


import dataclasses
import enum
import functools
import math
import re

# The following script is adapted from the script of TaPas.
# Original: https://github.com/google-research/tapas/master/wikisql_utils.py
from typing import Any, List, Text


UpperCAmelCase__            =							'''none'''
UpperCAmelCase__            =							'''none'''
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      List[str]:







 """simple docstring"""


 _lowercase											=value.split(__snake_case						)
 return len(__snake_case						) > 1 and any((len(__snake_case						) == 3 for x in split)						)
def     UpperCAmelCase_ (							__snake_case						)					->      List[str]:







 """simple docstring"""


 if isinstance(__snake_case					,						__snake_case						):
  return value
 if isinstance(__snake_case					,						__snake_case						):
  return float(__snake_case						)
 if not isinstance(__snake_case					,						__snake_case						):
  raise ValueError('''Argument value is not a string. Can\'t parse it as float'''						)
 _lowercase											=value

 try:
  # Example: 1,000.7
  if "." in sanitized and "," in sanitized:
   return float(sanitized.replace(''','''					,						''''''						)						)
  # 1,000
  if "," in sanitized and _split_thousands(''','''					,						__snake_case						):
   return float(sanitized.replace(''','''					,						''''''						)						)
  # 5,5556
  if "," in sanitized and sanitized.count(''','''						) == 1 and not _split_thousands(''','''					,						__snake_case						):
   return float(sanitized.replace(''','''					,						'''.'''						)						)
  # 0.0.0.1
  if sanitized.count('''.'''						) > 1:
   return float(sanitized.replace('''.'''					,						''''''						)						)
  # 0,0,0,1
  if sanitized.count(''','''						) > 1:
   return float(sanitized.replace(''','''					,						''''''						)						)
  return float(__snake_case						)
 except ValueError:
  # Avoid adding the sanitized value in the error message.
  raise ValueError('''Unable to convert value to float'''						)
def     UpperCAmelCase_ (							__snake_case						)					->      Tuple:







 """simple docstring"""


 if answer is None:
  return None
 try:
  _lowercase											=convert_to_float(__snake_case						)
  if isinstance(__snake_case					,						__snake_case						) and math.isnan(__snake_case						):
   return None
  return value
 except ValueError:
  return answer.lower()


UpperCAmelCase__            =							{
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}







class 				lowerCamelCase__       (	enum.Enum):
     SCREAMING_SNAKE_CASE__				      =      0
     SCREAMING_SNAKE_CASE__				      =      1
     SCREAMING_SNAKE_CASE__				      =      2
     SCREAMING_SNAKE_CASE__				      =      3
     SCREAMING_SNAKE_CASE__				      =      4
     SCREAMING_SNAKE_CASE__				      =      5







class 				lowerCamelCase__       (	enum.Enum):
     SCREAMING_SNAKE_CASE__				      =      0
     SCREAMING_SNAKE_CASE__				      =      1
     SCREAMING_SNAKE_CASE__				      =      2







@dataclasses.dataclass
class 				lowerCamelCase__       :
     SCREAMING_SNAKE_CASE__				      =      42
     SCREAMING_SNAKE_CASE__				      =      42
     SCREAMING_SNAKE_CASE__				      =      42



UpperCAmelCase__            =							re.compile(R'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)
def     UpperCAmelCase_ (							__snake_case						)					->      Any:







 """simple docstring"""


 return list(_TOKENIZER.findall(x.lower()						)						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      List[str]:







 """simple docstring"""


 if operator == _Operator.EQUALS:
  return src == tgt
 elif operator == _Operator.GREATER:
  return src > tgt
 elif operator == _Operator.LESSER:
  return src < tgt
 raise ValueError(F"Unknown operator: {operator}"						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      List[Any]:







 """simple docstring"""


 _lowercase											=table['''types''']
 return _TYPE_CONVERTER[types[column]](__snake_case						)
def     UpperCAmelCase_ (							__snake_case						)					->      str:







 """simple docstring"""


 return isinstance(__snake_case					,						__snake_case						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      Any:







 """simple docstring"""


 for cond in conditions:
  _lowercase											=row[cond.column]

  _lowercase											=_parse_value(__snake_case					,						cond.column					,						cond.cmp_value						)

  if _is_string(__snake_case						) and _is_string(__snake_case						):
   _lowercase											=_normalize_for_match(__snake_case						)
   _lowercase											=_normalize_for_match(__snake_case						)

  if not isinstance(__snake_case					,						type(__snake_case						)						):
   raise ValueError('''Type difference {} != {}'''.format(type(__snake_case						)					,						type(__snake_case						)						)						)

  if not _compare(cond.operator					,						__snake_case					,						__snake_case						):
   return False
 return True
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      Any:







 """simple docstring"""


 if not answer_coordinates:
  if aggregation_op == _Aggregation.COUNT:
   return 0.0
  else:
   return EMPTY_ANSWER_AGG

    # Count can support non numeric answers.
 if aggregation_op == _Aggregation.COUNT:
  return float(len(__snake_case						)						)

