



'''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 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[Any]       ):
       _A					:     Optional[Any]     = model.config

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

              if name == "encoder.norm.weight":
                     _A					:     Optional[int]     = 'encoder.layernorm.weight'
              if name == "encoder.norm.bias":
                     _A					:     Dict     = 'encoder.layernorm.bias'

       return name






def 				lowerCAmelCase__	(    lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      Any       ):
       for key in orig_state_dict.copy().keys():
              _A					:     Tuple     = orig_state_dict.pop(lowerCamelCase       )

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

                     if "weight" in key:
                            _A					:     List[str]     = val[:dim, :]
                            _A					:     List[Any]     = val[dim : dim * 2, :]
                            _A					:     Optional[Any]     = val[-dim:, :]
                     else:
                            _A					:     str     = val[:dim]
                            _A					:     Optional[int]     = 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 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      Union[str, Any]=None  ,lowerCamelCase					:      Any=False       ):
       # load original model
       _A					:     Optional[Any]     = DonutModel.from_pretrained(lowerCamelCase       ).eval()

       # load HuggingFace model
       _A					:     Tuple     = get_configs(lowerCamelCase       )
       _A					:     List[Any]     = DonutSwinModel(lowerCamelCase       )
       _A					:     Any     = MBartForCausalLM(lowerCamelCase       )
       _A					:     Tuple     = VisionEncoderDecoderModel(encoder=lowerCamelCase  ,decoder=lowerCamelCase       )
       model.eval()

       _A					:     str     = original_model.state_dict()
       _A					:     Any     = convert_state_dict(lowerCamelCase  ,lowerCamelCase       )
       model.load_state_dict(lowerCamelCase       )

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

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

       if model_name == "naver-clova-ix/donut-base-finetuned-docvqa":
              _A					:     Union[str, Any]     = '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
              _A					:     List[str]     = 'When is the coffee break?'
              _A					:     str     = task_prompt.replace('{user_input}'  ,lowerCamelCase       )
       elif model_name == "naver-clova-ix/donut-base-finetuned-rvlcdip":
              _A					:     Dict     = '<s_rvlcdip>'
       elif model_name in [
           "naver-clova-ix/donut-base-finetuned-cord-v1",
           "naver-clova-ix/donut-base-finetuned-cord-v1-2560",
       ]:
              _A					:     List[str]     = '<s_cord>'
       elif model_name == "naver-clova-ix/donut-base-finetuned-cord-v2":
              _A					:     Tuple     = 's_cord-v2>'
       elif model_name == "naver-clova-ix/donut-base-finetuned-zhtrainticket":
              _A					:     str     = '<s_zhtrainticket>'
       elif model_name in ["naver-clova-ix/donut-proto", "naver-clova-ix/donut-base"]:
              # use a random prompt
              _A					:     Tuple     = '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					:     List[Any]     = original_model.encoder.model.patch_embed(lowerCamelCase       )
       _A					:     Dict     = model.encoder.embeddings(lowerCamelCase       )
       assert torch.allclose(lowerCamelCase  ,lowerCamelCase  ,atol=1E-3       )

       # verify encoder hidden states
       _A					:     Optional[int]     = 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					:     List[Any]     = 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__":
   A				:					Optional[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.''',
   )

   A				:					Dict    						=			parser.parse_args()
   convert_donut_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''







import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

A				:					int    						=			2


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							List[str]				,							*,  # begin keyword-only arguments
        SCREAMING_SNAKE_CASE      :							Optional[Any]="<s>"				,							SCREAMING_SNAKE_CASE      :							int="<pad>"				,							SCREAMING_SNAKE_CASE      :							Optional[Any]="</s>"				,							SCREAMING_SNAKE_CASE      :							Tuple="<unk>"				,							SCREAMING_SNAKE_CASE      :							List[Any]=None				,							):
           _A  ,			_A  ,			_A  ,			_A					:     Any     = bos, unk, pad, eos
           _A					:     Optional[Any]     = []
           _A					:     Optional[Any]     = []
           _A					:     Optional[int]     = {}
           _A					:     Dict     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     str     = self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     Any     = self.add_symbol(SCREAMING_SNAKE_CASE)
           if extra_special_symbols:
                  for s in extra_special_symbols:
                         self.add_symbol(SCREAMING_SNAKE_CASE)
           _A					:     List[str]     = len(self.symbols)


