

'''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        _lowerCAmelCase					(						_UpperCamelCase						:     Optional[int]				)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =model.config

     _SCREAMING_SNAKE_CASE							    =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=1_28							, )
     _SCREAMING_SNAKE_CASE							    =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						:     Union[str, Any]				)      ->			Optional[Any]:
     """simple docstring"""




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

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

     return name


def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     Tuple				)      ->			Dict:
     """simple docstring"""




     for key in orig_state_dict.copy().keys():
          _SCREAMING_SNAKE_CASE							    =orig_state_dict.pop(_UpperCamelCase				)

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

               if "weight" in key:
                    _SCREAMING_SNAKE_CASE							    =val[:dim, :]
                    _SCREAMING_SNAKE_CASE							    =val[dim : dim * 2, :]
                    _SCREAMING_SNAKE_CASE							    =val[-dim:, :]
               else:
                    _SCREAMING_SNAKE_CASE							    =val[:dim]
                    _SCREAMING_SNAKE_CASE							    =val[dim : dim * 2]
                    _SCREAMING_SNAKE_CASE							    =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:
               _SCREAMING_SNAKE_CASE							    =val

     return orig_state_dict


def        _lowerCAmelCase					(						_UpperCamelCase						:     Tuple							, _UpperCamelCase						:     Dict=None							, _UpperCamelCase						:     Optional[int]=False				)      ->			List[str]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =DonutModel.from_pretrained(_UpperCamelCase				).eval()

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

     _SCREAMING_SNAKE_CASE							    =original_model.state_dict()
     _SCREAMING_SNAKE_CASE							    =convert_state_dict(_UpperCamelCase							, _UpperCamelCase				)
     model.load_state_dict(_UpperCamelCase				)

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

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

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

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

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

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



'''simple docstring'''
lowerCamelCase :      Any					 =     "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCamelCase :      int					 =     [{"type": "code", "content": INSTALL_CONTENT}]
lowerCamelCase :      str					 =     {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}
