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 :  int  =						"""none"""
__SCREAMING_SNAKE_CASE :  Any  =						"""none"""

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   int					)					->					Optional[Any]:


       """simple docstring"""







       _UpperCAmelCase  :						List[Any]							   =					value.split(_UpperCAmelCase					)
       return len(_UpperCAmelCase					) > 1 and any((len(_UpperCAmelCase					) == 3 for x in split)					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]					)					->					Optional[int]:


       """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"					)
       _UpperCAmelCase  :						str							   =					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 			UpperCamelCase_							(      _UpperCAmelCase						:   Tuple					)					->					List[str]:


       """simple docstring"""







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


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

class 			lowerCamelCase_			(enum.Enum					):
      '''simple docstring'''

      __UpperCamelCase:						Optional[int]      			=		0
      __UpperCamelCase:						List[Any]      			=		1
      __UpperCamelCase:						str      			=		2
      __UpperCamelCase:						Union[str, Any]      			=		3
      __UpperCamelCase:						List[str]      			=		4
      __UpperCamelCase:						Optional[Any]      			=		5

class 			lowerCamelCase_			(enum.Enum					):
      '''simple docstring'''

      __UpperCamelCase:						str      			=		0
      __UpperCamelCase:						Optional[int]      			=		1
      __UpperCamelCase:						Any      			=		2

@dataclasses.dataclass
class 			lowerCamelCase_			:
      '''simple docstring'''

      __UpperCamelCase:						str      			=		4_2
      __UpperCamelCase:						List[Any]      			=		4_2
      __UpperCamelCase:						str      			=		4_2

__SCREAMING_SNAKE_CASE :  Dict  =						re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]					)					->					List[str]:


       """simple docstring"""







       return list(_TOKENIZER.findall(x.lower()					)					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Union[str, Any]					)					->					Optional[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 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   Any					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Optional[Any]							   =					table["types"]
       return _TYPE_CONVERTER[types[column]](_UpperCAmelCase					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   str					)					->					Tuple:


       """simple docstring"""







       return isinstance(_UpperCAmelCase    ,			_UpperCAmelCase					)

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   Tuple    ,			_UpperCAmelCase						:   Union[str, Any]					)					->					str:


       """simple docstring"""







       for cond in conditions:
              _UpperCAmelCase  :						Any							   =					row[cond.column]

              _UpperCAmelCase  :						List[Any]							   =					_parse_value(_UpperCAmelCase    ,			cond.column    ,			cond.cmp_value					)

              if _is_string(_UpperCAmelCase					) and _is_string(_UpperCAmelCase					):
                     _UpperCAmelCase  :						Union[str, Any]							   =					_normalize_for_match(_UpperCAmelCase					)
                     _UpperCAmelCase  :						List[str]							   =					_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 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   List[Any]					)					->					Any:


       """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.
       _UpperCAmelCase  :						Union[str, Any]							   =					[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 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   Any					)					->					str:


       """simple docstring"""







       _UpperCAmelCase  :						Tuple							   =					sql_query["agg"]
       if aggregation_op_index >= 3:
              _UpperCAmelCase  :						int							   =					_Aggregation(_UpperCAmelCase					)
       else:
              _UpperCAmelCase  :						Dict							   =					_Aggregation.NONE

       _UpperCAmelCase  :						Optional[int]							   =					sql_query["sel"]
       _UpperCAmelCase  :						Union[str, Any]							   =					[
           _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"]					)
       ]

       _UpperCAmelCase  :						Any							   =					[]
       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):
              _UpperCAmelCase  :						Union[str, Any]							   =					{2: min, 1: max}
              _UpperCAmelCase  :						Optional[Any]							   =					[(table["rows"][i][j], index) for index, (i, j) in enumerate(_UpperCAmelCase					)]
              _UpperCAmelCase  :						Tuple							   =					functools.reduce(operators[sql_query["agg"]]    ,			_UpperCAmelCase					)

              _UpperCAmelCase  :						Optional[int]							   =					[indices[reduced[1]]]
              return ret, _Aggregation.NONE

       return indices, aggregation_op

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[Any]    ,			_UpperCAmelCase						:   Optional[int]    ,			_UpperCAmelCase						:   Any					)					->					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 			UpperCamelCase_							(      _UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   Any					)					->					int:


       """simple docstring"""







       _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					_get_answer_coordinates(_UpperCAmelCase    ,			_UpperCAmelCase					)
       _UpperCAmelCase  :						str							   =					_get_float_answer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       _UpperCAmelCase  :						Optional[int]							   =					_get_answer_text(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       # keep the original data the same with TaPas
       if len(_UpperCAmelCase					) == 0:
              _UpperCAmelCase  :						List[Any]							   =					[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,
)


__SCREAMING_SNAKE_CASE :  Union[str, Any]  =						{
    """configuration_gptsan_japanese""": ["""GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTSanJapaneseConfig"""],
    """tokenization_gptsan_japanese""": ["""GPTSanJapaneseTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __SCREAMING_SNAKE_CASE :  Optional[int]  =						[
      """GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """GPTSanJapaneseForConditionalGeneration""",
      """GPTSanJapaneseModel""",
      """GPTSanJapanesePreTrainedModel""",
  ]
  __SCREAMING_SNAKE_CASE :  Union[str, Any]  =						[
      """GPTSanJapaneseTokenizer""",
  ]


if TYPE_CHECKING:
  from .configuration_gptsan_japanese import GPTSAN_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTSanJapaneseConfig
  from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_gptsan_japanese import (
        GPTSAN_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
        GPTSanJapaneseForConditionalGeneration,
        GPTSanJapaneseModel,
        GPTSanJapanesePreTrainedModel,
    )
    from .tokenization_gptsan_japanese import GPTSanJapaneseTokenizer


else:
  import sys

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