    def __eq__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return self.indices == other.indices


    def __getitem__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           if idx < len(self.symbols):
                  return self.symbols[idx]
           return self.unk_word


    def __len__(  self      :							Union[str, Any]):
           return len(self.symbols)


    def __contains__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           return sym in self.indices


    @classmethod
    def        A   (  cls      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Any     = cls()
           d.add_from_file(SCREAMING_SNAKE_CASE)
           return d


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[int]=1				,							SCREAMING_SNAKE_CASE      :							int=False):
           if word in self.indices and not overwrite:
                  _A					:     str     = self.indices[word]
                  _A					:     List[str]     = self.count[idx] + n
                  return idx
           else:
                  _A					:     Optional[Any]     = len(self.symbols)
                  _A					:     Union[str, Any]     = idx
                  self.symbols.append(SCREAMING_SNAKE_CASE)
                  self.count.append(SCREAMING_SNAKE_CASE)
                  return idx


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           return 0


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[Any]):
           if isinstance(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE):
                  try:
                         with open(SCREAMING_SNAKE_CASE				,							'r'				,							encoding='utf-8') as fd:
                                self.add_from_file(SCREAMING_SNAKE_CASE)
                  except FileNotFoundError as fnfe:
                         raise fnfe
                  except UnicodeError:
                         raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(SCREAMING_SNAKE_CASE))
                  return

           _A					:     Union[str, Any]     = f.readlines()
           _A					:     Any     = self._load_meta(SCREAMING_SNAKE_CASE)

           for line in lines[indices_start_line:]:
                  try:
                         _A  ,			_A					:     List[str]     = line.rstrip().rsplit(' '				,							1)
                         if field == "#fairseq:overwrite":
                                _A					:     int     = True
                                _A  ,			_A					:     List[str]     = line.rsplit(' '				,							1)
                         else:
                                _A					:     Union[str, Any]     = False
                         _A					:     List[str]     = int(SCREAMING_SNAKE_CASE)
                         _A					:     Optional[Any]     = line
                         if word in self and not overwrite:
                                raise RuntimeError(
                                    'Duplicate word found when loading Dictionary: \'{}\'. '
                                    'Duplicate words can overwrite earlier ones by adding the '
                                    '#fairseq:overwrite flag at the end of the corresponding row '
                                    'in the dictionary file. If using the Camembert model, please '
                                    'download an updated copy of the model file.'.format(SCREAMING_SNAKE_CASE))
                         self.add_symbol(SCREAMING_SNAKE_CASE				,							n=SCREAMING_SNAKE_CASE				,							overwrite=SCREAMING_SNAKE_CASE)
                  except ValueError:
                         raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'')






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       # (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
       # e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
       _A					:     Union[str, Any]     = dict((re.sub(R'@@$'  ,''  ,lowerCamelCase       ), v) if k.endswith('@@'       ) else (re.sub(R'$'  ,'</w>'  ,lowerCamelCase       ), v) for k, v in d.items()       )
       _A					:     Optional[Any]     = '<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[F'{k}</w>']
              _A					:     str     = d[k]  # restore
       return da






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]       ):
       # prep
       if not os.path.exists(lowerCamelCase       ):
              raise ValueError(F'path {biogpt_checkpoint_path} does not exist!'       )
       os.makedirs(lowerCamelCase  ,exist_ok=lowerCamelCase       )
       print(F'Writing results to {pytorch_dump_folder_path}'       )

       # handle various types of models

       _A					:     Dict     = os.path.join(lowerCamelCase  ,'checkpoint.pt'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {checkpoint_file} does not exist!'       )
       _A					:     int     = torch.load(lowerCamelCase  ,map_location='cpu'       )

