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


snake_case__				= logging.get_logger(__name__)

snake_case__				= '▁'

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

snake_case__				= {
    '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',
    },
}

snake_case__				= {
    'facebook/m2m100_418M': 1024,
}

# fmt: off
snake_case__				= {
    '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  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  VOCAB_FILES_NAMES
     A_										=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_										=  PRETRAINED_VOCAB_FILES_MAP
     A_										=  ['input_ids', 'attention_mask']

     A_										=  []
     A_										=  []



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






           """simple docstring"""
           _lowerCamelCase			    =				{} if sp_model_kwargs is None else sp_model_kwargs

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

           _lowerCamelCase			    =				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_				,				)

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

           _lowerCamelCase			    =				len(self.encoder						)

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

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

           _lowerCamelCase			    =				num_madeup_words



     @property
     def 		UpperCamelCase_						(					self						) ->				int:






           """simple docstring"""
           return len(self.encoder						) + len(self.lang_token_to_id						)



     @property
     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           return self._src_lang



     @src_lang.setter
     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				new_src_lang
           self.set_src_lang_special_tokens(self._src_lang						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[str]:






           """simple docstring"""
           return self.sp_model.encode(A_				,				out_type=A_						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				List[Any]:






           """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 		UpperCamelCase_						(					self				,				A_						) ->				str:






           """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 		UpperCamelCase_						(					self				,				A_						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				''''''
           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
                       _lowerCamelCase			    =				[]
                 else:
                       current_sub_tokens.append(A_						)
           out_string += self.sp_model.decode(A_						)
           return out_string.strip()



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None				,				A_ = False						) ->				List[int]:






           """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_						)

           _lowerCamelCase			    =				[1] * len(self.prefix_tokens						)
           _lowerCamelCase			    =				[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 		UpperCamelCase_						(					self				,				A_				,				A_ = 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 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				{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						) ->				Dict:






           """simple docstring"""
           _lowerCamelCase			    =				self.__dict__.copy()
           _lowerCamelCase			    =				None
           return state



     def __setstate__(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				d

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

           _lowerCamelCase			    =				load_spm(self.spm_file				,				self.sp_model_kwargs						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				Tuple[str]:






           """simple docstring"""
           _lowerCamelCase			    =				Path(A_						)
           if not save_dir.is_dir():
                 raise OSError(F'{save_directory} should be a directory'						)
           _lowerCamelCase			    =				save_dir / (
               (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
           )
           _lowerCamelCase			    =				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:
                       _lowerCamelCase			    =				self.sp_model.serialized_model_proto()
                       fi.write(A_						)

           return (str(A_						), str(A_						))



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = "en"				,				A_ = None				,				A_ = "ro"				,				**A_				,				) ->				BatchEncoding:






           """simple docstring"""
           _lowerCamelCase			    =				src_lang
           _lowerCamelCase			    =				tgt_lang
           self.set_src_lang_special_tokens(self.src_lang						)
           return super().prepare_seqaseq_batch(A_				,				A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				**A_						) ->				Tuple:






           """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'''						)
           _lowerCamelCase			    =				src_lang
           _lowerCamelCase			    =				self(A_				,				add_special_tokens=A_				,				**A_						)
           _lowerCamelCase			    =				self.get_lang_id(A_						)
           _lowerCamelCase			    =				tgt_lang_id
           return inputs



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           self.set_src_lang_special_tokens(self.src_lang						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           self.set_tgt_lang_special_tokens(self.tgt_lang						)



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_lang_token(A_						)
           _lowerCamelCase			    =				self.lang_token_to_id[lang_token]
           _lowerCamelCase			    =				[self.cur_lang_id]
           _lowerCamelCase			    =				[self.eos_token_id]



     def 		UpperCamelCase_						(					self				,				A_						) ->				None:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_lang_token(A_						)
           _lowerCamelCase			    =				self.lang_token_to_id[lang_token]
           _lowerCamelCase			    =				[self.cur_lang_id]
           _lowerCamelCase			    =				[self.eos_token_id]



     def 		UpperCamelCase_						(					self				,				A_						) ->				str:






           """simple docstring"""
           return self.lang_code_to_token[lang]





     def 		UpperCamelCase_						(					self				,				A_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_lang_token(A_						)
           return self.lang_token_to_id[lang_token]





def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    sentencepiece.SentencePieceProcessor:



      '''simple docstring'''

      _lowerCamelCase			    =				sentencepiece.SentencePieceProcessor(**__UpperCAmelCase		)
      spm.Load(str(__UpperCAmelCase		)		)
      return spm
def       __magic_name__(  __UpperCAmelCase		)						->    Union[Dict, List]:



