




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       a					:

     '''simple docstring'''





     def __init__(     self       :      int ,   *,  # begin keyword-only arguments
         __snake_case       :      Union[str, Any]="<s>" ,   __snake_case       :      List[str]="<pad>" ,   __snake_case       :      Union[str, Any]="</s>" ,   __snake_case       :      Optional[int]="<unk>" ,   __snake_case       :      Optional[Any]=None ,   ):
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					bos, unk, pad, eos
         UpperCAmelCase_										=					[]
         UpperCAmelCase_										=					[]
         UpperCAmelCase_										=					{}
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					self.add_symbol(__snake_case    )
         if extra_special_symbols:
             for s in extra_special_symbols:
                 self.add_symbol(__snake_case    )
         UpperCAmelCase_										=					len(self.symbols    )
     def __eq__(     self       :      Any ,   __snake_case       :      str    ):
         return self.indices == other.indices
     def __getitem__(     self       :      Dict ,   __snake_case       :      Optional[int]    ):
         if idx < len(self.symbols    ):
             return self.symbols[idx]
         return self.unk_word
     def __len__(     self       :      Any    ):
         return len(self.symbols    )
     def __contains__(     self       :      Any ,   __snake_case       :      Tuple    ):
         return sym in self.indices
     @classmethod
     def   lowerCamelCase_       (     cls       :      Any ,   __snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					cls()
         d.add_from_file(__snake_case    )
         return d
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      Optional[int] ,   __snake_case       :      Optional[Any]=1 ,   __snake_case       :      Optional[Any]=False    ):
         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(__snake_case    )
             self.count.append(__snake_case    )
             return idx
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Optional[Any]    ):
         return 0




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

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

         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(__snake_case    )
                 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(__snake_case    )    )
                 self.add_symbol(__snake_case ,   n=__snake_case ,   overwrite=__snake_case    )
             except ValueError:
                 raise ValueError('''Incorrect dictionary format, expected \'<token> <cnt> [flags]\''''    )




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							Any:
    # (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'''@@$'''	,					''''''	,					__UpperCamelCase				), v) if k.endswith('''@@'''				) else (re.sub(R'''$'''	,					'''</w>'''	,					__UpperCamelCase				), 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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						List[Any]				)  ->							Optional[Any]:
    # prep
    if not os.path.exists(__UpperCamelCase				):
        raise ValueError(f'path {biogpt_checkpoint_path} does not exist!'				)
    os.makedirs(__UpperCamelCase	,					exist_ok=__UpperCamelCase				)
    print(f'Writing results to {pytorch_dump_folder_path}'				)

    # handle various types of models

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

    UpperCAmelCase_										=					chkpt['''cfg''']['''model''']

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

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

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

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

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

    UpperCAmelCase_										=					{
        '''bos_token''': '''<s>''',
        '''eos_token''': '''</s>''',
        '''model_max_length''': 1024,
        '''pad_token''': '''<pad>''',
        '''special_tokens_map_file''': None,
        '''tokenizer_class''': '''BioGptTokenizer''',
        '''unk_token''': '''<unk>''',
    }

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

    # model
    UpperCAmelCase_										=					chkpt['''model''']

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

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

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

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

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

    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)











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						float	,					__UpperCamelCase      :						float				)  ->							float:

    if density <= 0:
        raise ValueError('''Impossible fluid density'''				)
    if bulk_modulus <= 0:
        raise ValueError('''Impossible bulk modulus'''				)

    return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
 import doctest

 doctest.testmod()





