
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


UpperCAmelCase__						:	str	     = logging.get_logger(__name__)

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

UpperCAmelCase__						:	int	     = {
    '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'
        ),
    },
}

UpperCAmelCase__						:	List[str]	     = {
    '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,
}

UpperCAmelCase__						:	Optional[Any]	     = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Union[str, Any]												=       VOCAB_FILES_NAMES
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_VOCAB_FILES_MAP
   __UpperCamelCase						:       Union[str, Any]												=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCamelCase						:       str												=       ['''input_ids''', '''attention_mask''', '''token_type_ids''']
   __UpperCamelCase						:       Any												=       DebertaTokenizer


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Optional[Any]=None     ,     lowerCAmelCase_	:				int=None     ,     lowerCAmelCase_	:				Dict=None     ,     lowerCAmelCase_	:				int="replace"     ,     lowerCAmelCase_	:				List[str]="[CLS]"     ,     lowerCAmelCase_	:				str="[SEP]"     ,     lowerCAmelCase_	:				int="[SEP]"     ,     lowerCAmelCase_	:				Any="[CLS]"     ,     lowerCAmelCase_	:				Dict="[UNK]"     ,     lowerCAmelCase_	:				Optional[int]="[PAD]"     ,     lowerCAmelCase_	:				List[str]="[MASK]"     ,     lowerCAmelCase_	:				Optional[Any]=False     ,     **lowerCAmelCase_	:				List[Any]     ,     ):

      """simple docstring"""
      super().__init__(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     tokenizer_file=lowerCAmelCase_     ,     errors=lowerCAmelCase_     ,     bos_token=lowerCAmelCase_     ,     eos_token=lowerCAmelCase_     ,     unk_token=lowerCAmelCase_     ,     sep_token=lowerCAmelCase_     ,     cls_token=lowerCAmelCase_     ,     pad_token=lowerCAmelCase_     ,     mask_token=lowerCAmelCase_     ,     add_prefix_space=lowerCAmelCase_     ,     **lowerCAmelCase_     ,     )
      _A:    List[str]						     =			kwargs.pop('''add_bos_token'''     ,     lowerCAmelCase_	)

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

      _A:    Optional[Any]						     =			add_prefix_space


   @property
   def 					__magic_name__							(   self	:				Optional[Any]	):

      """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 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      _A:    Dict						     =			AddedToken(lowerCAmelCase_     ,     lstrip=lowerCAmelCase_     ,     rstrip=lowerCAmelCase_	) if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else value
      _A:    str						     =			value


   def 					__magic_name__							(   self	:				Optional[Any]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

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


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				List[int]     ,     lowerCAmelCase_	:				Optional[List[int]] = None	):

      """simple docstring"""
      _A:    Dict						     =			[self.sep_token_id]
      _A:    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 					__magic_name__							(   self	:				Tuple     ,     *lowerCAmelCase_	:				Dict     ,     **lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			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 					__magic_name__							(   self	:				List[Any]     ,     *lowerCAmelCase_	:				Optional[Any]     ,     **lowerCAmelCase_	:				Any	):

      """simple docstring"""
      _A:    Optional[Any]						     =			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 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Optional[str] = None	):

      """simple docstring"""
      _A:    Union[str, Any]						     =			self._tokenizer.model.save(lowerCAmelCase_     ,     name=lowerCAmelCase_	)
      return tuple(lowerCAmelCase_	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				"Conversation"	):

      """simple docstring"""
      _A:    List[Any]						     =			[]
      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:
         _A:    Union[str, Any]						     =			input_ids[-self.model_max_length :]
      return input_ids





import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


UpperCAmelCase__						:	Any	     = logging.get_logger(__name__)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				List[str]	):

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__	) < version.parse('''1.12.0'''	):
         raise ImportError(
             F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
             '''PytorchGELUTanh. Please upgrade torch.'''	)


