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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


snake_case__				= logging.get_logger(__name__)

snake_case__				= '▁'

snake_case__				= {'vocab_file': 'prophetnet.tokenizer'}

snake_case__				= {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

snake_case__				= {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

snake_case__				= {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}
def       __magic_name__(  __UpperCAmelCase		)						->    Any:



      '''simple docstring'''

      _lowerCamelCase			    =				collections.OrderedDict()
      with open(__UpperCAmelCase ,						'''r''' ,						encoding='''utf-8'''		) as reader:
            _lowerCamelCase			    =				reader.readlines()
      for index, token in enumerate(__UpperCAmelCase		):
            _lowerCamelCase			    =				token.rstrip('''\n'''		)
            _lowerCamelCase			    =				index
      return vocab




class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

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



     def __init__(					self				,				A_				,				A_="[SEP]"				,				A_="[SEP]"				,				A_="[SEP]"				,				A_="[UNK]"				,				A_="[PAD]"				,				A_="[CLS]"				,				A_="[MASK]"				,				A_ = None				,				**A_				,				) ->				None:






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

           super().__init__(
               bos_token=A_				,				eos_token=A_				,				sep_token=A_				,				unk_token=A_				,				pad_token=A_				,				cls_token=A_				,				mask_token=A_				,				sp_model_kwargs=self.sp_model_kwargs				,				**A_				,				)

           try:
                 import sentencepiece as spm
           except ImportError:
                 logger.warning(
                     '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                     ''' pip install sentencepiece'''						)
                 raise

           _lowerCamelCase			    =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
           self.sp_model.Load(str(A_						)						)
           _lowerCamelCase			    =				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'

           # put special tokens and [unused] tokens into the vocab
           _lowerCamelCase			    =				{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

           for i in range(10						):
                 _lowerCamelCase			    =				F'[unused{i}]'
                 _lowerCamelCase			    =				5 + i

           # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
           _lowerCamelCase			    =				12
           _lowerCamelCase			    =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}
           for k in self.fairseq_tokens_to_ids.keys():
                 self.unique_no_split_tokens.append(A_						)



     def __getstate__(					self						) ->				List[str]:






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



     def __setstate__(					self				,				A_						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				d
           try:
                 import sentencepiece as spm
           except ImportError:
                 logger.warning(
                     '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                     ''' pip install sentencepiece'''						)
                 raise

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

           _lowerCamelCase			    =				spm.SentencePieceProcessor(**self.sp_model_kwargs						)
           self.sp_model.Load(self.vocab_file						)



     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_						)

           if token_ids_a is None:
                 return ([0] * len(A_						)) + [1]
           return ([0] * len(A_						)) + [1] + ([0] * len(A_						)) + [1]



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






           """simple docstring"""
           _lowerCamelCase			    =				[self.sep_token_id]

           if token_ids_a is None:
                 return len(token_ids_a + sep						) * [0]
           return len(token_ids_a + sep + sep + token_ids_a + sep						) * [0]



     @property
     def 		UpperCamelCase_						(					self						) ->				Dict:






           """simple docstring"""
           return len(self.sp_model						) + self.fairseq_offset



     def 		UpperCamelCase_						(					self						) ->				Tuple:






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






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



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






           """simple docstring"""
           if token in self.fairseq_tokens_to_ids:
                 return self.fairseq_tokens_to_ids[token]
           _lowerCamelCase			    =				self.sp_model.PieceToId(A_						)

           # 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 		UpperCamelCase_						(					self				,				A_						) ->				Tuple:






           """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 		UpperCamelCase_						(					self				,				A_						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				''''''.join(A_						).replace(A_				,				''' '''						).strip()
           return out_string



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






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

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

           return (out_vocab_file,)





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






           """simple docstring"""

           if token_ids_a is None:
                 return token_ids_a + [self.sep_token_id]
           _lowerCamelCase			    =				[self.sep_token_id]
           return token_ids_a + sep + token_ids_a + sep
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)