





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


_lowerCamelCase      							=   logging.get_logger(__name__)

_lowerCamelCase      							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_lowerCamelCase      							=   {
    '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',
    },
}

_lowerCamelCase      							=   {
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      UpperCAmelCase__							=   VOCAB_FILES_NAMES
      UpperCAmelCase__							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase__							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase__							=   ["""input_ids""", """attention_mask"""]
      UpperCAmelCase__							=   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				:  Dict						=		kwargs.pop('''add_bos_token'''   ,				a__)

             _lowerCamelCase				:  Any						=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
             if pre_tok_state.get('''add_prefix_space'''   ,				a__) != add_prefix_space:
                    _lowerCamelCase				:  List[Any]						=		getattr(a__   ,				pre_tok_state.pop('''type'''))
                    _lowerCamelCase				:  Dict						=		add_prefix_space
                    _lowerCamelCase				:  str						=		pre_tok_class(**a__)

             _lowerCamelCase				:  int						=		add_prefix_space


      def 					__snake_case      (		self   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		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 					__snake_case      (		self   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		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 					__snake_case      (		self   ,				a__   ,				a__ = None):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		self._tokenizer.model.save(a__   ,				name=a__)
             return tuple(a__)



      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  int						=		[]
             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				:  Dict						=		input_ids[-self.model_max_length :]
             return input_ids








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase      							=   {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase      							=   [
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       _lowerCamelCase      							=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

