





"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


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

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

lowerCAmelCase:	List[str]							      ={
    "vocab_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
        "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
        "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
        "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
        "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
        "microsoft/deberta-xlarge-mnli": (
            "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
        ),
    },
}

lowerCAmelCase:	Optional[Any]							      ={
    "microsoft/deberta-base": 512,
    "microsoft/deberta-large": 512,
    "microsoft/deberta-xlarge": 512,
    "microsoft/deberta-base-mnli": 512,
    "microsoft/deberta-large-mnli": 512,
    "microsoft/deberta-xlarge-mnli": 512,
}

lowerCAmelCase:	int							      ={
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					VOCAB_FILES_NAMES
   __UpperCAmelCase        =					PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase        =					["""input_ids""", """attention_mask""", """token_type_ids"""]
   __UpperCAmelCase        =					DebertaTokenizer



   def __init__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case="replace"      ,   snake_case="[CLS]"      ,   snake_case="[SEP]"      ,   snake_case="[SEP]"      ,   snake_case="[CLS]"      ,   snake_case="[UNK]"      ,   snake_case="[PAD]"      ,   snake_case="[MASK]"      ,   snake_case=False      ,   **snake_case      ,   ) ->    Optional[int]:







      """simple docstring"""
      super().__init__(
          snake_case      ,   snake_case      ,   tokenizer_file=snake_case      ,   errors=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   unk_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   **snake_case      ,   )
      lowercase    :			int  =       kwargs.pop("""add_bos_token"""      ,   snake_case	)

      lowercase    :			int  =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get("""add_prefix_space"""      ,   snake_case	) != add_prefix_space:
         lowercase    :			Tuple  =       getattr(snake_case      ,   pre_tok_state.pop("""type"""	)	)
         lowercase    :			Union[str, Any]  =       add_prefix_space
         lowercase    :			Optional[int]  =       pre_tok_class(**snake_case	)

      lowercase    :			Union[str, Any]  =       add_prefix_space



   @property
   def    _UpperCAmelCase	(       self	) ->    str:







      """simple docstring"""
      if self._mask_token is None:
         if self.verbose:
            logger.error("""Using mask_token, but it is not set yet."""	)
         return None
      return str(self._mask_token	)



   @mask_token.setter
   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    Dict:







      """simple docstring"""
      lowercase    :			List[str]  =       AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case	) if isinstance(snake_case      ,   snake_case	) else value
      lowercase    :			Union[str, Any]  =       value



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = None	) ->    List[int]:







      """simple docstring"""
      if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowercase    :			List[Any]  =       [self.cls_token_id]
      lowercase    :			List[str]  =       [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = None	) ->    List[int]:







      """simple docstring"""
      lowercase    :			Any  =       [self.sep_token_id]
      lowercase    :			List[Any]  =       [self.cls_token_id]

      if token_ids_a is None:
         return len(cls + token_ids_a + sep	) * [0]
      return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    BatchEncoding:







      """simple docstring"""
      lowercase    :			Any  =       kwargs.get("""is_split_into_words"""      ,   snake_case	)
      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(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    BatchEncoding:







      """simple docstring"""
      lowercase    :			int  =       kwargs.get("""is_split_into_words"""      ,   snake_case	)

      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(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   snake_case      ,   snake_case = None	) ->    Tuple[str]:







      """simple docstring"""
      lowercase    :			List[str]  =       self._tokenizer.model.save(snake_case      ,   name=snake_case	)
      return tuple(snake_case	)





   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    List[int]:







      """simple docstring"""
      lowercase    :			Optional[Any]  =       []
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(snake_case      ,   add_special_tokens=snake_case	) + [self.eos_token_id]	)

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









"""simple docstring"""
import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
   import transformer_engine.pytorch as te




def 			__snake_case						(      __A ,__A=True ,__A=True ,__A=True    )		->				Union[str, Any]:
   if not is_fpa_available():
      raise ImportError("""Using `convert_model` requires transformer_engine to be installed."""    )
   for name, module in model.named_children():
      if isinstance(__A ,nn.Linear    ) and to_transformer_engine and _convert_linear:
         # Return early if the linear layer weights are not multiples of 16
         if any(p % 16 != 0 for p in module.weight.shape    ):
            return
         lowercase    :			Optional[int]  =       module.bias is not None
         lowercase    :			Union[str, Any]  =       te.Linear(
             module.in_features ,module.out_features ,bias=__A ,params_dtype=module.weight.dtype    )
         lowercase    :			Union[str, Any]  =       module.weight.data.clone()
         if has_bias:
            lowercase    :			Dict  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,nn.LayerNorm    ) and to_transformer_engine and _convert_ln:
         lowercase    :			List[str]  =       te.LayerNorm(module.normalized_shape[0] ,eps=module.eps ,params_dtype=module.weight.dtype    )
         lowercase    :			Optional[Any]  =       module.weight.data.clone()
         lowercase    :			Any  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,te.Linear    ) and not to_transformer_engine and _convert_linear:
         lowercase    :			Any  =       module.bias is not None
         lowercase    :			List[Any]  =       nn.Linear(
             module.in_features ,module.out_features ,bias=__A ,params_dtype=module.weight.dtype    )
         lowercase    :			Union[str, Any]  =       module.weight.data.clone()
         if has_bias:
            lowercase    :			Optional[Any]  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      elif isinstance(__A ,te.LayerNorm    ) and not to_transformer_engine and _convert_ln:
         lowercase    :			Any  =       nn.LayerNorm(module.normalized_shape[0] ,eps=module.eps ,params_dtype=module.weight.dtype    )
         lowercase    :			str  =       module.weight.data.clone()
         lowercase    :			List[str]  =       module.bias.data.clone()

         setattr(__A ,__A ,__A    )
      else:
         convert_model(
             __A ,to_transformer_engine=__A ,_convert_linear=_convert_linear ,_convert_ln=_convert_ln ,)







def 			__snake_case						(      __A    )		->				Union[str, Any]:
   if not is_fpa_available():
      raise ImportError("""Using `has_transformer_engine_layers` requires transformer_engine to be installed."""    )
   for m in model.modules():
      if isinstance(__A ,(te.LayerNorm, te.Linear)    ):
         return True
   return False


