




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 (
    BertTokenizer,
    ViltConfig,
    ViltForImageAndTextRetrieval,
    ViltForImagesAndTextClassification,
    ViltForMaskedLM,
    ViltForQuestionAnswering,
    ViltImageProcessor,
    ViltProcessor,
)
from transformers.utils import logging


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







def lowerCamelCase__  (     _lowercase					,		_lowercase=False					,		_lowercase=False					,		_lowercase=False ):

     '''simple docstring'''


     UpperCAmelCase_     :				Union[str, Any]  =     []
     for i in range(config.num_hidden_layers ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'''transformer.blocks.{i}.norm1.weight''', f'''vilt.encoder.layer.{i}.layernorm_before.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.norm1.bias''', f'''vilt.encoder.layer.{i}.layernorm_before.bias''') )
          rename_keys.append(
              (f'''transformer.blocks.{i}.attn.proj.weight''', f'''vilt.encoder.layer.{i}.attention.output.dense.weight''') )
          rename_keys.append(
              (f'''transformer.blocks.{i}.attn.proj.bias''', f'''vilt.encoder.layer.{i}.attention.output.dense.bias''') )
          rename_keys.append((f'''transformer.blocks.{i}.norm2.weight''', f'''vilt.encoder.layer.{i}.layernorm_after.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.norm2.bias''', f'''vilt.encoder.layer.{i}.layernorm_after.bias''') )
          rename_keys.append(
              (f'''transformer.blocks.{i}.mlp.fc1.weight''', f'''vilt.encoder.layer.{i}.intermediate.dense.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.mlp.fc1.bias''', f'''vilt.encoder.layer.{i}.intermediate.dense.bias''') )
          rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.weight''', f'''vilt.encoder.layer.{i}.output.dense.weight''') )
          rename_keys.append((f'''transformer.blocks.{i}.mlp.fc2.bias''', f'''vilt.encoder.layer.{i}.output.dense.bias''') )

     # embeddings
     rename_keys.extend(
         [
             # text embeddings
             ('''text_embeddings.word_embeddings.weight''', '''vilt.embeddings.text_embeddings.word_embeddings.weight'''),
             (
                 '''text_embeddings.position_embeddings.weight''',
                 '''vilt.embeddings.text_embeddings.position_embeddings.weight''',
             ),
             ('''text_embeddings.position_ids''', '''vilt.embeddings.text_embeddings.position_ids'''),
             (
                 '''text_embeddings.token_type_embeddings.weight''',
                 '''vilt.embeddings.text_embeddings.token_type_embeddings.weight''',
             ),
             ('''text_embeddings.LayerNorm.weight''', '''vilt.embeddings.text_embeddings.LayerNorm.weight'''),
             ('''text_embeddings.LayerNorm.bias''', '''vilt.embeddings.text_embeddings.LayerNorm.bias'''),
             # patch embeddings
             ('''transformer.cls_token''', '''vilt.embeddings.cls_token'''),
             ('''transformer.patch_embed.proj.weight''', '''vilt.embeddings.patch_embeddings.projection.weight'''),
             ('''transformer.patch_embed.proj.bias''', '''vilt.embeddings.patch_embeddings.projection.bias'''),
             ('''transformer.pos_embed''', '''vilt.embeddings.position_embeddings'''),
             # token type embeddings
             ('''token_type_embeddings.weight''', '''vilt.embeddings.token_type_embeddings.weight'''),
         ] )

     # final layernorm + pooler
     rename_keys.extend(
         [
             ('''transformer.norm.weight''', '''vilt.layernorm.weight'''),
             ('''transformer.norm.bias''', '''vilt.layernorm.bias'''),
             ('''pooler.dense.weight''', '''vilt.pooler.dense.weight'''),
             ('''pooler.dense.bias''', '''vilt.pooler.dense.bias'''),
         ] )

