
# Lint as: python3

import hashlib
from typing import Union


def  A__ (       __lowerCamelCase						):
   if isinstance(__lowerCamelCase,  __lowerCamelCase						):
      # Data already in bytes, returns as it as
      return hash_data
   elif isinstance(__lowerCamelCase,  __lowerCamelCase						):
      # We keep the data as it as for it ot be later encoded to UTF-8
      # However replace `\\` with `/` for Windows compatibility
      SCREAMING_SNAKE_CASE_	=      hash_data.replace('''\\''',  '''/'''						)
   elif isinstance(__lowerCamelCase,  __lowerCamelCase						):
      SCREAMING_SNAKE_CASE_	=      str(__lowerCamelCase						)
   else:
      # If data is not of the required type, raise error
      raise InvalidKeyError(__lowerCamelCase						)

   return hash_data.encode('''utf-8'''						)





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def __init__(							self ,     _A	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      '''\nFAILURE TO GENERATE DATASET: Invalid key type detected'''
          SCREAMING_SNAKE_CASE_	=      F'''\nFound Key {hash_data} of type {type(_A	)}'''
          SCREAMING_SNAKE_CASE_	=      '''\nKeys should be either str, int or bytes type'''
          super().__init__(F'''{self.prefix}{self.err_msg}{self.suffix}'''	)







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       def __init__(							self ,     _A ,     _A ,     _A=""	)				->				str:
          SCREAMING_SNAKE_CASE_	=      key
          SCREAMING_SNAKE_CASE_	=      duplicate_key_indices
          SCREAMING_SNAKE_CASE_	=      fix_msg
          SCREAMING_SNAKE_CASE_	=      '''Found multiple examples generated with the same key'''
          if len(_A	) <= 20:
             SCREAMING_SNAKE_CASE_	=      F'''\nThe examples at index {", ".join(_A	)} have the key {key}'''
          else:
             SCREAMING_SNAKE_CASE_	=      F'''\nThe examples at index {", ".join(duplicate_key_indices[:20]	)}... ({len(_A	) - 20} more) have the key {key}'''
          SCREAMING_SNAKE_CASE_	=      '''\n''' + fix_msg if fix_msg else ''''''
          super().__init__(F'''{self.prefix}{self.err_msg}{self.suffix}'''	)







class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      hashlib.mda(_as_bytes(_A	)	)




       def     _UpperCamelCase	(							self ,     _A	)				->				int:
          SCREAMING_SNAKE_CASE_	=      self._split_mda.copy()
          SCREAMING_SNAKE_CASE_	=      _as_bytes(_A	)
          mda.update(_A	)
          # Convert to integer with hexadecimal conversion
          return int(mda.hexdigest() ,     16	)




from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCAmelCase   						=     logging.get_logger(__name__)

__UpperCAmelCase   						=     {
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}





class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       UpperCAmelCase_				       ="deberta"



       def __init__(							self ,     _A=50265 ,     _A=768 ,     _A=12 ,     _A=12 ,     _A=3072 ,     _A="gelu" ,     _A=0.1 ,     _A=0.1 ,     _A=512 ,     _A=0 ,     _A=0.02 ,     _A=1E-7 ,     _A=False ,     _A=-1 ,     _A=0 ,     _A=True ,     _A=None ,     _A=0 ,     _A="gelu" ,     **_A ,     )				->				Optional[int]:
          super().__init__(**_A	)

          SCREAMING_SNAKE_CASE_	=      hidden_size
          SCREAMING_SNAKE_CASE_	=      num_hidden_layers
          SCREAMING_SNAKE_CASE_	=      num_attention_heads
          SCREAMING_SNAKE_CASE_	=      intermediate_size
          SCREAMING_SNAKE_CASE_	=      hidden_act
          SCREAMING_SNAKE_CASE_	=      hidden_dropout_prob
          SCREAMING_SNAKE_CASE_	=      attention_probs_dropout_prob
          SCREAMING_SNAKE_CASE_	=      max_position_embeddings
          SCREAMING_SNAKE_CASE_	=      type_vocab_size
          SCREAMING_SNAKE_CASE_	=      initializer_range
          SCREAMING_SNAKE_CASE_	=      relative_attention
          SCREAMING_SNAKE_CASE_	=      max_relative_positions
          SCREAMING_SNAKE_CASE_	=      pad_token_id
          SCREAMING_SNAKE_CASE_	=      position_biased_input

          # Backwards compatibility
          if type(_A	) == str:
             SCREAMING_SNAKE_CASE_	=      [x.strip() for x in pos_att_type.lower().split('''|'''	)]

          SCREAMING_SNAKE_CASE_	=      pos_att_type
          SCREAMING_SNAKE_CASE_	=      vocab_size
          SCREAMING_SNAKE_CASE_	=      layer_norm_eps

          SCREAMING_SNAKE_CASE_	=      kwargs.get('''pooler_hidden_size''' ,     _A	)
          SCREAMING_SNAKE_CASE_	=      pooler_dropout
          SCREAMING_SNAKE_CASE_	=      pooler_hidden_act







class 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE	):






       """simple docstring"""


       @property
       def     _UpperCamelCase	(							self	)				->				Mapping[str, Mapping[int, str]]:
          if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_	=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
             SCREAMING_SNAKE_CASE_	=      {0: '''batch''', 1: '''sequence'''}
          if self._config.type_vocab_size > 0:
             return OrderedDict(
                 [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]	)
          else:
             return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]	)



       @property
       def     _UpperCamelCase	(							self	)				->				int:
          return 12




       def     _UpperCamelCase	(							self ,     _A ,     _A = -1 ,     _A = -1 ,     _A = -1 ,     _A = False ,     _A = None ,     _A = 3 ,     _A = 40 ,     _A = 40 ,     _A = None ,     )				->				Mapping[str, Any]:
          SCREAMING_SNAKE_CASE_	=      super().generate_dummy_inputs(preprocessor=_A ,     framework=_A	)
          if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
             del dummy_inputs["token_type_ids"]
          return dummy_inputs


