
'''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__		: List[Any]  							=							"none"
a__		: int  							=							"none"





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[int]:







      """simple docstring"""
      __A													=      value.split(UpperCAmelCase						)
      return len(UpperCAmelCase						) > 1 and any((len(UpperCAmelCase						) == 3 for x in split)						)





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







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

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





def 							snake_case				(		UpperCAmelCase						)->      Optional[int]:







      """simple docstring"""
      if answer is None:
            return None
      try:
            __A													=      convert_to_float(UpperCAmelCase						)
            if isinstance(UpperCAmelCase   ,	UpperCAmelCase						) and math.isnan(UpperCAmelCase						):
                  return None
            return value
      except ValueError:
            return answer.lower()


a__		: Optional[int]  							=							{
    "text": lambda x: x,
    "real": convert_to_float,
}


class 			UpperCamelCase__	(     enum.Enum):
 UpperCAmelCase__     :     List[str]    					=       0
 UpperCAmelCase__     :     Any    					=       1
 UpperCAmelCase__     :     Any    					=       2
 UpperCAmelCase__     :     Optional[int]    					=       3
 UpperCAmelCase__     :     Tuple    					=       4
 UpperCAmelCase__     :     int    					=       5


class 			UpperCamelCase__	(     enum.Enum):
 UpperCAmelCase__     :     Union[str, Any]    					=       0
 UpperCAmelCase__     :     List[Any]    					=       1
 UpperCAmelCase__     :     Optional[int]    					=       2


@dataclasses.dataclass
class 			UpperCamelCase__	:
 UpperCAmelCase__     :     Text
 UpperCAmelCase__     :     _Operator
 UpperCAmelCase__     :     Any
a__		: str  							=							re.compile(R"\w+|[^\w\s]+", re.UNICODE | re.MULTILINE | re.DOTALL)





def 							snake_case				(		UpperCAmelCase						)->      List[Any]:







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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      List[Any]:







      """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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Dict:







      """simple docstring"""
      __A													=      table['types']
      return _TYPE_CONVERTER[types[column]](UpperCAmelCase						)





def 							snake_case				(		UpperCAmelCase						)->      str:







      """simple docstring"""
      return isinstance(UpperCAmelCase   ,	UpperCAmelCase						)





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      for cond in conditions:
            __A													=      row[cond.column]

            __A													=      _parse_value(UpperCAmelCase   ,	cond.column   ,	cond.cmp_value						)

            if _is_string(UpperCAmelCase						) and _is_string(UpperCAmelCase						):
                  __A													=      _normalize_for_match(UpperCAmelCase						)
                  __A													=      _normalize_for_match(UpperCAmelCase						)

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

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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Tuple:







      """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(UpperCAmelCase						)						)

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

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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __A													=      sql_query['agg']
      if aggregation_op_index >= 3:
            __A													=      _Aggregation(UpperCAmelCase						)
      else:
            __A													=      _Aggregation.NONE

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

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

      if not indices:
            return [], aggregation_op

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

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

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

      return indices, aggregation_op





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[Any]:







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





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      List:







      """simple docstring"""
      __A						,   __A													=      _get_answer_coordinates(UpperCAmelCase   ,	UpperCAmelCase						)
      __A													=      _get_float_answer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)
      __A													=      _get_answer_text(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)
      # keep the original data the same with TaPas
      if len(UpperCAmelCase						) == 0:
            __A													=      [EMPTY_ANSWER]
      return answer_text













"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'UniSpeechSatForAudioFrameClassification',
		    'UniSpeechSatForCTC',
		    'UniSpeechSatForPreTraining',
		    'UniSpeechSatForSequenceClassification',
		    'UniSpeechSatForXVector',
		    'UniSpeechSatModel',
		    'UniSpeechSatPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_snake_case	:						Union[str, Any]					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


