






import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							import PIL

							from transformers import FlavaImageProcessor
							from transformers.image_utils import PILImageResampling
							from transformers.models.flava.image_processing_flava import (
							    FLAVA_CODEBOOK_MEAN,
							    FLAVA_CODEBOOK_STD,
							    FLAVA_IMAGE_MEAN,
							    FLAVA_IMAGE_STD,
							)
else:
							A__     :      List[Any]             =      None
class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				def __init__(					self						:   Dict,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   List[Any]=7,  lowerCamelCase						:   str=3,  lowerCamelCase						:   Optional[int]=30,  lowerCamelCase						:   Optional[Any]=400,  lowerCamelCase						:   Union[str, Any]=True,  lowerCamelCase						:   Any=None,  lowerCamelCase						:   Tuple=True,  lowerCamelCase						:   List[str]=None,  lowerCamelCase						:   Tuple=None,  lowerCamelCase						:   str=True,  lowerCamelCase						:   str=1 / 255,  lowerCamelCase						:   Union[str, Any]=True,  lowerCamelCase						:   Optional[int]=FLAVA_IMAGE_MEAN,  lowerCamelCase						:   int=FLAVA_IMAGE_STD,  lowerCamelCase						:   Tuple=14,  lowerCamelCase						:   Tuple=75,  lowerCamelCase						:   Union[str, Any]=None,  lowerCamelCase						:   Optional[int]=16,  lowerCamelCase						:   Optional[int]=0.3,  lowerCamelCase						:   Tuple=None,  lowerCamelCase						:   Dict=True,  lowerCamelCase						:   str=None,  lowerCamelCase						:   int=None,  lowerCamelCase						:   str=True,  lowerCamelCase						:   List[Any]=None,  lowerCamelCase						:   Any=True,  lowerCamelCase						:   Optional[int]=True,  lowerCamelCase						:   Dict=FLAVA_CODEBOOK_MEAN,  lowerCamelCase						:   Tuple=FLAVA_CODEBOOK_STD,  ):

								'''simple docstring'''
								lowercase__       			=      size if size is not None else {'''height''': 224, '''width''': 224}
								lowercase__       			=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
								lowercase__       			=      codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
								lowercase__       			=      codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      num_channels
								lowercase__       			=      do_resize
								lowercase__       			=      do_rescale
								lowercase__       			=      rescale_factor
								lowercase__       			=      min_resolution
								lowercase__       			=      max_resolution
								lowercase__       			=      size
								lowercase__       			=      resample if resample is not None else PILImageResampling.BICUBIC
								lowercase__       			=      do_normalize
								lowercase__       			=      image_mean
								lowercase__       			=      image_std
								lowercase__       			=      do_center_crop
								lowercase__       			=      crop_size

								lowercase__       			=      input_size_patches
								lowercase__       			=      total_mask_patches
								lowercase__       			=      mask_group_max_patches
								lowercase__       			=      mask_group_min_patches
								lowercase__       			=      mask_group_min_aspect_ratio
								lowercase__       			=      mask_group_max_aspect_ratio

								lowercase__       			=      codebook_do_resize
								lowercase__       			=      codebook_size
								lowercase__       			=      codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
								lowercase__       			=      codebook_do_center_crop
								lowercase__       			=      codebook_crop_size
								lowercase__       			=      codebook_do_map_pixels
								lowercase__       			=      codebook_do_normalize
								lowercase__       			=      codebook_image_mean
								lowercase__       			=      codebook_image_std




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "resample": self.resample,
								    "do_rescale": self.do_rescale,
								    "rescale_factor": self.rescale_factor,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "input_size_patches": self.input_size_patches,
								    "total_mask_patches": self.total_mask_patches,
								    "mask_group_max_patches": self.mask_group_max_patches,
								    "mask_group_min_patches": self.mask_group_min_patches,
								    "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
								    "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
								    "codebook_do_resize": self.codebook_do_resize,
								    "codebook_size": self.codebook_size,
								    "codebook_resample": self.codebook_resample,
								    "codebook_do_center_crop": self.codebook_do_center_crop,
								    "codebook_crop_size": self.codebook_crop_size,
								    "codebook_do_map_pixels": self.codebook_do_map_pixels,
								    "codebook_do_normalize": self.codebook_do_normalize,
								    "codebook_image_mean": self.codebook_image_mean,
								    "codebook_image_std": self.codebook_image_std,
								}




