
from typing import Dict, Optional

import numpy as np

import datasets


__a         =   '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,\nthe mean IoU of the image is calculated by taking the IoU of each class and averaging them.\n'

__a         =   '\nArgs:\n    predictions (`List[ndarray]`):\n        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    references (`List[ndarray]`):\n        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.\n    num_labels (`int`):\n        Number of classes (categories).\n    ignore_index (`int`):\n        Index that will be ignored during evaluation.\n    nan_to_num (`int`, *optional*):\n        If specified, NaN values will be replaced by the number defined by the user.\n    label_map (`dict`, *optional*):\n        If specified, dictionary mapping old label indices to new label indices.\n    reduce_labels (`bool`, *optional*, defaults to `False`):\n        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,\n        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.\n\nReturns:\n    `Dict[str, float | ndarray]` comprising various elements:\n    - *mean_iou* (`float`):\n        Mean Intersection-over-Union (IoU averaged over all categories).\n    - *mean_accuracy* (`float`):\n        Mean accuracy (averaged over all categories).\n    - *overall_accuracy* (`float`):\n        Overall accuracy on all images.\n    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):\n        Per category accuracy.\n    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):\n        Per category IoU.\n\nExamples:\n\n    >>> import numpy as np\n\n    >>> mean_iou = datasets.load_metric("mean_iou")\n\n    >>> # suppose one has 3 different segmentation maps predicted\n    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])\n    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])\n\n    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])\n    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])\n\n    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])\n    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])\n\n    >>> predicted = [predicted_1, predicted_2, predicted_3]\n    >>> ground_truth = [actual_1, actual_2, actual_3]\n\n    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mean_iou\': 0.47750000000000004, \'mean_accuracy\': 0.5916666666666666, \'overall_accuracy\': 0.5263157894736842, \'per_category_iou\': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), \'per_category_accuracy\': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}\n'

__a         =   '\\n@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,\nauthor = {{MMSegmentation Contributors}},\nlicense = {Apache-2.0},\nmonth = {7},\ntitle = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},\nurl = {https://github.com/open-mmlab/mmsegmentation},\nyear = {2020}\n}'




def 							a		(				snake_case__:   Optional[Any]		,  snake_case__:   str		,  snake_case__:   Optional[int]		,  snake_case__:   bool		,  snake_case__:   Optional[Dict[int, int]] = None		,  snake_case__:   bool = False		,  ):


				'''simple docstring'''
				if label_map is not None:
								for old_id, new_id in label_map.items():
												lowercase_	    =   new_id

    # turn into Numpy arrays
				lowercase_	    =   np.array(snake_case__   )
				lowercase_	    =   np.array(snake_case__   )

				if reduce_labels:
								lowercase_	    =   255
								lowercase_	    =   label - 1
								lowercase_	    =   255

				lowercase_	    =   label != ignore_index
				lowercase_	    =   np.not_equal(snake_case__		,  snake_case__   )
				lowercase_	    =   pred_label[mask]
				lowercase_	    =   np.array(snake_case__   )[mask]

				lowercase_	    =   pred_label[pred_label == label]

				lowercase_	    =   np.histogram(snake_case__		,  bins=snake_case__		,  range=(0, num_labels - 1)   )[0]
				lowercase_	    =   np.histogram(snake_case__		,  bins=snake_case__		,  range=(0, num_labels - 1)   )[0]
				lowercase_	    =   np.histogram(snake_case__		,  bins=snake_case__		,  range=(0, num_labels - 1)   )[0]

				lowercase_	    =   area_pred_label + area_label - area_intersect

				return area_intersect, area_union, area_pred_label, area_label




def 							a		(				snake_case__:   int		,  snake_case__:   Optional[int]		,  snake_case__:   Any		,  snake_case__:   bool		,  snake_case__:   Optional[Dict[int, int]] = None		,  snake_case__:   bool = False		,  ):