       _A					:     Dict     = chkpt['cfg']['model']

       # dicts
       _A					:     Any     = os.path.join(lowerCamelCase  ,'dict.txt'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {dict_file} does not exist!'       )
       _A					:     Any     = Dictionary.load(lowerCamelCase       )
       _A					:     Optional[int]     = rewrite_dict_keys(src_dict.indices       )
       _A					:     List[Any]     = len(lowerCamelCase       )
       _A					:     str     = os.path.join(lowerCamelCase  ,VOCAB_FILES_NAMES['vocab_file']       )
       print(F'Generating {src_vocab_file} of {src_vocab_size} records'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # merges_file (bpecodes)
       _A					:     Optional[int]     = os.path.join(lowerCamelCase  ,'bpecodes'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {bpecodes_file} does not exist!'       )

       _A					:     Dict     = os.path.join(lowerCamelCase  ,VOCAB_FILES_NAMES['merges_file']       )
       shutil.copyfile(lowerCamelCase  ,lowerCamelCase       )

       # model config
       _A					:     str     = os.path.join(lowerCamelCase  ,'config.json'       )

       _A					:     int     = {
           'activation_dropout': args['activation_dropout'],
           'architectures': ['BioGptForCausalLM'],
           'attention_probs_dropout_prob': args['attention_dropout'],
           'bos_token_id': 0,
           'eos_token_id': 2,
           'hidden_act': args['activation_fn'],
           'hidden_dropout_prob': args['dropout'],
           'hidden_size': args['decoder_embed_dim'],
           'initializer_range': 0.02,
           'intermediate_size': args['decoder_ffn_embed_dim'],
           'layer_norm_eps': 1E-12,
           'layerdrop': args['decoder_layerdrop'],
           'max_position_embeddings': args['max_target_positions'],
           'model_type': 'biogpt',
           'num_attention_heads': args['decoder_attention_heads'],
           'num_hidden_layers': args['decoder_layers'],
           'pad_token_id': 1,
           'scale_embedding': not args['no_scale_embedding'],
           'tie_word_embeddings': args['share_decoder_input_output_embed'],
           'vocab_size': src_vocab_size,
       }

       # good hparam defaults to start with

       print(F'Generating {biogpt_model_config_file}'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # tokenizer config
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = {
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 1024,
           'pad_token': '<pad>',
           'special_tokens_map_file': None,
           'tokenizer_class': 'BioGptTokenizer',
           'unk_token': '<unk>',
       }

       print(F'Generating {biogpt_tokenizer_config_file}'       )
       with open(lowerCamelCase  ,'w'  ,encoding='utf-8'       ) as f:
              f.write(json.dumps(lowerCamelCase  ,ensure_ascii=lowerCamelCase  ,indent=lowerCamelCase       )       )

       # model
       _A					:     List[Any]     = chkpt['model']

       # remove unneeded keys
       _A					:     int     = [
           'decoder.version',
       ]
       for k in ignore_keys:
              model_state_dict.pop(lowerCamelCase  ,lowerCamelCase       )

       _A					:     Any     = list(model_state_dict.keys()       )
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'       ):
                     _A					:     str     = model_state_dict.pop(lowerCamelCase       )
              else:
                     _A					:     Dict     = model_state_dict.pop(lowerCamelCase       )

       _A					:     Any     = BioGptConfig.from_pretrained(lowerCamelCase       )
       _A					:     Union[str, Any]     = BioGptForCausalLM(lowerCamelCase       )

       # check that it loads ok
       model_new.load_state_dict(lowerCamelCase       )

       # save
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
       print(F'Generating {pytorch_weights_dump_path}'       )
       torch.save(lowerCamelCase  ,lowerCamelCase       )

       print('Conversion is done!'       )


if __name__ == "__main__":
   A				:					Optional[Any]    						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--biogpt_checkpoint_path''',
       default=None,
       type=str,
       required=True,
       help=(
           '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
           ''' bpecodes, etc.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   A				:					int    						=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






