



"""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


__snake_case							:     Tuple   =					logging.get_logger(__name__)

__snake_case							:     List[Any]   =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case							:     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',
    },
}

__snake_case							:     Optional[int]   =					{
    'gpt2': 1_024,
    'gpt2-medium': 1_024,
    'gpt2-large': 1_024,
    'gpt2-xl': 1_024,
    'distilgpt2': 1_024,
}



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE					   =			PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE					   =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE					   =			['input_ids', 'attention_mask']
	SCREAMING_SNAKE_CASE					   =			GPTaTokenizer

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

				"""simple docstring"""

				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 ,    )

				__lowerCAmelCase   :						List[str]				= kwargs.pop("add_bos_token" ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
				if pre_tok_state.get("add_prefix_space" ,    _SCREAMING_SNAKE_CASE) != add_prefix_space:
							__lowerCAmelCase   :						List[Any]				= getattr(_SCREAMING_SNAKE_CASE ,    pre_tok_state.pop("type"))
							__lowerCAmelCase   :						Dict				= add_prefix_space
							__lowerCAmelCase   :						Any				= pre_tok_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[str]				= add_prefix_space

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    *_SCREAMING_SNAKE_CASE:	Dict ,    **_SCREAMING_SNAKE_CASE:	List[str])       -> BatchEncoding:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[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 							_SCREAMING_SNAKE_CASE     (  self:	Dict ,    *_SCREAMING_SNAKE_CASE:	int ,    **_SCREAMING_SNAKE_CASE:	int)       -> BatchEncoding:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= 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 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Optional[str] = None)       -> Tuple[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE ,    name=_SCREAMING_SNAKE_CASE)
				return tuple(_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	"Conversation")       -> List[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= []
				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:
							__lowerCAmelCase   :						List[Any]				= input_ids[-self.model_max_length :]
				return input_ids




"""simple docstring"""






import re

from filelock import FileLock


try:
	import nltk

	__snake_case							:     Any   =					True
except (ImportError, ModuleNotFoundError):
	__snake_case							:     Optional[int]   =					False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)


def     _lowercase       (						__snake_case			)		->     str:
			re.sub("<n>"      ,""      ,__snake_case			)  # remove pegasus newline char
			assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
			return "\n".join(nltk.sent_tokenize(__snake_case			)			)