






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 		lowercase_					(	lowercase   ):






      '''simple docstring'''




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







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




       """simple docstring"""


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

       a		  =      kwargs.pop('''add_bos_token''' ,					__UpperCAmelCase  )

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

       a		  =      add_prefix_space







      def       __lowerCAmelCase					(      self      :						Dict ,					*__UpperCAmelCase      :						Optional[int] ,					**__UpperCAmelCase      :						Dict  )    ->BatchEncoding:




       """simple docstring"""


       a		  =      kwargs.get('''is_split_into_words''' ,					__UpperCAmelCase  )
       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(*__UpperCAmelCase ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						int ,					*__UpperCAmelCase      :						Any ,					**__UpperCAmelCase      :						Any  )    ->BatchEncoding:




       """simple docstring"""


       a		  =      kwargs.get('''is_split_into_words''' ,					__UpperCAmelCase  )

       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(*__UpperCAmelCase ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Optional[Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						Optional[str] = None  )    ->Tuple[str]:




       """simple docstring"""


       a		  =      self._tokenizer.model.save(__UpperCAmelCase ,					name=__UpperCAmelCase  )
       return tuple(__UpperCAmelCase  )







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




       """simple docstring"""


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

       if len(__UpperCAmelCase  ) > self.model_max_length:
        a		  =      input_ids[-self.model_max_length :]
       return input_ids










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				   =		{
    "configuration_nllb_moe": [
        "NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "NllbMoeConfig",
    ]
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__				   =		[
         "NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
         "NllbMoeForConditionalGeneration",
         "NllbMoeModel",
         "NllbMoePreTrainedModel",
         "NllbMoeTop2Router",
         "NllbMoeSparseMLP",
     ]


if TYPE_CHECKING:
     from .configuration_nllb_moe import (
         NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
         NllbMoeConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_nllb_moe import (
              NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
              NllbMoeForConditionalGeneration,
              NllbMoeModel,
              NllbMoePreTrainedModel,
              NllbMoeSparseMLP,
              NllbMoeTopaRouter,
          )


else:
     import sys

     UpperCAmelCase__				   =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


