



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


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

a_						:     Optional[int]	     =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

a_						:     Dict	     =		{
    '''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''',
    },
}

a_						:     Optional[Any]	     =		{
    '''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__  (						SCREAMING_SNAKE_CASE__):
     """simple docstring"""



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







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






           '''simple docstring'''



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

           lowerCamelCase								=		kwargs.pop("add_bos_token"				,			_lowercase    )

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

           lowerCamelCase								=		add_prefix_space







     def 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		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 						_a      (self				,			*__a				,			**__a    ):






           '''simple docstring'''



           lowerCamelCase								=		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 						_a      (self				,			__a				,			__a = None    ):






           '''simple docstring'''



           lowerCamelCase								=		self._tokenizer.model.save(_lowercase				,			name=_lowercase    )
           return tuple(_lowercase    )







     def 						_a      (self				,			__a    ):






           '''simple docstring'''



           lowerCamelCase								=		[]
           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:
                 lowerCamelCase								=		input_ids[-self.model_max_length :]
           return input_ids




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_						:     int	     =		{'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Dict	     =		[
         'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'VanForImageClassification',
         'VanModel',
         'VanPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     a_						:     Union[str, Any]	     =		_LazyModule(__name__, globals()['__file__'], _import_structure)