




'''simple docstring'''



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-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}






class 							_a				(			UpperCAmelCase__ ):





       '''simple docstring'''






       A						:				Tuple        =						'deberta-v2'







       def __init__(      self,							A=128_100,							A=1_536,							A=24,							A=24,							A=6_144,							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,							):







             '''simple docstring'''







             super().__init__(**lowerCamelCase__  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						hidden_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						num_hidden_layers
             SCREAMING_SNAKE_CASE   :      Dict    					=						num_attention_heads
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						intermediate_size
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						hidden_act
             SCREAMING_SNAKE_CASE   :      List[str]    					=						hidden_dropout_prob
             SCREAMING_SNAKE_CASE   :      Dict    					=						attention_probs_dropout_prob
             SCREAMING_SNAKE_CASE   :      Any    					=						max_position_embeddings
             SCREAMING_SNAKE_CASE   :      str    					=						type_vocab_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						initializer_range
             SCREAMING_SNAKE_CASE   :      str    					=						relative_attention
             SCREAMING_SNAKE_CASE   :      str    					=						max_relative_positions
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						pad_token_id
             SCREAMING_SNAKE_CASE   :      Dict    					=						position_biased_input

             # Backwards compatibility
             if type(lowerCamelCase__  ) == str:
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[x.strip() for x in pos_att_type.lower().split('|'  )]

             SCREAMING_SNAKE_CASE   :      str    					=						pos_att_type
             SCREAMING_SNAKE_CASE   :      str    					=						vocab_size
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						layer_norm_eps

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						kwargs.get('pooler_hidden_size',							lowerCamelCase__  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pooler_dropout
             SCREAMING_SNAKE_CASE   :      List[str]    					=						pooler_hidden_act






class 							_a				(			UpperCAmelCase__ ):





       '''simple docstring'''






       @property
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             if self.task == "multiple-choice":
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{0: "batch", 1: "choice", 2: "sequence"}
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						{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  ):







             '''simple docstring'''







             return 12







       def        UpperCamelCase_							(      self,							A,							A = -1,							A = -1,							A = -1,							A = False,							A = None,							A = 3,							A = 40,							A = 40,							A = None,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						super().generate_dummy_inputs(preprocessor=lowerCamelCase__,							framework=lowerCamelCase__  )
             if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                   del dummy_inputs["token_type_ids"]
             return dummy_inputs



def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							list[list[str]]       =	[[] for _ in range(lowercase					)]
   lowerCAmelCase_:							Optional[Any]       =	key - 1

   if key <= 0:
      raise ValueError("Height of grid can't be 0 or negative"					)
   if key == 1 or len(lowercase					) <= key:
      return input_string

   for position, character in enumerate(lowercase					):
      lowerCAmelCase_:							Optional[Any]       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Any       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      temp_grid[num].append(lowercase					)
   lowerCAmelCase_:							Optional[int]       =	["".join(lowercase					) for row in temp_grid]
   lowerCAmelCase_:							int       =	"".join(lowercase					)

   return output_string
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							Tuple       =	[]
   lowerCAmelCase_:							str       =	key - 1

   if key <= 0:
      raise ValueError("Height of grid can't be 0 or negative"					)
   if key == 1:
      return input_string

   lowerCAmelCase_:							list[list[str]]       =	[[] for _ in range(lowercase					)]  # generates template
   for position in range(len(lowercase					)					):
      lowerCAmelCase_:							List[str]       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Optional[int]       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      temp_grid[num].append("*"					)

   lowerCAmelCase_:							Optional[Any]       =	0
   for row in temp_grid:  # fills in the characters
      lowerCAmelCase_:							Tuple       =	input_string[counter : counter + len(lowercase					)]
      grid.append(list(lowercase					)					)
      counter += len(lowercase					)

   lowerCAmelCase_:							int       =	""  # reads as zigzag
   for position in range(len(lowercase					)					):
      lowerCAmelCase_:							str       =	position % (lowest * 2)  # puts it in bounds
      lowerCAmelCase_:							Optional[int]       =	min(lowercase							,				lowest * 2 - num					)  # creates zigzag pattern
      output_string += grid[num][0]
      grid[num].pop(0					)
   return output_string
def 		snake_case__	(		lowercase					):
   lowerCAmelCase_:							Dict       =	{}
   for key_guess in range(1							,				len(lowercase					)					):  # tries every key
      lowerCAmelCase_:							int       =	decrypt(lowercase							,				lowercase					)
   return results


if __name__ == "__main__":
 import doctest

 doctest.testmod()