




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


__magic_name__  :  Tuple					=       logging.get_logger(__name__)

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

__magic_name__  :  Optional[Any]					=       {
    """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""",
    },
}

__magic_name__  :  int					=       {
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}





class     SCREAMING_SNAKE_CASE__		(_a			):
     lowercase_ :      List[str]				= VOCAB_FILES_NAMES
     lowercase_ :      Optional[Any]				= PRETRAINED_VOCAB_FILES_MAP
     lowercase_ :      List[Any]				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase_ :      int				= ["input_ids", "attention_mask"]
     lowercase_ :      Union[str, Any]				= GPTaTokenizer
     def __init__(						self	:		Dict       ,						__lowerCamelCase	:		Optional[int]=None       ,						__lowerCamelCase	:		Dict=None       ,						__lowerCamelCase	:		str=None       ,						__lowerCamelCase	:		str="<|endoftext|>"       ,						__lowerCamelCase	:		str="<|endoftext|>"       ,						__lowerCamelCase	:		Tuple="<|endoftext|>"       ,						__lowerCamelCase	:		List[str]=False       ,						**__lowerCamelCase	:		int       ,						):



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

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

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

       lowerCAmelCase__										=      add_prefix_space
     def        A__      (						self	:		List[str]       ,						*__lowerCamelCase	:		Dict       ,						**__lowerCamelCase	:		int						):



       """simple docstring"""
       lowerCAmelCase__										=      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	:		Tuple       ,						*__lowerCamelCase	:		Tuple       ,						**__lowerCamelCase	:		List[str]						):



       """simple docstring"""
       lowerCAmelCase__										=      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	:		Optional[int]       ,						__lowerCamelCase	:		str       ,						__lowerCamelCase	:		Optional[str] = None						):



       """simple docstring"""
       lowerCAmelCase__										=      self._tokenizer.model.save(__lowerCamelCase       ,						name=__lowerCamelCase						)
       return tuple(__lowerCamelCase						)



     def        A__      (						self	:		Optional[int]       ,						__lowerCamelCase	:		"Conversation"						):



       """simple docstring"""
       lowerCAmelCase__										=      []
       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:
         lowerCAmelCase__										=      input_ids[-self.model_max_length :]
       return input_ids







from typing import TYPE_CHECKING

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


__magic_name__  :  List[str]					=       {
    """configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
    """tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  Union[str, Any]					=       [
          """M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """M2M100ForConditionalGeneration""",
          """M2M100Model""",
          """M2M100PreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
      from .tokenization_mam_aaa import MaMaaaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mam_aaa import (
                M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                MaMaaaForConditionalGeneration,
                MaMaaaModel,
                MaMaaaPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  List[str]					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

