






'''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


SCREAMING_SNAKE_CASE_				      =							"""none"""
SCREAMING_SNAKE_CASE_				      =							"""none"""






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	Optional[int]									=						value.split(SCREAMING_SNAKE_CASE__		)
 return len(SCREAMING_SNAKE_CASE__		) > 1 and any((len(SCREAMING_SNAKE_CASE__		) == 3 for x in split)		)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  return value
 if isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  return float(SCREAMING_SNAKE_CASE__		)
 if not isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  raise ValueError('Argument value is not a string. Can\'t parse it as float'		)
 __a    :	Tuple									=						value

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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if answer is None:
  return None
 try:
  __a    :	int									=						convert_to_float(SCREAMING_SNAKE_CASE__		)
  if isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) and math.isnan(SCREAMING_SNAKE_CASE__		):
   return None
  return value
 except ValueError:
  return answer.lower()


SCREAMING_SNAKE_CASE_				      =							{
    """text""": lambda x: x,
    """real""": convert_to_float,
}




class 							lowerCAmelCase					(							enum.Enum   ):







   """simple docstring"""







   _A						=   0
   _A						=   1
   _A						=   2
   _A						=   3
   _A						=   4
   _A						=   5




class 							lowerCAmelCase					(							enum.Enum   ):







   """simple docstring"""







   _A						=   0
   _A						=   1
   _A						=   2




@dataclasses.dataclass
class 							lowerCAmelCase					:







   """simple docstring"""







   _A						=   42
   _A						=   42
   _A						=   42
SCREAMING_SNAKE_CASE_				      =							re.compile(r"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 return list(_TOKENIZER.findall(x.lower()		)		)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 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   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	Tuple									=						table['''types''']
 return _TYPE_CONVERTER[types[column]](SCREAMING_SNAKE_CASE__		)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 return isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 for cond in conditions:
  __a    :	List[Any]									=						row[cond.column]

  __a    :	int									=						_parse_value(SCREAMING_SNAKE_CASE__       ,		cond.column       ,		cond.cmp_value		)

  if _is_string(SCREAMING_SNAKE_CASE__		) and _is_string(SCREAMING_SNAKE_CASE__		):
   __a    :	List[str]									=						_normalize_for_match(SCREAMING_SNAKE_CASE__		)
   __a    :	Optional[int]									=						_normalize_for_match(SCREAMING_SNAKE_CASE__		)

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

  if not _compare(cond.operator       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
   return False
 return True






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 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(SCREAMING_SNAKE_CASE__		)		)

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

 if aggregation_op == _Aggregation.SUM:
  return float(sum(SCREAMING_SNAKE_CASE__		)		)
 elif aggregation_op == _Aggregation.AVERAGE:
  return sum(SCREAMING_SNAKE_CASE__		) / len(SCREAMING_SNAKE_CASE__		)
 else:
  raise ValueError(f'''Unknown aggregation: {aggregation_op}'''		)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	List[Any]									=						sql_query['''agg''']
 if aggregation_op_index >= 3:
  __a    :	Dict									=						_Aggregation(SCREAMING_SNAKE_CASE__		)
 else:
  __a    :	int									=						_Aggregation.NONE

 __a    :	Union[str, Any]									=						sql_query['''sel''']
 __a    :	str									=						[
     _Condition(SCREAMING_SNAKE_CASE__       ,		_Operator(SCREAMING_SNAKE_CASE__		)       ,		SCREAMING_SNAKE_CASE__		)
     for column, operator, cmp_value in zip(
         sql_query['conds']['column_index']       ,		sql_query['conds']['operator_index']       ,		sql_query['conds']['condition']		)
 ]

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

 if not indices:
  return [], aggregation_op

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

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

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

 return indices, aggregation_op






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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	int									=						_get_answer_coordinates(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
 __a    :	str									=						_get_float_answer(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
 __a    :	str									=						_get_answer_text(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
 # keep the original data the same with TaPas
 if len(SCREAMING_SNAKE_CASE__		) == 0:
  __a    :	Optional[int]									=						[EMPTY_ANSWER]
 return answer_text













'''simple docstring'''


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

SCREAMING_SNAKE_CASE_				      =							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   lowerCAmelCase__			(					cls		):
   return cls






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		SCREAMING_SNAKE_CASE__ = None       ,		):
 __a    :	int									=						device
 __a    :	Union[str, Any]									=						shape[0]

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

 if generator is not None:
  __a    :	Optional[int]									=						generator.device.type if not isinstance(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		) else generator[0].device.type
  if gen_device_type != device.type and gen_device_type == "cpu":
   __a    :	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(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
  __a    :	Optional[int]									=						(1,) + shape[1:]
  __a    :	Dict									=						[
      torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=generator[i]       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		)
      for i in range(SCREAMING_SNAKE_CASE__		)
  ]
  __a    :	List[str]									=						torch.cat(SCREAMING_SNAKE_CASE__       ,		dim=0		).to(SCREAMING_SNAKE_CASE__		)
 else:
  __a    :	Tuple									=						torch.randn(SCREAMING_SNAKE_CASE__       ,		generator=SCREAMING_SNAKE_CASE__       ,		device=SCREAMING_SNAKE_CASE__       ,		dtype=SCREAMING_SNAKE_CASE__       ,		layout=SCREAMING_SNAKE_CASE__		).to(SCREAMING_SNAKE_CASE__		)

 return latents






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 if is_torch_version('<'       ,		'2.0.0'		) or not hasattr(SCREAMING_SNAKE_CASE__       ,		'_dynamo'		):
  return False
 return isinstance(SCREAMING_SNAKE_CASE__       ,		torch._dynamo.eval_frame.OptimizedModule		)




