





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			(      lowerCAmelCase					):
       _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=128    ,       )
       _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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       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     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase=None    ,       lowerCAmelCase=False					):
       # 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__":
       lowercase    : 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.""",
       )

       lowercase    : int    	=  parser.parse_args()
       convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_big_bird import BigBirdTokenizer
else:
       lowercase    : Union[str, Any]    	=  None

lowercase    : Union[str, Any]    	=  logging.get_logger(__name__)
lowercase    : Tuple    	=  {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowercase    : Any    	=  {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase    : Tuple    	=  {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


lowercase    : List[str]    	=  """▁"""

class  __A(						__UpperCAmelCase ):
     __A	  =       VOCAB_FILES_NAMES
     __A	  =       PRETRAINED_VOCAB_FILES_MAP
     __A	  =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	  =       BigBirdTokenizer
     __A	  =       ["input_ids", "attention_mask"]
     __A	  =       []

     def __init__(      self,       A=None,       A=None,       A="<unk>",       A="<s>",       A="</s>",       A="<pad>",       A="[SEP]",       A="[MASK]",       A="[CLS]",       **A,       ):





            """simple docstring"""

            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else bos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else eos_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else unk_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else pad_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else cls_token
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else sep_token

            # Mask token behave like a normal word, i.e. include the space before it
            _UpperCamelCase              =						AddedToken(A,       lstrip=A,       rstrip=A      ) if isinstance(A,       A      ) else mask_token

            super().__init__(
                A,       tokenizer_file=A,       bos_token=A,       eos_token=A,       unk_token=A,       sep_token=A,       pad_token=A,       cls_token=A,       mask_token=A,       **A,       )

            _UpperCamelCase              =						vocab_file
            _UpperCamelCase              =						False if not self.vocab_file else True

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]
            if token_ids_a is None:
                   return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep

     def 	_UpperCamelCase     (      self,       A,       A = None,       A = False      ):





            """simple docstring"""

            if already_has_special_tokens:
                   if token_ids_a is not None:
                          raise ValueError(
                              '''You should not supply a second sequence if the provided sequence of '''
                              '''ids is already formatted with special tokens for the model.'''      )
                   return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is None:
                   return [1] + ([0] * len(A      )) + [1]
            return [1] + ([0] * len(A      )) + [1] + ([0] * len(A      )) + [1]

     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            _UpperCamelCase              =						[self.sep_token_id]
            _UpperCamelCase              =						[self.cls_token_id]

            if token_ids_a is None:
                   return len(cls + token_ids_a + sep      ) * [0]
            return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]



     def 	_UpperCamelCase     (      self,       A,       A = None      ):





            """simple docstring"""

            if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                       '''tokenizer.'''      )

            if not os.path.isdir(A      ):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
                   return
            _UpperCamelCase              =						os.path.join(
                A,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

            if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ):
                   copyfile(self.vocab_file,       A      )

            return (out_vocab_file,)






