






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.swin.modeling_tf_swin import (
							    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFSwinForImageClassification,
							    TFSwinForMaskedImageModeling,
							    TFSwinModel,
							)


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 				_UpperCAmelCase :

				"""simple docstring"""




				def __init__(					self						:   Optional[Any],  lowerCamelCase						:   Optional[Any],  lowerCamelCase						:   List[str]=13,  lowerCamelCase						:   Dict=32,  lowerCamelCase						:   List[str]=2,  lowerCamelCase						:   Union[str, Any]=3,  lowerCamelCase						:   Tuple=16,  lowerCamelCase						:   int=[1, 2, 1],  lowerCamelCase						:   List[Any]=[2, 2, 4],  lowerCamelCase						:   Union[str, Any]=2,  lowerCamelCase						:   str=2.0,  lowerCamelCase						:   Dict=True,  lowerCamelCase						:   Dict=0.0,  lowerCamelCase						:   Optional[int]=0.0,  lowerCamelCase						:   Tuple=0.1,  lowerCamelCase						:   Dict="gelu",  lowerCamelCase						:   Any=False,  lowerCamelCase						:   Any=True,  lowerCamelCase						:   Tuple=0.02,  lowerCamelCase						:   int=1E-5,  lowerCamelCase						:   Tuple=True,  lowerCamelCase						:   int=None,  lowerCamelCase						:   List[Any]=True,  lowerCamelCase						:   Optional[int]=10,  lowerCamelCase						:   int=8,  ):

								'''simple docstring'''
								lowercase__       			=      parent
								lowercase__       			=      batch_size
								lowercase__       			=      image_size
								lowercase__       			=      patch_size
								lowercase__       			=      num_channels
								lowercase__       			=      embed_dim
								lowercase__       			=      depths
								lowercase__       			=      num_heads
								lowercase__       			=      window_size
								lowercase__       			=      mlp_ratio
								lowercase__       			=      qkv_bias
								lowercase__       			=      hidden_dropout_prob
								lowercase__       			=      attention_probs_dropout_prob
								lowercase__       			=      drop_path_rate
								lowercase__       			=      hidden_act
								lowercase__       			=      use_absolute_embeddings
								lowercase__       			=      patch_norm
								lowercase__       			=      layer_norm_eps
								lowercase__       			=      initializer_range
								lowercase__       			=      is_training
								lowercase__       			=      scope
								lowercase__       			=      use_labels
								lowercase__       			=      type_sequence_label_size
								lowercase__       			=      encoder_stride




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

								'''simple docstring'''
								lowercase__       			=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

								lowercase__       			=      None
								if self.use_labels:
												lowercase__       			=      ids_tensor([self.batch_size],  self.type_sequence_label_size  )

								lowercase__       			=      self.get_config()

								return config, pixel_values, labels




				def     lowercase__       (					self						:   Any  ):

								'''simple docstring'''
								return SwinConfig(
								    image_size=self.image_size,  patch_size=self.patch_size,  num_channels=self.num_channels,  embed_dim=self.embed_dim,  depths=self.depths,  num_heads=self.num_heads,  window_size=self.window_size,  mlp_ratio=self.mlp_ratio,  qkv_bias=self.qkv_bias,  hidden_dropout_prob=self.hidden_dropout_prob,  attention_probs_dropout_prob=self.attention_probs_dropout_prob,  drop_path_rate=self.drop_path_rate,  hidden_act=self.hidden_act,  use_absolute_embeddings=self.use_absolute_embeddings,  path_norm=self.patch_norm,  layer_norm_eps=self.layer_norm_eps,  initializer_range=self.initializer_range,  encoder_stride=self.encoder_stride,  )




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   Tuple,  lowerCamelCase						:   str,  lowerCamelCase						:   int  ):

