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()

UpperCAmelCase_								=							2





class      lowerCAmelCase						:



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



     def __eq__(					self				,      lowerCAmelCase__       ):
            return self.indices == other.indices



     def __getitem__(					self				,      lowerCAmelCase__       ):
            if idx < len(self.symbols       ):
                   return self.symbols[idx]
            return self.unk_word



     def __len__(					self       ):
            return len(self.symbols       )



     def __contains__(					self				,      lowerCAmelCase__       ):
            return sym in self.indices



     @classmethod
     def 			a__  (					cls				,      lowerCAmelCase__       ):
            _A=	cls()
            d.add_from_file(lowerCAmelCase__       )
            return d



     def 			a__  (					self				,      lowerCAmelCase__				,      lowerCAmelCase__=1				,      lowerCAmelCase__=False       ):
            if word in self.indices and not overwrite:
                   _A=	self.indices[word]
                   _A=	self.count[idx] + n
                   return idx
            else:
                   _A=	len(self.symbols       )
                   _A=	idx
                   self.symbols.append(lowerCAmelCase__       )
                   self.count.append(lowerCAmelCase__       )
                   return idx



     def 			a__  (					self				,      lowerCAmelCase__       ):
            return 0





     def 			a__  (					self				,      lowerCAmelCase__       ):
            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

            _A=	f.readlines()
            _A=	self._load_meta(lowerCAmelCase__       )

            for line in lines[indices_start_line:]:
                   try:
                          _A,			_A=	line.rstrip().rsplit(' '				,      1       )
                          if field == "#fairseq:overwrite":
                                 _A=	True
                                 _A,			_A=	line.rsplit(' '				,      1       )
                          else:
                                 _A=	False
                          _A=	int(lowerCAmelCase__       )
                          _A=	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 							UpperCamelCase				(    lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''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

       _A=	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=	torch.load(lowerCAmelCase_       ,					map_location='cpu'   )

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

       # dicts
       _A=	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=	Dictionary.load(lowerCAmelCase_   )
       _A=	rewrite_dict_keys(src_dict.indices   )
       _A=	len(lowerCAmelCase_   )
       _A=	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=	os.path.join(lowerCAmelCase_       ,					'bpecodes'   )
       if not os.path.isfile(lowerCAmelCase_   ):
              raise ValueError(F"path to the file {bpecodes_file} does not exist!"   )

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

       # model config
       _A=	os.path.join(lowerCAmelCase_       ,					'config.json'   )

       _A=	{
           '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=	os.path.join(lowerCAmelCase_       ,					lowerCAmelCase_   )

       _A=	{
           'bos_token': '<s>',
           'eos_token': '</s>',
           'model_max_length': 10_24,
           '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=	chkpt['model']

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

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

       _A=	BioGptConfig.from_pretrained(lowerCAmelCase_   )
       _A=	BioGptForCausalLM(lowerCAmelCase_   )

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

       # save
       _A=	os.path.join(lowerCAmelCase_       ,					lowerCAmelCase_   )
       print(F"Generating {pytorch_weights_dump_path}"   )
       torch.save(lowerCAmelCase_       ,					lowerCAmelCase_   )

       print('Conversion is done!'   )


