




from __future__ import annotations

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.file_utils import is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, slow

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 numpy as np
	import tensorflow as tf

	from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel
	from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import MobileViTImageProcessor
class 				__snake_case     (_a						):



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					self.config_class(**self.inputs_dict					)
								self.parent.assertTrue(hasattr(_UpperCAmelCase						,		"""hidden_sizes"""					)					)
								self.parent.assertTrue(hasattr(_UpperCAmelCase						,		"""neck_hidden_sizes"""					)					)
								self.parent.assertTrue(hasattr(_UpperCAmelCase						,		"""num_attention_heads"""					)					)






class 				__snake_case     :



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						List[Any]=13						,		_UpperCAmelCase					:						List[str]=32						,		_UpperCAmelCase					:						int=2						,		_UpperCAmelCase					:						Tuple=3						,		_UpperCAmelCase					:						List[str]=640						,		_UpperCAmelCase					:						List[str]=4						,		_UpperCAmelCase					:						List[str]="silu"						,		_UpperCAmelCase					:						Dict=3						,		_UpperCAmelCase					:						Any=32						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						int=0.1						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.02						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						Tuple=True						,		_UpperCAmelCase					:						Optional[Any]=10						,		_UpperCAmelCase					:						List[str]=None						,		)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					parent
								_lowerCAmelCase			:			Optional[int]        =					batch_size
								_lowerCAmelCase			:			Optional[Any]        =					image_size
								_lowerCAmelCase			:			Union[str, Any]        =					patch_size
								_lowerCAmelCase			:			Optional[Any]        =					num_channels
								_lowerCAmelCase			:			str        =					last_hidden_size
								_lowerCAmelCase			:			Dict        =					num_attention_heads
								_lowerCAmelCase			:			Optional[Any]        =					hidden_act
								_lowerCAmelCase			:			Optional[int]        =					conv_kernel_size
								_lowerCAmelCase			:			List[str]        =					output_stride
								_lowerCAmelCase			:			Optional[int]        =					hidden_dropout_prob
								_lowerCAmelCase			:			List[Any]        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			Union[str, Any]        =					classifier_dropout_prob
								_lowerCAmelCase			:			List[str]        =					use_labels
								_lowerCAmelCase			:			Tuple        =					is_training
								_lowerCAmelCase			:			List[str]        =					num_labels
								_lowerCAmelCase			:			List[Any]        =					initializer_range
								_lowerCAmelCase			:			List[str]        =					scope



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								_lowerCAmelCase			:			Union[str, Any]        =					None
								_lowerCAmelCase			:			Optional[Any]        =					None
								if self.use_labels:
												_lowerCAmelCase			:			Union[str, Any]        =					ids_tensor([self.batch_size]						,		self.num_labels					)
												_lowerCAmelCase			:			List[Any]        =					ids_tensor([self.batch_size, self.image_size, self.image_size]						,		self.num_labels					)

								_lowerCAmelCase			:			List[str]        =					self.get_config()

								return config, pixel_values, labels, pixel_labels



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]					)     ->			Any:





								'''simple docstring'''




								return MobileViTConfig(
								    image_size=self.image_size						,		patch_size=self.patch_size						,		num_channels=self.num_channels						,		num_attention_heads=self.num_attention_heads						,		hidden_act=self.hidden_act						,		conv_kernel_size=self.conv_kernel_size						,		output_stride=self.output_stride						,		hidden_dropout_prob=self.hidden_dropout_prob						,		attention_probs_dropout_prob=self.attention_probs_dropout_prob						,		classifier_dropout_prob=self.classifier_dropout_prob						,		initializer_range=self.initializer_range						,		)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						Optional[int]						,		_UpperCAmelCase					:						str						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						Dict					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					TFMobileViTModel(config=_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					model(_UpperCAmelCase						,		training=_UpperCAmelCase					)
								_lowerCAmelCase			:			Any        =					self.image_size // self.output_stride
								self.parent.assertEqual(
								    result.last_hidden_state.shape						,		(self.batch_size, self.last_hidden_size, expected_height, expected_width)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]						,		_UpperCAmelCase					:						Any						,		_UpperCAmelCase					:						Optional[Any]						,		_UpperCAmelCase					:						Tuple						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					self.num_labels
								_lowerCAmelCase			:			int        =					TFMobileViTForImageClassification(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					model(_UpperCAmelCase						,		labels=_UpperCAmelCase						,		training=_UpperCAmelCase					)
								self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Optional[Any]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						Dict						,		_UpperCAmelCase					:						Optional[Any]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					self.num_labels
								_lowerCAmelCase			:			List[str]        =					TFMobileViTForSemanticSegmentation(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					self.image_size // self.output_stride