 # If we have just one answer, if float returns it or try a conversion.
 _lowercase											=[table['''rows'''][i][j] for (i, j) in answer_coordinates]
 if len(__snake_case						) == 1:
  try:
   return convert_to_float(values[0]						)
  except ValueError as e:
   if aggregation_op != _Aggregation.NONE:
    raise e

 if aggregation_op == _Aggregation.NONE:
  return None

 # Other aggregation only support numeric values. Bail out if we have strings.
 if not all((isinstance(__snake_case					,						(int, float)						) for v in values)						):
  return None

 if aggregation_op == _Aggregation.SUM:
  return float(sum(__snake_case						)						)
 elif aggregation_op == _Aggregation.AVERAGE:
  return sum(__snake_case						) / len(__snake_case						)
 else:
  raise ValueError(F"Unknown aggregation: {aggregation_op}"						)
def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      Union[str, Any]:







 """simple docstring"""


 _lowercase											=sql_query['''agg''']
 if aggregation_op_index >= 3:
  _lowercase											=_Aggregation(__snake_case						)
 else:
  _lowercase											=_Aggregation.NONE

 _lowercase											=sql_query['''sel''']
 _lowercase											=[
     _Condition(__snake_case					,						_Operator(__snake_case						)					,						__snake_case						)
     for column, operator, cmp_value in zip(
         sql_query['''conds''']['''column_index''']					,						sql_query['''conds''']['''operator_index''']					,						sql_query['''conds''']['''condition''']						)
 ]

 _lowercase											=[]
 for row in range(len(table['''rows''']						)						):
  if _respect_conditions(__snake_case					,						table['''rows'''][row]					,						__snake_case						):
   indices.append((row, target_column)						)

 if not indices:
  return [], aggregation_op

 if len(__snake_case						) == 1:
  return indices, aggregation_op

 # Parsing of MIN/MAX.
 if aggregation_op_index in (1, 2):
  _lowercase											={2: min, 1: max}
  _lowercase											=[(table['''rows'''][i][j], index) for index, (i, j) in enumerate(__snake_case						)]
  _lowercase											=functools.reduce(operators[sql_query['''agg''']]					,						__snake_case						)

  _lowercase											=[indices[reduced[1]]]
  return ret, _Aggregation.NONE

 return indices, aggregation_op
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case						)					->      Any:







 """simple docstring"""


 if float_answer is not None:
  return [str(__snake_case						)]
 return [str(table['''real_rows'''][r][c]						) for r, c in answer_coordinates]



def     UpperCAmelCase_ (							__snake_case					,						__snake_case						)					->      List:







 """simple docstring"""


 _lowercase				,   _lowercase											=_get_answer_coordinates(__snake_case					,						__snake_case						)
 _lowercase											=_get_float_answer(__snake_case					,						__snake_case					,						__snake_case						)
 _lowercase											=_get_answer_text(__snake_case					,						__snake_case					,						__snake_case						)
 # keep the original data the same with TaPas
 if len(__snake_case						) == 0:
  _lowercase											=[EMPTY_ANSWER]
 return answer_text





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 				lowerCamelCase__       (	lowerCAmelCase):
     SCREAMING_SNAKE_CASE__				      =      '''deberta'''





     def __init__(self		,					UpperCAmelCase=5_0_2_6_5		,					UpperCAmelCase=7_6_8		,					UpperCAmelCase=1_2		,					UpperCAmelCase=1_2		,					UpperCAmelCase=3_0_7_2		,					UpperCAmelCase="gelu"		,					UpperCAmelCase=0.1		,					UpperCAmelCase=0.1		,					UpperCAmelCase=5_1_2		,					UpperCAmelCase=0		,					UpperCAmelCase=0.02		,					UpperCAmelCase=1e-7		,					UpperCAmelCase=False		,					UpperCAmelCase=-1		,					UpperCAmelCase=0		,					UpperCAmelCase=True		,					UpperCAmelCase=None		,					UpperCAmelCase=0		,					UpperCAmelCase="gelu"		,					**UpperCAmelCase		,					)					->       Dict:
      super().__init__(**UpperCAmelCase	)

      _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(UpperCAmelCase	) == 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'''		,					UpperCAmelCase	)
      _lowercase											=pooler_dropout
      _lowercase											=pooler_hidden_act







class 				lowerCamelCase__       (	lowerCAmelCase):





     @property
     def 					__A (self	)					->       Mapping[str, Mapping[int, str]]:
      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 					__A (self	)					->       int:
      return 1_2





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = -1		,					UpperCAmelCase = False		,					UpperCAmelCase = None		,					UpperCAmelCase = 3		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = 4_0		,					UpperCAmelCase = None		,					)					->       Mapping[str, Any]:
      _lowercase											=super().generate_dummy_inputs(preprocessor=UpperCAmelCase		,					framework=UpperCAmelCase	)
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
       del dummy_inputs["token_type_ids"]
      return dummy_inputs

