





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 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_		:      List[str]           =						value.split(__a )
	return len(__a ) > 1 and any((len(__a ) == 3 for x in split) )


def 				SCREAMING_SNAKE_CASE__    (     __a ):
	if isinstance(__a ,			__a ):
		return value
	if isinstance(__a ,			__a ):
		return float(__a )
	if not isinstance(__a ,			__a ):
		raise ValueError('Argument value is not a string. Can\'t parse it as float' )
	snake_case_		:      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(',' ,			__a ):
			return float(sanitized.replace(',' ,			'' ) )
		# 5,5556
		if "," in sanitized and sanitized.count(',' ) == 1 and not _split_thousands(',' ,			__a ):
			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(__a )
	except ValueError:
		# Avoid adding the sanitized value in the error message.
		raise ValueError('Unable to convert value to float' )


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


_SCREAMING_SNAKE_CASE   						=		{
    """text""": lambda x: x,
    """real""": convert_to_float,
}
class SCREAMING_SNAKE_CASE_    (       enum.Enum       ):
						__magic_name__:      Union[str, Any]			   =							0
						__magic_name__:      Optional[int]			   =							1
						__magic_name__:      List[str]			   =							2
						__magic_name__:      Union[str, Any]			   =							3
						__magic_name__:      Tuple			   =							4
						__magic_name__:      Optional[Any]			   =							5
class SCREAMING_SNAKE_CASE_    (       enum.Enum       ):
						__magic_name__:      List[str]			   =							0
						__magic_name__:      Any			   =							1
						__magic_name__:      Optional[Any]			   =							2
@dataclasses.dataclass
class SCREAMING_SNAKE_CASE_    :
						__magic_name__:      Text
						__magic_name__:      _Operator
						__magic_name__:      Any
_SCREAMING_SNAKE_CASE   						=		re.compile(R"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)


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


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	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 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	snake_case_		:      List[str]           =						table['types']
	return _TYPE_CONVERTER[types[column]](__a )


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


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

		snake_case_		:      Dict           =						_parse_value(__a ,			cond.column ,			cond.cmp_value )

		if _is_string(__a ) and _is_string(__a ):
			snake_case_		:      Optional[int]           =						_normalize_for_match(__a )
			snake_case_		:      Tuple           =						_normalize_for_match(__a )

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

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


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a ):
	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(__a ) )

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

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


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

	snake_case_		:      Dict           =						sql_query['sel']
	snake_case_		:      Optional[int]           =						[
	    _Condition(__a ,			_Operator(__a ) ,			__a )
	    for column, operator, cmp_value in zip(
	        sql_query['conds']['column_index'] ,			sql_query['conds']['operator_index'] ,			sql_query['conds']['condition'] )
	]

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

	if not indices:
		return [], aggregation_op

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

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

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

	return indices, aggregation_op


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


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


"""simple docstring"""


import platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand







def        __a (		A   ):






     '''simple docstring'''



     return EnvironmentCommand()






class  a__     ( _a						):


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				parser.add_parser("env",					help="Print relevant system environment info."	)
        download_parser.set_defaults(func=_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				{
            "`datasets` version": version,
            "Platform": platform.platform(),
            "Python version": platform.python_version(),
            "Huggingface_hub version": huggingface_hub.__version__,
            "PyArrow version": pyarrow.__version__,
            "Pandas version": pandas.__version__,
        }

        print("\nCopy-and-paste the text below in your GitHub issue.\n"	)
        print(self.format_dict(_UpperCAmelCase	)	)

        return info


   @staticmethod
   def 						snake_case__						(  _UpperCAmelCase	):
        '''simple docstring'''



        return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"




