


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 			_a  (				SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[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__					:    List[Any]          =   MBartConfig(
     is_decoder=SCREAMING_SNAKE_CASE    , is_encoder_decoder=SCREAMING_SNAKE_CASE    , add_cross_attention=SCREAMING_SNAKE_CASE    , decoder_layers=original_config.decoder_layer    , max_position_embeddings=original_config.max_position_embeddings    , vocab_size=len(
         model.decoder.tokenizer     )    , scale_embedding=SCREAMING_SNAKE_CASE    , add_final_layer_norm=SCREAMING_SNAKE_CASE    , )

 return encoder_config, decoder_config

def 			_a  (				SCREAMING_SNAKE_CASE	:				Dict     ):







 """simple docstring"""



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

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

 return name

def 			_a  (				SCREAMING_SNAKE_CASE	:				Union[str, Any]    , SCREAMING_SNAKE_CASE	:				int     ):







 """simple docstring"""



 for key in orig_state_dict.copy().keys():
  UpperCamelCase__					:    int          =   orig_state_dict.pop(SCREAMING_SNAKE_CASE     )

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

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

 return orig_state_dict

def 			_a  (				SCREAMING_SNAKE_CASE	:				Optional[int]    , SCREAMING_SNAKE_CASE	:				List[Any]=None    , SCREAMING_SNAKE_CASE	:				Any=False     ):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   DonutModel.from_pretrained(SCREAMING_SNAKE_CASE     ).eval()

 # load HuggingFace model
 UpperCamelCase__       ,				UpperCamelCase__					:    Optional[int]          =   get_configs(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[str]          =   DonutSwinModel(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    str          =   MBartForCausalLM(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Union[str, Any]          =   VisionEncoderDecoderModel(encoder=SCREAMING_SNAKE_CASE    , decoder=SCREAMING_SNAKE_CASE     )
 model.eval()

 UpperCamelCase__					:    Union[str, Any]          =   original_model.state_dict()
 UpperCamelCase__					:    Optional[int]          =   convert_state_dict(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 model.load_state_dict(SCREAMING_SNAKE_CASE     )

 # verify results on scanned document
 UpperCamelCase__					:    Union[str, Any]          =   load_dataset('''hf-internal-testing/example-documents'''     )
 UpperCamelCase__					:    Tuple          =   dataset['''test'''][0]['''image'''].convert('''RGB'''     )

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

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

 UpperCamelCase__					:    Optional[int]          =   original_model.encoder.model.patch_embed(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__       ,				UpperCamelCase__					:    Any          =   model.encoder.embeddings(SCREAMING_SNAKE_CASE     )
 assert torch.allclose(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , atol=1E-3     )

 # verify encoder hidden states
 UpperCamelCase__					:    List[Any]          =   original_model.encoder(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Tuple          =   model.encoder(SCREAMING_SNAKE_CASE     ).last_hidden_state
 assert torch.allclose(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , atol=1E-2     )

 # verify decoder hidden states
 UpperCamelCase__					:    Optional[int]          =   original_model(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ).logits
 UpperCamelCase__					:    Optional[int]          =   model(SCREAMING_SNAKE_CASE    , decoder_input_ids=SCREAMING_SNAKE_CASE     ).logits
 assert torch.allclose(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE    , 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(SCREAMING_SNAKE_CASE     )
  processor.save_pretrained(SCREAMING_SNAKE_CASE     )

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






__UpperCamelCase       :       List[Any]						       =				256
# Modulus to hash a string
__UpperCamelCase       :       Union[str, Any]						       =				100_0003

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )
 if p_len > t_len:
  return False

 UpperCamelCase__					:    Any          =   0
 UpperCamelCase__					:    str          =   0
 UpperCamelCase__					:    List[Any]          =   1

 # Calculating the hash of pattern and substring of text
 for i in range(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Any          =   (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
  UpperCamelCase__					:    List[str]          =   (ord(text[i]     ) + text_hash * alphabet_size) % modulus
  if i == p_len - 1:
   continue
  UpperCamelCase__					:    Dict          =   (modulus_power * alphabet_size) % modulus

 for i in range(0    , t_len - p_len + 1     ):
  if text_hash == p_hash and text[i : i + p_len] == pattern:
   return True
  if i == t_len - p_len:
   continue
  # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
  UpperCamelCase__					:    Optional[int]          =   (
      (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
      + ord(text[i + p_len]     )
  ) % modulus
 return False

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   '''abc1abc12'''
 UpperCamelCase__					:    Dict          =   '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 UpperCamelCase__					:    List[str]          =   '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ) and not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 2)
 UpperCamelCase__					:    Optional[int]          =   '''ABABX'''
 UpperCamelCase__					:    int          =   '''ABABZABABYABABX'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 3)
 UpperCamelCase__					:    int          =   '''AAAB'''
 UpperCamelCase__					:    str          =   '''ABAAAAAB'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 4)
 UpperCamelCase__					:    Union[str, Any]          =   '''abcdabcy'''
 UpperCamelCase__					:    List[str]          =   '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 5)
 UpperCamelCase__					:    Tuple          =   '''Lü'''
 UpperCamelCase__					:    Any          =   '''Lüsai'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''Lue'''
 assert not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 print('''Success.'''     )


if __name__ == "__main__":
       test_rabin_karp()


