




UpperCAmelCase_    ={
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

UpperCAmelCase_    =[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

UpperCAmelCase_    =[0 for x in range(len(graph))]
UpperCAmelCase_    =[]



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							lowerCAmelCase =		1
							while stack:
														lowerCAmelCase =		stack.pop()
														print(_snake_case    ,			clothes[current_clothing]				)
														order += 1



def    UpperCAmelCase							(  _snake_case    ,			_snake_case    ,			_snake_case				):
							lowerCAmelCase =		1
							for v in graph[u]:
														if not visited[v]:
																					depth_first_search(_snake_case    ,			_snake_case    ,			_snake_case				)

							stack.append(_snake_case				)



def    UpperCAmelCase							(  _snake_case    ,			_snake_case				):
							for v in range(len(_snake_case				)				):
														if not visited[v]:
																					depth_first_search(_snake_case    ,			_snake_case    ,			_snake_case				)


if __name__ == "__main__":
		topological_sort(graph, visited)
		print(stack)
		print_stack(stack, clothes)









from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
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 import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
		from PIL import Image

		from transformers import ConvNextImageProcessor


class 				__UpperCamelCase      :





				'''simple docstring'''



				def __init__(			self							,			UpperCAmelCase_							,			UpperCAmelCase_=13							,			UpperCAmelCase_=32							,			UpperCAmelCase_=3							,			UpperCAmelCase_=4							,			UpperCAmelCase_=[10, 20, 30, 40]							,			UpperCAmelCase_=[2, 2, 3, 2]							,			UpperCAmelCase_=True							,			UpperCAmelCase_=True							,			UpperCAmelCase_=37							,			UpperCAmelCase_="gelu"							,			UpperCAmelCase_=10							,			UpperCAmelCase_=0.02							,			UpperCAmelCase_=3							,			UpperCAmelCase_=None							,			):
											lowerCAmelCase =		parent
											lowerCAmelCase =		batch_size
											lowerCAmelCase =		image_size
											lowerCAmelCase =		num_channels
											lowerCAmelCase =		num_stages
											lowerCAmelCase =		hidden_sizes
											lowerCAmelCase =		depths
											lowerCAmelCase =		is_training
											lowerCAmelCase =		use_labels
											lowerCAmelCase =		intermediate_size
											lowerCAmelCase =		hidden_act
											lowerCAmelCase =		type_sequence_label_size
											lowerCAmelCase =		initializer_range
											lowerCAmelCase =		scope



				def    __snake_case		(			self    ):
											lowerCAmelCase =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

											lowerCAmelCase =		self.get_config()

											return config, pixel_values, labels



				def    __snake_case		(			self    ):
											return ConvNextConfig(
											    num_channels=self.num_channels							,			hidden_sizes=self.hidden_sizes							,			depths=self.depths							,			num_stages=self.num_stages							,			hidden_act=self.hidden_act							,			is_decoder=UpperCAmelCase_							,			initializer_range=self.initializer_range							,			)



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		TFConvNextModel(config=UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			training=UpperCAmelCase_    )
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape							,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,			)



				def    __snake_case		(			self							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
											lowerCAmelCase =		self.type_sequence_label_size
											lowerCAmelCase =		TFConvNextForImageClassification(UpperCAmelCase_    )
											lowerCAmelCase =		model(UpperCAmelCase_							,			labels=UpperCAmelCase_							,			training=UpperCAmelCase_    )
											self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.type_sequence_label_size)    )






				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.prepare_config_and_inputs()
											lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase =		config_and_inputs
											lowerCAmelCase =		{'''pixel_values''': pixel_values}
											return config, inputs_dict




@require_tf
class 				__UpperCamelCase      (			__UpperCAmelCase  ,       __UpperCAmelCase  ,       unittest.TestCase	):





				'''simple docstring'''



				__a				:			Union[str, Any]					    =(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
				__a				:			Optional[int]					    =(
				    {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
				    if is_tf_available()
				    else {}
				)