				'''simple docstring'''
				lowercase_	    =   np.zeros((num_labels,)		,  dtype=np.floataa   )
				lowercase_	    =   np.zeros((num_labels,)		,  dtype=np.floataa   )
				lowercase_	    =   np.zeros((num_labels,)		,  dtype=np.floataa   )
				lowercase_	    =   np.zeros((num_labels,)		,  dtype=np.floataa   )
				for result, gt_seg_map in zip(snake_case__		,  snake_case__   ):
								lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   intersect_and_union(
								    snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )
								total_area_intersect += area_intersect
								total_area_union += area_union
								total_area_pred_label += area_pred_label
								total_area_label += area_label
				return total_area_intersect, total_area_union, total_area_pred_label, total_area_label




def 							a		(				snake_case__:   Union[str, Any]		,  snake_case__:   Any		,  snake_case__:   List[Any]		,  snake_case__:   bool		,  snake_case__:   Optional[int] = None		,  snake_case__:   Optional[Dict[int, int]] = None		,  snake_case__:   bool = False		,  ):


				'''simple docstring'''
				lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   total_intersect_and_union(
				    snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__		,  snake_case__   )

				# compute metrics
				lowercase_	    =   {}

				lowercase_	    =   total_area_intersect.sum() / total_area_label.sum()
				lowercase_	    =   total_area_intersect / total_area_union
				lowercase_	    =   total_area_intersect / total_area_label

				lowercase_	    =   np.nanmean(snake_case__   )
				lowercase_	    =   np.nanmean(snake_case__   )
				lowercase_	    =   all_acc
				lowercase_	    =   iou
				lowercase_	    =   acc

				if nan_to_num is not None:
								lowercase_	    =   {metric: np.nan_to_num(snake_case__		,  nan=snake_case__   ) for metric, metric_value in metrics.items()}

				return metrics


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,		_KWARGS_DESCRIPTION				)
class 					lowercase__(							datasets.Metric				):



	"""simple docstring"""




	def 		_lowercase    (			self    :     str			)			->	Optional[int]:
					return datasets.MetricInfo(
					    description=_DESCRIPTION			,				citation=_CITATION			,				inputs_description=_KWARGS_DESCRIPTION			,				features=datasets.Features(
					        # 1st Seq - height dim, 2nd - width dim
					        {
					            '''predictions''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''			)			)			),
					            '''references''': datasets.Sequence(datasets.Sequence(datasets.Value('''uint16'''			)			)			),
					        }			)			,				reference_urls=[
					        '''https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'''
					    ]			,				)




	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     bool			,				SCREAMING_SNAKE_CASE_    :     Optional[int] = None			,				SCREAMING_SNAKE_CASE_    :     Optional[Dict[int, int]] = None			,				SCREAMING_SNAKE_CASE_    :     bool = False			,				)			->	str:
					lowercase_	    =   mean_iou(
					    results=SCREAMING_SNAKE_CASE_			,				gt_seg_maps=SCREAMING_SNAKE_CASE_			,				num_labels=SCREAMING_SNAKE_CASE_			,				ignore_index=SCREAMING_SNAKE_CASE_			,				nan_to_num=SCREAMING_SNAKE_CASE_			,				label_map=SCREAMING_SNAKE_CASE_			,				reduce_labels=SCREAMING_SNAKE_CASE_			,				)
					return iou_result






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFDebertaVaForMaskedLM,
						    TFDebertaVaForQuestionAnswering,
						    TFDebertaVaForSequenceClassification,
						    TFDebertaVaForTokenClassification,
						    TFDebertaVaModel,
						)
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     str=1_3			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=7			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=True			,				SCREAMING_SNAKE_CASE_    :     Tuple=9_9			,				SCREAMING_SNAKE_CASE_    :     List[str]=3_2			,				SCREAMING_SNAKE_CASE_    :     int=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=4			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=3_7			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="gelu"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=5_1_2			,				SCREAMING_SNAKE_CASE_    :     str=1_6			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=2			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0.02			,				SCREAMING_SNAKE_CASE_    :     List[Any]=False			,				SCREAMING_SNAKE_CASE_    :     List[Any]=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="None"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=3			,				SCREAMING_SNAKE_CASE_    :     List[Any]=4			,				SCREAMING_SNAKE_CASE_    :     str=None			,				)			->	str:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   seq_length
					lowercase_	    =   is_training
					lowercase_	    =   use_input_mask
					lowercase_	    =   use_token_type_ids
					lowercase_	    =   use_labels
					lowercase_	    =   vocab_size
					lowercase_	    =   hidden_size
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   num_attention_heads
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   hidden_dropout_prob
					lowercase_	    =   attention_probs_dropout_prob
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   type_vocab_size
					lowercase_	    =   type_sequence_label_size
					lowercase_	    =   initializer_range
					lowercase_	    =   num_labels
					lowercase_	    =   num_choices
					lowercase_	    =   relative_attention
					lowercase_	    =   position_biased_input
					lowercase_	    =   pos_att_type
					lowercase_	    =   scope
	def 		_lowercase    (			self    :     List[Any]			)			->	List[Any]:
					lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size			)

