




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


lowercase_		=							'''none'''
lowercase_		=							'''none'''







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






					"""simple docstring"""






					__magic_name__					:							Optional[int]		   =							value.split(UpperCAmelCase       )
					return len(UpperCAmelCase       ) > 1 and any((len(UpperCAmelCase       ) == 3 for x in split)       )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->str:






					"""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'''       )
					__magic_name__					:							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  lowerCAmelCase  (	UpperCAmelCase       )  ->List[Any]:






					"""simple docstring"""






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


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





class 		A__				(   enum.Enum ):
	lowerCamelCase__		: Any				    =0
	lowerCamelCase__		: Dict				    =1
	lowerCamelCase__		: List[str]				    =2
	lowerCamelCase__		: Union[str, Any]				    =3
	lowerCamelCase__		: str				    =4
	lowerCamelCase__		: Optional[Any]				    =5





class 		A__				(   enum.Enum ):
	lowerCamelCase__		: Tuple				    =0
	lowerCamelCase__		: Any				    =1
	lowerCamelCase__		: List[str]				    =2





@dataclasses.dataclass
class 		A__				:
	lowerCamelCase__		: Text
	lowerCamelCase__		: _Operator
	lowerCamelCase__		: Any

lowercase_		=							re.compile(r'''\w+|[^\w\s]+''', re.UNICODE | re.MULTILINE | re.DOTALL)







def  lowerCAmelCase  (	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






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







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






					"""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  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[str]:






					"""simple docstring"""






					__magic_name__					:							Any		   =							table['''types''']
					return _TYPE_CONVERTER[types[column]](UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase       )  ->Optional[Any]:






					"""simple docstring"""






					return isinstance(UpperCAmelCase,	UpperCAmelCase       )







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






					"""simple docstring"""






					for cond in conditions:
										__magic_name__					:							str		   =							row[cond.column]

										__magic_name__					:							Optional[Any]		   =							_parse_value(UpperCAmelCase,	cond.column,	cond.cmp_value       )

										if _is_string(UpperCAmelCase       ) and _is_string(UpperCAmelCase       ):
															__magic_name__					:							int		   =							_normalize_for_match(UpperCAmelCase       )
															__magic_name__					:							Optional[Any]		   =							_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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[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.
					__magic_name__					:							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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->Tuple:






					"""simple docstring"""






					__magic_name__					:							Dict		   =							sql_query['''agg''']
					if aggregation_op_index >= 3:
										__magic_name__					:							str		   =							_Aggregation(UpperCAmelCase       )
					else:
										__magic_name__					:							str		   =							_Aggregation.NONE

					__magic_name__					:							Optional[int]		   =							sql_query['''sel''']
					__magic_name__					:							str		   =							[
					    _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''']       )
					]

					__magic_name__					:							Optional[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):
										__magic_name__					:							int		   =							{2: min, 1: max}
										__magic_name__					:							str		   =							[(table['''rows'''][i][j], index) for index, (i, j) in enumerate(UpperCAmelCase       )]
										__magic_name__					:							Tuple		   =							functools.reduce(operators[sql_query['''agg''']],	UpperCAmelCase       )

										__magic_name__					:							List[Any]		   =							[indices[reduced[1]]]
										return ret, _Aggregation.NONE

					return indices, aggregation_op







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->List[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  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->List:






					"""simple docstring"""






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










from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase__  :	Dict     =logging.get_logger(__name__)


if is_vision_available():
							import PIL

class 					__A   (  a			):
				__A            = ["""pixel_values"""]


				def __init__(						self      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = 1 / 255      ,       UpperCAmelCase_ = True      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = True      ,       **UpperCAmelCase_      ,       ):
								super().__init__(**UpperCAmelCase_							)
								lowerCamelCase		       =size if size is not None else {"""shortest_edge""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)

								lowerCamelCase		       =do_resize
								lowerCamelCase		       =size
								lowerCamelCase		       =resample
								lowerCamelCase		       =do_center_crop
								lowerCamelCase		       =crop_size
								lowerCamelCase		       =do_rescale
								lowerCamelCase		       =rescale_factor
								lowerCamelCase		       =do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								lowerCamelCase		       =image_std if image_std is not None else OPENAI_CLIP_STD
								lowerCamelCase		       =do_convert_rgb


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = PILImageResampling.BICUBIC      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =get_resize_output_image_size(
								    UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       default_to_square=UpperCAmelCase_							)
								return resize(UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								return center_crop(UpperCAmelCase_      ,       size=(size["""height"""], size["""width"""])      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return rescale(UpperCAmelCase_      ,       scale=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       **UpperCAmelCase_      ,       ):
								return normalize(UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_      ,       data_format=UpperCAmelCase_      ,       **UpperCAmelCase_							)


				def _snake_case				(						self      ,       UpperCAmelCase_      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = None      ,       UpperCAmelCase_ = ChannelDimension.FIRST      ,       **UpperCAmelCase_      ,       ):
								lowerCamelCase		       =do_resize if do_resize is not None else self.do_resize
								lowerCamelCase		       =size if size is not None else self.size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_      ,       default_to_square=UpperCAmelCase_							)
								lowerCamelCase		       =resample if resample is not None else self.resample
								lowerCamelCase		       =do_center_crop if do_center_crop is not None else self.do_center_crop
								lowerCamelCase		       =crop_size if crop_size is not None else self.crop_size
								lowerCamelCase		       =get_size_dict(UpperCAmelCase_							)
								lowerCamelCase		       =do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase		       =rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase		       =do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase		       =image_mean if image_mean is not None else self.image_mean
								lowerCamelCase		       =image_std if image_std is not None else self.image_std
								lowerCamelCase		       =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								lowerCamelCase		       =make_list_of_images(UpperCAmelCase_							)

								if not valid_images(UpperCAmelCase_							):
												raise ValueError(
												    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
												    """torch.Tensor, tf.Tensor or jax.ndarray."""							)

								if do_resize and size is None:
												raise ValueError("""Size must be specified if do_resize is True."""							)

								if do_center_crop and crop_size is None:
												raise ValueError("""Crop size must be specified if do_center_crop is True."""							)

								if do_rescale and rescale_factor is None:
												raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError("""Image mean and std must be specified if do_normalize is True."""							)

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
												lowerCamelCase		       =[convert_to_rgb(UpperCAmelCase_							) for image in images]

								# All transformations expect numpy arrays.
								lowerCamelCase		       =[to_numpy_array(UpperCAmelCase_							) for image in images]

								if do_resize:
												lowerCamelCase		       =[self.resize(image=UpperCAmelCase_      ,       size=UpperCAmelCase_      ,       resample=UpperCAmelCase_							) for image in images]

								if do_center_crop:
												lowerCamelCase		       =[self.center_crop(image=UpperCAmelCase_      ,       size=UpperCAmelCase_							) for image in images]

								if do_rescale:
												lowerCamelCase		       =[self.rescale(image=UpperCAmelCase_      ,       scale=UpperCAmelCase_							) for image in images]

								if do_normalize:
												lowerCamelCase		       =[self.normalize(image=UpperCAmelCase_      ,       mean=UpperCAmelCase_      ,       std=UpperCAmelCase_							) for image in images]

								lowerCamelCase		       =[to_channel_dimension_format(UpperCAmelCase_      ,       UpperCAmelCase_							) for image in images]

								lowerCamelCase		       ={"""pixel_values""": images}
								return BatchFeature(data=UpperCAmelCase_      ,       tensor_type=UpperCAmelCase_							)





