]				)
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            _lowerCamelCase					: List[str]		    = name.replace(F'refinenet{layer_idx}'					,						F'fusion_stage.layers.{abs(layer_idx-4				)}'				)
      if "out_conv" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("out_conv"					,						"projection"				)
      if "resConfUnit1" in name:
            _lowerCamelCase					: Any		    = name.replace("resConfUnit1"					,						"residual_layer1"				)
      if "resConfUnit2" in name:
            _lowerCamelCase					: List[str]		    = name.replace("resConfUnit2"					,						"residual_layer2"				)
      if "conv1" in name:
            _lowerCamelCase					: str		    = name.replace("conv1"					,						"convolution1"				)
      if "conv2" in name:
            _lowerCamelCase					: Dict		    = name.replace("conv2"					,						"convolution2"				)
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            _lowerCamelCase					: str		    = name.replace("pretrained.act_postprocess1.0.project.0"					,						"neck.reassemble_stage.readout_projects.0.0"				)
      if "pretrained.act_postprocess2.0.project.0" in name:
            _lowerCamelCase					: int		    = name.replace("pretrained.act_postprocess2.0.project.0"					,						"neck.reassemble_stage.readout_projects.1.0"				)
      if "pretrained.act_postprocess3.0.project.0" in name:
            _lowerCamelCase					: Optional[Any]		    = name.replace("pretrained.act_postprocess3.0.project.0"					,						"neck.reassemble_stage.readout_projects.2.0"				)
      if "pretrained.act_postprocess4.0.project.0" in name:
            _lowerCamelCase					: List[Any]		    = name.replace("pretrained.act_postprocess4.0.project.0"					,						"neck.reassemble_stage.readout_projects.3.0"				)

      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            _lowerCamelCase					: List[Any]		    = name.replace("pretrained.act_postprocess1.3"					,						"neck.reassemble_stage.layers.0.projection"				)
      if "pretrained.act_postprocess1.4" in name:
            _lowerCamelCase					: List[Any]		    = name.replace("pretrained.act_postprocess1.4"					,						"neck.reassemble_stage.layers.0.resize"				)
      if "pretrained.act_postprocess2.3" in name:
            _lowerCamelCase					: str		    = name.replace("pretrained.act_postprocess2.3"					,						"neck.reassemble_stage.layers.1.projection"				)
      if "pretrained.act_postprocess2.4" in name:
            _lowerCamelCase					: Optional[int]		    = name.replace("pretrained.act_postprocess2.4"					,						"neck.reassemble_stage.layers.1.resize"				)
      if "pretrained.act_postprocess3.3" in name:
            _lowerCamelCase					: Optional[Any]		    = name.replace("pretrained.act_postprocess3.3"					,						"neck.reassemble_stage.layers.2.projection"				)
      if "pretrained.act_postprocess4.3" in name:
            _lowerCamelCase					: List[Any]		    = name.replace("pretrained.act_postprocess4.3"					,						"neck.reassemble_stage.layers.3.projection"				)
      if "pretrained.act_postprocess4.4" in name:
            _lowerCamelCase					: Optional[Any]		    = name.replace("pretrained.act_postprocess4.4"					,						"neck.reassemble_stage.layers.3.resize"				)
      if "pretrained" in name:
            _lowerCamelCase					: int		    = name.replace("pretrained"					,						"dpt"				)
      if "bn" in name:
            _lowerCamelCase					: Any		    = name.replace("bn"					,						"batch_norm"				)
      if "head" in name:
            _lowerCamelCase					: int		    = name.replace("head"					,						"head.head"				)
      if "encoder.norm" in name:
            _lowerCamelCase					: Any		    = name.replace("encoder.norm"					,						"layernorm"				)
      if "auxlayer" in name:
            _lowerCamelCase					: Union[str, Any]		    = name.replace("auxlayer"					,						"auxiliary_head.head"				)
      if "backbone" in name:
            _lowerCamelCase					: Any		    = name.replace("backbone"					,						"backbone.bit.encoder"				)

