



'''simple docstring'''

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
				from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import DPTImageProcessor

class __snake_case:


						'''simple docstring'''



						def __init__( self     ,	A_     ,	A_=2     ,	A_=32     ,	A_=16     ,	A_=3     ,	A_=True     ,	A_=True     ,	A_=32     ,	A_=4     ,	A_=[0, 1, 2, 3]     ,	A_=4     ,	A_=37     ,	A_="gelu"     ,	A_=0.1     ,	A_=0.1     ,	A_=0.0_2     ,	A_=3     ,	A_=[1, 384, 24, 24]     ,	A_=True     ,	A_=None     ,	)     ->     str:
							lowerCAmelCase   =     parent
							lowerCAmelCase   =     batch_size
							lowerCAmelCase   =     image_size
							lowerCAmelCase   =     patch_size
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     is_training
							lowerCAmelCase   =     use_labels
							lowerCAmelCase   =     hidden_size
							lowerCAmelCase   =     num_hidden_layers
							lowerCAmelCase   =     backbone_out_indices
							lowerCAmelCase   =     num_attention_heads
							lowerCAmelCase   =     intermediate_size
							lowerCAmelCase   =     hidden_act
							lowerCAmelCase   =     hidden_dropout_prob
							lowerCAmelCase   =     attention_probs_dropout_prob
							lowerCAmelCase   =     initializer_range
							lowerCAmelCase   =     num_labels
							lowerCAmelCase   =     backbone_featmap_shape
							lowerCAmelCase   =     scope
							lowerCAmelCase   =     is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							lowerCAmelCase   =     (image_size // patch_size) ** 2
							lowerCAmelCase   =     num_patches + 1


						def     __snake_case   ( self				)     ->     str:
							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.image_size, self.image_size]     ,	self.num_labels				)

							lowerCAmelCase   =     self.get_config()

							return config, pixel_values, labels


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase   =     {
							    """global_padding""": """same""",
							    """layer_type""": """bottleneck""",
							    """depths""": [3, 4, 9],
							    """out_features""": ["""stage1""", """stage2""", """stage3"""],
							    """embedding_dynamic_padding""": True,
							    """hidden_sizes""": [96, 192, 384, 768],
							    """num_groups""": 2,
							}

							return DPTConfig(
							    image_size=self.image_size     ,	patch_size=self.patch_size     ,	num_channels=self.num_channels     ,	hidden_size=self.hidden_size     ,	num_hidden_layers=self.num_hidden_layers     ,	backbone_out_indices=self.backbone_out_indices     ,	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     ,	is_decoder=A_     ,	initializer_range=self.initializer_range     ,	is_hybrid=self.is_hybrid     ,	backbone_config=A_     ,	backbone_featmap_shape=self.backbone_featmap_shape     ,	)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     Optional[int]:
							lowerCAmelCase   =     DPTModel(config=A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.last_hidden_state.shape     ,	(self.batch_size, self.seq_length, self.hidden_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     List[str]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     DPTForDepthEstimation(A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_				)
							self.parent.assertEqual(result.predicted_depth.shape     ,	(self.batch_size, self.image_size, self.image_size)				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_				)     ->     List[Any]:
							lowerCAmelCase   =     self.num_labels
							lowerCAmelCase   =     DPTForSemanticSegmentation(A_				)
							model.to(A_				)
							model.eval()
							lowerCAmelCase   =     model(A_     ,	labels=A_				)
							self.parent.assertEqual(
							    result.logits.shape     ,	(self.batch_size, self.num_labels, self.image_size, self.image_size)				)




						def     __snake_case   ( self				)     ->     Tuple:
							lowerCAmelCase   =     self.prepare_config_and_inputs()
							lowerCAmelCase,       lowerCAmelCase,       lowerCAmelCase   =     config_and_inputs
							lowerCAmelCase   =     {"""pixel_values""": pixel_values}
							return config, inputs_dict







@require_torch
class __snake_case(				_lowerCAmelCase		,							_lowerCAmelCase		,							unittest.TestCase ):


						'''simple docstring'''



						UpperCAmelCase							:   str									=		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						UpperCAmelCase							:   Union[str, Any]									=		(
						    {
						        "depth-estimation": DPTForDepthEstimation,
						        "feature-extraction": DPTModel,
						        "image-segmentation": DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase							:   Optional[int]									=		False
						UpperCAmelCase							:   Tuple									=		False
						UpperCAmelCase							:   Tuple									=		False


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     DPTModelTester(self				)
							lowerCAmelCase   =     ConfigTester(self     ,	config_class=A_     ,	has_text_modality=A_     ,	hidden_size=37				)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							self.config_tester.run_common_tests()


						@unittest.skip(reason="""DPT does not use inputs_embeds"""				)
						def     __snake_case   ( self				)     ->     Any:
							pass


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

							for model_class in self.all_model_classes:
								lowerCAmelCase   =     model_class(A_				)
								self.assertIsInstance(model.get_input_embeddings()     ,	(nn.Module)				)
								lowerCAmelCase   =     model.get_output_embeddings()
								self.assertTrue(x is None or isinstance(A_     ,	nn.Linear				)				)


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

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

								lowerCAmelCase   =     ["""pixel_values"""]
								self.assertListEqual(arg_names[:1]     ,	A_				)


						def     __snake_case   ( self				)     ->     Optional[Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*A_				)


