
"""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[int]									=				2

class __lowerCAmelCase	:





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

     '''simple docstring'''




     __UpperCamelCase       						= bos, unk, pad, eos
     __UpperCamelCase       						= []
     __UpperCamelCase       						= []
     __UpperCamelCase       						= {}
     __UpperCamelCase       						= self.add_symbol(_a   )
     __UpperCamelCase       						= self.add_symbol(_a   )
     __UpperCamelCase       						= self.add_symbol(_a   )
     __UpperCamelCase       						= self.add_symbol(_a   )
     if extra_special_symbols:
      for s in extra_special_symbols:
       self.add_symbol(_a   )
     __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(_a   )
     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(_a   )
      self.count.append(_a   )
      return idx





    def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

     '''simple docstring'''




     return 0







    def 		UpperCAmelCase      (			self			,  __UpperCAmelCase   ):

     '''simple docstring'''




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

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

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






def 							A		(       snake_case   :Union[str, Any]	)   ->			Dict:
 # (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'@@$'       ,      ''       ,      __lowerCAmelCase	), v) if k.endswith('@@'	) else (re.sub(r'$'       ,      '</w>'       ,      __lowerCAmelCase	), 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   :str       ,      snake_case   :Optional[int]	)   ->			Optional[Any]:
 # 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

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

 __UpperCamelCase       						= chkpt["""cfg"""]["""model"""]

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

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

 # model config
 __UpperCamelCase       						= os.path.join(__lowerCAmelCase       ,      '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(__lowerCAmelCase       ,      'w'       ,      encoding='utf-8'	) as f:
  f.write(json.dumps(__lowerCAmelCase       ,      ensure_ascii=__lowerCAmelCase       ,      indent=__lowerCAmelCase	)	)

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

 __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(__lowerCAmelCase       ,      'w'       ,      encoding='utf-8'	) as f:
  f.write(json.dumps(__lowerCAmelCase       ,      ensure_ascii=__lowerCAmelCase       ,      indent=__lowerCAmelCase	)	)

 # model
 __UpperCamelCase       						= chkpt["""model"""]

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

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

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

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

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

 print('Conversion is done!'	)


if __name__ == "__main__":
      UpperCamelCase			:       Union[str, 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."
      )
      UpperCamelCase			:       Dict									=				parser.parse_args()
      convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""

import argparse
from collections import defaultdict






def 							A		(       snake_case   :Tuple       ,      snake_case   :List[Any]       ,      snake_case   :List[Any]       ,      snake_case   :Union[str, Any]       ,      snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 __UpperCamelCase       						= f'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= f'class {class_name}('
 __UpperCamelCase       						= f'{4 * " "}def {test_name}('
 __UpperCamelCase       						= f'{8 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= f'{1_6 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= 0
 __UpperCamelCase       						= 0

 __UpperCamelCase       						= []
 for line in lines:
  if line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and in_func and (line.startswith(snake_case	) or line.startswith(snake_case	)):
   __UpperCamelCase       						= len(line.split(correct_line.split()[0]	)[0]	)
   count += 1

   if count == done_test[_id]:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(f'{spaces * " "}{correct_line}'	)
   __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= False
  else:
   new_lines.append(snake_case	)

 with open(snake_case       ,      'w'	) as f:
  for line in new_lines:
   f.write(snake_case	)






def 							A		(       snake_case   :List[str]       ,      snake_case   :str=None	)   ->			Tuple:
 if fail is not None:
  with open(snake_case       ,      'r'	) as f:
   __UpperCamelCase       						= {l.strip() for l in f.readlines()}
 else:
  __UpperCamelCase       						= None

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= defaultdict(snake_case	)
 for line in correct_lines:
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= line.split(';'	)
  if test_failures is None or "::".join([file, class_name, test_name]	) in test_failures:
   overwrite_file(snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)


if __name__ == "__main__":
      UpperCamelCase			:       str									=				argparse.ArgumentParser()
      parser.add_argument("--correct_filename", help="filename of tests with expected result")
      parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
      UpperCamelCase			:       List[Any]									=				parser.parse_args()

      main(args.correct_filename, args.fail_filename)