      if ".." in name:
            _lowerCamelCase					: Any		    = name.replace(".."					,						"."				)

      if "stem.conv" in name:
            _lowerCamelCase					: Optional[int]		    = name.replace("stem.conv"					,						"bit.embedder.convolution"				)
      if "blocks" in name:
            _lowerCamelCase					: Dict		    = name.replace("blocks"					,						"layers"				)
      if "convolution" in name and "backbone" in name:
            _lowerCamelCase					: str		    = name.replace("convolution"					,						"conv"				)
      if "layer" in name and "backbone" in name:
            _lowerCamelCase					: Any		    = name.replace("layer"					,						"layers"				)
      if "backbone.bit.encoder.bit" in name:
            _lowerCamelCase					: Dict		    = name.replace("backbone.bit.encoder.bit"					,						"backbone.bit"				)
      if "embedder.conv" in name:
            _lowerCamelCase					: List[str]		    = name.replace("embedder.conv"					,						"embedder.convolution"				)
      if "backbone.bit.encoder.stem.norm" in name:
            _lowerCamelCase					: int		    = name.replace("backbone.bit.encoder.stem.norm"					,						"backbone.bit.embedder.norm"				)
      return name







def 		A_							(						_lowerCAmelCase :     Optional[int]					,						_lowerCAmelCase :     str				):



      """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)
            _lowerCamelCase					: List[str]		    = state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight'				)
            _lowerCamelCase					: Optional[Any]		    = state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias'				)
            # next, add query, keys and values (in that order) to the state dict
            _lowerCamelCase					: Any		    = in_proj_weight[: config.hidden_size, :]
            _lowerCamelCase					: int		    = in_proj_bias[: config.hidden_size]
            _lowerCamelCase					: List[Any]		    = in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            _lowerCamelCase					: int		    = in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            _lowerCamelCase					: Union[str, Any]		    = in_proj_weight[
                -config.hidden_size :, :
            ]
            _lowerCamelCase					: str		    = in_proj_bias[-config.hidden_size :]







def 		A_							(						):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCamelCase					: Union[str, Any]		    = Image.open(requests.get(UpperCamelCase__					,						stream=UpperCamelCase__				).raw				)
      return im







@torch.no_grad()
def 		A_							(						_lowerCAmelCase :     Tuple					,						_lowerCAmelCase :     str					,						_lowerCAmelCase :     List[str]					,						_lowerCAmelCase :     Optional[Any]					,						_lowerCAmelCase :     Any				):



      """simple docstring"""




      _lowerCamelCase					: Optional[Any]		    = get_dpt_config(UpperCamelCase__				)
      # load original state_dict from URL
      # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
      _lowerCamelCase					: int		    = torch.load(UpperCamelCase__					,						map_location="cpu"				)
      # remove certain keys
      remove_ignore_keys_(UpperCamelCase__				)
      # rename keys
      for key in state_dict.copy().keys():
            _lowerCamelCase					: Any		    = state_dict.pop(UpperCamelCase__				)
            _lowerCamelCase					: Tuple		    = val
      # read in qkv matrices
      read_in_q_k_v(UpperCamelCase__					,						UpperCamelCase__				)

      # load HuggingFace model
      _lowerCamelCase					: Any		    = DPTForSemanticSegmentation(UpperCamelCase__				) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__				)
      model.load_state_dict(UpperCamelCase__				)
      model.eval()

      # Check outputs on an image
      _lowerCamelCase					: str		    = 480 if """ade""" in checkpoint_url else 384
      _lowerCamelCase					: Optional[int]		    = DPTImageProcessor(size=UpperCamelCase__				)

      _lowerCamelCase					: Dict		    = prepare_img()
      _lowerCamelCase					: Any		    = image_processor(UpperCamelCase__					,						return_tensors="pt"				)