if __name__ == "__main__":
      UpperCAmelCase_								=							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.'''
      )
      UpperCAmelCase_								=							parser.parse_args()
      convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)
UpperCAmelCase_								=							{
    '''Pillow''': '''Pillow<10.0.0''',
    '''accelerate''': '''accelerate>=0.20.3''',
    '''av''': '''av==9.2.0''',
    '''beautifulsoup4''': '''beautifulsoup4''',
    '''black''': '''black~=23.1''',
    '''codecarbon''': '''codecarbon==1.2.0''',
    '''cookiecutter''': '''cookiecutter==1.7.3''',
    '''dataclasses''': '''dataclasses''',
    '''datasets''': '''datasets!=2.5.0''',
    '''decord''': '''decord==0.6.0''',
    '''deepspeed''': '''deepspeed>=0.9.3''',
    '''diffusers''': '''diffusers''',
    '''dill''': '''dill<0.3.5''',
    '''evaluate''': '''evaluate>=0.2.0''',
    '''fairscale''': '''fairscale>0.3''',
    '''faiss-cpu''': '''faiss-cpu''',
    '''fastapi''': '''fastapi''',
    '''filelock''': '''filelock''',
    '''flax''': '''flax>=0.4.1,<=0.7.0''',
    '''ftfy''': '''ftfy''',
    '''fugashi''': '''fugashi>=1.0''',
    '''GitPython''': '''GitPython<3.1.19''',
    '''hf-doc-builder''': '''hf-doc-builder>=0.3.0''',
    '''huggingface-hub''': '''huggingface-hub>=0.14.1,<1.0''',
    '''importlib_metadata''': '''importlib_metadata''',
    '''ipadic''': '''ipadic>=1.0.0,<2.0''',
    '''isort''': '''isort>=5.5.4''',
    '''jax''': '''jax>=0.2.8,!=0.3.2,<=0.4.13''',
    '''jaxlib''': '''jaxlib>=0.1.65,<=0.4.13''',
    '''jieba''': '''jieba''',
    '''kenlm''': '''kenlm''',
    '''keras-nlp''': '''keras-nlp>=0.3.1''',
    '''librosa''': '''librosa''',
    '''nltk''': '''nltk''',
    '''natten''': '''natten>=0.14.6''',
    '''numpy''': '''numpy>=1.17''',
    '''onnxconverter-common''': '''onnxconverter-common''',
    '''onnxruntime-tools''': '''onnxruntime-tools>=1.4.2''',
    '''onnxruntime''': '''onnxruntime>=1.4.0''',
    '''opencv-python''': '''opencv-python''',
    '''optuna''': '''optuna''',
    '''optax''': '''optax>=0.0.8,<=0.1.4''',
    '''packaging''': '''packaging>=20.0''',
    '''parameterized''': '''parameterized''',
    '''phonemizer''': '''phonemizer''',
    '''protobuf''': '''protobuf''',
    '''psutil''': '''psutil''',
    '''pyyaml''': '''pyyaml>=5.1''',
    '''pydantic''': '''pydantic<2''',
    '''pytest''': '''pytest>=7.2.0''',
    '''pytest-timeout''': '''pytest-timeout''',
    '''pytest-xdist''': '''pytest-xdist''',
    '''python''': '''python>=3.8.0''',
    '''ray[tune]''': '''ray[tune]''',
    '''regex''': '''regex!=2019.12.17''',
    '''requests''': '''requests''',
    '''rhoknp''': '''rhoknp>=1.1.0,<1.3.1''',
    '''rjieba''': '''rjieba''',
    '''rouge-score''': '''rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1''',
    '''ruff''': '''ruff>=0.0.241,<=0.0.259''',
    '''sacrebleu''': '''sacrebleu>=1.4.12,<2.0.0''',
    '''sacremoses''': '''sacremoses''',
    '''safetensors''': '''safetensors>=0.3.1''',
    '''sagemaker''': '''sagemaker>=2.31.0''',
    '''scikit-learn''': '''scikit-learn''',
    '''sentencepiece''': '''sentencepiece>=0.1.91,!=0.1.92''',
    '''sigopt''': '''sigopt''',
    '''starlette''': '''starlette''',
    '''sudachipy''': '''sudachipy>=0.6.6''',
    '''sudachidict_core''': '''sudachidict_core>=20220729''',
    '''tensorflow-cpu''': '''tensorflow-cpu>=2.6,<2.14''',
    '''tensorflow''': '''tensorflow>=2.6,<2.14''',
    '''tensorflow-text''': '''tensorflow-text<2.14''',
    '''tf2onnx''': '''tf2onnx''',
    '''timeout-decorator''': '''timeout-decorator''',
    '''timm''': '''timm''',
    '''tokenizers''': '''tokenizers>=0.11.1,!=0.11.3,<0.14''',
    '''torch''': '''torch>=1.9,!=1.12.0''',
    '''torchaudio''': '''torchaudio''',
    '''torchvision''': '''torchvision''',
    '''pyctcdecode''': '''pyctcdecode>=0.4.0''',
    '''tqdm''': '''tqdm>=4.27''',
    '''unidic''': '''unidic>=1.0.2''',
    '''unidic_lite''': '''unidic_lite>=1.0.7''',
    '''urllib3''': '''urllib3<2.0.0''',
    '''uvicorn''': '''uvicorn''',
}