




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 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   Optional[Any]:
   snake_case      :				Dict				=       model.config

   snake_case      :				Optional[int]				=       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						,)
   snake_case      :				Tuple				=       MBartConfig(
       is_decoder=lowercase						,is_encoder_decoder=lowercase						,add_cross_attention=lowercase						,decoder_layers=original_config.decoder_layer						,max_position_embeddings=original_config.max_position_embeddings						,vocab_size=len(
           model.decoder.tokenizer     )						,scale_embedding=lowercase						,add_final_layer_norm=lowercase						,)

   return encoder_config, decoder_config




def 						SCREAMING_SNAKE_CASE__					(    lowercase     )  ->   int:




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

      if name == "encoder.norm.weight":
         snake_case      :				Optional[Any]				=       """encoder.layernorm.weight"""
      if name == "encoder.norm.bias":
         snake_case      :				Optional[int]				=       """encoder.layernorm.bias"""

   return name




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase     )  ->   Tuple:




   for key in orig_state_dict.copy().keys():
      snake_case      :				Optional[Any]				=       orig_state_dict.pop(lowercase     )

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

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

   return orig_state_dict




def 						SCREAMING_SNAKE_CASE__					(    lowercase						,lowercase=None						,lowercase=False     )  ->   List[Any]:
   snake_case      :				List[str]				=       DonutModel.from_pretrained(lowercase     ).eval()

   # load HuggingFace model
   snake_case	,					snake_case      :				Tuple				=       get_configs(lowercase     )
   snake_case      :				Optional[Any]				=       DonutSwinModel(lowercase     )
   snake_case      :				Tuple				=       MBartForCausalLM(lowercase     )
   snake_case      :				Optional[int]				=       VisionEncoderDecoderModel(encoder=lowercase						,decoder=lowercase     )
   model.eval()

   snake_case      :				str				=       original_model.state_dict()
   snake_case      :				Dict				=       convert_state_dict(lowercase						,lowercase     )
   model.load_state_dict(lowercase     )

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

   snake_case      :				Optional[int]				=       XLMRobertaTokenizerFast.from_pretrained(lowercase						,from_slow=lowercase     )
   snake_case      :				Optional[int]				=       DonutImageProcessor(
       do_align_long_axis=original_model.config.align_long_axis						,size=original_model.config.input_size[::-1]     )
   snake_case      :				int				=       DonutProcessor(lowercase						,lowercase     )
   snake_case      :				List[str]				=       processor(lowercase						,return_tensors="""pt"""     ).pixel_values

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

   snake_case      :				Dict				=       original_model.encoder.model.patch_embed(lowercase     )
   snake_case	,					snake_case      :				Union[str, Any]				=       model.encoder.embeddings(lowercase     )
   assert torch.allclose(lowercase						,lowercase						,atol=1E-3     )

   # verify encoder hidden states
   snake_case      :				Any				=       original_model.encoder(lowercase     )
   snake_case      :				Optional[int]				=       model.encoder(lowercase     ).last_hidden_state
   assert torch.allclose(lowercase						,lowercase						,atol=1E-2     )

   # verify decoder hidden states
   snake_case      :				Any				=       original_model(lowercase						,lowercase						,lowercase     ).logits
   snake_case      :				Optional[Any]				=       model(lowercase						,decoder_input_ids=lowercase     ).logits
   assert torch.allclose(lowercase						,lowercase						,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(lowercase     )
      processor.save_pretrained(lowercase     )

   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__":
       lowerCamelCase       :       Any        =    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.',
       )

       lowerCamelCase       :       Dict        =    parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

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


if is_torch_available():
       import torch


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__					=   {"vocab_file": "spiece.model"}

SCREAMING_SNAKE_CASE__					=   {
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

SCREAMING_SNAKE_CASE__					=   {
    "AI-Sweden/gpt-sw3-126m": 2_048,
    "AI-Sweden/gpt-sw3-350m": 2_048,
    "AI-Sweden/gpt-sw3-1.6b": 2_048,
    "AI-Sweden/gpt-sw3-6.7b": 2_048,
    "AI-Sweden/gpt-sw3-20b": 2_048,
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']





      def __init__(       self      ,						lowercase      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase = None      ,						**lowercase      ,						)		->     None:
            lowerCAmelCase  						=       {} if sp_model_kwargs is None else sp_model_kwargs

            lowerCAmelCase  						=       kwargs.get("""name_or_path""" )
            if name_or_path is None:
                  logger.warning(
                      """name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"""
                      """ you are testing the model, this can safely be ignored""" )
                  lowerCAmelCase  						=       """None"""

            # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
            lowerCAmelCase  						=       """<|endoftext|>""" if eos_token is None else eos_token
            lowerCAmelCase  						=       """<unk>""" if unk_token is None else unk_token
            if "gpt-sw3-7b" in name_or_path:
                  lowerCAmelCase  						=       unk_token if pad_token is None else pad_token
                  lowerCAmelCase  						=       eos_token if bos_token is None else bos_token
            else:
                  lowerCAmelCase  						=       """<pad>""" if pad_token is None else pad_token
                  lowerCAmelCase  						=       """<s>""" if bos_token is None else bos_token

            super().__init__(
                do_lower_case=lowercase      ,						remove_space=lowercase      ,						keep_accents=lowercase      ,						bos_token=lowercase      ,						eos_token=lowercase      ,						unk_token=lowercase      ,						pad_token=lowercase      ,						sp_model_kwargs=self.sp_model_kwargs      ,						**lowercase      ,						)

            lowerCAmelCase  						=       do_lower_case
            lowerCAmelCase  						=       remove_space
            lowerCAmelCase  						=       keep_accents
            lowerCAmelCase  						=       vocab_file

            lowerCAmelCase  						=       spm.SentencePieceProcessor(**self.sp_model_kwargs )
            self.sp_model.Load(lowercase )

            # Used for whitespace normalization in input texts
            # fmt : off
            lowerCAmelCase  						=       {""" """, """ """, """ """, """ """, """ """, """　""", """ """, """ """, """ """, """ """, """￼""", """"""}
            # fmt : on

            # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
            lowerCAmelCase  						=       re.compile(
                f'[{"".join(map(lowercase      ,						list(range(0      ,						9 ) ) + list(range(11      ,						32 ) ) + list(range(127      ,						160 ) ) + [160, 173, 8_203] ) )}]' )





      def __getstate__(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.__dict__.copy()
            lowerCAmelCase  						=       None
            return state





      def __setstate__(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       d

            # 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 )





      @property
      # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
      def     _snake_case			(       self )		->     int:
            return len(self.sp_model )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       self.non_printing_characters_re.sub(""""""      ,						lowercase )

            # Normalize whitespaces
            lowerCAmelCase  						=       """""".join([char if char not in self.whitespaces else """ """ for char in text] )

            # NFC Unicode normalization
            lowerCAmelCase  						=       unicodedata.normalize("""NFC"""      ,						lowercase )
            return text





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     List[str]:
            lowerCAmelCase  						=       self.preprocess_text(lowercase )
            return self.sp_model.encode(lowercase      ,						out_type=lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     int:
            return self.sp_model.PieceToId(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     str:
            return self.sp_model.IdToPiece(lowercase )





      @staticmethod
      def     _snake_case			(       lowercase )		->     str:
            return out_string





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       []
            lowerCAmelCase  						=       """"""
            lowerCAmelCase  						=       False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                        if not prev_is_special:
                              out_string += " "

                        out_string += self.sp_model.decode(lowercase ) + token
                        lowerCAmelCase  						=       True
                        lowerCAmelCase  						=       []
                  else:
                        current_sub_tokens.append(lowercase )
                        lowerCAmelCase  						=       False
            out_string += self.sp_model.decode(lowercase )

            return out_string





      def     _snake_case			(       self )		->     Dict[str, int]:
            lowerCAmelCase  						=       {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
            vocab.update(self.added_tokens_encoder )
            return vocab





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            if not os.path.isdir(lowercase ):
                  logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
                  return
            lowerCAmelCase  						=       os.path.join(
                lowercase      ,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

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

            return (out_vocab_file,)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False )		->     Union[List[int], List[List[int]], "torch.Tensor"]:

            if isinstance(lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       self.preprocess_text(lowercase )
                  lowerCAmelCase  						=       self.sp_model.encode(lowercase )
            else:
                  lowerCAmelCase  						=       [self.preprocess_text(lowercase ) for t in text]
                  lowerCAmelCase  						=       self.sp_model.encode(lowercase )

            if return_tensors is True or return_tensors == "pt":
                  lowerCAmelCase  						=       torch.tensor(lowercase )

            return token_ids





      def     _snake_case			(       self      ,						lowercase )		->     str:
            return self.sp_model.decode(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     List[int]:
            lowerCAmelCase  						=       [f'User: {text}' if is_user else f'Bot: {text}' for is_user, text in conversation.iter_texts()]
            lowerCAmelCase  						=       (
                f'{self.eos_token}{self.bos_token}' + f'{self.bos_token}'.join(lowercase ) + f'{self.bos_token}Bot:'
            )
            return self.encode(text=lowercase )