					lowercase_	    =   None
					if self.use_input_mask:
									lowercase_	    =   random_attention_mask([self.batch_size, self.seq_length]			)

					lowercase_	    =   None
					if self.use_token_type_ids:
									lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size			)

					lowercase_	    =   None
					lowercase_	    =   None
					lowercase_	    =   None
					if self.use_labels:
									lowercase_	    =   ids_tensor([self.batch_size]			,				self.type_sequence_label_size			)
									lowercase_	    =   ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels			)

					lowercase_	    =   DebertaVaConfig(
					    vocab_size=self.vocab_size			,				hidden_size=self.hidden_size			,				num_hidden_layers=self.num_hidden_layers			,				num_attention_heads=self.num_attention_heads			,				intermediate_size=self.intermediate_size			,				hidden_act=self.hidden_act			,				hidden_dropout_prob=self.hidden_dropout_prob			,				attention_probs_dropout_prob=self.attention_probs_dropout_prob			,				max_position_embeddings=self.max_position_embeddings			,				type_vocab_size=self.type_vocab_size			,				relative_attention=self.relative_attention			,				position_biased_input=self.position_biased_input			,				initializer_range=self.initializer_range			,				return_dict=SCREAMING_SNAKE_CASE_			,				)

					return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
	def 		_lowercase    (			self    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     List[Any]			)			->	List[Any]:
					lowercase_	    =   TFDebertaVaModel(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

					lowercase_	    =   [input_ids, input_mask]
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)

					self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.seq_length, self.hidden_size)			)
	def 		_lowercase    (			self    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	Union[str, Any]:
					lowercase_	    =   TFDebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size)			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	List[str]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   TFDebertaVaForSequenceClassification(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)
	def 		_lowercase    (			self    :     Optional[int]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	Optional[Any]:
					lowercase_	    =   self.num_labels
					lowercase_	    =   TFDebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.num_labels)			)
	def 		_lowercase    (			self    :     Union[str, Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Optional[int]			)			->	str:
					lowercase_	    =   TFDebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   {
					    '''input_ids''': input_ids,
					    '''attention_mask''': input_mask,
					    '''token_type_ids''': token_type_ids,
					}

					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			)
					self.parent.assertEqual(result.start_logits.shape			,				(self.batch_size, self.seq_length)			)
					self.parent.assertEqual(result.end_logits.shape			,				(self.batch_size, self.seq_length)			)




	def 		_lowercase    (			self    :     List[str]			)			->	Optional[int]:
					lowercase_	    =   self.prepare_config_and_inputs()
					(
					    (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      
					)	    =   config_and_inputs
					lowercase_	    =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
					return config, inputs_dict

@require_tf
class 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :str       	=      (
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	a     :int       	=      (
	    {
	        'feature-extraction': TFDebertaVaModel,
	        'fill-mask': TFDebertaVaForMaskedLM,
	        'question-answering': TFDebertaVaForQuestionAnswering,
	        'text-classification': TFDebertaVaForSequenceClassification,
	        'token-classification': TFDebertaVaForTokenClassification,
	        'zero-shot': TFDebertaVaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	a     :int       	=      False
	a     :Any       	=      False
	def 		_lowercase    (			self    :     Dict			)			->	str:
					lowercase_	    =   TFDebertaVaModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				hidden_size=3_7			)
	def 		_lowercase    (			self    :     Tuple			)			->	Dict:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Dict			)			->	List[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Optional[int]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Any			)			->	str:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Optional[Any]:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_			)




	@slow
	def 		_lowercase    (			self    :     Tuple			)			->	Dict:
					lowercase_	    =   TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''			)
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)

@require_tf
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@unittest.skip(reason='''Model not available yet'''			)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Union[str, Any]:
					pass




	@slow
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					lowercase_	    =   TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''			)
					lowercase_	    =   tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]			)
					lowercase_	    =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]			)
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			)[0]

					lowercase_	    =   tf.constant(
					    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]			)
					tf.debugging.assert_near(output[:, 1:4, 1:4]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-4			)