     # classifier head(s)
     if vqa_model:
          # classification head
          rename_keys.extend(
              [
                  ('''vqa_classifier.0.weight''', '''classifier.0.weight'''),
                  ('''vqa_classifier.0.bias''', '''classifier.0.bias'''),
                  ('''vqa_classifier.1.weight''', '''classifier.1.weight'''),
                  ('''vqa_classifier.1.bias''', '''classifier.1.bias'''),
                  ('''vqa_classifier.3.weight''', '''classifier.3.weight'''),
                  ('''vqa_classifier.3.bias''', '''classifier.3.bias'''),
              ] )
     elif nlvr_model:
          # classification head
          rename_keys.extend(
              [
                  ('''nlvr2_classifier.0.weight''', '''classifier.0.weight'''),
                  ('''nlvr2_classifier.0.bias''', '''classifier.0.bias'''),
                  ('''nlvr2_classifier.1.weight''', '''classifier.1.weight'''),
                  ('''nlvr2_classifier.1.bias''', '''classifier.1.bias'''),
                  ('''nlvr2_classifier.3.weight''', '''classifier.3.weight'''),
                  ('''nlvr2_classifier.3.bias''', '''classifier.3.bias'''),
              ] )
     else:
          pass

     return rename_keys







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


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







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     ['''head.weight''', '''head.bias''']
     for k in ignore_keys:
          state_dict.pop(_lowercase					,		_lowercase )







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     dct.pop(_lowercase )
     UpperCAmelCase_     :				Dict  =     val







@torch.no_grad()
def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     ViltConfig(image_size=384					,		patch_size=32					,		tie_word_embeddings=_lowercase )
     UpperCAmelCase_     :				int  =     False
     UpperCAmelCase_     :				str  =     False
     UpperCAmelCase_     :				int  =     False
     UpperCAmelCase_     :				Dict  =     False
     if "vqa" in checkpoint_url:
          UpperCAmelCase_     :				Optional[Any]  =     True
          UpperCAmelCase_     :				str  =     3129
          UpperCAmelCase_     :				List[str]  =     '''huggingface/label-files'''
          UpperCAmelCase_     :				List[Any]  =     '''vqa2-id2label.json'''
          UpperCAmelCase_     :				Any  =     json.load(open(hf_hub_download(_lowercase					,		_lowercase					,		repo_type='''dataset''' )					,		'''r''' ) )
          UpperCAmelCase_     :				Dict  =     {int(_lowercase ): v for k, v in idalabel.items()}
          UpperCAmelCase_     :				int  =     idalabel
          UpperCAmelCase_     :				List[Any]  =     {v: k for k, v in idalabel.items()}
          UpperCAmelCase_     :				Optional[Any]  =     ViltForQuestionAnswering(_lowercase )
     elif "nlvr" in checkpoint_url:
          UpperCAmelCase_     :				Optional[Any]  =     True
          UpperCAmelCase_     :				List[str]  =     2
          UpperCAmelCase_     :				Any  =     {0: '''False''', 1: '''True'''}
          UpperCAmelCase_     :				Optional[int]  =     {v: k for k, v in config.idalabel.items()}
          UpperCAmelCase_     :				int  =     3
          UpperCAmelCase_     :				Optional[int]  =     ViltForImagesAndTextClassification(_lowercase )
     elif "irtr" in checkpoint_url:
          UpperCAmelCase_     :				List[Any]  =     True
          UpperCAmelCase_     :				Tuple  =     ViltForImageAndTextRetrieval(_lowercase )
     elif "mlm_itm" in checkpoint_url:
          UpperCAmelCase_     :				List[str]  =     True
          UpperCAmelCase_     :				Any  =     ViltForMaskedLM(_lowercase )
     else:
          raise ValueError('''Unknown model type''' )

     # load state_dict of original model, remove and rename some keys
     UpperCAmelCase_     :				Optional[int]  =     torch.hub.load_state_dict_from_url(_lowercase					,		map_location='''cpu''' )['''state_dict''']
     UpperCAmelCase_     :				int  =     create_rename_keys(_lowercase					,		_lowercase					,		_lowercase					,		_lowercase )
     for src, dest in rename_keys:
          rename_key(_lowercase					,		_lowercase					,		_lowercase )
     read_in_q_k_v(_lowercase					,		_lowercase )
     if mlm_model or irtr_model:
          UpperCAmelCase_     :				Any  =     ['''itm_score.fc.weight''', '''itm_score.fc.bias''']
          for k in ignore_keys:
               state_dict.pop(_lowercase					,		_lowercase )

