'''simple docstring'''

import argparse
import json

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

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation







def __UpperCamelCase(   _A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  3_84
   UpperCAmelCase__							:				List[str]   				=  7
   if "tiny" in model_name:
      UpperCAmelCase__							:				Tuple   				=  96
      UpperCAmelCase__							:				List[str]   				=  (2, 2, 6, 2)
      UpperCAmelCase__							:				Any   				=  (3, 6, 12, 24)
   elif "small" in model_name:
      UpperCAmelCase__							:				Optional[Any]   				=  96
      UpperCAmelCase__							:				Optional[Any]   				=  (2, 2, 18, 2)
      UpperCAmelCase__							:				List[str]   				=  (3, 6, 12, 24)
   elif "base" in model_name:
      UpperCAmelCase__							:				Tuple   				=  1_28
      UpperCAmelCase__							:				Any   				=  (2, 2, 18, 2)
      UpperCAmelCase__							:				str   				=  (4, 8, 16, 32)
      UpperCAmelCase__							:				int   				=  12
      UpperCAmelCase__							:				int   				=  5_12
   elif "large" in model_name:
      UpperCAmelCase__							:				Any   				=  1_92
      UpperCAmelCase__							:				Optional[Any]   				=  (2, 2, 18, 2)
      UpperCAmelCase__							:				Optional[int]   				=  (6, 12, 24, 48)
      UpperCAmelCase__							:				int   				=  12
      UpperCAmelCase__							:				str   				=  7_68

   # set label information
   UpperCAmelCase__							:				Optional[Any]   				=  1_50
   UpperCAmelCase__							:				Optional[int]   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				Dict   				=  '''ade20k-id2label.json'''
   UpperCAmelCase__							:				Tuple   				=  json.load(open(hf_hub_download(_A  ,			_A  ,			repo_type='''dataset'''    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				str   				=  {int(_A    ): v for k, v in idalabel.items()}
   UpperCAmelCase__							:				Union[str, Any]   				=  {v: k for k, v in idalabel.items()}

   UpperCAmelCase__							:				Tuple   				=  SwinConfig(
       embed_dim=_A  ,			depths=_A  ,			num_heads=_A  ,			window_size=_A  ,			out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']  ,			)
   UpperCAmelCase__							:				Union[str, Any]   				=  UperNetConfig(
       backbone_config=_A  ,			auxiliary_in_channels=_A  ,			num_labels=_A  ,			idalabel=_A  ,			labelaid=_A  ,			)

   return config







def __UpperCamelCase(   _A					:				int    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  []

   # fmt: off
   # stem
   rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')    )
   rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')    )
   rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')    )
   rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')    )
   # stages
   for i in range(len(config.backbone_config.depths    )    ):
      for j in range(config.backbone_config.depths[i]    ):
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.norm2.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight''')    )
         rename_keys.append((F'''backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias''', F'''backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias''')    )

      if i < 3:
         rename_keys.append((F'''backbone.stages.{i}.downsample.reduction.weight''', F'''backbone.encoder.layers.{i}.downsample.reduction.weight''')    )
         rename_keys.append((F'''backbone.stages.{i}.downsample.norm.weight''', F'''backbone.encoder.layers.{i}.downsample.norm.weight''')    )
         rename_keys.append((F'''backbone.stages.{i}.downsample.norm.bias''', F'''backbone.encoder.layers.{i}.downsample.norm.bias''')    )
      rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''')    )
      rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''')    )

   # decode head
   rename_keys.extend(
       [
           ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
           ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
           ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
           ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
       ]    )
   # fmt: on

   return rename_keys







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				List[Any]  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  dct.pop(_A    )
   UpperCAmelCase__							:				List[Any]   				=  val







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  [int(backbone_config.embed_dim * 2**i    ) for i in range(len(backbone_config.depths    )    )]
   for i in range(len(backbone_config.depths    )    ):
      UpperCAmelCase__							:				int   				=  num_features[i]
      for j in range(backbone_config.depths[i]    ):
         # fmt: off
         # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
         UpperCAmelCase__							:				List[str]   				=  state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'''    )
         UpperCAmelCase__							:				Union[str, Any]   				=  state_dict.pop(F'''backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'''    )
         # next, add query, keys and values (in that order) to the state dict
         UpperCAmelCase__							:				List[Any]   				=  in_proj_weight[:dim, :]
         UpperCAmelCase__							:				Dict   				=  in_proj_bias[: dim]
         UpperCAmelCase__							:				int   				=  in_proj_weight[
             dim : dim * 2, :
         ]
         UpperCAmelCase__							:				Dict   				=  in_proj_bias[
             dim : dim * 2
         ]
         UpperCAmelCase__							:				Optional[int]   				=  in_proj_weight[
             -dim :, :
         ]
         UpperCAmelCase__							:				Optional[Any]   				=  in_proj_bias[-dim :]
         # fmt: on







