'''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_	   = "none"
A_	   = "none"


def        UpperCAmelCase__   (				_lowerCAmelCase    :			int,      _lowerCAmelCase    :			List[str])    ->		str:



						"""simple docstring"""

						snake_case :       str				=    value.split(_lowerCAmelCase)
						return len(_lowerCAmelCase) > 1 and any((len(_lowerCAmelCase) == 3 for x in split))


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[int])    ->		Optional[int]:



						"""simple docstring"""

						if isinstance(_lowerCAmelCase,      _lowerCAmelCase):
												return value
						if isinstance(_lowerCAmelCase,      _lowerCAmelCase):
												return float(_lowerCAmelCase)
						if not isinstance(_lowerCAmelCase,      _lowerCAmelCase):
												raise ValueError("Argument value is not a string. Can't parse it as float")
						snake_case :       Union[str, Any]				=    value

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			int)    ->		List[str]:



						"""simple docstring"""

						if answer is None:
												return None
						try:
												snake_case :       Optional[int]				=    convert_to_float(_lowerCAmelCase)
												if isinstance(_lowerCAmelCase,      _lowerCAmelCase) and math.isnan(_lowerCAmelCase):
																		return None
												return value
						except ValueError:
												return answer.lower()


A_	   = {
    "text": lambda x: x,
    "real": convert_to_float,
}




class 		SCREAMING_SNAKE_CASE_					(enum.Enum      ):



			"""simple docstring"""






			__magic_name__ :				int   							=       0
			__magic_name__ :				Optional[int]   							=       1
			__magic_name__ :				int   							=       2
			__magic_name__ :				str   							=       3
			__magic_name__ :				List[str]   							=       4
			__magic_name__ :				Any   							=       5







class 		SCREAMING_SNAKE_CASE_					(enum.Enum      ):



			"""simple docstring"""






			__magic_name__ :				int   							=       0
			__magic_name__ :				Optional[Any]   							=       1
			__magic_name__ :				Any   							=       2







@dataclasses.dataclass
class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			__magic_name__ :				Text
			__magic_name__ :				_Operator
			__magic_name__ :				Any







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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[Any])    ->		Any:



						"""simple docstring"""

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Optional[int])    ->		Dict:



						"""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__   (				_lowerCAmelCase    :			List[str],      _lowerCAmelCase    :			Optional[Any],      _lowerCAmelCase    :			Tuple)    ->		Dict:



						"""simple docstring"""

						snake_case :       List[Any]				=    table["types"]
						return _TYPE_CONVERTER[types[column]](_lowerCAmelCase)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[str])    ->		str:



						"""simple docstring"""

						return isinstance(_lowerCAmelCase,      _lowerCAmelCase)


def        UpperCAmelCase__   (				_lowerCAmelCase    :			int,      _lowerCAmelCase    :			str,      _lowerCAmelCase    :			Union[str, Any])    ->		Optional[int]:



						"""simple docstring"""

						for cond in conditions:
												snake_case :       Dict				=    row[cond.column]

												snake_case :       List[str]				=    _parse_value(_lowerCAmelCase,      cond.column,      cond.cmp_value)

												if _is_string(_lowerCAmelCase) and _is_string(_lowerCAmelCase):
																		snake_case :       Optional[int]				=    _normalize_for_match(_lowerCAmelCase)
																		snake_case :       List[str]				=    _normalize_for_match(_lowerCAmelCase)

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

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			Optional[int],      _lowerCAmelCase    :			List[str],      _lowerCAmelCase    :			Tuple)    ->		int:



						"""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(_lowerCAmelCase))

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

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


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			Optional[Any])    ->		str:



						"""simple docstring"""

						snake_case :       Any				=    sql_query["agg"]
						if aggregation_op_index >= 3:
												snake_case :       List[str]				=    _Aggregation(_lowerCAmelCase)
						else:
												snake_case :       Union[str, Any]				=    _Aggregation.NONE

						snake_case :       str				=    sql_query["sel"]
						snake_case :       int				=    [
						    _Condition(_lowerCAmelCase,      _Operator(_lowerCAmelCase),      _lowerCAmelCase)
						    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(_lowerCAmelCase,      table["rows"][row],      _lowerCAmelCase):
																		indices.append((row, target_column))

						if not indices:
												return [], aggregation_op

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

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

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

						return indices, aggregation_op


def        UpperCAmelCase__   (				_lowerCAmelCase    :			str,      _lowerCAmelCase    :			Any,      _lowerCAmelCase    :			str)    ->		List[str]:



						"""simple docstring"""

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



def        UpperCAmelCase__   (				_lowerCAmelCase    :			List[str],      _lowerCAmelCase    :			Dict)    ->		List:



						"""simple docstring"""

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






'''simple docstring'''

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			__magic_name__ :				Tuple   							=       ["""image_processor""", """tokenizer"""]
			__magic_name__ :				List[str]   							=       """AutoImageProcessor"""
			__magic_name__ :				List[str]   							=       """AutoTokenizer"""



			def __init__(							self				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):
									snake_case :       Any				=    None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead."				,			snake_case_				,			)
															snake_case :       str				=    kwargs.pop("feature_extractor"					)

									snake_case :       Optional[int]				=    image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError("You have to specify an image_processor."					)
									if tokenizer is None:
															raise ValueError("You have to specify a tokenizer."					)

									super().__init__(snake_case_				,			snake_case_					)
									snake_case :       Optional[int]				=    self.image_processor



			def __call__(							self				,			snake_case_=None				,			snake_case_=None				,			snake_case_=None				,			**snake_case_					):

									if text is None and images is None:
															raise ValueError("You have to specify either text or images. Both cannot be none."					)

									if text is not None:
															snake_case :       Optional[int]				=    self.tokenizer(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if images is not None:
															snake_case :       List[Any]				=    self.image_processor(snake_case_				,			return_tensors=snake_case_				,			**snake_case_					)

									if text is not None and images is not None:
															snake_case :       str				=    image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**snake_case_					)				,			tensor_type=snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.batch_decode(*snake_case_				,			**snake_case_					)



			def   lowerCamelCase (							self				,			*snake_case_				,			**snake_case_					):
									return self.tokenizer.decode(*snake_case_				,			**snake_case_					)



			@property
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    self.tokenizer.model_input_names
									snake_case :       Any				=    self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)



			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			snake_case_				,			)
									return self.image_processor_class






			@property
			def   lowerCamelCase (							self					):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			snake_case_				,			)
									return self.image_processor





