

'''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


lowerCAmelCase__   		=      logging.get_logger(__name__)

lowerCAmelCase__   		=      {
    '''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      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Optional[Any]							     =     'deberta-v2'






  def __init__( self  :	int   ,lowercase__  :	Optional[Any]=1_2_8_1_0_0   ,lowercase__  :	str=1_5_3_6   ,lowercase__  :	List[Any]=2_4   ,lowercase__  :	Union[str, Any]=2_4   ,lowercase__  :	Optional[Any]=6_1_4_4   ,lowercase__  :	Union[str, Any]="gelu"   ,lowercase__  :	Union[str, Any]=0.1   ,lowercase__  :	Tuple=0.1   ,lowercase__  :	Dict=5_1_2   ,lowercase__  :	List[str]=0   ,lowercase__  :	int=0.0_2   ,lowercase__  :	str=1e-7   ,lowercase__  :	Any=False   ,lowercase__  :	Union[str, Any]=-1   ,lowercase__  :	Optional[int]=0   ,lowercase__  :	List[str]=True   ,lowercase__  :	List[str]=None   ,lowercase__  :	Optional[int]=0   ,lowercase__  :	str="gelu"   ,**lowercase__  :	Optional[Any]   ,):
   super().__init__(**lowercase__     )

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

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

   __lowercase				    =					pos_att_type
   __lowercase				    =					vocab_size
   __lowercase				    =					layer_norm_eps

   __lowercase				    =					kwargs.get('''pooler_hidden_size'''   ,lowercase__     )
   __lowercase				    =					pooler_dropout
   __lowercase				    =					pooler_hidden_act






class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  @property
  def    SCREAMING_SNAKE_CASE ( self  :	Dict     ):
   if self.task == "multiple-choice":
    __lowercase				    =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
   else:
    __lowercase				    =					{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    SCREAMING_SNAKE_CASE ( self  :	str     ):
   return 1_2






  def    SCREAMING_SNAKE_CASE ( self  :	List[Any]   ,lowercase__  :	Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,lowercase__  :	int = -1   ,lowercase__  :	int = -1   ,lowercase__  :	int = -1   ,lowercase__  :	bool = False   ,lowercase__  :	Optional["TensorType"] = None   ,lowercase__  :	int = 3   ,lowercase__  :	int = 4_0   ,lowercase__  :	int = 4_0   ,lowercase__  :	"PreTrainedTokenizerBase" = None   ,):
   __lowercase				    =					super().generate_dummy_inputs(preprocessor=lowercase__   ,framework=lowercase__     )
   if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
    del dummy_inputs["token_type_ids"]
   return dummy_inputs






"""simple docstring"""






import argparse
from collections import defaultdict


def      _lowercase							(    __snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )		->  Optional[int]:
			__lowerCAmelCase :    str				=			F"""{file}_{class_name}_{test_name}"""
			done_test[_id] += 1

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    List[str]				=			f.readlines()

			__lowerCAmelCase :    Tuple				=			F"""class {class_name}("""
			__lowerCAmelCase :    Tuple				=			F"""{4 * ' '}def {test_name}("""
			__lowerCAmelCase :    List[str]				=			F"""{8 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    List[Any]				=			F"""{16 * ' '}{correct_line.split()[0]}"""
			__lowerCAmelCase :    Any				=			False
			__lowerCAmelCase :    List[Any]				=			False
			__lowerCAmelCase :    Optional[Any]				=			False
			__lowerCAmelCase :    str				=			False
			__lowerCAmelCase :    Optional[int]				=			0
			__lowerCAmelCase :    Dict				=			0

			__lowerCAmelCase :    int				=			[]
			for line in lines:
						if line.startswith(__snake_case      ):
									__lowerCAmelCase :    Any				=			True
						elif in_class and line.startswith(__snake_case      ):
									__lowerCAmelCase :    str				=			True
						elif in_class and in_func and (line.startswith(__snake_case      ) or line.startswith(__snake_case      )):
									__lowerCAmelCase :    Optional[Any]				=			len(line.split(correct_line.split()[0]      )[0]      )
									count += 1

									if count == done_test[_id]:
												__lowerCAmelCase :    Optional[int]				=			True

						if in_class and in_func and in_line:
									if ")" not in line:
												continue
									else:
												__lowerCAmelCase :    Dict				=			True

						if in_class and in_func and in_line and insert_line:
									new_lines.append(F"""{spaces * ' '}{correct_line}"""      )
									__lowerCAmelCase :    Union[str, Any]				=			False
						else:
									new_lines.append(__snake_case      )

			with open(__snake_case   ,"w"      ) as f:
						for line in new_lines:
									f.write(__snake_case      )


def      _lowercase							(    __snake_case   ,__snake_case=None      )		->  int:
			if fail is not None:
						with open(__snake_case   ,"r"      ) as f:
									__lowerCAmelCase :    Union[str, Any]				=			{l.strip() for l in f.readlines()}
			else:
						__lowerCAmelCase :    Optional[Any]				=			None

			with open(__snake_case   ,"r"      ) as f:
						__lowerCAmelCase :    Union[str, Any]				=			f.readlines()

			__lowerCAmelCase :    Optional[Any]				=			defaultdict(__snake_case      )
			for line in correct_lines:
						__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			line.split(";"      )
						if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
									overwrite_file(__snake_case   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case      )


if __name__ == "__main__":
	__snake_case				:  Union[str, Any] =       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	__snake_case				:  int =       parser.parse_args()

	main(args.correct_filename, args.fail_filename)