								_lowerCAmelCase			:			str        =					model(_UpperCAmelCase						,		training=_UpperCAmelCase					)
								self.parent.assertEqual(
								    result.logits.shape						,		(self.batch_size, self.num_labels, expected_height, expected_width)					)

								_lowerCAmelCase			:			Optional[Any]        =					model(_UpperCAmelCase						,		labels=_UpperCAmelCase						,		training=_UpperCAmelCase					)
								self.parent.assertEqual(
								    result.logits.shape						,		(self.batch_size, self.num_labels, expected_height, expected_width)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					self.prepare_config_and_inputs()
								_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[int]        =					config_and_inputs
								_lowerCAmelCase			:			List[Any]        =					{"""pixel_values""": pixel_values}
								return config, inputs_dict






@require_tf
class 				__snake_case     (_a  ,		_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   (
				    (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation)
				    if is_tf_available()
				    else ()
				)
				lowerCAmelCase__			=   (
				    {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification}
				    if is_tf_available()
				    else {}
				)

				lowerCAmelCase__			=   False
				lowerCAmelCase__			=   False
				lowerCAmelCase__			=   False
				lowerCAmelCase__			=   False
				lowerCAmelCase__			=   False



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					TFMobileViTModelTester(self					)
								_lowerCAmelCase			:			Union[str, Any]        =					TFMobileViTConfigTester(self						,		config_class=_UpperCAmelCase						,		has_text_modality=_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Tuple:





								'''simple docstring'''




								self.config_tester.run_common_tests()



				@unittest.skip(reason="""MobileViT does not use inputs_embeds"""					)
				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Any:





								'''simple docstring'''




								pass



				@unittest.skip(reason="""MobileViT does not support input and output embeddings"""					)
				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Optional[int]:





								'''simple docstring'''




								pass



				@unittest.skip(reason="""MobileViT does not output attentions"""					)
				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Optional[Any]:





								'''simple docstring'''




								pass



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[str]        =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCAmelCase			:			Optional[int]        =					model_class(_UpperCAmelCase					)
												_lowerCAmelCase			:			Tuple        =					inspect.signature(model.call					)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												_lowerCAmelCase			:			List[str]        =					[*signature.parameters.keys()]

												_lowerCAmelCase			:			List[str]        =					["""pixel_values"""]
												self.assertListEqual(arg_names[:1]						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple					)     ->			Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			int        =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								def check_hidden_states_output(_UpperCAmelCase					:						Dict						,		_UpperCAmelCase					:						Any						,		_UpperCAmelCase					:						Any					):
												_lowerCAmelCase			:			List[Any]        =					model_class(_UpperCAmelCase					)

												_lowerCAmelCase			:			List[str]        =					model(**self._prepare_for_class(_UpperCAmelCase						,		_UpperCAmelCase					)					)

												_lowerCAmelCase			:			Optional[int]        =					outputs.hidden_states

												_lowerCAmelCase			:			int        =					5
												self.assertEqual(len(_UpperCAmelCase					)						,		_UpperCAmelCase					)

												# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
												# with the width and height being successively divided by 2.
												_lowerCAmelCase			:			List[str]        =					2
												for i in range(len(_UpperCAmelCase					)					):
																self.assertListEqual(
																    list(hidden_states[i].shape[-2:]					)						,		[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]						,		)
																divisor *= 2

												self.assertEqual(self.model_tester.output_stride						,		divisor // 2					)

								_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												_lowerCAmelCase			:			int        =					True
												check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												_lowerCAmelCase			:			Dict        =					True

												check_hidden_states_output(_UpperCAmelCase						,		_UpperCAmelCase						,		_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			str:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase					)



				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0						,		reason="""TF does not support backprop for grouped convolutions on CPU."""						,		)
				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			str:





								'''simple docstring'''




								super().test_dataset_conversion()



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						List[str]						,		_UpperCAmelCase					:						Dict=2E-1						,		_UpperCAmelCase					:						List[str]=2E-1					)     ->			Optional[int]:





								'''simple docstring'''




								self.assertTrue(np.allclose(_UpperCAmelCase						,		_UpperCAmelCase						,		atol=_UpperCAmelCase						,		rtol=_UpperCAmelCase					)					)



				@unittest.skipIf(
				    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""					)					) == 0						,		reason="""TF does not support backprop for grouped convolutions on CPU."""						,		)
				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												# Since `TFMobileViTModel` cannot operate with the default `fit()` method.
												if model_class.__name__ != "TFMobileViTModel":
																_lowerCAmelCase			:			Dict        =					model_class(_UpperCAmelCase					)
																if getattr(_UpperCAmelCase						,		"""hf_compute_loss"""						,		_UpperCAmelCase					):
																				super().test_keras_fit()