def __UpperCamelCase(   _A					:				Optional[int]    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				Dict   				=  x.shape
   UpperCAmelCase__							:				List[Any]   				=  x.reshape(_A  ,			4  ,			in_channel // 4    )
   UpperCAmelCase__							:				Optional[Any]   				=  x[:, [0, 2, 1, 3], :].transpose(1  ,			2    ).reshape(_A  ,			_A    )
   return x







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__				,      UpperCAmelCase__							:				Union[str, Any]   				=  x.shape
   UpperCAmelCase__							:				Dict   				=  x.reshape(_A  ,			in_channel // 4  ,			4    )
   UpperCAmelCase__							:				int   				=  x[:, :, [0, 2, 1, 3]].transpose(1  ,			2    ).reshape(_A  ,			_A    )

   return x







def __UpperCamelCase(   _A					:				Optional[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  x.shape[0]
   UpperCAmelCase__							:				Optional[Any]   				=  x.reshape(4  ,			in_channel // 4    )
   UpperCAmelCase__							:				Optional[Any]   				=  x[[0, 2, 1, 3], :].transpose(0  ,			1    ).reshape(_A    )
   return x







def __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  x.shape[0]
   UpperCAmelCase__							:				List[Any]   				=  x.reshape(in_channel // 4  ,			4    )
   UpperCAmelCase__							:				Optional[Any]   				=  x[:, [0, 2, 1, 3]].transpose(0  ,			1    ).reshape(_A    )
   return x







def __UpperCamelCase(   _A					:				Optional[int]  ,			_A					:				Dict  ,			_A					:				Any    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Any   				=  {
       '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
       '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
       '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
       '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
   }
   UpperCAmelCase__							:				str   				=  model_name_to_url[model_name]
   UpperCAmelCase__							:				Tuple   				=  torch.hub.load_state_dict_from_url(_A  ,			map_location='''cpu'''  ,			file_name=_A    )[
       '''state_dict'''
   ]

   for name, param in state_dict.items():
      print(_A  ,			param.shape    )

   UpperCAmelCase__							:				List[str]   				=  get_upernet_config(_A    )
   UpperCAmelCase__							:				Tuple   				=  UperNetForSemanticSegmentation(_A    )
   model.eval()

   # replace "bn" => "batch_norm"
   for key in state_dict.copy().keys():
      UpperCAmelCase__							:				str   				=  state_dict.pop(_A    )
      if "bn" in key:
         UpperCAmelCase__							:				List[str]   				=  key.replace('''bn'''  ,			'''batch_norm'''    )
      UpperCAmelCase__							:				str   				=  val

   # rename keys
   UpperCAmelCase__							:				int   				=  create_rename_keys(_A    )
   for src, dest in rename_keys:
      rename_key(_A  ,			_A  ,			_A    )
   read_in_q_k_v(_A  ,			config.backbone_config    )

   # fix downsample parameters
   for key, value in state_dict.items():
      if "downsample" in key:
         if "reduction" in key:
            UpperCAmelCase__							:				Tuple   				=  reverse_correct_unfold_reduction_order(_A    )
         if "norm" in key:
            UpperCAmelCase__							:				int   				=  reverse_correct_unfold_norm_order(_A    )

   model.load_state_dict(_A    )

   # verify on image
   UpperCAmelCase__							:				Dict   				=  '''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
   UpperCAmelCase__							:				int   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    ).convert('''RGB'''    )

   UpperCAmelCase__							:				List[str]   				=  SegformerImageProcessor()
   UpperCAmelCase__							:				Optional[Any]   				=  processor(_A  ,			return_tensors='''pt'''    ).pixel_values

   with torch.no_grad():
      UpperCAmelCase__							:				Tuple   				=  model(_A    )
      UpperCAmelCase__							:				List[str]   				=  outputs.logits

   print(logits.shape    )
   print('''First values of logits:'''  ,			logits[0, 0, :3, :3]    )
   # assert values
   if model_name == "upernet-swin-tiny":
      UpperCAmelCase__							:				Dict   				=  torch.tensor(
          [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]    )
   elif model_name == "upernet-swin-small":
      UpperCAmelCase__							:				List[Any]   				=  torch.tensor(
          [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]    )
   elif model_name == "upernet-swin-base":
      UpperCAmelCase__							:				Optional[int]   				=  torch.tensor(
          [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]    )
   elif model_name == "upernet-swin-large":
      UpperCAmelCase__							:				Tuple   				=  torch.tensor(
          [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]    )
   print('''Logits:'''  ,			outputs.logits[0, 0, :3, :3]    )
   assert torch.allclose(outputs.logits[0, 0, :3, :3]  ,			_A  ,			atol=1e-4    )
   print('''Looks ok!'''    )

