


"""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 						_UpperCamelCase						:
							'''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, 3_84, 24, 24]					,     __a=True					,     __a=None					,     ):
										__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			):
										__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			):
										__lowerCAmelCase       					=    {
										    "global_padding": "same",
										    "layer_type": "bottleneck",
										    "depths": [3, 4, 9],
										    "out_features": ["stage1", "stage2", "stage3"],
										    "embedding_dynamic_padding": True,
										    "hidden_sizes": [96, 1_92, 3_84, 7_68],
										    "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			):
										__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			):
										__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			):
										__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			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    config_and_inputs
										__lowerCAmelCase       					=    {"pixel_values": pixel_values}
										return config, inputs_dict
@require_torch
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







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

							__UpperCAmelCase		:					Union[str, Any]          =False
							__UpperCAmelCase		:					Any          =False
							__UpperCAmelCase		:					Optional[int]          =False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    DPTModelTester(self			)
										__lowerCAmelCase       					=    ConfigTester(self					,     config_class=__a					,     has_text_modality=__a					,     hidden_size=37			)

							def     snake_case       (  self			):
										self.config_tester.run_common_tests()

							@unittest.skip(reason="DPT does not use inputs_embeds"			)
							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(__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			):
										__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			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*__a			)

							def     snake_case       (  self			):
										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			):
										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			):
										__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			):
										pass

							@slow
							def     snake_case       (  self			):
										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
													__lowerCAmelCase       					=    DPTModel.from_pretrained(__a			)
													self.assertIsNotNone(__a			)



							def     snake_case       (  self			):
										# 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 _lowerCamelCase			(	):

			'''simple docstring'''




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


@require_torch
@require_vision
@slow
class 						_UpperCamelCase						(     unittest.TestCase	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__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, 3_84, 3_84)			)
										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] / 1_00					,     __a					,     atol=1e-4			)			)








"""simple docstring"""


import gc
import threading
import time

import psutil
import torch
class 						_UpperCamelCase						:
							'''simple docstring'''







							def __init__(  self			):
										__lowerCAmelCase       					=    psutil.Process()
										__lowerCAmelCase       					=    False

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    -1

										while True:
													__lowerCAmelCase       					=    max(self.process.memory_info().rss					,     self.cpu_memory_peak			)

													# can't sleep or will not catch the peak right (this comment is here on purpose)
													if not self.peak_monitoring:
																break

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    True
										__lowerCAmelCase       					=    threading.Thread(target=self.peak_monitor			)
										__lowerCAmelCase       					=    True
										self.thread.start()



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    False
										self.thread.join()
										return self.cpu_memory_peak
A  :     Any    					=    PeakCPUMemory()


def _lowerCamelCase			(	):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    torch.cuda.memory_allocated(_UpperCamelCase   )
			torch.cuda.reset_peak_memory_stats()

			return measures


def _lowerCamelCase			(	_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase       					=    (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase       					=    (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()   ):
						__lowerCAmelCase       					=    (torch.cuda.memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20
						__lowerCAmelCase       					=    (torch.cuda.max_memory_allocated(_UpperCamelCase   ) - start_measures[str(_UpperCamelCase   )]) / 2**20

			return measures


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			print(f"{description}:"   )
			print(f"- Time: {measures['time']:.2f}s"   )
			for i in range(torch.cuda.device_count()   ):
						print(f"- GPU {i} allocated: {measures[str(_UpperCamelCase   )]:.2f}MiB"   )
						__lowerCAmelCase       					=    measures[f"{i}-peak"]
						print(f"- GPU {i} peak: {peak:.2f}MiB"   )
			print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB"   )
			print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"   )




