
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    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ):
 __a :  Optional[int]						=  model.config

 __a :  List[str]						=  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 :  Optional[Any]						=  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    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 if "encoder.model" in name:
  __a :  Optional[int]						=  name.replace('''encoder.model'''  ,   '''encoder'''  )
 if "decoder.model" in name:
  __a :  Union[str, Any]						=  name.replace('''decoder.model'''  ,   '''decoder'''  )
 if "patch_embed.proj" in name:
  __a :  Tuple						=  name.replace('''patch_embed.proj'''  ,   '''embeddings.patch_embeddings.projection'''  )
 if "patch_embed.norm" in name:
  __a :  Optional[int]						=  name.replace('''patch_embed.norm'''  ,   '''embeddings.norm'''  )
 if name.startswith('''encoder'''  ):
  if "layers" in name:
   __a :  Dict						=  '''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 :  Dict						=  name.replace('''attn'''  ,   '''attention.self'''  )
  if "norm1" in name:
   __a :  Optional[int]						=  name.replace('''norm1'''  ,   '''layernorm_before'''  )
  if "norm2" in name:
   __a :  str						=  name.replace('''norm2'''  ,   '''layernorm_after'''  )
  if "mlp.fc1" in name:
   __a :  int						=  name.replace('''mlp.fc1'''  ,   '''intermediate.dense'''  )
  if "mlp.fc2" in name:
   __a :  Dict						=  name.replace('''mlp.fc2'''  ,   '''output.dense'''  )

  if name == "encoder.norm.weight":
   __a :  List[Any]						=  '''encoder.layernorm.weight'''
  if name == "encoder.norm.bias":
   __a :  int						=  '''encoder.layernorm.bias'''

 return name




def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				Tuple  ):
 for key in orig_state_dict.copy().keys():
  __a :  Optional[Any]						=  orig_state_dict.pop(lowerCAmelCase__  )

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

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

 return orig_state_dict




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Any=None  ,   lowerCAmelCase__							:				Optional[int]=False  ):
 # load original model
 __a :  List[str]						=  DonutModel.from_pretrained(lowerCAmelCase__  ).eval()

 # load HuggingFace model
 __a				,       __a :  int						=  get_configs(lowerCAmelCase__  )
 __a :  List[Any]						=  DonutSwinModel(lowerCAmelCase__  )
 __a :  Optional[Any]						=  MBartForCausalLM(lowerCAmelCase__  )
 __a :  int						=  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 :  str						=  load_dataset('''hf-internal-testing/example-documents'''  )
 __a :  Any						=  dataset['''test'''][0]['''image'''].convert('''RGB'''  )

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

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

 __a :  Tuple						=  original_model.encoder.model.patch_embed(lowerCAmelCase__  )
 __a				,       __a :  Optional[int]						=  model.encoder.embeddings(lowerCAmelCase__  )
 assert torch.allclose(lowerCAmelCase__  ,   lowerCAmelCase__  ,   atol=1e-3  )

 # verify encoder hidden states
 __a :  Optional[int]						=  original_model.encoder(lowerCAmelCase__  )
 __a :  List[str]						=  model.encoder(lowerCAmelCase__  ).last_hidden_state
 assert torch.allclose(lowerCAmelCase__  ,   lowerCAmelCase__  ,   atol=1e-2  )

 # verify decoder hidden states
 __a :  str						=  original_model(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ).logits
 __a :  Tuple						=  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__":
      lowercase__       =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.',
      )

      lowercase__       =parser.parse_args()
      convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


def    __UpperCamelCase     (				lowerCAmelCase__							:				str  ):
 if n_term == "":
  return []
 __a :  list						=  []
 for temp in range(int(lowerCAmelCase__  )  ):
  series.append(f"1/{temp + 1}" if series else '''1'''  )
 return series


if __name__ == "__main__":
      lowercase__       =input('Enter the last number (nth term) of the Harmonic Series')
      print('Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n')
      print(harmonic_series(nth_term))
