






"""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       snake_case  (A_				:str				):







  '''simple docstring'''

  a					:      Any    =       model.config

  a					:      Tuple    =       DonutSwinConfig(
      image_size=original_config.input_size     ,      patch_size=4     ,      depths=original_config.encoder_layer     ,      num_heads=[4, 8, 1_6, 3_2]     ,      window_size=original_config.window_size     ,      embed_dim=1_2_8     ,      )
  a					:      str    =       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       snake_case  (A_				:Optional[Any]				):







  '''simple docstring'''

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

    if name == "encoder.norm.weight":
      a					:      List[str]    =       'encoder.layernorm.weight'
    if name == "encoder.norm.bias":
      a					:      str    =       'encoder.layernorm.bias'

  return name




def       snake_case  (A_				:int     ,      A_				:Dict				):







  '''simple docstring'''

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

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

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

  return orig_state_dict




def       snake_case  (A_				:int     ,      A_				:str=None     ,      A_				:Tuple=False				):







  '''simple docstring'''

  a					:      Tuple    =       DonutModel.from_pretrained(__lowerCamelCase				).eval()

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

  a					:      Dict    =       original_model.state_dict()
  a					:      str    =       convert_state_dict(__lowerCamelCase     ,      __lowerCamelCase				)
  model.load_state_dict(__lowerCamelCase				)

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

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

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

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

  # verify encoder hidden states
  a					:      Dict    =       original_model.encoder(__lowerCamelCase				)
  a					:      Tuple    =       model.encoder(__lowerCamelCase				).last_hidden_state
  assert torch.allclose(__lowerCamelCase     ,      __lowerCamelCase     ,      atol=1E-2				)

  # verify decoder hidden states
  a					:      int    =       original_model(__lowerCamelCase     ,      __lowerCamelCase     ,      __lowerCamelCase				).logits
  a					:      int    =       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__":
   _UpperCamelCase    :   Tuple   			=						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    :   Any   			=						parser.parse_args()
   convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""


def       snake_case  (A_				:list[int]     ,      A_				:str				):







  '''simple docstring'''

  a					:      Optional[int]    =       int(A_				)

  # Initialize Result
  a					:      int    =       []

  # Traverse through all denomination
  for denomination in reversed(A_				):
    # Find denominations
    while int(A_				) >= int(A_				):
      total_value -= int(A_				)
      answer.append(A_				)  # Append the "answers" array

  return answer


# Driver Code
if __name__ == "__main__":
   _UpperCamelCase    :   Dict   			=						[]
   _UpperCamelCase    :   str   			=						'0'

   if (
       input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
       == "y"
   ):
      _UpperCamelCase    :   Dict   			=						int(input('Enter the number of denominations you want to add: ').strip())

      for i in range(0, n):
         denominations.append(int(input(f'''Denomination {i}: ''').strip()))
      _UpperCamelCase    :   Optional[int]   			=						input('Enter the change you want to make in Indian Currency: ').strip()
   else:
      # All denominations of Indian Currency if user does not enter
      _UpperCamelCase    :   Union[str, Any]   			=						[1, 2, 5, 10, 20, 50, 100, 500, 2000]
      _UpperCamelCase    :   Dict   			=						input('Enter the change you want to make: ').strip()

   if int(value) == 0 or int(value) < 0:
      print('The total value cannot be zero or negative.')

   else:
      print(f'''Following is minimal change for {value}: ''')
      _UpperCamelCase    :   Tuple   			=						find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=' ')




