




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


UpperCamelCase__					    =    logging.get_logger(__name__)

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

UpperCamelCase__					    =    {
    """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""",
    },
}

UpperCamelCase__					    =    {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}
class     a__					(      snake_case__   ):
						_a							:					int      =					VOCAB_FILES_NAMES
						_a							:					List[Any]      =					PRETRAINED_VOCAB_FILES_MAP
						_a							:					Optional[int]      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a							:					int      =					["""input_ids""", """attention_mask"""]
						_a							:					str      =					GPTaTokenizer






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




											"""simple docstring"""



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

											__lowerCAmelCase            = kwargs.pop("add_bos_token"	,    _A    )

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

											__lowerCAmelCase            = add_prefix_space






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




											"""simple docstring"""



											__lowerCAmelCase            = kwargs.get("is_split_into_words"	,    _A    )
											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(*_A	,    **_A    )






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




											"""simple docstring"""



											__lowerCAmelCase            = kwargs.get("is_split_into_words"	,    _A    )

											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(*_A	,    **_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A = None    ):




											"""simple docstring"""



											__lowerCAmelCase            = self._tokenizer.model.save(_A	,    name=_A    )
											return tuple(_A    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A    ):




											"""simple docstring"""



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

											if len(_A    ) > self.model_max_length:
																__lowerCAmelCase            = input_ids[-self.model_max_length :]
											return input_ids










from typing import TYPE_CHECKING

from ....utils import _LazyModule


UpperCamelCase__					    =    {"""tokenization_tapex""": ["""TapexTokenizer"""]}


if TYPE_CHECKING:
					from .tokenization_tapex import TapexTokenizer


else:
					import sys

					UpperCamelCase__					    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)




