



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_								=       2



class 			__lowerCAmelCase  :




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



    '''simple docstring'''
    __lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase				,__lowerCamelCase    				=     bos, unk, pad, eos
    __lowerCamelCase    				=     []
    __lowerCamelCase    				=     []
    __lowerCamelCase    				=     {}
    __lowerCamelCase    				=     self.add_symbol(UpperCAmelCase_					)
    __lowerCamelCase    				=     self.add_symbol(UpperCAmelCase_					)
    __lowerCamelCase    				=     self.add_symbol(UpperCAmelCase_					)
    __lowerCamelCase    				=     self.add_symbol(UpperCAmelCase_					)
    if extra_special_symbols:
       for s in extra_special_symbols:
          self.add_symbol(UpperCAmelCase_					)
    __lowerCamelCase    				=     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 							lowerCamelCase   (    cls						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     cls()
    d.add_from_file(UpperCAmelCase_					)
    return d




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



    '''simple docstring'''
    if word in self.indices and not overwrite:
       __lowerCamelCase    				=     self.indices[word]
       __lowerCamelCase    				=     self.count[idx] + n
       return idx
    else:
       __lowerCamelCase    				=     len(self.symbols					)
       __lowerCamelCase    				=     idx
       self.symbols.append(UpperCAmelCase_					)
       self.count.append(UpperCAmelCase_					)
       return idx




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    return 0




 def 							lowerCamelCase   (    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

    __lowerCamelCase    				=     f.readlines()
    __lowerCamelCase    				=     self._load_meta(UpperCAmelCase_					)

    for line in lines[indices_start_line:]:
       try:
          __lowerCamelCase				,__lowerCamelCase    				=     line.rstrip().rsplit(''' '''						,					1					)
          if field == "#fairseq:overwrite":
             __lowerCamelCase    				=     True
             __lowerCamelCase				,__lowerCamelCase    				=     line.rsplit(''' '''						,					1					)
          else:
             __lowerCamelCase    				=     False
          __lowerCamelCase    				=     int(UpperCAmelCase_					)
          __lowerCamelCase    				=     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__      (		_UpperCamelCase      :			List[str]				):
   __lowerCamelCase    				=     dict((re.sub(R'''@@$'''			,''''''			,_snake_case				), v) if k.endswith('''@@'''				) else (re.sub(R'''$'''			,'''</w>'''			,_snake_case				), v) for k, v in d.items()				)
   __lowerCamelCase    				=     '''<s> <pad> </s> <unk>'''.split()
   # restore the special tokens
   for k in keep_keys:
      del da[F"""{k}</w>"""]
      __lowerCamelCase    				=     d[k]  # restore
   return da





def 		a__      (		_UpperCamelCase      :			str			,_UpperCamelCase      :			Tuple				):


   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

   __lowerCamelCase    				=     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!"""				)
   __lowerCamelCase    				=     torch.load(_snake_case			,map_location='''cpu'''				)

   __lowerCamelCase    				=     chkpt['''cfg''']['''model''']

   # dicts
   __lowerCamelCase    				=     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!"""				)
   __lowerCamelCase    				=     Dictionary.load(_snake_case				)
   __lowerCamelCase    				=     rewrite_dict_keys(src_dict.indices				)
   __lowerCamelCase    				=     len(_snake_case				)
   __lowerCamelCase    				=     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)
   __lowerCamelCase    				=     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!"""				)

   __lowerCamelCase    				=     os.path.join(_snake_case			,VOCAB_FILES_NAMES['''merges_file''']				)
   shutil.copyfile(_snake_case			,_snake_case				)

   # model config
   __lowerCamelCase    				=     os.path.join(_snake_case			,'''config.json'''				)

   __lowerCamelCase    				=     {
       '''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
   __lowerCamelCase    				=     os.path.join(_snake_case			,_snake_case				)

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

   # model
   __lowerCamelCase    				=     chkpt['''model''']

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

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

   __lowerCamelCase    				=     BioGptConfig.from_pretrained(_snake_case				)
   __lowerCamelCase    				=     BioGptForCausalLM(_snake_case				)

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

   # save
   __lowerCamelCase    				=     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__":
  a_								=       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_								=       parser.parse_args()
  convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)











'''simple docstring'''





def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Union[str, Any]  ,_snake_case	:    Dict  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]  ,_snake_case	:    List[str]							):





    '''simple docstring'''


    if index == r:
        for j in range(_snake_case							):
            print(data[j]  ,end=" "							)
        print(" "							)
        return
    #  When no more elements are there to put in data[]
    if i >= n:
        return
    # current is included, put next at next location
    lowercase__									=    arr[i]
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,index + 1  ,_snake_case  ,i + 1							)
    # current is excluded, replace it with
    # next (Note that i+1 is passed, but
    # index is not changed)
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,_snake_case  ,i + 1							)
    # The main function that prints all combinations
    # of size r in arr[] of size n. This function
    # mainly uses combinationUtil()




def 							lowerCamelCase	(					_snake_case	:    Optional[Any]  ,_snake_case	:    Tuple  ,_snake_case	:    Union[str, Any]							):





    '''simple docstring'''


    lowercase__									=    [0] * r
    # Print all combination using temporary array 'data[]'
    combination_util(_snake_case  ,_snake_case  ,_snake_case  ,0  ,_snake_case  ,0							)


if __name__ == "__main__":
 # Driver code to check the function above
 SCREAMING_SNAKE_CASE__          =  [10, 20, 30, 40, 50]
 print_combination(arr, len(arr), 3)
 # This code is contributed by Ambuj sahu