						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*A_				)


						def     __snake_case   ( self				)     ->     Tuple:
							for model_class in self.all_model_classes:
								if model_class.__name__ == "DPTForDepthEstimation":
									continue

								lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase   =     True

								if model_class in get_values(A_				):
									continue

								lowerCAmelCase   =     model_class(A_				)
								model.to(A_				)
								model.train()
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_     ,	return_labels=A_				)
								lowerCAmelCase   =     model(**A_				).loss
								loss.backward()


						def     __snake_case   ( self				)     ->     Any:
							for model_class in self.all_model_classes:
								if model_class.__name__ == "DPTForDepthEstimation":
									continue

								lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase   =     False
								lowerCAmelCase   =     True

								if model_class in get_values(A_				) or not model_class.supports_gradient_checkpointing:
									continue
								lowerCAmelCase   =     model_class(A_				)
								model.to(A_				)
								model.gradient_checkpointing_enable()
								model.train()
								lowerCAmelCase   =     self._prepare_for_class(A_     ,	A_     ,	return_labels=A_				)
								lowerCAmelCase   =     model(**A_				).loss
								loss.backward()


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

							lowerCAmelCase   =     _config_zero_init(A_				)
							for model_class in self.all_model_classes:
								lowerCAmelCase   =     model_class(config=A_				)
								# Skip the check for the backbone
								lowerCAmelCase   =     []
								for name, module in model.named_modules():
									if module.__class__.__name__ == "DPTViTHybridEmbeddings":
										lowerCAmelCase   =     [f'{name}.{key}' for key in module.state_dict().keys()]
										break

								for name, param in model.named_parameters():
									if param.requires_grad:
										if name in backbone_params:
											continue
										self.assertIn(
										    ((param.data.mean() * 1e9).round() / 1e9).item()     ,	[0.0, 1.0]     ,	msg=f'Parameter {name} of model {model_class} seems not properly initialized'     ,	)


						@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""				)
						def     __snake_case   ( self				)     ->     Union[str, Any]:
							pass


						@slow
						def     __snake_case   ( self				)     ->     Optional[Any]:
							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
								lowerCAmelCase   =     DPTModel.from_pretrained(A_				)
								self.assertIsNotNone(A_				)




						def     __snake_case   ( self				)     ->     int:
							# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
							lowerCAmelCase,       lowerCAmelCase   =     self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase   =     """add"""
							with self.assertRaises(A_				):
								lowerCAmelCase   =     DPTForDepthEstimation(A_				)







def 		_snake_case		( )      ->						Dict:







	"""simple docstring"""
	lowerCAmelCase   =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
	return image





@require_torch
@require_vision
@slow
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self				)     ->     Union[str, Any]:
							lowerCAmelCase   =     DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""				)
							lowerCAmelCase   =     DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""				).to(A_				)

							lowerCAmelCase   =     prepare_img()
							lowerCAmelCase   =     image_processor(images=A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.predicted_depth

							# verify the predicted depth
							lowerCAmelCase   =     torch.Size((1, 384, 384)				)
							self.assertEqual(predicted_depth.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]				).to(A_				)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100     ,	A_     ,	atol=1e-4				)				)




'''simple docstring'''

class __snake_case:


						'''simple docstring'''



						def __init__( self				)     ->     None:
							lowerCAmelCase   =     {}  # Mapping from char to TrieNode
							lowerCAmelCase   =     False


						def     __snake_case   ( self     ,	A_				)     ->     None:
							for word in words:
								self.insert(A_				)


						def     __snake_case   ( self     ,	A_				)     ->     None:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									lowerCAmelCase   =     TrieNode()
								lowerCAmelCase   =     curr.nodes[char]
							lowerCAmelCase   =     True


						def     __snake_case   ( self     ,	A_				)     ->     bool:
							lowerCAmelCase   =     self
							for char in word:
								if char not in curr.nodes:
									return False
								lowerCAmelCase   =     curr.nodes[char]
							return curr.is_leaf




						def     __snake_case   ( self     ,	A_				)     ->     None:

							def _delete(A_     ,	A_     ,	A_				) -> bool:
								if index == len(A_				):
									# If word does not exist
									if not curr.is_leaf:
										return False
									lowerCAmelCase   =     False
									return len(curr.nodes				) == 0
								lowerCAmelCase   =     word[index]
								lowerCAmelCase   =     curr.nodes.get(A_				)
								# If char not in current trie node
								if not char_node:
									return False
								# Flag to check if node can be deleted
								lowerCAmelCase   =     _delete(A_     ,	A_     ,	index + 1				)
								if delete_curr:
									del curr.nodes[char]
									return len(curr.nodes				) == 0
								return delete_curr

							_delete(self     ,	A_     ,	0				)







def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						TrieNode							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	if node.is_leaf:
		print(_SCREAMING_SNAKE_CASE							,		end=""" """							)

	for key, value in node.nodes.items():
		print_words(_SCREAMING_SNAKE_CASE							,		word + key							)






def 		_snake_case		( )      ->						bool:







	"""simple docstring"""
	lowerCAmelCase   =     """banana bananas bandana band apple all beast""".split()
	lowerCAmelCase   =     TrieNode()
	root.insert_many(_SCREAMING_SNAKE_CASE							)
	# print_words(root, "")
	assert all(root.find(_SCREAMING_SNAKE_CASE							) for word in words							)
	assert root.find("""banana"""							)
	assert not root.find("""bandanas"""							)
	assert not root.find("""apps"""							)
	assert root.find("""apple"""							)
	assert root.find("""all"""							)
	root.delete("""all"""							)
	assert not root.find("""all"""							)
	root.delete("""banana"""							)
	assert not root.find("""banana"""							)
	assert root.find("""bananas"""							)
	return True






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						bool							)      ->						None:







	"""simple docstring"""
	print(str(_SCREAMING_SNAKE_CASE							)							,		"""works!""" if passes else """doesn't work :("""							)






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	assert test_trie()






def 		_snake_case		( )      ->						None:







	"""simple docstring"""
	print_results("""Testing trie functionality"""							,		test_trie()							)


if __name__ == "__main__":
				main()