





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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE_						   =       {
    """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""",
    },
}

SCREAMING_SNAKE_CASE_						   =       {
    """gpt2""": 1_0_2_4,
    """gpt2-medium""": 1_0_2_4,
    """gpt2-large""": 1_0_2_4,
    """gpt2-xl""": 1_0_2_4,
    """distilgpt2""": 1_0_2_4,
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Tuple			=			VOCAB_FILES_NAMES
     __snake_case    :	List[str]			=			PRETRAINED_VOCAB_FILES_MAP
     __snake_case    :	Optional[Any]			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case    :	Any			=			["input_ids", "attention_mask"]
     __snake_case    :	int			=			GPTaTokenizer






     def __init__(							self						:    Dict						,lowerCamelCase__						:    int=None						,lowerCamelCase__						:    Dict=None						,lowerCamelCase__						:    Dict=None						,lowerCamelCase__						:    Union[str, Any]="<|endoftext|>"						,lowerCamelCase__						:    Optional[Any]="<|endoftext|>"						,lowerCamelCase__						:    Any="<|endoftext|>"						,lowerCamelCase__						:    Tuple=False						,**lowerCamelCase__						:    Dict						,)     ->		Dict:




           '''simple docstring'''


           super().__init__(
               lowerCamelCase__						,lowerCamelCase__						,tokenizer_file=lowerCamelCase__						,unk_token=lowerCamelCase__						,bos_token=lowerCamelCase__						,eos_token=lowerCamelCase__						,add_prefix_space=lowerCamelCase__						,**lowerCamelCase__						,)

           SCREAMING_SNAKE_CASE		=						kwargs.pop("""add_bos_token"""						,lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,lowerCamelCase__       ) != add_prefix_space:
                 SCREAMING_SNAKE_CASE		=						getattr(lowerCamelCase__						,pre_tok_state.pop("""type"""       )       )
                 SCREAMING_SNAKE_CASE		=						add_prefix_space
                 SCREAMING_SNAKE_CASE		=						pre_tok_class(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						add_prefix_space






     def 				SCREAMING_SNAKE_CASE__  (							self						:    List[Any]						,*lowerCamelCase__						:    Dict						,**lowerCamelCase__						:    Tuple       )     ->		BatchEncoding:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.get("""is_split_into_words"""						,lowerCamelCase__       )
           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(*lowerCamelCase__						,**lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str						,*lowerCamelCase__						:    Dict						,**lowerCamelCase__						:    int       )     ->		BatchEncoding:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						kwargs.get("""is_split_into_words"""						,lowerCamelCase__       )

           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(*lowerCamelCase__						,**lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int						,lowerCamelCase__						:    str						,lowerCamelCase__						:    Optional[str] = None       )     ->		Tuple[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self._tokenizer.model.save(lowerCamelCase__						,name=lowerCamelCase__       )
           return tuple(lowerCamelCase__       )






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict						,lowerCamelCase__						:    "Conversation"       )     ->		List[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]
           for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(lowerCamelCase__						,add_special_tokens=lowerCamelCase__       ) + [self.eos_token_id]       )

           if len(lowerCamelCase__       ) > self.model_max_length:
                 SCREAMING_SNAKE_CASE		=						input_ids[-self.model_max_length :]
           return input_ids









import os
from distutils.util import strtobool



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) ->   Tuple:


      '''simple docstring'''


      for e in env_keys:
            SCREAMING_SNAKE_CASE		=						int(os.environ.get(_SCREAMING_SNAKE_CASE ,						-1	)	)
            if val >= 0:
                  return val
      return default



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE=False	) ->   Optional[int]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						os.environ.get(_SCREAMING_SNAKE_CASE ,						str(_SCREAMING_SNAKE_CASE	)	)
      return strtobool(_SCREAMING_SNAKE_CASE	) == 1  # As its name indicates `strtobool` actually returns an int...



def    __lowercase   (      _SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE="no"	) ->   Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						os.environ.get(_SCREAMING_SNAKE_CASE ,						str(_SCREAMING_SNAKE_CASE	)	)
      return value


