





'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


UpperCAmelCase_  							=				logging.get_logger(__name__)


UpperCAmelCase_  							=				{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
UpperCAmelCase_  							=				{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase_  							=				{
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}






class 					lowercase__ (			_UpperCAmelCase       ):

   '''simple docstring'''


   a		:							Optional[Any]     	=	VOCAB_FILES_NAMES
   a		:							Optional[Any]     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Optional[int]     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[Any]     	=	["""input_ids""", """attention_mask"""]
   a		:							List[str]     	=	BartTokenizer




   def __init__(					self,	__magic_name__=None,	__magic_name__=None,	__magic_name__=None,	__magic_name__="replace",	__magic_name__="<s>",	__magic_name__="</s>",	__magic_name__="</s>",	__magic_name__="<s>",	__magic_name__="<unk>",	__magic_name__="<pad>",	__magic_name__="<mask>",	__magic_name__=False,	__magic_name__=True,	**__magic_name__,	)				->   List[Any]:






       """simple docstring"""
       super().__init__(
           lowercase__,	lowercase__,	tokenizer_file=lowercase__,	errors=lowercase__,	bos_token=lowercase__,	eos_token=lowercase__,	sep_token=lowercase__,	cls_token=lowercase__,	unk_token=lowercase__,	pad_token=lowercase__,	mask_token=lowercase__,	add_prefix_space=lowercase__,	trim_offsets=lowercase__,	**lowercase__,	)

       UpperCamelCase__							:       int      =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space''',	lowercase__							) != add_prefix_space:
           UpperCamelCase__							:       List[str]      =   getattr(lowercase__,	pre_tok_state.pop('''type'''							)							)
           UpperCamelCase__							:       int      =   add_prefix_space
           UpperCamelCase__							:       List[Any]      =   pre_tok_class(**lowercase__							)

       UpperCamelCase__							:       List[str]      =   add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCamelCase__							:       int      =   """post_processor"""
       UpperCamelCase__							:       Union[str, Any]      =   getattr(self.backend_tokenizer,	lowercase__,	lowercase__							)
       if tokenizer_component_instance:
           UpperCamelCase__							:       List[Any]      =   json.loads(tokenizer_component_instance.__getstate__()							)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
               UpperCamelCase__							:       int      =   tuple(state['''sep''']							)
           if "cls" in state:
               UpperCamelCase__							:       List[Any]      =   tuple(state['''cls''']							)

           UpperCamelCase__							:       str      =   False

           if state.get('''add_prefix_space''',	lowercase__							) != add_prefix_space:
               UpperCamelCase__							:       Optional[int]      =   add_prefix_space
               UpperCamelCase__							:       int      =   True

           if state.get('''trim_offsets''',	lowercase__							) != trim_offsets:
               UpperCamelCase__							:       int      =   trim_offsets
               UpperCamelCase__							:       int      =   True

           if changes_to_apply:
               UpperCamelCase__							:       int      =   getattr(lowercase__,	state.pop('''type'''							)							)
               UpperCamelCase__							:       List[str]      =   component_class(**lowercase__							)
               setattr(self.backend_tokenizer,	lowercase__,	lowercase__							)




   @property
   def    UpperCamelCase__						(					self							)				->   Tuple:






       """simple docstring"""
       if self._mask_token is None:
           if self.verbose:
               logger.error('''Using mask_token, but it is not set yet.'''							)
           return None
       return str(self._mask_token							)




   @mask_token.setter
   def    UpperCamelCase__						(					self,	__magic_name__							)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   AddedToken(lowercase__,	lstrip=lowercase__,	rstrip=lowercase__							) if isinstance(lowercase__,	lowercase__							) else value
       UpperCamelCase__							:       Tuple      =   value




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Union[str, Any]      =   kwargs.get('''is_split_into_words''',	lowercase__							)

       if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               '''to use it with pretokenized inputs.'''							)

       return super()._batch_encode_plus(*lowercase__,	**lowercase__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   kwargs.get('''is_split_into_words''',	lowercase__							)

       if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               '''to use it with pretokenized inputs.'''							)

       return super()._encode_plus(*lowercase__,	**lowercase__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Tuple:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   self._tokenizer.model.save(lowercase__,	name=lowercase__							)
       return tuple(lowercase__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__=None							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       int      =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
           return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   [self.sep_token_id]
       UpperCamelCase__							:       List[Any]      =   [self.cls_token_id]

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








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 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]   )   ->			Union[str, Any]:
    UpperCamelCase__							:       List[str]      =   model.config