    # load state dict into HuggingFace model
     model.eval()
     if mlm_model:
          UpperCAmelCase_,       UpperCAmelCase_     :				int  =     model.load_state_dict(_lowercase					,		strict=_lowercase )
          assert missing_keys == ["mlm_score.decoder.bias"]
     else:
          model.load_state_dict(_lowercase )

     # Define processor
     UpperCAmelCase_     :				Dict  =     ViltImageProcessor(size=384 )
     UpperCAmelCase_     :				Optional[Any]  =     BertTokenizer.from_pretrained('''bert-base-uncased''' )
     UpperCAmelCase_     :				Optional[int]  =     ViltProcessor(_lowercase					,		_lowercase )

     # Forward pass on example inputs (image + text)
     if nlvr_model:
          UpperCAmelCase_     :				int  =     Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''					,		stream=_lowercase ).raw )
          UpperCAmelCase_     :				Any  =     Image.open(requests.get('''https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg'''					,		stream=_lowercase ).raw )
          UpperCAmelCase_     :				Any  =     (
              '''The left image contains twice the number of dogs as the right image, and at least two dogs in total are'''
              ''' standing.'''
          )
          UpperCAmelCase_     :				Tuple  =     processor(_lowercase					,		_lowercase					,		return_tensors='''pt''' )
          UpperCAmelCase_     :				Optional[int]  =     processor(_lowercase					,		_lowercase					,		return_tensors='''pt''' )
          UpperCAmelCase_     :				Tuple  =     model(
              input_ids=encoding_a.input_ids					,		pixel_values=encoding_a.pixel_values					,		pixel_values_a=encoding_a.pixel_values					,		)
     else:
          UpperCAmelCase_     :				str  =     Image.open(requests.get('''http://images.cocodataset.org/val2017/000000039769.jpg'''					,		stream=_lowercase ).raw )
          if mlm_model:
               UpperCAmelCase_     :				List[str]  =     '''a bunch of [MASK] laying on a [MASK].'''
          else:
               UpperCAmelCase_     :				Optional[Any]  =     '''How many cats are there?'''
          UpperCAmelCase_     :				str  =     processor(_lowercase					,		_lowercase					,		return_tensors='''pt''' )
          UpperCAmelCase_     :				Any  =     model(**_lowercase )

     # Verify outputs
     if mlm_model:
          UpperCAmelCase_     :				Optional[int]  =     torch.Size([1, 11, 30522] )
          UpperCAmelCase_     :				str  =     torch.tensor([-12.5061, -12.5123, -12.5174] )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]					,		_lowercase					,		atol=1E-4 )

          # verify masked token prediction equals "cats"
          UpperCAmelCase_     :				str  =     outputs.logits[0, 4, :].argmax(-1 ).item()
          assert tokenizer.decode([predicted_id] ) == "cats"
     elif vqa_model:
          UpperCAmelCase_     :				List[str]  =     torch.Size([1, 3129] )
          UpperCAmelCase_     :				Union[str, Any]  =     torch.tensor([-15.9495, -18.1472, -10.3041] )
          assert torch.allclose(outputs.logits[0, :3]					,		_lowercase					,		atol=1E-4 )
          assert outputs.logits.shape == expected_shape
          assert torch.allclose(outputs.logits[0, 0, :3]					,		_lowercase					,		atol=1E-4 )

          # verify vqa prediction equals "2"
          UpperCAmelCase_     :				List[str]  =     outputs.logits.argmax(-1 ).item()
          assert model.config.idalabel[predicted_idx] == "2"
     elif nlvr_model:
          UpperCAmelCase_     :				Union[str, Any]  =     torch.Size([1, 2] )
          UpperCAmelCase_     :				List[str]  =     torch.tensor([-2.8721, 2.1291] )
          assert torch.allclose(outputs.logits[0, :3]					,		_lowercase					,		atol=1E-4 )
          assert outputs.logits.shape == expected_shape