      '''simple docstring'''

      with open(__UpperCAmelCase ,						'''r'''		) as f:
            return json.load(__UpperCAmelCase		)




def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    None:



      '''simple docstring'''

      with open(__UpperCAmelCase ,						'''w'''		) as f:
            json.dump(__UpperCAmelCase ,						__UpperCAmelCase ,						indent=2		)
import argparse

import torch
from datasets import load_dataset
from donut import DonutModel

from transformers import (
    DonutImageProcessor,
    DonutProcessor,
    DonutSwinConfig,
    DonutSwinModel,
    MBartConfig,
    MBartForCausalLM,
    VisionEncoderDecoderModel,
    XLMRobertaTokenizerFast,
)
def       __magic_name__(  __UpperCAmelCase		)						->    str:



      '''simple docstring'''

      _lowerCamelCase			    =				model.config

      _lowerCamelCase			    =				DonutSwinConfig(
          image_size=original_config.input_size ,						patch_size=4 ,						depths=original_config.encoder_layer ,						num_heads=[4, 8, 16, 32] ,						window_size=original_config.window_size ,						embed_dim=128 ,						)
      _lowerCamelCase			    =				MBartConfig(
          is_decoder=__UpperCAmelCase ,						is_encoder_decoder=__UpperCAmelCase ,						add_cross_attention=__UpperCAmelCase ,						decoder_layers=original_config.decoder_layer ,						max_position_embeddings=original_config.max_position_embeddings ,						vocab_size=len(
              model.decoder.tokenizer		) ,						scale_embedding=__UpperCAmelCase ,						add_final_layer_norm=__UpperCAmelCase ,						)

      return encoder_config, decoder_config
def       __magic_name__(  __UpperCAmelCase		)						->    Tuple:



      '''simple docstring'''

      if "encoder.model" in name:
            _lowerCamelCase			    =				name.replace('''encoder.model''' ,						'''encoder'''		)
      if "decoder.model" in name:
            _lowerCamelCase			    =				name.replace('''decoder.model''' ,						'''decoder'''		)
      if "patch_embed.proj" in name:
            _lowerCamelCase			    =				name.replace('''patch_embed.proj''' ,						'''embeddings.patch_embeddings.projection'''		)
      if "patch_embed.norm" in name:
            _lowerCamelCase			    =				name.replace('''patch_embed.norm''' ,						'''embeddings.norm'''		)
      if name.startswith('''encoder'''		):
            if "layers" in name:
                  _lowerCamelCase			    =				'''encoder.''' + name
            if "attn.proj" in name:
                  _lowerCamelCase			    =				name.replace('''attn.proj''' ,						'''attention.output.dense'''		)
            if "attn" in name and "mask" not in name:
                  _lowerCamelCase			    =				name.replace('''attn''' ,						'''attention.self'''		)
            if "norm1" in name:
                  _lowerCamelCase			    =				name.replace('''norm1''' ,						'''layernorm_before'''		)
            if "norm2" in name:
                  _lowerCamelCase			    =				name.replace('''norm2''' ,						'''layernorm_after'''		)
            if "mlp.fc1" in name:
                  _lowerCamelCase			    =				name.replace('''mlp.fc1''' ,						'''intermediate.dense'''		)
            if "mlp.fc2" in name:
                  _lowerCamelCase			    =				name.replace('''mlp.fc2''' ,						'''output.dense'''		)

            if name == "encoder.norm.weight":
                  _lowerCamelCase			    =				'''encoder.layernorm.weight'''
            if name == "encoder.norm.bias":
                  _lowerCamelCase			    =				'''encoder.layernorm.bias'''

      return name
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase		)						->    Dict:



      '''simple docstring'''

      for key in orig_state_dict.copy().keys():
            _lowerCamelCase			    =				orig_state_dict.pop(__UpperCAmelCase		)

            if "qkv" in key:
                  _lowerCamelCase			    =				key.split('''.'''		)
                  _lowerCamelCase			    =				int(key_split[3]		)
                  _lowerCamelCase			    =				int(key_split[5]		)
                  _lowerCamelCase			    =				model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

                  if "weight" in key:
                        _lowerCamelCase			    =				val[:dim, :]
                        _lowerCamelCase			    =				val[dim : dim * 2, :]
                        _lowerCamelCase			    =				val[-dim:, :]
                  else:
                        _lowerCamelCase			    =				val[:dim]
                        _lowerCamelCase			    =				val[dim : dim * 2]
                        _lowerCamelCase			    =				val[-dim:]
            elif "attn_mask" in key or key in ["encoder.model.norm.weight", "encoder.model.norm.bias"]:
                  # HuggingFace implementation doesn't use attn_mask buffer
                  # and model doesn't use final LayerNorms for the encoder
                  pass
            else:
                  _lowerCamelCase			    =				val

      return orig_state_dict
def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=None ,						__UpperCAmelCase=False		)						->    int:



