





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


__UpperCamelCase:  List[Any]   =   """none"""
__UpperCamelCase:  Optional[int]   =   """none"""


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int      ,						_lowercase    :   Dict )      ->       str:




		'''simple docstring'''
		lowercase__				:			Optional[Any]            =		value.split(_lowercase )
		return len(_lowercase ) > 1 and any((len(_lowercase ) == 3 for x in split) )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[Any] )      ->       Optional[Any]:




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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[int] )      ->       Any:




		'''simple docstring'''
		if answer is None:
				return None
		try:
				lowercase__				:			str            =		convert_to_float(_lowercase )
				if isinstance(_lowercase      ,						_lowercase ) and math.isnan(_lowercase ):
						return None
				return value
		except ValueError:
				return answer.lower()


__UpperCamelCase:  str   =   {
    """text""": lambda x: x,
    """real""": convert_to_float,
}


class 			__lowerCAmelCase				(					enum.Enum			):


	'''simple docstring'''





	_A       	=							0
	_A       	=							1
	_A       	=							2
	_A       	=							3
	_A       	=							4
	_A       	=							5


class 			__lowerCAmelCase				(					enum.Enum			):


	'''simple docstring'''





	_A       	=							0
	_A       	=							1
	_A       	=							2


@dataclasses.dataclass
class 			__lowerCAmelCase				:


	'''simple docstring'''





	_A       	=							42
	_A       	=							42
	_A       	=							42
__UpperCamelCase:  List[Any]   =   re.compile(r"""\w+|[^\w\s]+""", re.UNICODE | re.MULTILINE | re.DOTALL)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Union[str, Any] )      ->       List[str]:




		'''simple docstring'''
		return list(_TOKENIZER.findall(x.lower() ) )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str      ,						_lowercase    :   Optional[Any]      ,						_lowercase    :   Union[str, Any] )      ->       Union[str, 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 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Any      ,						_lowercase    :   List[Any]      ,						_lowercase    :   int )      ->       Optional[Any]:




		'''simple docstring'''
		lowercase__				:			str            =		table['types']
		return _TYPE_CONVERTER[types[column]](_lowercase )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int )      ->       List[str]:




		'''simple docstring'''
		return isinstance(_lowercase      ,						_lowercase )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[Any]      ,						_lowercase    :   str      ,						_lowercase    :   Tuple )      ->       List[str]:




		'''simple docstring'''
		for cond in conditions:
				lowercase__				:			List[Any]            =		row[cond.column]

				lowercase__				:			int            =		_parse_value(_lowercase      ,						cond.column      ,						cond.cmp_value )

				if _is_string(_lowercase ) and _is_string(_lowercase ):
						lowercase__				:			Dict            =		_normalize_for_match(_lowercase )
						lowercase__				:			Tuple            =		_normalize_for_match(_lowercase )

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

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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Tuple      ,						_lowercase    :   Any      ,						_lowercase    :   Tuple )      ->       Tuple:




		'''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(_lowercase ) )

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

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


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Dict      ,						_lowercase    :   Optional[int] )      ->       Optional[int]:




		'''simple docstring'''
		lowercase__				:			Optional[int]            =		sql_query['agg']
		if aggregation_op_index >= 3:
				lowercase__				:			Dict            =		_Aggregation(_lowercase )
		else:
				lowercase__				:			Optional[Any]            =		_Aggregation.NONE

		lowercase__				:			Any            =		sql_query['sel']
		lowercase__				:			Optional[Any]            =		[
		    _Condition(_lowercase      ,						_Operator(_lowercase )      ,						_lowercase )
		    for column, operator, cmp_value in zip(
		        sql_query['conds']['column_index']      ,						sql_query['conds']['operator_index']      ,						sql_query['conds']['condition'] )
		]

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

		if not indices:
				return [], aggregation_op

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

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

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

		return indices, aggregation_op


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   List[str]      ,						_lowercase    :   Dict      ,						_lowercase    :   str )      ->       Dict:




		'''simple docstring'''
		if float_answer is not None:
				return [str(_lowercase )]
		return [str(table['real_rows'][r][c] ) for r, c in answer_coordinates]





def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Union[str, Any]      ,						_lowercase    :   List[Any] )      ->       List:




		'''simple docstring'''
		lowercase__      ,							lowercase__				:			Optional[Any]            =		_get_answer_coordinates(_lowercase      ,						_lowercase )
		lowercase__				:			Union[str, Any]            =		_get_float_answer(_lowercase      ,						_lowercase      ,						_lowercase )
		lowercase__				:			Optional[Any]            =		_get_answer_text(_lowercase      ,						_lowercase      ,						_lowercase )
		# keep the original data the same with TaPas
		if len(_lowercase ) == 0:
				lowercase__				:			int            =		[EMPTY_ANSWER]
		return answer_text









from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION


class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any] ):
			super().__init__()

			self.register_buffer('watermark_image',				torch.zeros((62, 62, 4) ) )
			lowercase__				:			int            =		None





	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	List[PIL.Image.Image],				lowerCamelCase_:	Dict=None ):
			# copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

			lowercase__				:			str            =		images[0].height
			lowercase__				:			Optional[Any]            =		images[0].width

			lowercase__				:			List[Any]            =		sample_size or h

			lowercase__				:			Any            =		min(h / sample_size,				w / sample_size )
			lowercase__      ,							lowercase__				:			Optional[int]            =		(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

			lowercase__      ,							lowercase__				:			Any            =		1024**2, img_w * img_h
			lowercase__				:			Union[str, Any]            =		(Sa / Sa) ** 0.5
			lowercase__      ,							lowercase__      ,							lowercase__				:			Optional[int]            =		int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

			if self.watermark_image_as_pil is None:
					lowercase__				:			Tuple            =		self.watermark_image.to(torch.uinta ).cpu().numpy()
					lowercase__				:			str            =		Image.fromarray(lowerCamelCase_,				mode='RGBA' )
					lowercase__				:			Tuple            =		watermark_image

			lowercase__				:			Optional[int]            =		self.watermark_image_as_pil.resize(
			    (wm_size, wm_size),				PIL_INTERPOLATION['bicubic'],				reducing_gap=lowerCamelCase_ )

			for pil_img in images:
					pil_img.paste(lowerCamelCase_,				box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y),				mask=wm_img.split()[-1] )

			return images