     Path(_lowercase ).mkdir(exist_ok=_lowercase )
     print(f'''Saving model and processor to {pytorch_dump_folder_path}''' )
     model.save_pretrained(_lowercase )
     processor.save_pretrained(_lowercase )


if __name__ == "__main__":
     __a							     =					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint_url',
         default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
         type=str,
         help='URL of the checkpoint you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     __a							     =					parser.parse_args()
     convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



"""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__					=logging.get_logger(__name__)

UpperCAmelCase__					="▁"

UpperCAmelCase__					={
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase__					={
    "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__					={
    "facebook/m2m100_418M": 1024,
}

# fmt: off
UpperCAmelCase__					={
    "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  lowerCamelCase__    (		_a     ):
      a			:  str           =		VOCAB_FILES_NAMES
      a			:  Dict           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a			:  int           =		PRETRAINED_VOCAB_FILES_MAP
      a			:  Union[str, Any]           =		["""input_ids""", """attention_mask"""]

      a			:  List[int]           =		[]
      a			:  List[int]           =		[]
      def __init__(		self						:						Optional[Any]   ,							A_						:						str   ,							A_						:						Dict   ,							A_						:						str=None   ,							A_						:						Dict=None   ,							A_						:						str="<s>"   ,							A_						:						Any="</s>"   ,							A_						:						List[Any]="</s>"   ,							A_						:						List[str]="<pad>"   ,							A_						:						Optional[int]="<unk>"   ,							A_						:						str="m2m100"   ,							A_						:						Optional[Dict[str, Any]] = None   ,							A_						:						Tuple=8   ,							**A_						:						Dict   ,							):






       '''simple docstring'''


       __lowercase						=						{} if sp_model_kwargs is None else sp_model_kwargs

       __lowercase						=						language_codes
       __lowercase						=						FAIRSEQ_LANGUAGE_CODES[language_codes]
       __lowercase						=						{lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

       __lowercase						=						kwargs.get("""additional_special_tokens"""   ,							[]				)
       kwargs["additional_special_tokens"] += [
           self.get_lang_token(A_				)
           for lang_code in fairseq_language_code
           if self.get_lang_token(A_				) not in kwargs["additional_special_tokens"]
       ]

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

       __lowercase						=						vocab_file
       __lowercase						=						load_json(A_				)
       __lowercase						=						{v: k for k, v in self.encoder.items()}
       __lowercase						=						spm_file
       __lowercase						=						load_spm(A_   ,							self.sp_model_kwargs				)

       __lowercase						=						len(self.encoder				)

       __lowercase						=						{
           self.get_lang_token(A_				): self.encoder_size + i for i, lang_code in enumerate(A_				)
       }
       __lowercase						=						{lang_code: self.encoder_size + i for i, lang_code in enumerate(A_				)}
       __lowercase						=						{v: k for k, v in self.lang_token_to_id.items()}

       __lowercase						=						src_lang if src_lang is not None else """en"""
       __lowercase						=						tgt_lang
       __lowercase						=						self.get_lang_id(self._src_lang				)
       self.set_src_lang_special_tokens(self._src_lang				)

       __lowercase						=						num_madeup_words
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       return len(self.encoder				) + len(self.lang_token_to_id				)
      @property
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       return self._src_lang
      @src_lang.setter
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str				):






       '''simple docstring'''


       __lowercase						=						new_src_lang
       self.set_src_lang_special_tokens(self._src_lang				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str				):






       '''simple docstring'''


       return self.sp_model.encode(A_   ,							out_type=A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						str				):






       '''simple docstring'''


       if token in self.lang_token_to_id:
        return self.lang_token_to_id[token]
       return self.encoder.get(A_   ,							self.encoder[self.unk_token]				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						int				):






       '''simple docstring'''


       if index in self.id_to_lang_token:
        return self.id_to_lang_token[index]
       return self.decoder.get(A_   ,							self.unk_token				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						[]
       __lowercase						=						""""""
       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(A_				) + token
         __lowercase						=						[]
        else:
         current_sub_tokens.append(A_				)
       out_string += self.sp_model.decode(A_				)
       return out_string.strip()
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]   ,							A_						:						List[int]   ,							A_						:						Optional[List[int]] = None   ,							A_						:						bool = False				):






