



'''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()

lowerCamelCase	 =       2





class _UpperCamelCase		:





    '''simple docstring'''





    def __init__(							self  :  List[str]  ,       *,  # begin keyword-only arguments
        _lowerCAmelCase  :  Optional[Any]="<s>"  ,       _lowerCAmelCase  :  int="<pad>"  ,       _lowerCAmelCase  :  Tuple="</s>"  ,       _lowerCAmelCase  :  List[str]="<unk>"  ,       _lowerCAmelCase  :  Any=None  ,       ):







           '''simple docstring'''

           __lowercase	,				__lowercase	,				__lowercase	,				__lowercase       	=bos, unk, pad, eos
           __lowercase       	=[]
           __lowercase       	=[]
           __lowercase       	={}
           __lowercase       	=self.add_symbol(lowerCamelCase_)
           __lowercase       	=self.add_symbol(lowerCamelCase_)
           __lowercase       	=self.add_symbol(lowerCamelCase_)
           __lowercase       	=self.add_symbol(lowerCamelCase_)
           if extra_special_symbols:
                  for s in extra_special_symbols:
                         self.add_symbol(lowerCamelCase_)
           __lowercase       	=len(self.symbols)

    def __eq__(							self  :  Tuple  ,       _lowerCAmelCase  :  List[Any]):







           '''simple docstring'''

           return self.indices == other.indices

    def __getitem__(							self  :  Union[str, Any]  ,       _lowerCAmelCase  :  List[str]):







           '''simple docstring'''

           if idx < len(self.symbols):
                  return self.symbols[idx]
           return self.unk_word

    def __len__(							self  :  Tuple):







           '''simple docstring'''

           return len(self.symbols)

    def __contains__(							self  :  Tuple  ,       _lowerCAmelCase  :  List[Any]):







           '''simple docstring'''

           return sym in self.indices

    @classmethod
    def 				__lowerCamelCase     (							cls  :  Optional[int]  ,       _lowerCAmelCase  :  List[Any]):







           '''simple docstring'''

           __lowercase       	=cls()
           d.add_from_file(lowerCamelCase_)
           return d

    def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Any  ,       _lowerCAmelCase  :  int=1  ,       _lowerCAmelCase  :  Tuple=False):







           '''simple docstring'''

           if word in self.indices and not overwrite:
                  __lowercase       	=self.indices[word]
                  __lowercase       	=self.count[idx] + n
                  return idx
           else:
                  __lowercase       	=len(self.symbols)
                  __lowercase       	=idx
                  self.symbols.append(lowerCamelCase_)
                  self.count.append(lowerCamelCase_)
                  return idx

    def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  int):







           '''simple docstring'''

           return 0

    def 				__lowerCamelCase     (							self  :  Tuple  ,       _lowerCAmelCase  :  Union[str, Any]):







           '''simple docstring'''

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

           __lowercase       	=f.readlines()
           __lowercase       	=self._load_meta(lowerCamelCase_)

           for line in lines[indices_start_line:]:
                  try:
                         __lowercase	,				__lowercase       	=line.rstrip().rsplit(' '  ,       1)
                         if field == "#fairseq:overwrite":
                                __lowercase       	=True
                                __lowercase	,				__lowercase       	=line.rsplit(' '  ,       1)
                         else:
                                __lowercase       	=False
                         __lowercase       	=int(lowerCamelCase_)
                         __lowercase       	=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(lowerCamelCase_))
                         self.add_symbol(lowerCamelCase_  ,       n=lowerCamelCase_  ,       overwrite=lowerCamelCase_)
                  except ValueError:
                         raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\'')



def   _A       (   _lowerCAmelCase					):



       """simple docstring"""



       __lowercase       	=dict((re.sub(r'@@$'  ,   ''  ,   _lowerCAmelCase					), v) if k.endswith('@@'					) else (re.sub(r'$'  ,   '</w>'  ,   _lowerCAmelCase					), v) for k, v in d.items()					)
       __lowercase       	='<s> <pad> </s> <unk>'.split()
       # restore the special tokens
       for k in keep_keys:
              del da[f"""{k}</w>"""]
              __lowercase       	=d[k]  # restore
       return da



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



       """simple docstring"""



       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

       __lowercase       	=os.path.join(_lowerCAmelCase  ,   'checkpoint.pt'					)
       if not os.path.isfile(_lowerCAmelCase					):
              raise ValueError(f"""path to the file {checkpoint_file} does not exist!"""					)
       __lowercase       	=torch.load(_lowerCAmelCase  ,   map_location='cpu'					)

       __lowercase       	=chkpt['cfg']['model']

       # dicts
       __lowercase       	=os.path.join(_lowerCAmelCase  ,   'dict.txt'					)
       if not os.path.isfile(_lowerCAmelCase					):
              raise ValueError(f"""path to the file {dict_file} does not exist!"""					)
       __lowercase       	=Dictionary.load(_lowerCAmelCase					)
       __lowercase       	=rewrite_dict_keys(src_dict.indices					)
       __lowercase       	=len(_lowerCAmelCase					)
       __lowercase       	=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)
       __lowercase       	=os.path.join(_lowerCAmelCase  ,   'bpecodes'					)
       if not os.path.isfile(_lowerCAmelCase					):
              raise ValueError(f"""path to the file {bpecodes_file} does not exist!"""					)