      '''simple docstring'''

      _lowerCamelCase			    =				DonutModel.from_pretrained(__UpperCAmelCase		).eval()

      # load HuggingFace model
      _lowerCamelCase    ,					_lowerCamelCase			    =				get_configs(__UpperCAmelCase		)
      _lowerCamelCase			    =				DonutSwinModel(__UpperCAmelCase		)
      _lowerCamelCase			    =				MBartForCausalLM(__UpperCAmelCase		)
      _lowerCamelCase			    =				VisionEncoderDecoderModel(encoder=__UpperCAmelCase ,						decoder=__UpperCAmelCase		)
      model.eval()

      _lowerCamelCase			    =				original_model.state_dict()
      _lowerCamelCase			    =				convert_state_dict(__UpperCAmelCase ,						__UpperCAmelCase		)
      model.load_state_dict(__UpperCAmelCase		)

      # verify results on scanned document
      _lowerCamelCase			    =				load_dataset('''hf-internal-testing/example-documents'''		)
      _lowerCamelCase			    =				dataset['''test'''][0]['''image'''].convert('''RGB'''		)

      _lowerCamelCase			    =				XLMRobertaTokenizerFast.from_pretrained(__UpperCAmelCase ,						from_slow=__UpperCAmelCase		)
      _lowerCamelCase			    =				DonutImageProcessor(
          do_align_long_axis=original_model.config.align_long_axis ,						size=original_model.config.input_size[::-1]		)
      _lowerCamelCase			    =				DonutProcessor(__UpperCAmelCase ,						__UpperCAmelCase		)
      _lowerCamelCase			    =				processor(__UpperCAmelCase ,						return_tensors='''pt'''		).pixel_values

      if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
            _lowerCamelCase			    =				'''<s_docvqa><s_question>{user_input}</s_question><s_answer>'''
            _lowerCamelCase			    =				'''When is the coffee break?'''
            _lowerCamelCase			    =				task_prompt.replace('''{user_input}''' ,						__UpperCAmelCase		)
      elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
            _lowerCamelCase			    =				'''<s_rvlcdip>'''
      elif model_name in [
          "naver-clova-ix/donut-base-finetuned-cord-v1",
          "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
      ]:
            _lowerCamelCase			    =				'''<s_cord>'''
      elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
            _lowerCamelCase			    =				'''s_cord-v2>'''
      elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
            _lowerCamelCase			    =				'''<s_zhtrainticket>'''
      elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
            # use a random prompt
            _lowerCamelCase			    =				'''hello world'''
      else:
            raise ValueError('''Model name not supported'''		)
      _lowerCamelCase			    =				original_model.decoder.tokenizer(__UpperCAmelCase ,						add_special_tokens=__UpperCAmelCase ,						return_tensors='''pt'''		)[
          '''input_ids'''
      ]

      _lowerCamelCase			    =				original_model.encoder.model.patch_embed(__UpperCAmelCase		)
      _lowerCamelCase    ,					_lowerCamelCase			    =				model.encoder.embeddings(__UpperCAmelCase		)
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase ,						atol=1E-3		)

      # verify encoder hidden states
      _lowerCamelCase			    =				original_model.encoder(__UpperCAmelCase		)
      _lowerCamelCase			    =				model.encoder(__UpperCAmelCase		).last_hidden_state
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase ,						atol=1E-2		)

      # verify decoder hidden states
      _lowerCamelCase			    =				original_model(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase		).logits
      _lowerCamelCase			    =				model(__UpperCAmelCase ,						decoder_input_ids=__UpperCAmelCase		).logits
      assert torch.allclose(__UpperCAmelCase ,						__UpperCAmelCase ,						atol=1E-3		)
      print('''Looks ok!'''		)

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

      if push_to_hub:
            model.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1] ,						commit_message='''Update model'''		)
            processor.push_to_hub('''nielsr/''' + model_name.split('''/'''		)[-1] ,						commit_message='''Update model'''		)


if __name__ == "__main__":
     snake_case__				= argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='naver-clova-ix/donut-base-finetuned-docvqa',
         required=False,
         type=str,
         help='Name of the original model you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default=None,
         required=False,
         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 and processor to the 🤗 hub.',
     )

     snake_case__				= parser.parse_args()
     convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)