






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[Any]      =				'''none'''
a	: Any      =				'''none'''






def        lowerCamelCase__   (					__lowerCamelCase    :  Tuple						,	__lowerCamelCase    :  Optional[int]	):
  __UpperCAmelCase     =						value.split(snake_case__	)
  return len(snake_case__	) > 1 and any((len(snake_case__	) == 3 for x in split)	)






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[Any]	):
  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"""	)
  __UpperCAmelCase     =						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        lowerCamelCase__   (					__lowerCamelCase    :  Any	):
  if answer is None:
    return None
  try:
    __UpperCAmelCase     =						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()


a	: List[Any]      =				{
    '''text''': lambda x: x,
    '''real''': convert_to_float,
}
class 					a					(				enum.Enum     ):



      """simple docstring"""


      a						:				List[str]							 =			0
      a						:				Dict							 =			1
      a						:				Tuple							 =			2
      a						:				List[str]							 =			3
      a						:				List[Any]							 =			4
      a						:				Dict							 =			5


class 					a					(				enum.Enum     ):



      """simple docstring"""


      a						:				Tuple							 =			0
      a						:				List[Any]							 =			1
      a						:				Dict							 =			2


@dataclasses.dataclass
class 					a					:



      """simple docstring"""


      a						:				Text
      a						:				_Operator
      a						:				Any


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






def        lowerCamelCase__   (					__lowerCamelCase    :  str	):
  return list(_TOKENIZER.findall(x.lower()	)	)






def        lowerCamelCase__   (					__lowerCamelCase    :  Any						,	__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  Optional[Any]	):
  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__   (					__lowerCamelCase    :  int						,	__lowerCamelCase    :  int						,	__lowerCamelCase    :  str	):
  __UpperCAmelCase     =						table["""types"""]
  return _TYPE_CONVERTER[types[column]](snake_case__	)






def        lowerCamelCase__   (					__lowerCamelCase    :  Any	):
  return isinstance(snake_case__						,	snake_case__	)






def        lowerCamelCase__   (					__lowerCamelCase    :  Union[str, Any]						,	__lowerCamelCase    :  str						,	__lowerCamelCase    :  Tuple	):
  for cond in conditions:
    __UpperCAmelCase     =						row[cond.column]

    __UpperCAmelCase     =						_parse_value(snake_case__						,	cond.column						,	cond.cmp_value	)

    if _is_string(snake_case__	) and _is_string(snake_case__	):
      __UpperCAmelCase     =						_normalize_for_match(snake_case__	)
      __UpperCAmelCase     =						_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        lowerCamelCase__   (					__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  List[str]						,	__lowerCamelCase    :  int	):
  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.
  __UpperCAmelCase     =						[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        lowerCamelCase__   (					__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  int	):
  __UpperCAmelCase     =						sql_query["""agg"""]
  if aggregation_op_index >= 3:
    __UpperCAmelCase     =						_Aggregation(snake_case__	)
  else:
    __UpperCAmelCase     =						_Aggregation.NONE

  __UpperCAmelCase     =						sql_query["""sel"""]
  __UpperCAmelCase     =						[
      _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"""]	)
  ]

  __UpperCAmelCase     =						[]
  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):
    __UpperCAmelCase     =						{2: min, 1: max}
    __UpperCAmelCase     =						[(table["""rows"""][i][j], index) for index, (i, j) in enumerate(snake_case__	)]
    __UpperCAmelCase     =						functools.reduce(operators[sql_query["""agg"""]]						,	snake_case__	)

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

  return indices, aggregation_op






def        lowerCamelCase__   (					__lowerCamelCase    :  List[str]						,	__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  List[Any]	):
  if float_answer is not None:
    return [str(snake_case__	)]
  return [str(table["""real_rows"""][r][c]	) for r, c in answer_coordinates]







def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  Optional[Any]	):
  __UpperCAmelCase   ,				__UpperCAmelCase     =						_get_answer_coordinates(snake_case__						,	snake_case__	)
  __UpperCAmelCase     =						_get_float_answer(snake_case__						,	snake_case__						,	snake_case__	)
  __UpperCAmelCase     =						_get_answer_text(snake_case__						,	snake_case__						,	snake_case__	)
  # keep the original data the same with TaPas
  if len(snake_case__	) == 0:
    __UpperCAmelCase     =						[EMPTY_ANSWER]
  return answer_text










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a	: str      =				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: str      =				[
           "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "HubertForCTC",
           "HubertForSequenceClassification",
           "HubertModel",
           "HubertPreTrainedModel",
       ]


try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a	: List[str]      =				[
           "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "TFHubertForCTC",
           "TFHubertModel",
           "TFHubertPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_hubert import (
                  HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  HubertForCTC,
                  HubertForSequenceClassification,
                  HubertModel,
                  HubertPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_hubert import (
                  TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFHubertForCTC,
                  TFHubertModel,
                  TFHubertPreTrainedModel,
              )


else:
       import sys

       a	: List[Any]      =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