       '''simple docstring'''


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

       __lowercase						=						[1] * len(self.prefix_tokens				)
       __lowercase						=						[1] * len(self.suffix_tokens				)
       if token_ids_a is None:
        return prefix_ones + ([0] * len(A_				)) + suffix_ones
       return prefix_ones + ([0] * len(A_				)) + ([0] * len(A_				)) + suffix_ones
      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						List[int]   ,							A_						:						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 SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						{self.convert_ids_to_tokens(A_				): i for i in range(self.vocab_size				)}
       vocab.update(self.added_tokens_encoder				)
       return vocab
      def __getstate__(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						self.__dict__.copy()
       __lowercase						=						None
       return state
      def __setstate__(		self						:						Optional[Any]   ,							A_						:						Dict				):






       '''simple docstring'''


       __lowercase						=						d

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

       __lowercase						=						load_spm(self.spm_file   ,							self.sp_model_kwargs				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple   ,							A_						:						str   ,							A_						:						Optional[str] = None				):






       '''simple docstring'''


       __lowercase						=						Path(A_				)
       if not save_dir.is_dir():
        raise OSError(F'''{save_directory} should be a directory'''				)
       __lowercase						=						save_dir / (
           (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
       )
       __lowercase						=						save_dir / (
           (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
       )

       save_json(self.encoder   ,							A_				)

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

       return (str(A_				), str(A_				))
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]   ,							A_						:						List[str]   ,							A_						:						str = "en"   ,							A_						:						Optional[List[str]] = None   ,							A_						:						str = "ro"   ,							**A_						:						List[Any]   ,							):






       '''simple docstring'''


       __lowercase						=						src_lang
       __lowercase						=						tgt_lang
       self.set_src_lang_special_tokens(self.src_lang				)
       return super().prepare_seqaseq_batch(A_   ,							A_   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						List[Any]   ,							A_						:						Optional[str]   ,							A_						:						Optional[str]   ,							**A_						:						Optional[Any]				):






       '''simple docstring'''


       if src_lang is None or tgt_lang is None:
        raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""				)
       __lowercase						=						src_lang
       __lowercase						=						self(A_   ,							add_special_tokens=A_   ,							**A_				)
       __lowercase						=						self.get_lang_id(A_				)
       __lowercase						=						tgt_lang_id
       return inputs
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       self.set_src_lang_special_tokens(self.src_lang				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       self.set_tgt_lang_special_tokens(self.tgt_lang				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]   ,							A_						:						str				):






       '''simple docstring'''


       __lowercase						=						self.get_lang_token(A_				)
       __lowercase						=						self.lang_token_to_id[lang_token]
       __lowercase						=						[self.cur_lang_id]
       __lowercase						=						[self.eos_token_id]
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]   ,							A_						:						str				):






       '''simple docstring'''


       __lowercase						=						self.get_lang_token(A_				)
       __lowercase						=						self.lang_token_to_id[lang_token]
       __lowercase						=						[self.cur_lang_id]
       __lowercase						=						[self.eos_token_id]
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str				):






       '''simple docstring'''


       return self.lang_code_to_token[lang]






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							A_						:						str				):






       '''simple docstring'''


       __lowercase						=						self.get_lang_token(A_				)
       return self.lang_token_to_id[lang_token]






def lowerCAmelCase_   (					UpperCamelCase__						:			str					, UpperCamelCase__						:			Dict[str, Any]			):






 """simple docstring"""







 __lowercase						=						sentencepiece.SentencePieceProcessor(**UpperCamelCase__			)
 spm.Load(str(UpperCamelCase__			)			)
 return spm


def lowerCAmelCase_   (					UpperCamelCase__						:			str			):






 """simple docstring"""







 with open(UpperCamelCase__					, """r"""			) as f:
  return json.load(UpperCamelCase__			)


def lowerCAmelCase_   (					UpperCamelCase__						:			List[str]					, UpperCamelCase__						:			str			):






 """simple docstring"""







 with open(UpperCamelCase__					, """w"""			) as f:
  json.dump(UpperCamelCase__					, UpperCamelCase__					, indent=2			)
