





'''simple docstring'''






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


A__       :     int          ="""none"""
A__       :     Any          ="""none"""
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							value.split(lowerCAmelCase						)
      return len(lowerCAmelCase						) > 1 and any((len(lowerCAmelCase						) == 3 for x in split)						)
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


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

      try:
            # Example: 1,000.7
            if "." in sanitized and "," in sanitized:
                  return float(sanitized.replace(""","""							,			""""""						)						)
            # 1,000
            if "," in sanitized and _split_thousands(""","""							,			lowerCAmelCase						):
                  return float(sanitized.replace(""","""							,			""""""						)						)
            # 5,5556
            if "," in sanitized and sanitized.count(""","""						) == 1 and not _split_thousands(""","""							,			lowerCAmelCase						):
                  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(lowerCAmelCase						)
      except ValueError:
            # Avoid adding the sanitized value in the error message.
            raise ValueError("""Unable to convert value to float"""						)
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


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


A__       :     Dict          ={
    """text""": lambda x: x,
    """real""": convert_to_float,
}




class 				UpperCAmelCase  (    enum.Enum     ):
      _lowercase:    Dict		  =     0
      _lowercase:    Dict		  =     1
      _lowercase:    Union[str, Any]		  =     2
      _lowercase:    str		  =     3
      _lowercase:    List[str]		  =     4
      _lowercase:    List[str]		  =     5




class 				UpperCAmelCase  (    enum.Enum     ):
      _lowercase:    List[Any]		  =     0
      _lowercase:    Optional[Any]		  =     1
      _lowercase:    str		  =     2




@dataclasses.dataclass
class 				UpperCAmelCase  :
      _lowercase:    int		  =     42
      _lowercase:    List[str]		  =     42
      _lowercase:    Any		  =     42




A__       :     Dict          =re.compile(r'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      return list(_TOKENIZER.findall(x.lower()						)						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """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__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							table["""types"""]
      return _TYPE_CONVERTER[types[column]](lowerCAmelCase						)
def      UpperCamelCase__   (	lowerCAmelCase						):





      """simple docstring"""


      return isinstance(lowerCAmelCase							,			lowerCAmelCase						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


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

            _lowerCAmelCase							  =							_parse_value(lowerCAmelCase							,			cond.column							,			cond.cmp_value						)

            if _is_string(lowerCAmelCase						) and _is_string(lowerCAmelCase						):
                  _lowerCAmelCase							  =							_normalize_for_match(lowerCAmelCase						)
                  _lowerCAmelCase							  =							_normalize_for_match(lowerCAmelCase						)

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

            if not _compare(cond.operator							,			lowerCAmelCase							,			lowerCAmelCase						):
                  return False
      return True
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """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(lowerCAmelCase						)						)

      # If we have just one answer, if float returns it or try a conversion.
      _lowerCAmelCase							  =							[table["""rows"""][i][j] for (i, j) in answer_coordinates]
      if len(lowerCAmelCase						) == 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(lowerCAmelCase							,			(int, float)						) for v in values)						):
            return None

      if aggregation_op == _Aggregation.SUM:
            return float(sum(lowerCAmelCase						)						)
      elif aggregation_op == _Aggregation.AVERAGE:
            return sum(lowerCAmelCase						) / len(lowerCAmelCase						)
      else:
            raise ValueError(f"Unknown aggregation: {aggregation_op}"						)
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


      _lowerCAmelCase							  =							sql_query["""agg"""]
      if aggregation_op_index >= 3:
            _lowerCAmelCase							  =							_Aggregation(lowerCAmelCase						)
      else:
            _lowerCAmelCase							  =							_Aggregation.NONE

      _lowerCAmelCase							  =							sql_query["""sel"""]
      _lowerCAmelCase							  =							[
          _Condition(lowerCAmelCase							,			_Operator(lowerCAmelCase						)							,			lowerCAmelCase						)
          for column, operator, cmp_value in zip(
              sql_query["""conds"""]["""column_index"""]							,			sql_query["""conds"""]["""operator_index"""]							,			sql_query["""conds"""]["""condition"""]						)
      ]

      _lowerCAmelCase							  =							[]
      for row in range(len(table["""rows"""]						)						):
            if _respect_conditions(lowerCAmelCase							,			table["""rows"""][row]							,			lowerCAmelCase						):
                  indices.append((row, target_column)						)

      if not indices:
            return [], aggregation_op

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

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

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

      return indices, aggregation_op
def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


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





def      UpperCamelCase__   (	lowerCAmelCase							,			lowerCAmelCase						):





      """simple docstring"""


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








'''simple docstring'''






import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




class 				UpperCAmelCase  (    nn.Linear     ):




      def __init__(    self    :				str    ,    __snake_case    :				int    ,    __snake_case    :				int    ,    __snake_case    :				bool = True    ,    __snake_case    :				str = "constant"    ,    __snake_case    :				float = 0.0    ,    __snake_case    :				str = "topK"    ,    )       ->   int:
            super(__snake_case    ,    self						).__init__(in_features=__snake_case    ,    out_features=__snake_case    ,    bias=__snake_case						)
            assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
            _lowerCAmelCase							  =							pruning_method

            if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
                  _lowerCAmelCase							  =							mask_scale
                  _lowerCAmelCase							  =							mask_init
                  _lowerCAmelCase							  =							nn.Parameter(torch.empty(self.weight.size()						)						)
                  self.init_mask()




      def        lowercase__					(    self    :				Any						)       ->   str:
            if self.mask_init == "constant":
                  init.constant_(self.mask_scores    ,    val=self.mask_scale						)
            elif self.mask_init == "uniform":
                  init.uniform_(self.mask_scores    ,    a=-self.mask_scale    ,    b=self.mask_scale						)
            elif self.mask_init == "kaiming":
                  init.kaiming_uniform_(self.mask_scores    ,    a=math.sqrt(5						)						)




      def        lowercase__					(    self    :				Dict    ,    __snake_case    :				torch.tensor    ,    __snake_case    :				float						)       ->   Union[str, Any]:
            # Get the mask
            if self.pruning_method == "topK":
                  _lowerCAmelCase							  =							TopKBinarizer.apply(self.mask_scores    ,    __snake_case						)
            elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
                  _lowerCAmelCase							  =							"""sigmoied""" in self.pruning_method
                  _lowerCAmelCase							  =							ThresholdBinarizer.apply(self.mask_scores    ,    __snake_case    ,    __snake_case						)
            elif self.pruning_method == "magnitude":
                  _lowerCAmelCase							  =							MagnitudeBinarizer.apply(self.weight    ,    __snake_case						)
            elif self.pruning_method == "l0":
                  _lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase							  =							-0.1, 1.1, 2 / 3
                  if self.training:
                        _lowerCAmelCase							  =							torch.zeros_like(self.mask_scores						).uniform_().clamp(0.00_01    ,    0.99_99						)
                        _lowerCAmelCase							  =							torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b						)
                  else:
                        _lowerCAmelCase							  =							torch.sigmoid(self.mask_scores						)
                  _lowerCAmelCase							  =							s * (r - l) + l
                  _lowerCAmelCase							  =							s_bar.clamp(min=0.0    ,    max=1.0						)
            # Mask weights with computed mask
            _lowerCAmelCase							  =							mask * self.weight
            # Compute output (linear layer) with masked weights
            return nn.functional.linear(__snake_case    ,    __snake_case    ,    self.bias						)