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			Union[str, Any]        =					self.model_tester.prepare_config_and_inputs_for_common()
								for model_class in self.all_model_classes:
												# set an ignore index to correctly test the masked loss used in
												# `TFMobileViTForSemanticSegmentation`.
												if model_class.__name__ != "TFMobileViTForSemanticSegmentation":
																_lowerCAmelCase			:			Union[str, Any]        =					5

												_lowerCAmelCase			:			int        =					model_class(_UpperCAmelCase					)
												if getattr(_UpperCAmelCase						,		"""hf_compute_loss"""						,		_UpperCAmelCase					):
																# The number of elements in the loss should be the same as the number of elements in the label
																_lowerCAmelCase			:			Optional[int]        =					self._prepare_for_class(inputs_dict.copy()						,		_UpperCAmelCase						,		return_labels=_UpperCAmelCase					)
																_lowerCAmelCase			:			Union[str, Any]        =					prepared_for_class[
																    sorted(prepared_for_class.keys() - inputs_dict.keys()						,		reverse=_UpperCAmelCase					)[0]
																]
																_lowerCAmelCase			:			List[str]        =					added_label.shape.as_list()[:1]

																# Test that model correctly compute the loss with kwargs
																_lowerCAmelCase			:			str        =					self._prepare_for_class(inputs_dict.copy()						,		_UpperCAmelCase						,		return_labels=_UpperCAmelCase					)
																_lowerCAmelCase			:			Union[str, Any]        =					{"""input_ids""", """pixel_values""", """input_features"""}
																_lowerCAmelCase			:			Dict        =					possible_input_names.intersection(set(_UpperCAmelCase					)					).pop()
																_lowerCAmelCase			:			Any        =					prepared_for_class.pop(_UpperCAmelCase					)

																_lowerCAmelCase			:			Optional[Any]        =					model(_UpperCAmelCase						,		**_UpperCAmelCase					)[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

																# Test that model correctly compute the loss when we mask some positions
																_lowerCAmelCase			:			Optional[Any]        =					self._prepare_for_class(inputs_dict.copy()						,		_UpperCAmelCase						,		return_labels=_UpperCAmelCase					)
																_lowerCAmelCase			:			Union[str, Any]        =					{"""input_ids""", """pixel_values""", """input_features"""}
																_lowerCAmelCase			:			List[str]        =					possible_input_names.intersection(set(_UpperCAmelCase					)					).pop()
																_lowerCAmelCase			:			List[str]        =					prepared_for_class.pop(_UpperCAmelCase					)
																if "labels" in prepared_for_class:
																				_lowerCAmelCase			:			List[str]        =					prepared_for_class["""labels"""].numpy()
																				if len(labels.shape					) > 1 and labels.shape[1] != 1:
																								# labels[0] = -100
																								_lowerCAmelCase			:			Optional[Any]        =					tf.convert_to_tensor(_UpperCAmelCase					)
																								_lowerCAmelCase			:			List[Any]        =					model(_UpperCAmelCase						,		**_UpperCAmelCase					)[0]
																								self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)
																								self.assertTrue(not np.any(np.isnan(loss.numpy()					)					)					)

                # Test that model correctly compute the loss with a dict
																_lowerCAmelCase			:			int        =					self._prepare_for_class(inputs_dict.copy()						,		_UpperCAmelCase						,		return_labels=_UpperCAmelCase					)
																_lowerCAmelCase			:			List[Any]        =					model(_UpperCAmelCase					)[0]
																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)

																# Test that model correctly compute the loss with a tuple
																_lowerCAmelCase			:			Any        =					self._prepare_for_class(inputs_dict.copy()						,		_UpperCAmelCase						,		return_labels=_UpperCAmelCase					)

																# Get keys that were added with the _prepare_for_class function
																_lowerCAmelCase			:			Union[str, Any]        =					prepared_for_class.keys() - inputs_dict.keys()
																_lowerCAmelCase			:			Dict        =					inspect.signature(model.call					).parameters
																_lowerCAmelCase			:			str        =					list(signature.keys()					)