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return nn.functional.gelu(lowerCAmelCase_     ,     approximate='''tanh'''	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi	) * (input + 0.044715 * torch.pow(lowerCAmelCase_     ,     3.0	))	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Tuple     ,     lowerCAmelCase_	:				bool = False	):

      """simple docstring"""
      super().__init__()
      if use_gelu_python:
         _A:    Union[str, Any]						     =			self._gelu_python
      else:
         _A:    Union[str, Any]						     =			nn.functional.gelu


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0	)	))


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return self.act(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1.0 + torch.tanh(input * 0.7978845608 * (1.0 + 0.044715 * input * input)	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * torch.sigmoid(1.702 * input	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				str     ,     lowerCAmelCase_	:				float     ,     lowerCAmelCase_	:				float	):

      """simple docstring"""
      if min > max:
         raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""	)

      super().__init__()
      _A:    Tuple						     =			min
      _A:    str						     =			max


   def 					__magic_name__							(   self	:				int     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return torch.clip(gelu(lowerCAmelCase_	)     ,     self.min     ,     self.max	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Optional[Any]	):

      """simple docstring"""
      super().__init__()
      _A:    Any						     =			math.sqrt(2 / math.pi	)


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.044715 * torch.pow(lowerCAmelCase_     ,     3	))	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return nn.functional.silu(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]	):

      """simple docstring"""
      super().__init__()
      if version.parse(torch.__version__	) < version.parse('''1.9.0'''	):
         _A:    Optional[Any]						     =			self._mish_python
      else:
         _A:    int						     =			nn.functional.mish


   def 					__magic_name__							(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input * torch.tanh(nn.functional.softplus(lowerCAmelCase_	)	)


   def 					__magic_name__							(   self	:				Any     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return self.act(lowerCAmelCase_	)






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Tensor	):

      """simple docstring"""
      return input






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Dict     ,     lowerCAmelCase_	:				List[str]     ,     lowerCAmelCase_	:				Tuple=0.707107     ,     lowerCAmelCase_	:				Union[str, Any]=0.282095	):

      """simple docstring"""
      _A:    Any						     =			(input - mu).div(sigma * math.sqrt(2.0	)	)
      return 0.5 * (1.0 + torch.erf(lowerCAmelCase_	))






class UpperCAmelCase  (		nn.Module  ):





   '''simple docstring'''


   def 					__magic_name__							(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				List[Any]	):

      """simple docstring"""
      _A:    Optional[int]						     =			nn.functional.relu(lowerCAmelCase_	)
      _A:    Any						     =			torch.square(lowerCAmelCase_	)
      return squared






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   def __getitem__(   self	:				List[Any]     ,     lowerCAmelCase_	:				Tuple	):

      """simple docstring"""
      _A:    Any						     =			super().__getitem__(lowerCAmelCase_	)
      _A     ,    _A:    List[str]						     =			content if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else (content, {})
      return cls(**lowerCAmelCase_	)

UpperCAmelCase__						:	List[str]	     = {
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -10, 'max': 10}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
UpperCAmelCase__						:	List[str]	     = ClassInstantier(ACTaCLS)






def     lowerCamelCase__				(   a ) ->  List[str]:
   if activation_string in ACTaFN:
      return ACTaFN[activation_string]
   else:
      raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )


# For backwards compatibility with: from activations import gelu_python
UpperCAmelCase__						:	Any	     = get_activation('gelu_python')
UpperCAmelCase__						:	Dict	     = get_activation('gelu_new')
UpperCAmelCase__						:	List[Any]	     = get_activation('gelu')
UpperCAmelCase__						:	Dict	     = get_activation('gelu_fast')
UpperCAmelCase__						:	str	     = get_activation('quick_gelu')
UpperCAmelCase__						:	Union[str, Any]	     = get_activation('silu')
UpperCAmelCase__						:	List[Any]	     = get_activation('mish')





UpperCAmelCase__						:	int	     = get_activation('linear')