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								return (self.size["height"], self.size["width"])




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								return (
								    (self.input_size_patches, self.input_size_patches)
								    if not isinstance(self.input_size_patches,  lowerCamelCase  )
								    else self.input_size_patches
								)




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								return (self.codebook_size["height"], self.codebook_size["width"])



@require_torch
@require_vision
class 				_UpperCAmelCase (	A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	FlavaImageProcessor if is_vision_available() else None
				lowercase__    							=	None




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      FlavaImageProcessingTester(self  )




				@property
				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								return self.image_processor_tester.prepare_image_processor_dict()




				def     lowercase__       (					self						:   int  ):

								'''simple docstring'''
								lowercase__       			=      self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(lowerCamelCase,  '''image_mean'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''image_std'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''do_normalize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''do_resize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''resample'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''crop_size'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''do_center_crop'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''do_rescale'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''rescale_factor'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''masking_generator'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_do_resize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_size'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_resample'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_do_center_crop'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_crop_size'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_do_map_pixels'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_do_normalize'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_image_mean'''  )  )
								self.assertTrue(hasattr(lowerCamelCase,  '''codebook_image_std'''  )  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size,  {'''height''': 224, '''width''': 224}  )
								self.assertEqual(image_processor.crop_size,  {'''height''': 224, '''width''': 224}  )
								self.assertEqual(image_processor.codebook_size,  {'''height''': 112, '''width''': 112}  )
								self.assertEqual(image_processor.codebook_crop_size,  {'''height''': 112, '''width''': 112}  )

								lowercase__       			=      self.image_processing_class.from_dict(
								    self.image_processor_dict,  size=42,  crop_size=84,  codebook_size=33,  codebook_crop_size=66  )
								self.assertEqual(image_processor.size,  {'''height''': 42, '''width''': 42}  )
								self.assertEqual(image_processor.crop_size,  {'''height''': 84, '''width''': 84}  )
								self.assertEqual(image_processor.codebook_size,  {'''height''': 33, '''width''': 33}  )
								self.assertEqual(image_processor.codebook_crop_size,  {'''height''': 66, '''width''': 66}  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								pass




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowercase__       			=      prepare_image_inputs(self.image_processor_tester,  equal_resolution=lowerCamelCase  )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,  PIL.Image.Image  )

								# Test not batched input
								lowercase__       			=      image_processing(image_inputs[0],  return_tensors='''pt'''  )

								# Test no bool masked pos
								self.assertFalse('''bool_masked_pos''' in encoded_images  )

								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_image_size()

								self.assertEqual(
								    encoded_images.pixel_values.shape,  (1, self.image_processor_tester.num_channels, expected_height, expected_width),  )

								# Test batched
								lowercase__       			=      image_processing(lowerCamelCase,  return_tensors='''pt'''  )
								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_image_size()

								# Test no bool masked pos
								self.assertFalse('''bool_masked_pos''' in encoded_images  )

								self.assertEqual(
								    encoded_images.pixel_values.shape,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Dict,  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      self.image_processing_class(**self.image_processor_dict  )
								# create random tensors
								lowercase__       			=      prepare_image_inputs(self.image_processor_tester,  equal_resolution=lowerCamelCase,  **lowerCamelCase  )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,  lowerCamelCase  )

								# Test not batched input
								lowercase__       			=      image_processing(image_inputs[0],  return_tensors='''pt'''  )

								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.pixel_values.shape,  (1, self.image_processor_tester.num_channels, expected_height, expected_width),  )

								lowercase__       			=      image_processing(lowerCamelCase,  return_image_mask=lowerCamelCase,  return_tensors='''pt'''  )

								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.pixel_values.shape,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),  )

								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_mask_size()
								self.assertEqual(
								    encoded_images.bool_masked_pos.shape,  (
								        self.image_processor_tester.batch_size,
								        expected_height,
								        expected_width,
								    ),  )

								# Test batched
								lowercase__       			=      image_processing(lowerCamelCase,  return_tensors='''pt'''  ).pixel_values

								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.shape,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),  )

								# Test masking
								lowercase__       			=      image_processing(lowerCamelCase,  return_image_mask=lowerCamelCase,  return_tensors='''pt'''  )

								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_image_size()
								self.assertEqual(
								    encoded_images.pixel_values.shape,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),  )