								'''simple docstring'''
								lowercase__       			=      TFSwinModel(config=lowerCamelCase  )
								lowercase__       			=      model(lowerCamelCase  )

								lowercase__       			=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
								lowercase__       			=      int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, expected_seq_len, expected_dim)  )




				def     lowercase__       (					self						:   List[str],  lowerCamelCase						:   Dict,  lowerCamelCase						:   List[Any],  lowerCamelCase						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      TFSwinForMaskedImageModeling(config=lowerCamelCase  )
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(
								    result.logits.shape,  (self.batch_size, self.num_channels, self.image_size, self.image_size)  )

								# test greyscale images
								lowercase__       			=      1
								lowercase__       			=      TFSwinForMaskedImageModeling(lowerCamelCase  )

								lowercase__       			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, 1, self.image_size, self.image_size)  )




				def     lowercase__       (					self						:   Tuple,  lowerCamelCase						:   Any,  lowerCamelCase						:   Any,  lowerCamelCase						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.type_sequence_label_size
								lowercase__       			=      TFSwinForImageClassification(lowerCamelCase  )
								lowercase__       			=      model(lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.type_sequence_label_size)  )

								# test greyscale images
								lowercase__       			=      1
								lowercase__       			=      TFSwinForImageClassification(lowerCamelCase  )
								lowercase__       			=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
								lowercase__       			=      model(lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.type_sequence_label_size)  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__       			=      self.prepare_config_and_inputs()
								lowercase__			,						lowercase__			,						lowercase__       			=      config_and_inputs
								lowercase__       			=      {'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_tf
class 				_UpperCAmelCase (	A__		,A__		,unittest.TestCase     ):

				"""simple docstring"""




				lowercase__    							=	(
				    (
				        TFSwinModel,
				        TFSwinForImageClassification,
				        TFSwinForMaskedImageModeling,
				    )
				    if is_tf_available()
				    else ()
				)
				lowercase__    							=	(
				    {"""feature-extraction""": TFSwinModel, """image-classification""": TFSwinForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False
				lowercase__    							=	False




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      TFSwinModelTester(self  )
								lowercase__       			=      ConfigTester(self,  config_class=lowerCamelCase,  embed_dim=37  )




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								self.create_and_test_config_common_properties()
								self.config_tester.create_and_test_config_to_json_string()
								self.config_tester.create_and_test_config_to_json_file()
								self.config_tester.create_and_test_config_from_and_save_pretrained()
								self.config_tester.create_and_test_config_with_num_labels()
								self.config_tester.check_config_can_be_init_without_params()
								self.config_tester.check_config_arguments_init()




				def     lowercase__       (					self						:   Dict  ):

								'''simple docstring'''
								return




				def     lowercase__       (					self						:   str  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase  )




				def     lowercase__       (					self						:   Optional[Any]  ):

								'''simple docstring'''
								lowercase__       			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase  )




				@unittest.skip(reason='''Swin does not use inputs_embeds'''  )
				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								pass




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__       			=      model_class(lowerCamelCase  )
												self.assertIsInstance(model.get_input_embeddings(),  tf.keras.layers.Layer  )
												lowercase__       			=      model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(lowerCamelCase,  tf.keras.layers.Dense  )  )




				def     lowercase__       (					self						:   Optional[int]  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												lowercase__       			=      model_class(lowerCamelCase  )
												lowercase__       			=      inspect.signature(model.call  )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												lowercase__       			=      [*signature.parameters.keys()]

												lowercase__       			=      ['''pixel_values''']
												self.assertListEqual(arg_names[:1],  lowerCamelCase  )




				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__       			=      True

								for model_class in self.all_model_classes:
												lowercase__       			=      True
												lowercase__       			=      False
												lowercase__       			=      True
												lowercase__       			=      model_class(lowerCamelCase  )
												lowercase__       			=      model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )
												lowercase__       			=      outputs.attentions
												lowercase__       			=      len(self.model_tester.depths  )
												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												lowercase__       			=      True
												lowercase__       			=      config.window_size**2
												lowercase__       			=      model_class(lowerCamelCase  )
												lowercase__       			=      model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )
												lowercase__       			=      outputs.attentions
												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]  ),  [self.model_tester.num_heads[0], window_size_squared, window_size_squared],  )
												lowercase__       			=      len(lowerCamelCase  )

