

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				:							Optional[int]					=			'none'
a				:							Dict					=			'none'






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase       )		->str:


 '''simple docstring'''
 snake_case   : Tuple	  =      value.split(__lowerCamelCase       )
 return len(__lowerCamelCase       ) > 1 and any((len(__lowerCamelCase       ) == 3 for x in split)       )






def    _UpperCAmelCase	(   __lowerCamelCase       )		->List[str]:


 '''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'''       )
 snake_case   : Dict	  =      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       )		->List[Any]:


 '''simple docstring'''


 if answer is None:
  return None
 try:
  snake_case   : Optional[Any]	  =      convert_to_float(__lowerCamelCase       )
  if isinstance(__lowerCamelCase ,     __lowerCamelCase       ) and math.isnan(__lowerCamelCase       ):
   return None
  return value
 except ValueError:
  return answer.lower()


a				:							Union[str, Any]					=			{
    'text': lambda x: x,
    'real': convert_to_float,
}

class      lowerCamelCase	(  enum.Enum     ):
       _UpperCamelCase    		=      0
       _UpperCamelCase    		=      1
       _UpperCamelCase    		=      2
       _UpperCamelCase    		=      3
       _UpperCamelCase    		=      4
       _UpperCamelCase    		=      5







class      lowerCamelCase	(  enum.Enum     ):
       _UpperCamelCase    		=      0
       _UpperCamelCase    		=      1
       _UpperCamelCase    		=      2







@dataclasses.dataclass
class      lowerCamelCase	:
       _UpperCamelCase    		=      42
       _UpperCamelCase    		=      42
       _UpperCamelCase    		=      42







a				:							Optional[int]					=			re.compile(r'\w+|[^\w\s]+', re.UNICODE | re.MULTILINE | re.DOTALL)






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Dict:


 '''simple docstring'''
 return list(_TOKENIZER.findall(x.lower()       )       )






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )		->Dict:


 '''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       )		->Any:


 '''simple docstring'''
 snake_case   : Tuple	  =      table['''types''']
 return _TYPE_CONVERTER[types[column]](__lowerCamelCase       )






def    _UpperCAmelCase	(   __lowerCamelCase       )		->List[str]:


 '''simple docstring'''
 return isinstance(__lowerCamelCase ,     __lowerCamelCase       )






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )		->List[Any]:


 '''simple docstring'''


 for cond in conditions:
  snake_case   : Tuple	  =      row[cond.column]

  snake_case   : Union[str, Any]	  =      _parse_value(__lowerCamelCase ,     cond.column ,     cond.cmp_value       )

  if _is_string(__lowerCamelCase       ) and _is_string(__lowerCamelCase       ):
   snake_case   : int	  =      _normalize_for_match(__lowerCamelCase       )
   snake_case   : Any	  =      _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       )		->Dict:


 '''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.
 snake_case   : Dict	  =      [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       )		->str:


 '''simple docstring'''
 snake_case   : Optional[Any]	  =      sql_query['''agg''']
 if aggregation_op_index >= 3:
  snake_case   : str	  =      _Aggregation(__lowerCamelCase       )
 else:
  snake_case   : List[Any]	  =      _Aggregation.NONE

 snake_case   : Union[str, Any]	  =      sql_query['''sel''']
 snake_case   : Any	  =      [
     _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''']       )
 ]

 snake_case   : Any	  =      []
 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):
  snake_case   : List[str]	  =      {2: min, 1: max}
  snake_case   : List[Any]	  =      [(table['''rows'''][i][j], index) for index, (i, j) in enumerate(__lowerCamelCase       )]
  snake_case   : int	  =      functools.reduce(operators[sql_query['''agg''']] ,     __lowerCamelCase       )

  snake_case   : str	  =      [indices[reduced[1]]]
  return ret, _Aggregation.NONE

 return indices, aggregation_op






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )		->List[Any]:


 '''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       )		->List:


 '''simple docstring'''
 snake_case  ,					snake_case   : List[str]	  =      _get_answer_coordinates(__lowerCamelCase ,     __lowerCamelCase       )
 snake_case   : Dict	  =      _get_float_answer(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )
 snake_case   : Union[str, Any]	  =      _get_answer_text(__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase       )
 # keep the original data the same with TaPas
 if len(__lowerCamelCase       ) == 0:
  snake_case   : Any	  =      [EMPTY_ANSWER]
 return answer_text










from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
  import torch

a				:							Union[str, Any]					=			logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
  from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):






  def    _UpperCAmelCase	(   cls       )		->str:


   '''simple docstring'''
   return cls






def    _UpperCAmelCase	(   __lowerCamelCase ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     __lowerCamelCase = None ,     )		->Union[str, Any]:


 '''simple docstring'''
 snake_case   : Union[str, Any]	  =      device
 snake_case   : Any	  =      shape[0]

 snake_case   : Any	  =      layout or torch.strided
 snake_case   : Union[str, Any]	  =      device or torch.device('''cpu'''       )

 if generator is not None:
  snake_case   : Any	  =      generator.device.type if not isinstance(__lowerCamelCase ,     __lowerCamelCase       ) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   snake_case   : Optional[int]	  =      '''cpu'''
   if device != "mps":
    logger.info(
        f'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
        f''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
        f''' slighly speed up this function by passing a generator that was created on the {device} device.'''       )
  elif gen_device_type != device.type and gen_device_type == "cuda":
   raise ValueError(f'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''       )

 if isinstance(__lowerCamelCase ,     __lowerCamelCase       ):
  snake_case   : Tuple	  =      (1,) + shape[1:]
  snake_case   : List[Any]	  =      [
      torch.randn(__lowerCamelCase ,     generator=generator[i] ,     device=__lowerCamelCase ,     dtype=__lowerCamelCase ,     layout=__lowerCamelCase       )
      for i in range(__lowerCamelCase       )
  ]
  snake_case   : int	  =      torch.cat(__lowerCamelCase ,     dim=0       ).to(__lowerCamelCase       )
 else:
  snake_case   : Union[str, Any]	  =      torch.randn(__lowerCamelCase ,     generator=__lowerCamelCase ,     device=__lowerCamelCase ,     dtype=__lowerCamelCase ,     layout=__lowerCamelCase       ).to(__lowerCamelCase       )

 return latents






def    _UpperCAmelCase	(   __lowerCamelCase       )		->Tuple:


 '''simple docstring'''
 if is_torch_version('''<''' ,     '''2.0.0'''       ) or not hasattr(__lowerCamelCase ,     '''_dynamo'''       ):
  return False
 return isinstance(__lowerCamelCase ,     torch._dynamo.eval_frame.OptimizedModule       )