    UpperCamelCase__							:       Optional[Any]      =   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	,     )
    UpperCamelCase__							:       Union[str, Any]      =   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 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			Tuple:
    if "encoder.model" in name:
        UpperCamelCase__							:       str      =   name.replace('''encoder.model'''	,     '''encoder'''   )
    if "decoder.model" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''decoder.model'''	,     '''decoder'''   )
    if "patch_embed.proj" in name:
        UpperCamelCase__							:       Dict      =   name.replace('''patch_embed.proj'''	,     '''embeddings.patch_embeddings.projection'''   )
    if "patch_embed.norm" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''patch_embed.norm'''	,     '''embeddings.norm'''   )
    if name.startswith('''encoder'''   ):
        if "layers" in name:
            UpperCamelCase__							:       Optional[Any]      =   '''encoder.''' + name
        if "attn.proj" in name:
            UpperCamelCase__							:       Optional[int]      =   name.replace('''attn.proj'''	,     '''attention.output.dense'''   )
        if "attn" in name and "mask" not in name:
            UpperCamelCase__							:       str      =   name.replace('''attn'''	,     '''attention.self'''   )
        if "norm1" in name:
            UpperCamelCase__							:       List[str]      =   name.replace('''norm1'''	,     '''layernorm_before'''   )
        if "norm2" in name:
            UpperCamelCase__							:       Union[str, Any]      =   name.replace('''norm2'''	,     '''layernorm_after'''   )
        if "mlp.fc1" in name:
            UpperCamelCase__							:       Any      =   name.replace('''mlp.fc1'''	,     '''intermediate.dense'''   )
        if "mlp.fc2" in name:
            UpperCamelCase__							:       List[str]      =   name.replace('''mlp.fc2'''	,     '''output.dense'''   )

        if name == "encoder.norm.weight":
            UpperCamelCase__							:       Optional[Any]      =   '''encoder.layernorm.weight'''
        if name == "encoder.norm.bias":
            UpperCamelCase__							:       Any      =   '''encoder.layernorm.bias'''

    return name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str   )   ->			Dict:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:       Dict      =   orig_state_dict.pop(__UpperCAmelCase   )

        if "qkv" in key:
            UpperCamelCase__							:       Tuple      =   key.split('''.'''   )
            UpperCamelCase__							:       str      =   int(key_split[3]   )
            UpperCamelCase__							:       List[Any]      =   int(key_split[5]   )
            UpperCamelCase__							:       Optional[Any]      =   model.encoder.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                UpperCamelCase__							:       Tuple      =   val[:dim, :]
                UpperCamelCase__							:       Dict      =   val[dim : dim * 2, :]
                UpperCamelCase__							:       Dict      =   val[-dim:, :]
            else:
                UpperCamelCase__							:       Optional[int]      =   val[:dim]
                UpperCamelCase__							:       str      =   val[dim : dim * 2]
                UpperCamelCase__							:       int      =   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:
            UpperCamelCase__							:       Any      =   val

    return orig_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[Any]	,     __UpperCAmelCase:    List[Any]=None	,     __UpperCAmelCase:    Tuple=False   )   ->			Optional[int]:
    # load original model
    UpperCamelCase__							:       List[Any]      =   DonutModel.from_pretrained(__UpperCAmelCase   ).eval()

    # load HuggingFace model
    UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   get_configs(__UpperCAmelCase   )
    UpperCamelCase__							:       int      =   DonutSwinModel(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   MBartForCausalLM(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[int]      =   VisionEncoderDecoderModel(encoder=__UpperCAmelCase	,     decoder=__UpperCAmelCase   )
    model.eval()

    UpperCamelCase__							:       List[Any]      =   original_model.state_dict()
    UpperCamelCase__							:       List[Any]      =   convert_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )

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

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

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

    UpperCamelCase__							:       List[str]      =   original_model.encoder.model.patch_embed(__UpperCAmelCase   )
    UpperCamelCase__     ,UpperCamelCase__							:       List[str]      =   model.encoder.embeddings(__UpperCAmelCase   )
    assert torch.allclose(__UpperCAmelCase	,     __UpperCAmelCase	,     atol=1e-3   )

    # verify encoder hidden states
    UpperCamelCase__							:       Dict      =   original_model.encoder(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   model.encoder(__UpperCAmelCase   ).last_hidden_state
    assert torch.allclose(__UpperCAmelCase	,     __UpperCAmelCase	,     atol=1e-2   )

    # verify decoder hidden states
    UpperCamelCase__							:       Optional[Any]      =   original_model(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   ).logits
    UpperCamelCase__							:       str      =   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__":
  UpperCAmelCase_  							=				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.',
  )

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

