




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							    =	'none'
_A							    =	'none'
def 						lowerCAmelCase_       (	__a	,	__a ):





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: Any	     =value.split(__a )
							return len(__a ) > 1 and any((len(__a ) == 3 for x in split) )
def 						lowerCAmelCase_       (	__a ):





							"""simple docstring"""





							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''' )
							SCREAMING_SNAKE_CASE			: int	     =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 						lowerCAmelCase_       (	__a ):





							"""simple docstring"""





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


_A							    =	{
    'text': lambda x: x,
    'real': convert_to_float,
}


class 							_lowerCAmelCase		(     enum.Enum	):
				lowerCamelCase__									=    0
				lowerCamelCase__									=    1
				lowerCamelCase__									=    2
				lowerCamelCase__									=    3
				lowerCamelCase__									=    4
				lowerCamelCase__									=    5







class 							_lowerCAmelCase		(     enum.Enum	):
				lowerCamelCase__									=    0
				lowerCamelCase__									=    1
				lowerCamelCase__									=    2







@dataclasses.dataclass
class 							_lowerCAmelCase		:
				lowerCamelCase__									=    42
				lowerCamelCase__									=    42
				lowerCamelCase__									=    42







_A							    =	re.compile(r"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)
def 						lowerCAmelCase_       (	__a ):





							"""simple docstring"""





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





							"""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 						lowerCAmelCase_       (	__a	,	__a	,	__a ):





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: str	     =table['''types''']
							return _TYPE_CONVERTER[types[column]](__a )
def 						lowerCAmelCase_       (	__a ):





							"""simple docstring"""





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





							"""simple docstring"""





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

														SCREAMING_SNAKE_CASE			: Any	     =_parse_value(__a	,	cond.column	,	cond.cmp_value )

														if _is_string(__a ) and _is_string(__a ):
																					SCREAMING_SNAKE_CASE			: str	     =_normalize_for_match(__a )
																					SCREAMING_SNAKE_CASE			: Any	     =_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 						lowerCAmelCase_       (	__a	,	__a	,	__a ):





							"""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(__a ) )

							# If we have just one answer, if float returns it or try a conversion.
							SCREAMING_SNAKE_CASE			: 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 						lowerCAmelCase_       (	__a	,	__a ):





							"""simple docstring"""





							SCREAMING_SNAKE_CASE			: List[str]	     =sql_query['''agg''']
							if aggregation_op_index >= 3:
														SCREAMING_SNAKE_CASE			: List[str]	     =_Aggregation(__a )
							else:
														SCREAMING_SNAKE_CASE			: Dict	     =_Aggregation.NONE

							SCREAMING_SNAKE_CASE			: Optional[int]	     =sql_query['''sel''']
							SCREAMING_SNAKE_CASE			: Any	     =[
							    _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'''] )
							]

							SCREAMING_SNAKE_CASE			: Any	     =[]
							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):
														SCREAMING_SNAKE_CASE			: List[Any]	     ={2: min, 1: max}
														SCREAMING_SNAKE_CASE			: int	     =[(table['''rows'''][i][j], index) for index, (i, j) in enumerate(__a )]
														SCREAMING_SNAKE_CASE			: Dict	     =functools.reduce(operators[sql_query['''agg''']]	,	__a )

														SCREAMING_SNAKE_CASE			: Tuple	     =[indices[reduced[1]]]
														return ret, _Aggregation.NONE

							return indices, aggregation_op
def 						lowerCAmelCase_       (	__a	,	__a	,	__a ):





							"""simple docstring"""





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





							"""simple docstring"""





							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =_get_answer_coordinates(__a	,	__a )
							SCREAMING_SNAKE_CASE			: Dict	     =_get_float_answer(__a	,	__a	,	__a )
							SCREAMING_SNAKE_CASE			: Tuple	     =_get_answer_text(__a	,	__a	,	__a )
							# keep the original data the same with TaPas
							if len(__a ) == 0:
														SCREAMING_SNAKE_CASE			: str	     =[EMPTY_ANSWER]
							return answer_text












from typing import TYPE_CHECKING

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


_A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RegNetForImageClassification""",
							    """RegNetModel""",
							    """RegNetPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRegNetForImageClassification""",
							    """TFRegNetModel""",
							    """TFRegNetPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """FlaxRegNetForImageClassification""",
							    """FlaxRegNetModel""",
							    """FlaxRegNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_regnet import (
														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RegNetForImageClassification,
														    RegNetModel,
														    RegNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_regnet import (
														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRegNetForImageClassification,
														    TFRegNetModel,
														    TFRegNetPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_regnet import (
														    FlaxRegNetForImageClassification,
														    FlaxRegNetModel,
														    FlaxRegNetPreTrainedModel,
														)


else:
							import sys

							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)