								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_mask_size()
								self.assertEqual(
								    encoded_images.bool_masked_pos.shape,  (
								        self.image_processor_tester.batch_size,
								        expected_height,
								        expected_width,
								    ),  )




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								self._test_call_framework(np.ndarray,  prepare_kwargs={'''numpify''': True}  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								self._test_call_framework(torch.Tensor,  prepare_kwargs={'''torchify''': True}  )




				def     lowercase__       (					self						:   Union[str, Any]  ):

								'''simple docstring'''
								random.seed(1_234  )
								lowercase__       			=      self.image_processing_class(**self.image_processor_dict  )
								lowercase__       			=      prepare_image_inputs(self.image_processor_tester,  equal_resolution=lowerCamelCase,  torchify=lowerCamelCase  )

								# Test not batched input
								lowercase__       			=      image_processing(image_inputs[0],  return_image_mask=lowerCamelCase,  return_tensors='''pt'''  )
								self.assertEqual(encoded_images.bool_masked_pos.sum().item(),  75  )




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								lowercase__       			=      self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowercase__       			=      prepare_image_inputs(self.image_processor_tester,  equal_resolution=lowerCamelCase  )
								for image in image_inputs:
												self.assertIsInstance(lowerCamelCase,  PIL.Image.Image  )

								# Test not batched input
								lowercase__       			=      image_processing(image_inputs[0],  return_codebook_pixels=lowerCamelCase,  return_tensors='''pt'''  )
								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_codebook_image_size()
								self.assertEqual(
								    encoded_images.codebook_pixel_values.shape,  (1, self.image_processor_tester.num_channels, expected_height, expected_width),  )

								# Test batched
								lowercase__       			=      image_processing(lowerCamelCase,  return_codebook_pixels=lowerCamelCase,  return_tensors='''pt'''  )
								lowercase__			,						lowercase__       			=      self.image_processor_tester.get_expected_codebook_image_size()
								self.assertEqual(
								    encoded_images.codebook_pixel_values.shape,  (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        expected_height,
								        expected_width,
								    ),  )












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__     :      Dict             =			'none'
A__     :      Optional[Any]             =			'none'






def   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      value.split(lowerCamelCase_    )
				return len(lowerCamelCase_    ) > 1 and any((len(lowerCamelCase_    ) == 3 for x in split)    )






def   a      (						lowerCamelCase_    ):






				'''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'''    )
				lowercase__       			=      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   a      (						lowerCamelCase_    ):






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


A__     :      Tuple             =			{
    'text': lambda x: x,
    'real': convert_to_float,
}
class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2
				lowercase__    							=	3
				lowercase__    							=	4
				lowercase__    							=	5



class 				_UpperCAmelCase (	enum.Enum     ):

				"""simple docstring"""




				lowercase__    							=	0
				lowercase__    							=	1
				lowercase__    							=	2



@dataclasses.dataclass
class 				_UpperCAmelCase :

				"""simple docstring"""




				lowercase__    							=	42
				lowercase__    							=	42
				lowercase__    							=	42



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






def   a      (						lowerCamelCase_    ):






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






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''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   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      table['''types''']
				return _TYPE_CONVERTER[types[column]](lowerCamelCase_    )






def   a      (						lowerCamelCase_    ):






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






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






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

								lowercase__       			=      _parse_value(lowerCamelCase_		,       cond.column		,       cond.cmp_value    )

								if _is_string(lowerCamelCase_    ) and _is_string(lowerCamelCase_    ):
												lowercase__       			=      _normalize_for_match(lowerCamelCase_    )
												lowercase__       			=      _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   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''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.
				lowercase__       			=      [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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






				'''simple docstring'''
				lowercase__       			=      sql_query['''agg''']
				if aggregation_op_index >= 3:
								lowercase__       			=      _Aggregation(lowerCamelCase_    )
				else:
								lowercase__       			=      _Aggregation.NONE

				lowercase__       			=      sql_query['''sel''']
				lowercase__       			=      [
				    _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''']    )
				]

				lowercase__       			=      []
				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):
								lowercase__       			=      {2: min, 1: max}
								lowercase__       			=      [(table['''rows'''][i][j], index) for index, (i, j) in enumerate(lowerCamelCase_    )]
								lowercase__       			=      functools.reduce(operators[sql_query['''agg''']]		,       lowerCamelCase_    )

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

				return indices, aggregation_op






def   a      (						lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_    ):






				'''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   a      (						lowerCamelCase_		,       lowerCamelCase_    ):






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




