
'''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


_lowercase :  Union[str, Any]    							=       logging.get_logger(__name__)

_lowercase :  Optional[Any]    							=       {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase :  str    							=       {
    "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",
    },
}

_lowercase :  Dict    							=       {
    "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  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     VOCAB_FILES_NAMES
					lowerCAmelCase_        =     PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_        =     ['''input_ids''', '''attention_mask''']
					lowerCAmelCase_        =     GPTaTokenizer






					def __init__(   self				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE="<|endoftext|>"				,							__SCREAMING_SNAKE_CASE="<|endoftext|>"				,							__SCREAMING_SNAKE_CASE="<|endoftext|>"				,							__SCREAMING_SNAKE_CASE=False				,							**__SCREAMING_SNAKE_CASE				,							):


											"""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				,							)

											lowercase_	:     int													=       kwargs.pop('''add_bos_token'''				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Union[str, Any]													=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
											if pre_tok_state.get('''add_prefix_space'''				,							__SCREAMING_SNAKE_CASE		) != add_prefix_space:
																	lowercase_	:     Optional[Any]													=       getattr(__SCREAMING_SNAKE_CASE				,							pre_tok_state.pop('''type'''		)		)
																	lowercase_	:     List[Any]													=       add_prefix_space
																	lowercase_	:     Optional[int]													=       pre_tok_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     List[str]													=       add_prefix_space






					def 	_snake_case    (   self				,							*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[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()._batch_encode_plus(*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     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 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_	:     str													=       self._tokenizer.model.save(__SCREAMING_SNAKE_CASE				,							name=__SCREAMING_SNAKE_CASE		)
											return tuple(__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     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:
																	lowercase_	:     Union[str, Any]													=       input_ids[-self.model_max_length :]
											return input_ids







'''simple docstring'''
import re

from filelock import FileLock


try:
		import nltk

		__snake_case							      =     True
except (ImportError, ModuleNotFoundError):
		__snake_case							      =     False

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


def 			a      (			__a   )  ->	str:



					'''simple docstring'''






					re.sub('''<n>''' ,					'''''' ,					__a   )  # 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(__a   )   )