





"""simple docstring"""


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 					lowercase__    (					lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	int						=       model.config

      _UpperCamelCase      :	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		,)
      _UpperCamelCase      :	Any						=       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 					lowercase__    (					lowercase_			)					->		Optional[int]:




      """simple docstring"""

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

            if name == "encoder.norm.weight":
                  _UpperCamelCase      :	Union[str, Any]						=       "encoder.layernorm.weight"
            if name == "encoder.norm.bias":
                  _UpperCamelCase      :	Tuple						=       "encoder.layernorm.bias"

      return name




def 					lowercase__    (					lowercase_		,lowercase_			)					->		str:




      """simple docstring"""

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

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

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

      return orig_state_dict




def 					lowercase__    (					lowercase_		,lowercase_=None		,lowercase_=False			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	List[str]						=       DonutModel.from_pretrained(lowercase_			).eval()

      # load HuggingFace model
      _UpperCamelCase      :	Dict						=       get_configs(lowercase_			)
      _UpperCamelCase      :	Dict						=       DonutSwinModel(lowercase_			)
      _UpperCamelCase      :	int						=       MBartForCausalLM(lowercase_			)
      _UpperCamelCase      :	str						=       VisionEncoderDecoderModel(encoder=lowercase_		,decoder=lowercase_			)
      model.eval()

      _UpperCamelCase      :	str						=       original_model.state_dict()
      _UpperCamelCase      :	Union[str, Any]						=       convert_state_dict(lowercase_		,lowercase_			)
      model.load_state_dict(lowercase_			)

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

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

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

      _UpperCamelCase      :	List[Any]						=       original_model.encoder.model.patch_embed(lowercase_			)
      _UpperCamelCase      :	List[Any]						=       model.encoder.embeddings(lowercase_			)
      assert torch.allclose(lowercase_		,lowercase_		,atol=1e-3			)

      # verify encoder hidden states
      _UpperCamelCase      :	Any						=       original_model.encoder(lowercase_			)
      _UpperCamelCase      :	Any						=       model.encoder(lowercase_			).last_hidden_state
      assert torch.allclose(lowercase_		,lowercase_		,atol=1e-2			)

      # verify decoder hidden states
      _UpperCamelCase      :	Any						=       original_model(lowercase_		,lowercase_		,lowercase_			).logits
      _UpperCamelCase      :	List[str]						=       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__				  =   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__				  =   parser.parse_args()
  convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""


class      __SCREAMING_SNAKE_CASE    :







  '''simple docstring'''


  def __init__(		self   :       Optional[Any]					,   __a   :       list[int]	)		->				None:
        _UpperCamelCase      :	Tuple						=       len(__a	)
        _UpperCamelCase      :	Dict						=       [0] * len_array

        if len_array > 0:
              _UpperCamelCase      :	Optional[Any]						=       array[0]

        for i in range(1					,   __a	):
              _UpperCamelCase      :	Tuple						=       self.prefix_sum[i - 1] + array[i]

  def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       int					,   __a   :       int	)		->				int:
        if start == 0:
              return self.prefix_sum[end]

        return self.prefix_sum[end] - self.prefix_sum[start - 1]


  def    __SCREAMING_SNAKE_CASE				(		self   :       Optional[Any]					,   __a   :       int	)		->				bool:
        _UpperCamelCase      :	int						=       {0}
        for sum_item in self.prefix_sum:
              if sum_item - target_sum in sums:
                    return True

              sums.add(__a	)

        return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()