       __lowercase       	=os.path.join(_lowerCAmelCase  ,   VOCAB_FILES_NAMES['merges_file']					)
       shutil.copyfile(_lowerCAmelCase  ,   _lowerCAmelCase					)

       # model config
       __lowercase       	=os.path.join(_lowerCAmelCase  ,   'config.json'					)

       __lowercase       	={
           '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-1_2,
           '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
       __lowercase       	=os.path.join(_lowerCAmelCase  ,   _lowerCAmelCase					)

       __lowercase       	={
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 1_024,
           '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
       __lowercase       	=chkpt['model']

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

       __lowercase       	=list(model_state_dict.keys()					)
       for layer_name in layer_names:
              if layer_name.endswith('output_projection.weight'					):
                     __lowercase       	=model_state_dict.pop(_lowerCAmelCase					)
              else:
                     __lowercase       	=model_state_dict.pop(_lowerCAmelCase					)

       __lowercase       	=BioGptConfig.from_pretrained(_lowerCAmelCase					)
       __lowercase       	=BioGptForCausalLM(_lowerCAmelCase					)

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

       # save
       __lowercase       	=os.path.join(_lowerCAmelCase  ,   _lowerCAmelCase					)
       print(f"""Generating {pytorch_weights_dump_path}"""					)
       torch.save(_lowerCAmelCase  ,   _lowerCAmelCase					)

       print('Conversion is done!'					)


if __name__ == "__main__":
  lowerCamelCase	 =       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."""
  )
  lowerCamelCase	 =       parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType





class _UpperCamelCase		(     A		):





				'''simple docstring'''





				lowerCAmelCase__					    =    ["""image_processor""", """tokenizer"""]
				lowerCAmelCase__					    =    """ViltImageProcessor"""
				lowerCAmelCase__					    =    ("""BertTokenizer""", """BertTokenizerFast""")

				def __init__(							self  :  str  ,       _lowerCAmelCase  :  Optional[Any]=None  ,       _lowerCAmelCase  :  int=None  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	=None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																		    ' instead.'  ,       _lowerCAmelCase  ,       )
																		__lowercase       	=kwargs.pop('feature_extractor')

											__lowercase       	=image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																		raise ValueError('You need to specify an `image_processor`.')
											if tokenizer is None:
																		raise ValueError('You need to specify a `tokenizer`.')

											super().__init__(_lowerCAmelCase  ,       _lowerCAmelCase)
											__lowercase       	=self.image_processor

				def __call__(							self  :  List[Any]  ,       _lowerCAmelCase  :  Tuple  ,       _lowerCAmelCase  :  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Union[bool, str, PaddingStrategy] = False  ,       _lowerCAmelCase  :  Union[bool, str, TruncationStrategy] = None  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  int = 0  ,       _lowerCAmelCase  :  Optional[int] = None  ,       _lowerCAmelCase  :  Optional[bool] = None  ,       _lowerCAmelCase  :  Optional[bool] = None  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = False  ,       _lowerCAmelCase  :  bool = True  ,       _lowerCAmelCase  :  Optional[Union[str, TensorType]] = None  ,       **_lowerCAmelCase  :  Any  ,       ):







											'''simple docstring'''

											__lowercase       	=self.tokenizer(
											    text=_lowerCAmelCase  ,       add_special_tokens=_lowerCAmelCase  ,       padding=_lowerCAmelCase  ,       truncation=_lowerCAmelCase  ,       max_length=_lowerCAmelCase  ,       stride=_lowerCAmelCase  ,       pad_to_multiple_of=_lowerCAmelCase  ,       return_token_type_ids=_lowerCAmelCase  ,       return_attention_mask=_lowerCAmelCase  ,       return_overflowing_tokens=_lowerCAmelCase  ,       return_special_tokens_mask=_lowerCAmelCase  ,       return_offsets_mapping=_lowerCAmelCase  ,       return_length=_lowerCAmelCase  ,       verbose=_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase  ,       **_lowerCAmelCase  ,       )
											# add pixel_values + pixel_mask
											__lowercase       	=self.image_processor(_lowerCAmelCase  ,       return_tensors=_lowerCAmelCase)
											encoding.update(_lowerCAmelCase)

											return encoding

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Union[str, Any]):







											'''simple docstring'''

											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				def 				__lowerCamelCase     (							self  :  Union[str, Any]  ,       *_lowerCAmelCase  :  str  ,       **_lowerCAmelCase  :  Optional[Any]):







											'''simple docstring'''

											return self.tokenizer.decode(*_lowerCAmelCase  ,       **_lowerCAmelCase)

				@property
				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											__lowercase       	=self.tokenizer.model_input_names
											__lowercase       	=self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

				@property
				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,       _lowerCAmelCase  ,       )
											return self.image_processor_class

				@property
				def 				__lowerCamelCase     (							self  :  Tuple):







											'''simple docstring'''

											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,       _lowerCAmelCase  ,       )
											return self.image_processor