   if pytorch_dump_folder_path is not None:
      print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''    )
      model.save_pretrained(_A    )
      print(F'''Saving processor to {pytorch_dump_folder_path}'''    )
      processor.save_pretrained(_A    )

   if push_to_hub:
      print(F'''Pushing model and processor for {model_name} to hub'''    )
      model.push_to_hub(F'''openmmlab/{model_name}'''    )
      processor.push_to_hub(F'''openmmlab/{model_name}'''    )


if __name__ == "__main__":
  UpperCamelCase__     :	int				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--model_name',
      default='upernet-swin-tiny',
      type=str,
      choices=[f"""upernet-swin-{size}""" for size in ['tiny', 'small', 'base', 'large']],
      help='Name of the Swin + UperNet model you\'d like to convert.',
  )
  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__     :	Dict				=			parser.parse_args()
  convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


UpperCamelCase__     :	Optional[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	str				=			'▁'

UpperCamelCase__     :	Any				=			{
    'vocab_file': 'vocab.json',
    'spm_file': 'sentencepiece.bpe.model',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCamelCase__     :	Union[str, Any]				=			{
    'vocab_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
    },
    'spm_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_config_file': {
        'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
        'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
    },
}

UpperCamelCase__     :	Dict				=			{
    'facebook/m2m100_418M': 1_024,
}

# fmt: off
UpperCamelCase__     :	Optional[int]				=			{
    'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
    'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Optional[int]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_      :      Optional[int]      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Optional[int]      							=   ['''input_ids''', '''attention_mask''']

      UpperCAmelCase_      :      List[int]      							=   []
      UpperCAmelCase_      :      List[int]      							=   []




      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_="<s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="<pad>"					,lowerCamelCase_="<unk>"					,lowerCamelCase_="m2m100"					,lowerCamelCase_ = None					,lowerCamelCase_=8					,**lowerCamelCase_					,)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  {} if sp_model_kwargs is None else sp_model_kwargs

         UpperCAmelCase__							:				Dict   				=  language_codes
         UpperCAmelCase__							:				Union[str, Any]   				=  FAIRSEQ_LANGUAGE_CODES[language_codes]
         UpperCAmelCase__							:				Union[str, Any]   				=  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

         UpperCAmelCase__							:				Any   				=  kwargs.get('''additional_special_tokens'''					,[]							)
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(lowerCamelCase_							)
             for lang_code in fairseq_language_code
             if self.get_lang_token(lowerCamelCase_							) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=lowerCamelCase_					,tgt_lang=lowerCamelCase_					,bos_token=lowerCamelCase_					,eos_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,unk_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,language_codes=lowerCamelCase_					,sp_model_kwargs=self.sp_model_kwargs					,num_madeup_words=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				Optional[int]   				=  vocab_file
         UpperCAmelCase__							:				Optional[Any]   				=  load_json(lowerCamelCase_							)
         UpperCAmelCase__							:				List[str]   				=  {v: k for k, v in self.encoder.items()}
         UpperCAmelCase__							:				List[Any]   				=  spm_file
         UpperCAmelCase__							:				Any   				=  load_spm(lowerCamelCase_					,self.sp_model_kwargs							)

         UpperCAmelCase__							:				int   				=  len(self.encoder							)

         UpperCAmelCase__							:				Optional[int]   				=  {
             self.get_lang_token(lowerCamelCase_							): self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_							)
         }
         UpperCAmelCase__							:				List[Any]   				=  {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCamelCase_							)}
         UpperCAmelCase__							:				List[str]   				=  {v: k for k, v in self.lang_token_to_id.items()}

         UpperCAmelCase__							:				Optional[int]   				=  src_lang if src_lang is not None else '''en'''
         UpperCAmelCase__							:				int   				=  tgt_lang
         UpperCAmelCase__							:				int   				=  self.get_lang_id(self._src_lang							)
         self.set_src_lang_special_tokens(self._src_lang							)

         UpperCAmelCase__							:				Optional[int]   				=  num_madeup_words




      @property
      def 		lowerCAmelCase__					(				self							)			->			int:



         '''simple docstring'''



         return len(self.encoder							) + len(self.lang_token_to_id							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         return self._src_lang




