
"""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()

UpperCamelCase     :    Optional[Any]       =			2

class __lowerCAmelCase		:





     def __init__(     self     ,					*,  # begin keyword-only arguments
         __UpperCAmelCase="<s>"     ,					__UpperCAmelCase="<pad>"     ,					__UpperCAmelCase="</s>"     ,					__UpperCAmelCase="<unk>"     ,					__UpperCAmelCase=None     ,					):

      '''simple docstring'''




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





     def __eq__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return self.indices == other.indices





     def __getitem__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




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





     def __len__(     self				):

      '''simple docstring'''




      return len(self.symbols				)





     def __contains__(     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return sym in self.indices





     @classmethod
     def 						UpperCAmelCase (     cls     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	cls()
      d.add_from_file(__UpperCAmelCase				)
      return d





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase=1     ,					__UpperCAmelCase=False				):

      '''simple docstring'''




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





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




      return 0







     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase				):

      '''simple docstring'''




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

      __UpperCamelCase      =	f.readlines()
      __UpperCamelCase      =	self._load_meta(__UpperCAmelCase				)

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






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






def 							A						(    snake_case				:List[Any]    , snake_case				:List[Any]		)				->     Union[str, Any]:
 # prep
 if not os.path.exists(snake_case		):
  raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'		)
 os.makedirs(snake_case    , exist_ok=snake_case		)
 print(f'Writing results to {pytorch_dump_folder_path}'		)

 # handle various types of models

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

 __UpperCamelCase      =	chkpt['cfg']['model']

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

 # merges_file (bpecodes)
 __UpperCamelCase      =	os.path.join(snake_case    , 'bpecodes'		)
 if not os.path.isfile(snake_case		):
  raise ValueError(f'path to the file {bpecodes_file} does not exist!'		)

 __UpperCamelCase      =	os.path.join(snake_case    , VOCAB_FILES_NAMES['merges_file']		)
 shutil.copyfile(snake_case    , snake_case		)

 # model config
 __UpperCamelCase      =	os.path.join(snake_case    , 'config.json'		)

 __UpperCamelCase      =	{
     '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(snake_case    , 'w'    , encoding='utf-8'		) as f:
  f.write(json.dumps(snake_case    , ensure_ascii=snake_case    , indent=snake_case		)		)

 # tokenizer config
 __UpperCamelCase      =	os.path.join(snake_case    , snake_case		)

 __UpperCamelCase      =	{
     'bos_token': '<s>',
     'eos_token': '</s>',
     'model_max_length': 1_0_2_4,
     'pad_token': '<pad>',
     'special_tokens_map_file': None,
     'tokenizer_class': 'BioGptTokenizer',
     'unk_token': '<unk>',
 }

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

 # model
 __UpperCamelCase      =	chkpt['model']

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

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

 __UpperCamelCase      =	BioGptConfig.from_pretrained(snake_case		)
 __UpperCamelCase      =	BioGptForCausalLM(snake_case		)

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

 # save
 __UpperCamelCase      =	os.path.join(snake_case    , snake_case		)
 print(f'Generating {pytorch_weights_dump_path}'		)
 torch.save(snake_case    , snake_case		)

 print('Conversion is done!'		)


if __name__ == "__main__":
      UpperCamelCase     :    Dict       =			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     :    List[Any]       =			parser.parse_args()
      convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""

from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands






def 							A						(    )				->     Union[str, Any]:
 __UpperCamelCase      =	ArgumentParser('Transformers CLI tool'    , usage='transformers-cli <command> [<args>]'		)
 __UpperCamelCase      =	parser.add_subparsers(help='transformers-cli command helpers'		)

 # Register commands
 ConvertCommand.register_subcommand(snake_case		)
 DownloadCommand.register_subcommand(snake_case		)
 EnvironmentCommand.register_subcommand(snake_case		)
 RunCommand.register_subcommand(snake_case		)
 ServeCommand.register_subcommand(snake_case		)
 UserCommands.register_subcommand(snake_case		)
 AddNewModelCommand.register_subcommand(snake_case		)
 AddNewModelLikeCommand.register_subcommand(snake_case		)
 LfsCommands.register_subcommand(snake_case		)
 PTtoTFCommand.register_subcommand(snake_case		)

 # Let's go
 __UpperCamelCase      =	parser.parse_args()

 if not hasattr(snake_case    , 'func'		):
  parser.print_help()
  exit(1		)

 # Run
 __UpperCamelCase      =	args.func(snake_case		)
 service.run()


if __name__ == "__main__":
      main()



