

'''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


lowerCAmelCase__      		=			logging.get_logger(__name__)

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

lowerCAmelCase__      		=			{
    '''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''',
    },
}

lowerCAmelCase__      		=			{
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Dict           =    VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE					:						Tuple           =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE					:						Tuple           =    ['input_ids', 'attention_mask']
  SCREAMING_SNAKE_CASE					:						Optional[int]           =    GPTaTokenizer






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

   __lowercase													=				kwargs.pop('''add_bos_token'''      ,lowercase__    )

   __lowercase													=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
   if pre_tok_state.get('''add_prefix_space'''      ,lowercase__    ) != add_prefix_space:
    __lowercase													=				getattr(lowercase__      ,pre_tok_state.pop('''type'''    )    )
    __lowercase													=				add_prefix_space
    __lowercase													=				pre_tok_class(**lowercase__    )

   __lowercase													=				add_prefix_space






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,*lowercase__     :  Optional[int]      ,**lowercase__     :  Dict    ):
   __lowercase													=				kwargs.get('''is_split_into_words'''      ,lowercase__    )
   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(*lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,*lowercase__     :  Tuple      ,**lowercase__     :  Union[str, Any]    ):
   __lowercase													=				kwargs.get('''is_split_into_words'''      ,lowercase__    )

   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(*lowercase__      ,**lowercase__    )






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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  "Conversation"    ):
   __lowercase													=				[]
   for is_user, text in conversation.iter_texts():
    input_ids.extend(self.encode(lowercase__      ,add_special_tokens=lowercase__    ) + [self.eos_token_id]    )

   if len(lowercase__    ) > self.model_max_length:
    __lowercase													=				input_ids[-self.model_max_length :]
   return input_ids




'''simple docstring'''






from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def 	_A  (					A__   ):






 """simple docstring"""



 return DownloadCommand(args.model  ,       args.cache_dir  ,       args.force  ,       args.trust_remote_code   )






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  @staticmethod
  def       SCREAMING_SNAKE_CASE   (   lowercase__     :  ArgumentParser    ):
   __lowercase													=				parser.add_parser('''download'''    )
   download_parser.add_argument(
       '''--cache-dir'''      ,type=lowercase__      ,default=lowercase__      ,help='''Path to location to store the models'''    )
   download_parser.add_argument(
       '''--force'''      ,action='''store_true'''      ,help='''Force the model to be download even if already in cache-dir'''    )
   download_parser.add_argument(
       '''--trust-remote-code'''      ,action='''store_true'''      ,help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''      ,)
   download_parser.add_argument('''model'''      ,type=lowercase__      ,help='''Name of the model to download'''    )
   download_parser.set_defaults(func=lowercase__    )






  def __init__(   self     :  str      ,lowercase__     :  str      ,lowercase__     :  str      ,lowercase__     :  bool      ,lowercase__     :  bool    ):
   __lowercase													=				model
   __lowercase													=				cache
   __lowercase													=				force
   __lowercase													=				trust_remote_code






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   from ..models.auto import AutoModel, AutoTokenizer

   AutoModel.from_pretrained(
       self._model      ,cache_dir=self._cache      ,force_download=self._force      ,trust_remote_code=self._trust_remote_code    )
   AutoTokenizer.from_pretrained(
       self._model      ,cache_dir=self._cache      ,force_download=self._force      ,trust_remote_code=self._trust_remote_code    )

