


'''simple docstring'''







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


A				:					List[str]    						=			logging.get_logger(__name__)

A				:					int    						=			{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

A				:					List[Any]    						=			{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

A				:					Union[str, Any]    						=			{
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   VOCAB_FILES_NAMES
    a													=   PRETRAINED_VOCAB_FILES_MAP
    a													=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a													=   ["input_ids", "attention_mask"]
    a													=   GPTaTokenizer


    def __init__(  self      :							Any				,							SCREAMING_SNAKE_CASE      :							List[str]=None				,							SCREAMING_SNAKE_CASE      :							List[str]=None				,							SCREAMING_SNAKE_CASE      :							str=None				,							SCREAMING_SNAKE_CASE      :							Dict="<|endoftext|>"				,							SCREAMING_SNAKE_CASE      :							Optional[int]="<|endoftext|>"				,							SCREAMING_SNAKE_CASE      :							Any="<|endoftext|>"				,							SCREAMING_SNAKE_CASE      :							str=False				,							**SCREAMING_SNAKE_CASE      :							Tuple				,							):
           super().__init__(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							tokenizer_file=SCREAMING_SNAKE_CASE				,							unk_token=SCREAMING_SNAKE_CASE				,							bos_token=SCREAMING_SNAKE_CASE				,							eos_token=SCREAMING_SNAKE_CASE				,							add_prefix_space=SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE				,							)

           _A					:     str     = kwargs.pop('add_bos_token'				,							SCREAMING_SNAKE_CASE)

           _A					:     Any     = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
           if pre_tok_state.get('add_prefix_space'				,							SCREAMING_SNAKE_CASE) != add_prefix_space:
                  _A					:     Dict     = getattr(SCREAMING_SNAKE_CASE				,							pre_tok_state.pop('type'))
                  _A					:     Dict     = add_prefix_space
                  _A					:     Optional[int]     = pre_tok_class(**SCREAMING_SNAKE_CASE)

           _A					:     List[str]     = add_prefix_space


    def        A   (  self      :							List[Any]				,							*SCREAMING_SNAKE_CASE      :							int				,							**SCREAMING_SNAKE_CASE      :							Tuple):
           _A					:     Union[str, Any]     = kwargs.get('is_split_into_words'				,							SCREAMING_SNAKE_CASE)
           assert self.add_prefix_space or not is_split_into_words, (
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]				,							*SCREAMING_SNAKE_CASE      :							Any				,							**SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Union[str, Any]     = kwargs.get('is_split_into_words'				,							SCREAMING_SNAKE_CASE)

           assert self.add_prefix_space or not is_split_into_words, (
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*SCREAMING_SNAKE_CASE				,							**SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[str] = None):
           _A					:     Optional[int]     = self._tokenizer.model.save(SCREAMING_SNAKE_CASE				,							name=SCREAMING_SNAKE_CASE)
           return tuple(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							"Conversation"):
           _A					:     Dict     = []
           for is_user, text in conversation.iter_texts():
                  input_ids.extend(self.encode(SCREAMING_SNAKE_CASE				,							add_special_tokens=SCREAMING_SNAKE_CASE) + [self.eos_token_id])

           if len(SCREAMING_SNAKE_CASE) > self.model_max_length:
                  _A					:     Union[str, Any]     = input_ids[-self.model_max_length :]
           return input_ids










'''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()

A				:					int    						=			2


class 			__lowerCamelCase			:
    """simple docstring"""


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


    def __eq__(  self      :							int				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           return self.indices == other.indices


    def __getitem__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple):
           if idx < len(self.symbols):
                  return self.symbols[idx]
           return self.unk_word


    def __len__(  self      :							Union[str, Any]):
           return len(self.symbols)


    def __contains__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							List[str]):
           return sym in self.indices


    @classmethod
    def        A   (  cls      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Any     = cls()
           d.add_from_file(SCREAMING_SNAKE_CASE)
           return d


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[int]=1				,							SCREAMING_SNAKE_CASE      :							int=False):
           if word in self.indices and not overwrite:
                  _A					:     str     = self.indices[word]
                  _A					:     List[str]     = self.count[idx] + n
                  return idx
           else:
                  _A					:     Optional[Any]     = len(self.symbols)
                  _A					:     Union[str, Any]     = idx
                  self.symbols.append(SCREAMING_SNAKE_CASE)
                  self.count.append(SCREAMING_SNAKE_CASE)
                  return idx


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           return 0


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

           _A					:     Union[str, Any]     = f.readlines()
           _A					:     Any     = self._load_meta(SCREAMING_SNAKE_CASE)

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






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






def 				lowerCAmelCase__	(    lowerCamelCase					:      List[str]  ,lowerCamelCase					:      List[str]       ):
       # 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

       _A					:     Dict     = 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					:     int     = torch.load(lowerCamelCase  ,map_location='cpu'       )

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

       # dicts
       _A					:     Any     = 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					:     Any     = Dictionary.load(lowerCamelCase       )
       _A					:     Optional[int]     = rewrite_dict_keys(src_dict.indices       )
       _A					:     List[Any]     = len(lowerCamelCase       )
       _A					:     str     = 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					:     Optional[int]     = os.path.join(lowerCamelCase  ,'bpecodes'       )
       if not os.path.isfile(lowerCamelCase       ):
              raise ValueError(F'path to the file {bpecodes_file} does not exist!'       )

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

       # model config
       _A					:     str     = os.path.join(lowerCamelCase  ,'config.json'       )

       _A					:     int     = {
           '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					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )

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

       # model
       _A					:     List[Any]     = chkpt['model']

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

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

       _A					:     Any     = BioGptConfig.from_pretrained(lowerCamelCase       )
       _A					:     Union[str, Any]     = BioGptForCausalLM(lowerCamelCase       )

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

       # save
       _A					:     Union[str, Any]     = os.path.join(lowerCamelCase  ,lowerCamelCase       )
       print(F'Generating {pytorch_weights_dump_path}'       )
       torch.save(lowerCamelCase  ,lowerCamelCase       )

       print('Conversion is done!'       )


if __name__ == "__main__":
   A				:					Optional[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.'''
   )
   A				:					int    						=			parser.parse_args()
   convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)