												# Check attention is always last and order is fine
												lowercase__       			=      True
												lowercase__       			=      True
												lowercase__       			=      model_class(lowerCamelCase  )
												lowercase__       			=      model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )

												if hasattr(self.model_tester,  '''num_hidden_states_types'''  ):
																lowercase__       			=      self.model_tester.num_hidden_states_types
												else:
																# also another +1 for reshaped_hidden_states
																lowercase__       			=      2
												self.assertEqual(out_len + added_hidden_states,  len(lowerCamelCase  )  )

												lowercase__       			=      outputs.attentions

												self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]  ),  [self.model_tester.num_heads[0], window_size_squared, window_size_squared],  )




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

								'''simple docstring'''
								lowercase__       			=      model_class(lowerCamelCase  )
								lowercase__       			=      model(**self._prepare_for_class(lowerCamelCase,  lowerCamelCase  )  )
								lowercase__       			=      outputs.hidden_states

								lowercase__       			=      getattr(
								    self.model_tester,  '''expected_num_hidden_layers''',  len(self.model_tester.depths  ) + 1  )
								self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

								# Swin has a different seq_length
								lowercase__       			=      to_atuple(config.patch_size  )

								lowercase__       			=      (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

								self.assertListEqual(
								    list(hidden_states[0].shape[-2:]  ),  [num_patches, self.model_tester.embed_dim],  )

								lowercase__       			=      outputs.reshaped_hidden_states
								self.assertEqual(len(lowerCamelCase  ),  lowerCamelCase  )

								lowercase__			,						lowercase__			,						lowercase__			,						lowercase__       			=      reshaped_hidden_states[0].shape

								lowercase__       			=      tf.reshape(reshaped_hidden_states[0],  (batch_size, num_channels, height * width)  )
								lowercase__       			=      tf.transpose(lowerCamelCase,  (0, 2, 1)  )

								self.assertListEqual(
								    list(reshaped_hidden_states.shape[-2:]  ),  [num_patches, self.model_tester.embed_dim],  )




				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()

								lowercase__       			=      to_atuple(self.model_tester.image_size  )

								for model_class in self.all_model_classes:
												lowercase__       			=      True
												self.check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowercase__       			=      True

												self.check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase  )




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

								'''simple docstring'''
								lowercase__			,						lowercase__       			=      self.model_tester.prepare_config_and_inputs_for_common()
								lowercase__       			=      3

								lowercase__       			=      to_atuple(self.model_tester.image_size  )
								lowercase__       			=      to_atuple(config.patch_size  )

								lowercase__       			=      image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
								lowercase__       			=      image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

								for model_class in self.all_model_classes:
												lowercase__       			=      True
												self.check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  (padded_height, padded_width)  )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												lowercase__       			=      True
												self.check_hidden_states_output(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  (padded_height, padded_width)  )




				@slow
				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__       			=      TFSwinModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )



@require_vision
@require_tf
class 				_UpperCAmelCase (	unittest.TestCase     ):

				"""simple docstring"""




				@cached_property
				def     lowercase__       (					self						:   List[str]  ):

								'''simple docstring'''
								return (
								    AutoImageProcessor.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''  )
								    if is_vision_available()
								    else None
								)




				@slow
				def     lowercase__       (					self						:   Tuple  ):

								'''simple docstring'''
								lowercase__       			=      TFSwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''  )
								lowercase__       			=      self.default_image_processor

								lowercase__       			=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
								lowercase__       			=      image_processor(images=lowerCamelCase,  return_tensors='''tf'''  )

								# forward pass
								lowercase__       			=      model(lowerCamelCase  )

								# verify the logits
								lowercase__       			=      tf.TensorShape((1, 1_000)  )
								self.assertEqual(outputs.logits.shape,  lowerCamelCase  )
								lowercase__       			=      tf.constant([-0.0948, -0.6454, -0.0921]  )
								self.assertTrue(np.allclose(outputs.logits[0, :3],  lowerCamelCase,  atol=1E-4  )  )












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