				__a				:			Any					    =False
				__a				:			int					    =False
				__a				:			Union[str, Any]					    =False
				__a				:			Optional[Any]					    =False
				__a				:			List[Any]					    =False



				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModelTester(self    )
											lowerCAmelCase =		ConfigTester(
											    self							,			config_class=UpperCAmelCase_							,			has_text_modality=UpperCAmelCase_							,			hidden_size=37							,			)



				@unittest.skip(reason='''ConvNext does not use inputs_embeds'''    )
				def    __snake_case		(			self    ):
											pass



				@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    __snake_case		(			self    ):
											super().test_keras_fit()



				@unittest.skip(reason='''ConvNext does not support input and output embeddings'''    )
				def    __snake_case		(			self    ):
											pass



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

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

																		lowerCAmelCase =		['''pixel_values''']
																		self.assertListEqual(arg_names[:1]							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*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    __snake_case		(			self    ):
											super().test_dataset_conversion()



				def    __snake_case		(			self    ):
											def check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    ):
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		model(**self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )    )
																		lowerCAmelCase =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		lowerCAmelCase =		self.model_tester.num_stages
																		self.assertEqual(len(UpperCAmelCase_    )							,			expected_num_stages + 1    )

																		# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]    )							,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,			)

											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		True
																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

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

																		check_hidden_states_output(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )



				def    __snake_case		(			self    ):
											lowerCAmelCase ,  lowerCAmelCase =		self.model_tester.prepare_config_and_inputs_for_common()

											def check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_={}    ):
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    )
																		lowerCAmelCase =		model(UpperCAmelCase_							,			return_dict=UpperCAmelCase_							,			**UpperCAmelCase_    ).to_tuple()

																		def recursive_check(UpperCAmelCase_							,			UpperCAmelCase_    ):
																									if isinstance(UpperCAmelCase_							,			(List, Tuple)    ):
																																for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase_							,			UpperCAmelCase_    ):
																																							recursive_check(UpperCAmelCase_							,			UpperCAmelCase_    )
																									elif tuple_object is None:
																																return
																									else:
																																self.assertTrue(
																																    all(tf.equal(UpperCAmelCase_							,			UpperCAmelCase_    )    )							,			msg=(
																																        '''Tuple and dict output are not equal. Difference:'''
																																        F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object    )    )}"""
																																    )							,			)

																									recursive_check(UpperCAmelCase_							,			UpperCAmelCase_    )

											for model_class in self.all_model_classes:
																		lowerCAmelCase =		model_class(UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )

																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		lowerCAmelCase =		self._prepare_for_class(UpperCAmelCase_							,			UpperCAmelCase_							,			return_labels=UpperCAmelCase_    )
																		check_equivalence(UpperCAmelCase_							,			UpperCAmelCase_							,			UpperCAmelCase_							,			{'''output_hidden_states''': True}    )



				def    __snake_case		(			self    ):
											lowerCAmelCase =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_    )






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextModel.from_pretrained('''facebook/convnext-tiny-224'''    )
											self.assertIsNotNone(UpperCAmelCase_    )




def    UpperCAmelCase							(  ):
							lowerCAmelCase =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
							return image


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





				'''simple docstring'''



				@cached_property
				def    __snake_case		(			self    ):
											return ConvNextImageProcessor.from_pretrained('''facebook/convnext-tiny-224'''    ) if is_vision_available() else None






				@slow
				def    __snake_case		(			self    ):
											lowerCAmelCase =		TFConvNextForImageClassification.from_pretrained('''facebook/convnext-tiny-224'''    )

											lowerCAmelCase =		self.default_image_processor
											lowerCAmelCase =		prepare_img()
											lowerCAmelCase =		image_processor(images=UpperCAmelCase_							,			return_tensors='''tf'''    )

											# forward pass
											lowerCAmelCase =		model(**UpperCAmelCase_    )

											# verify the logits
											lowerCAmelCase =		tf.TensorShape((1, 10_00)    )
											self.assertEqual(outputs.logits.shape							,			UpperCAmelCase_    )

											lowerCAmelCase =		tf.constant([-0.0260, -0.4739, 0.1911]    )

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