      @src_lang.setter
      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  new_src_lang
         self.set_src_lang_special_tokens(self._src_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[str]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         if token in self.lang_token_to_id:
            return self.lang_token_to_id[token]
         return self.encoder.get(lowerCamelCase_					,self.encoder[self.unk_token]							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         if index in self.id_to_lang_token:
            return self.id_to_lang_token[index]
         return self.decoder.get(lowerCamelCase_					,self.unk_token							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  []
         UpperCAmelCase__							:				str   				=  ''''''
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += self.sp_model.decode(lowerCamelCase_							) + token
               UpperCAmelCase__							:				str   				=  []
            else:
               current_sub_tokens.append(lowerCamelCase_							)
         out_string += self.sp_model.decode(lowerCamelCase_							)
         return out_string.strip()




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



         '''simple docstring'''



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

         UpperCAmelCase__							:				Dict   				=  [1] * len(self.prefix_tokens							)
         UpperCAmelCase__							:				Optional[Any]   				=  [1] * len(self.suffix_tokens							)
         if token_ids_a is None:
            return prefix_ones + ([0] * len(lowerCamelCase_							)) + suffix_ones
         return prefix_ones + ([0] * len(lowerCamelCase_							)) + ([0] * len(lowerCamelCase_							)) + suffix_ones




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = 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 		lowerCAmelCase__					(				self							)			->			Dict:



         '''simple docstring'''



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




      def __getstate__(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				str   				=  None
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  d

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

         UpperCAmelCase__							:				int   				=  load_spm(self.spm_file					,self.sp_model_kwargs							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  Path(lowerCamelCase_							)
         if not save_dir.is_dir():
            raise OSError(f'''{save_directory} should be a directory'''							)
         UpperCAmelCase__							:				Optional[int]   				=  save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
         )
         UpperCAmelCase__							:				str   				=  save_dir / (
             (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
         )

         save_json(self.encoder					,lowerCamelCase_							)

         if os.path.abspath(self.spm_file							) != os.path.abspath(lowerCamelCase_							) and os.path.isfile(self.spm_file							):
            copyfile(self.spm_file					,lowerCamelCase_							)
         elif not os.path.isfile(self.spm_file							):
            with open(lowerCamelCase_					,'''wb'''							) as fi:
               UpperCAmelCase__							:				str   				=  self.sp_model.serialized_model_proto()
               fi.write(lowerCamelCase_							)

         return (str(lowerCamelCase_							), str(lowerCamelCase_							))




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = "en"					,lowerCamelCase_ = None					,lowerCamelCase_ = "ro"					,**lowerCamelCase_					,)			->			BatchEncoding:



         '''simple docstring'''



         UpperCAmelCase__							:				int   				=  src_lang
         UpperCAmelCase__							:				Tuple   				=  tgt_lang
         self.set_src_lang_special_tokens(self.src_lang							)
         return super().prepare_seqaseq_batch(lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)			->			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'''							)
         UpperCAmelCase__							:				List[str]   				=  src_lang
         UpperCAmelCase__							:				List[str]   				=  self(lowerCamelCase_					,add_special_tokens=lowerCamelCase_					,**lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  self.get_lang_id(lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  tgt_lang_id
         return inputs




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



         '''simple docstring'''



         self.set_src_lang_special_tokens(self.src_lang							)




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



         '''simple docstring'''



         self.set_tgt_lang_special_tokens(self.tgt_lang							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  self.get_lang_token(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  self.lang_token_to_id[lang_token]
         UpperCAmelCase__							:				Union[str, Any]   				=  [self.cur_lang_id]
         UpperCAmelCase__							:				Dict   				=  [self.eos_token_id]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			None:



         '''simple docstring'''



         UpperCAmelCase__							:				Any   				=  self.get_lang_token(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  self.lang_token_to_id[lang_token]
         UpperCAmelCase__							:				Tuple   				=  [self.cur_lang_id]
         UpperCAmelCase__							:				str   				=  [self.eos_token_id]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         return self.lang_code_to_token[lang]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.get_lang_token(lowerCamelCase_							)
         return self.lang_token_to_id[lang_token]







def __UpperCamelCase(   _A					:				str  ,			_A					:				Dict[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  sentencepiece.SentencePieceProcessor(**_A    )
   spm.Load(str(_A    )    )
   return spm







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''r'''    ) as f:
      return json.load(_A    )







def __UpperCamelCase(   _A					:				List[str]  ,			_A					:				str    ):
   '''simple docstring'''
   with open(_A  ,			'''w'''    ) as f:
      json.dump(_A  ,			_A  ,			indent=2    )




