





'''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     (      _lowerCAmelCase    )->     List[Any]:
 __UpperCAmelCase					=   model.config

 __UpperCAmelCase					=   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       ,    )
 __UpperCAmelCase					=   MBartConfig(
     is_decoder=_lowerCAmelCase       ,    is_encoder_decoder=_lowerCAmelCase       ,    add_cross_attention=_lowerCAmelCase       ,    decoder_layers=original_config.decoder_layer       ,    max_position_embeddings=original_config.max_position_embeddings       ,    vocab_size=len(
         model.decoder.tokenizer    )       ,    scale_embedding=_lowerCAmelCase       ,    add_final_layer_norm=_lowerCAmelCase       ,    )

 return encoder_config, decoder_config







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

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

 return name







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     Union[str, Any]:
 for key in orig_state_dict.copy().keys():
  __UpperCAmelCase					=   orig_state_dict.pop(_lowerCAmelCase    )

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

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

 return orig_state_dict







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase=None       ,    _lowerCAmelCase=False    )->     Any:
 # load original model
 __UpperCAmelCase					=   DonutModel.from_pretrained(_lowerCAmelCase    ).eval()

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

 __UpperCAmelCase					=   original_model.state_dict()
 __UpperCAmelCase					=   convert_state_dict(_lowerCAmelCase       ,    _lowerCAmelCase    )
 model.load_state_dict(_lowerCAmelCase    )

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

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

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

 __UpperCAmelCase					=   original_model.encoder.model.patch_embed(_lowerCAmelCase    )
 __UpperCAmelCase					,    __UpperCAmelCase					=   model.encoder.embeddings(_lowerCAmelCase    )
 assert torch.allclose(_lowerCAmelCase       ,    _lowerCAmelCase       ,    atol=1e-3    )

 # verify encoder hidden states
 __UpperCAmelCase					=   original_model.encoder(_lowerCAmelCase    )
 __UpperCAmelCase					=   model.encoder(_lowerCAmelCase    ).last_hidden_state
 assert torch.allclose(_lowerCAmelCase       ,    _lowerCAmelCase       ,    atol=1e-2    )

 # verify decoder hidden states
 __UpperCAmelCase					=   original_model(_lowerCAmelCase       ,    _lowerCAmelCase       ,    _lowerCAmelCase    ).logits
 __UpperCAmelCase					=   model(_lowerCAmelCase       ,    decoder_input_ids=_lowerCAmelCase    ).logits
 assert torch.allclose(_lowerCAmelCase       ,    _lowerCAmelCase       ,    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(_lowerCAmelCase    )
  processor.save_pretrained(_lowerCAmelCase    )

 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__":
    _A:					Optional[int]							=				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.""",
    )

    _A:					Union[str, Any]							=				parser.parse_args()
    convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












'''simple docstring'''




from string import ascii_uppercase

_A:					Union[str, Any]							=				{char: i for i, char in enumerate(ascii_uppercase)}
_A:					str							=				dict(enumerate(ascii_uppercase))







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   len(_lowerCAmelCase    )
 __UpperCAmelCase					=   0
 while True:
  if x == i:
   __UpperCAmelCase					=   0
  if len(_lowerCAmelCase    ) == len(_lowerCAmelCase    ):
   break
  key += key[i]
  i += 1
 return key







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in message:
  if letter == " ":
   cipher_text += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] - dicta[key_new[i]]) % 26
   i += 1
   cipher_text += dicta[x]
 return cipher_text







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 for letter in cipher_text:
  if letter == " ":
   or_txt += " "
  else:
   __UpperCAmelCase					=   (dicta[letter] + dicta[key_new[i]] + 26) % 26
   i += 1
   or_txt += dicta[x]
 return or_txt







def _lowerCAmelCase     (      )->     None:
 __UpperCAmelCase					=   'THE GERMAN ATTACK'
 __UpperCAmelCase					=   'SECRET'
 __UpperCAmelCase					=   generate_key(_lowerCAmelCase       ,    _lowerCAmelCase    )
 __UpperCAmelCase					=   cipher_text(_lowerCAmelCase       ,    _lowerCAmelCase    )
 print(F'Encrypted Text = {s}'    )
 print(F'Original Text = {original_text(_lowerCAmelCase       ,    _lowerCAmelCase    )}'    )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()





