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_      =      logging.get_logger(__name__)

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

A_      =      {
    "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_      =      {
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}






class      __lowercase					(    _A    ):
      lowercase					 =     VOCAB_FILES_NAMES
      lowercase					 =     PRETRAINED_VOCAB_FILES_MAP
      lowercase					 =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      lowercase					 =     ['input_ids', 'attention_mask']
      lowercase					 =     GPTaTokenizer

      def __init__(	self				:      Optional[int]							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int=None							,							__lowerCamelCase				:      Any=None							,							__lowerCamelCase				:      Optional[Any]="<|endoftext|>"							,							__lowerCamelCase				:      int="<|endoftext|>"							,							__lowerCamelCase				:      Tuple="<|endoftext|>"							,							__lowerCamelCase				:      Any=False							,							**__lowerCamelCase				:      Optional[Any]							,							)							->		Dict:


         '''simple docstring'''

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

         lowercase							=		kwargs.pop('''add_bos_token'''							,							__lowerCamelCase   )

         lowercase							=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
         if pre_tok_state.get('''add_prefix_space'''							,							__lowerCamelCase   ) != add_prefix_space:
            lowercase							=		getattr(__lowerCamelCase							,							pre_tok_state.pop('''type'''   )   )
            lowercase							=		add_prefix_space
            lowercase							=		pre_tok_class(**__lowerCamelCase   )

         lowercase							=		add_prefix_space

      def        __a		(	self				:      Any							,							*__lowerCamelCase				:      Dict							,							**__lowerCamelCase				:      int   )							->		BatchEncoding:


         '''simple docstring'''

         lowercase							=		kwargs.get('''is_split_into_words'''							,							__lowerCamelCase   )
         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(*__lowerCamelCase							,							**__lowerCamelCase   )

      def        __a		(	self				:      Union[str, Any]							,							*__lowerCamelCase				:      List[str]							,							**__lowerCamelCase				:      Tuple   )							->		BatchEncoding:


         '''simple docstring'''

         lowercase							=		kwargs.get('''is_split_into_words'''							,							__lowerCamelCase   )

         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(*__lowerCamelCase							,							**__lowerCamelCase   )

      def        __a		(	self				:      Dict							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Optional[str] = None   )							->		Tuple[str]:


         '''simple docstring'''

         lowercase							=		self._tokenizer.model.save(__lowerCamelCase							,							name=__lowerCamelCase   )
         return tuple(__lowerCamelCase   )



      def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      "Conversation"   )							->		List[int]:


         '''simple docstring'''

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

         if len(__lowerCamelCase   ) > self.model_max_length:
            lowercase							=		input_ids[-self.model_max_length :]
         return input_ids






import logging

from transformers import PretrainedConfig


A_      =      logging.getLogger(__name__)


A_      =      {
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}






class      __lowercase					(    _A    ):
						lowercase					 =     'bertabs'

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      Tuple=3_05_22							,							__lowerCamelCase				:      Tuple=5_12							,							__lowerCamelCase				:      List[Any]=6							,							__lowerCamelCase				:      Any=5_12							,							__lowerCamelCase				:      Any=8							,							__lowerCamelCase				:      Union[str, Any]=5_12							,							__lowerCamelCase				:      Tuple=0.2							,							__lowerCamelCase				:      str=6							,							__lowerCamelCase				:      int=7_68							,							__lowerCamelCase				:      int=8							,							__lowerCamelCase				:      List[Any]=20_48							,							__lowerCamelCase				:      Union[str, Any]=0.2							,							**__lowerCamelCase				:      Dict							,							)							->		Dict:


									'''simple docstring'''

									super().__init__(**__lowerCamelCase   )

									lowercase							=		vocab_size
									lowercase							=		max_pos

									lowercase							=		enc_layers
									lowercase							=		enc_hidden_size
									lowercase							=		enc_heads
									lowercase							=		enc_ff_size
									lowercase							=		enc_dropout

									lowercase							=		dec_layers
									lowercase							=		dec_hidden_size
									lowercase							=		dec_heads
									lowercase							=		dec_ff_size
									lowercase							=		dec_dropout