      # forward pass
      _lowerCamelCase					: Dict		    = model(**UpperCamelCase__				).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__				).predicted_depth

      if show_prediction:
            _lowerCamelCase					: Any		    = (
                torch.nn.functional.interpolate(
                    outputs.unsqueeze(1				)					,						size=(image.size[1], image.size[0])					,						mode="bicubic"					,						align_corners=UpperCamelCase__					,						)
                .squeeze()
                .cpu()
                .numpy()
            )

            Image.fromarray((prediction / prediction.max()) * 255				).show()

      if pytorch_dump_folder_path is not None:
            Path(UpperCamelCase__				).mkdir(exist_ok=UpperCamelCase__				)
            print(F'Saving model to {pytorch_dump_folder_path}'				)
            model.save_pretrained(UpperCamelCase__				)
            print(F'Saving image processor to {pytorch_dump_folder_path}'				)
            image_processor.save_pretrained(UpperCamelCase__				)

      if push_to_hub:
            model.push_to_hub("ybelkada/dpt-hybrid-midas"				)
            image_processor.push_to_hub("ybelkada/dpt-hybrid-midas"				)


if __name__ == "__main__":
 UpperCAmelCase_							: Dict =							argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--checkpoint_url',
     default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
     type=str,
     help='URL of the original DPT checkpoint you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path',
     default=None,
     type=str,
     required=False,
     help='Path to the output PyTorch model directory.',
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
 )
 parser.add_argument(
     '--model_name',
     default='dpt-large',
     type=str,
     help='Name of the model, in case you\'re pushing to the hub.',
 )
 parser.add_argument(
     '--show_prediction',
     action='store_true',
 )

 UpperCAmelCase_							: Optional[Any] =							parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )





'''simple docstring'''


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

import sentencepiece as spm

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


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

lowerCAmelCase_      :  str				     =  '▁'

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

lowerCAmelCase_      :  Optional[Any]				     =  {
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    }
}

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

# fmt: off
lowerCAmelCase_      :  Optional[Any]				     =  ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']







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

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






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


           '''simple docstring'''

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

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

           a_     :							int     						=  kwargs.get("""additional_special_tokens"""						,						[]       )
           kwargs["additional_special_tokens"] += [
               code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
           ]

           super().__init__(
               src_lang=lowercase__						,						tgt_lang=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						sp_model_kwargs=self.sp_model_kwargs						,						**lowercase__						,						)

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

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

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

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

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

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

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






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


           '''simple docstring'''

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






      @property
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           return self._src_lang






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


           '''simple docstring'''

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






      def __getstate__( self   :  Any       ):


           '''simple docstring'''

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






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


           '''simple docstring'''

           a_     :							Any     						=  d

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

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






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

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






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


           '''simple docstring'''

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






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


           '''simple docstring'''

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

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






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


           '''simple docstring'''

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






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


           '''simple docstring'''

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






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


           '''simple docstring'''

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

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

           return (out_vocab_file,)






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


           '''simple docstring'''

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

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






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


           '''simple docstring'''

           if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
           # We don't expect to process pairs, but leave the pair logic for API consistency
           return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






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


           '''simple docstring'''

           if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
           a_     :							Union[str, Any]     						=  src_lang
           a_     :							Dict     						=  self(lowercase__						,						add_special_tokens=lowercase__						,						return_tensors=lowercase__						,						**lowercase__       )
           a_     :							Tuple     						=  self.convert_tokens_to_ids(lowercase__       )
           a_     :							Optional[Any]     						=  tgt_lang_id
           return inputs






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


           '''simple docstring'''

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






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           return self.set_src_lang_special_tokens(self.src_lang       )






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


           '''simple docstring'''

           return self.set_tgt_lang_special_tokens(self.tgt_lang       )






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


           '''simple docstring'''

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






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


           '''simple docstring'''

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