





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int     )      ->						Optional[Any]:
    _lowercase							= model.config

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

    return encoder_config, decoder_config

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Dict     )      ->						Any:



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

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

    return name

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Union[str, Any]			,							snake_case__  :int     )      ->						Optional[int]:



    for key in orig_state_dict.copy().keys():
        _lowercase							= orig_state_dict.pop(snake_case__     )

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

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

    return orig_state_dict

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :Optional[int]			,							snake_case__  :List[Any]=None			,							snake_case__  :Any=False     )      ->						List[Any]:
    _lowercase							= DonutModel.from_pretrained(snake_case__     ).eval()

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

    _lowercase							= original_model.state_dict()
    _lowercase							= convert_state_dict(snake_case__			,							snake_case__     )
    model.load_state_dict(snake_case__     )

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

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

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

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

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

    # verify decoder hidden states
    _lowercase							= original_model(snake_case__			,							snake_case__			,							snake_case__     ).logits
    _lowercase							= model(snake_case__			,							decoder_input_ids=snake_case__     ).logits
    assert torch.allclose(snake_case__			,							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(snake_case__     )
        processor.save_pretrained(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__":
      snake_case 				=       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.""",
      )

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






import string

import numpy

def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int			,							snake_case__  :int     )      ->						int:
  return b if a == 0 else greatest_common_divisor(b % a			,							snake_case__     )





class 				A_				:







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : Dict      =  string.ascii_uppercase + string.digits
       # This cipher takes alphanumerics into account
       # i.e. a total of 36 characters

       # take x and return x % len(key_string)
       SCREAMING_SNAKE_CASE_      : Dict      =  numpy.vectorize(lambda UpperCAmelCase	: x % 3_6	)

       SCREAMING_SNAKE_CASE_      : List[Any]      =  numpy.vectorize(UpperCAmelCase	)







       def __init__(    self	:	Optional[Any]						,__A	:	numpy.ndarray  )						->					None:
         _lowercase							= self.modulus(__A  )  # mod36 calc's on the encrypt key
         self.check_determinant()  # validate the determinant of the encryption key
         _lowercase							= encrypt_key.shape[0]







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	str  )						->					int:
         return self.key_string.index(__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	int  )						->					str:
         return self.key_string[round(__A  )]







       def    __UpperCAmelCase (    self	:	str  )						->					None:
         _lowercase							= round(numpy.linalg.det(self.encrypt_key  )  )

         if det < 0:
           _lowercase							= det % len(self.key_string  )

         _lowercase							= len(self.key_string  )
         if greatest_common_divisor(__A						,len(self.key_string  )  ) != 1:
           _lowercase							= (
               F"""determinant modular {req_l} of encryption key({det}) """
               F"""is not co prime w.r.t {req_l}.\nTry another key."""
           )
           raise ValueError(__A  )







       def    __UpperCAmelCase (    self	:	Any						,__A	:	str  )						->					str:
         _lowercase							= [char for char in text.upper() if char in self.key_string]

         _lowercase							= chars[-1]
         while len(__A  ) % self.break_key != 0:
           chars.append(__A  )

         return "".join(__A  )







       def    __UpperCAmelCase (    self	:	Optional[int]						,__A	:	str  )						->					str:
         _lowercase							= self.process_text(text.upper()  )
         _lowercase							= ''

         for i in range(0						,len(__A  ) - self.break_key + 1						,self.break_key  ):
           _lowercase							= text[i : i + self.break_key]
           _lowercase							= [self.replace_letters(__A  ) for char in batch]
           _lowercase							= numpy.array([vec]  ).T
           _lowercase							= self.modulus(self.encrypt_key.dot(__A  )  ).T.tolist()[
               0
           ]
           _lowercase							= ''.join(
               self.replace_digits(__A  ) for num in batch_encrypted  )
           encrypted += encrypted_batch

         return encrypted







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					numpy.ndarray:
         _lowercase							= round(numpy.linalg.det(self.encrypt_key  )  )

         if det < 0:
           _lowercase							= det % len(self.key_string  )
         _lowercase							= None
         for i in range(len(self.key_string  )  ):
           if (det * i) % len(self.key_string  ) == 1:
             _lowercase							= i
             break

         _lowercase							= (
             det_inv
             * numpy.linalg.det(self.encrypt_key  )
             * numpy.linalg.inv(self.encrypt_key  )
         )

         return self.to_int(self.modulus(__A  )  )







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	str  )						->					str:
         _lowercase							= self.make_decrypt_key()
         _lowercase							= self.process_text(text.upper()  )
         _lowercase							= ''

         for i in range(0						,len(__A  ) - self.break_key + 1						,self.break_key  ):
           _lowercase							= text[i : i + self.break_key]
           _lowercase							= [self.replace_letters(__A  ) for char in batch]
           _lowercase							= numpy.array([vec]  ).T
           _lowercase							= self.modulus(decrypt_key.dot(__A  )  ).T.tolist()[0]
           _lowercase							= ''.join(
               self.replace_digits(__A  ) for num in batch_decrypted  )
           decrypted += decrypted_batch

         return decrypted

def 							SCREAMING_SNAKE_CASE__       (  )      ->						None:
  _lowercase							= int(input('Enter the order of the encryption key: '     )     )
  _lowercase							= []

  print('Enter each row of the encryption key with space separated integers'     )
  for _ in range(snake_case__     ):
    _lowercase							= [int(snake_case__     ) for x in input().split()]
    hill_matrix.append(snake_case__     )

  _lowercase							= HillCipher(numpy.array(snake_case__     )     )

  print('Would you like to encrypt or decrypt some text? (1 or 2)'     )
  _lowercase							= input('\n1. Encrypt\n2. Decrypt\n'     )
  if option == "1":
    _lowercase							= input('What text would you like to encrypt?: '     )
    print('Your encrypted text is:'     )
    print(hc.encrypt(snake_case__     )     )
  elif option == "2":
    _lowercase							= input('What text would you like to decrypt?: '     )
    print('Your decrypted text is:'     )
    print(hc.decrypt(snake_case__     )     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      main()