																# Create a dictionary holding the location of the tensors in the tuple
																_lowerCAmelCase			:			Dict        =					{0: input_name}
																for label_key in label_keys:
																				_lowerCAmelCase			:			List[str]        =					signature_names.index(_UpperCAmelCase					)
																				_lowerCAmelCase			:			int        =					label_key
																_lowerCAmelCase			:			int        =					sorted(tuple_index_mapping.items()					)
																# Initialize a list with their default values, update the values and convert to a tuple
																_lowerCAmelCase			:			List[Any]        =					[]

																for name in signature_names:
																				if name != "kwargs":
																								list_input.append(signature[name].default					)

																for index, value in sorted_tuple_index_mapping:
																				_lowerCAmelCase			:			Dict        =					prepared_for_class[value]

																_lowerCAmelCase			:			Optional[int]        =					tuple(_UpperCAmelCase					)

																# Send to model
																_lowerCAmelCase			:			List[Any]        =					model(tuple_input[:-1]					)[0]

																self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]					)



				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			int:





								'''simple docstring'''




								for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase			:			Dict        =					TFMobileViTModel.from_pretrained(_UpperCAmelCase					)
												self.assertIsNotNone(_UpperCAmelCase					)






def 							_UpperCAmelCase     ():
				'''simple docstring'''
				_lowerCAmelCase			:			Union[str, Any]        =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
				return image





@require_tf
class 				__snake_case     (unittest.TestCase						):



				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					TFMobileViTForImageClassification.from_pretrained("""apple/mobilevit-xx-small"""					)

								_lowerCAmelCase			:			Tuple        =					MobileViTImageProcessor.from_pretrained("""apple/mobilevit-xx-small"""					)
								_lowerCAmelCase			:			Optional[Any]        =					prepare_img()
								_lowerCAmelCase			:			Union[str, Any]        =					image_processor(images=_UpperCAmelCase						,		return_tensors="""tf"""					)

								# forward pass
								_lowerCAmelCase			:			List[str]        =					model(**_UpperCAmelCase						,		training=_UpperCAmelCase					)

								# verify the logits
								_lowerCAmelCase			:			str        =					tf.TensorShape((1, 1000)					)
								self.assertEqual(outputs.logits.shape						,		_UpperCAmelCase					)

								_lowerCAmelCase			:			List[str]        =					tf.constant([-1.9364, -1.2327, -0.4653]					)

								tf.debugging.assert_near(outputs.logits[0, :3]						,		_UpperCAmelCase						,		atol=1E-4						,		rtol=1E-04					)



				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					TFMobileViTForSemanticSegmentation.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)

								_lowerCAmelCase			:			Optional[Any]        =					MobileViTImageProcessor.from_pretrained("""apple/deeplabv3-mobilevit-xx-small"""					)

								_lowerCAmelCase			:			int        =					prepare_img()
								_lowerCAmelCase			:			Any        =					image_processor(images=_UpperCAmelCase						,		return_tensors="""tf"""					)

								# forward pass
								_lowerCAmelCase			:			List[Any]        =					model(inputs.pixel_values						,		training=_UpperCAmelCase					)
								_lowerCAmelCase			:			Tuple        =					outputs.logits

								# verify the logits
								_lowerCAmelCase			:			Optional[int]        =					tf.TensorShape((1, 21, 32, 32)					)
								self.assertEqual(logits.shape						,		_UpperCAmelCase					)

								_lowerCAmelCase			:			Union[str, Any]        =					tf.constant(
								    [
								        [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
								        [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
								        [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
								    ]					)

								tf.debugging.assert_near(logits[0, :3, :3, :3]						,		_UpperCAmelCase						,		rtol=1E-4						,		atol=1E-4					)







def 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Dict        =					len(UpperCamelCase_				)
				# Create a stack and push index of fist element to it
				_lowerCAmelCase			:			Tuple        =					[]
				st.append(0				)

				# Span value of first element is always 1
				_lowerCAmelCase			:			List[Any]        =					1

				# Calculate span values for rest of the elements
				for i in range(1					,			UpperCamelCase_				):
								# Pop elements from stack while stack is not
								# empty and top of stack is smaller than price[i]
								while len(UpperCamelCase_				) > 0 and price[st[0]] <= price[i]:
												st.pop()

								# If stack becomes empty, then price[i] is greater
								# than all elements on left of it, i.e. price[0],
								# price[1], ..price[i-1]. Else the price[i]  is
								# greater than elements after top of stack
								_lowerCAmelCase			:			Union[str, Any]        =					i + 1 if len(UpperCamelCase_				) <= 0 else (i - st[0])

								# Push this element to stack
								st.append(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				for i in range(0					,			UpperCamelCase_				):
								print(arr[i]					,			end=""" """				)


# Driver program to test above function
_lowerCamelCase					:     Union[str, Any]					  =  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
_lowerCamelCase					:     List[Any]					  =  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))

