
"""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 __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					List[str]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					Union[str, Any]=2       ,   _snake_case						:					Dict=32       ,   _snake_case						:					Any=16       ,   _snake_case						:					Dict=3       ,   _snake_case						:					Optional[Any]=True       ,   _snake_case						:					Any=True       ,   _snake_case						:					Any=32       ,   _snake_case						:					Tuple=4       ,   _snake_case						:					str=[0, 1, 2, 3]       ,   _snake_case						:					Tuple=4       ,   _snake_case						:					List[str]=37       ,   _snake_case						:					str="gelu"       ,   _snake_case						:					Optional[Any]=0.1       ,   _snake_case						:					Optional[Any]=0.1       ,   _snake_case						:					int=0.0_2       ,   _snake_case						:					Optional[Any]=3       ,   _snake_case						:					List[Any]=[1, 384, 24, 24]       ,   _snake_case						:					Optional[int]=True       ,   _snake_case						:					Optional[int]=None       ,   )      -> Dict:







								"""simple docstring"""
								A_				 =						parent
								A_				 =						batch_size
								A_				 =						image_size
								A_				 =						patch_size
								A_				 =						num_channels
								A_				 =						is_training
								A_				 =						use_labels
								A_				 =						hidden_size
								A_				 =						num_hidden_layers
								A_				 =						backbone_out_indices
								A_				 =						num_attention_heads
								A_				 =						intermediate_size
								A_				 =						hidden_act
								A_				 =						hidden_dropout_prob
								A_				 =						attention_probs_dropout_prob
								A_				 =						initializer_range
								A_				 =						num_labels
								A_				 =						backbone_featmap_shape
								A_				 =						scope
								A_				 =						is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								A_				 =						(image_size // patch_size) ** 2
								A_				 =						num_patches + 1




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







								"""simple docstring"""
								A_				 =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

								A_				 =						None
								if self.use_labels:
													A_				 =						ids_tensor([self.batch_size, self.image_size, self.image_size]       ,   self.num_labels	)

								A_				 =						self.get_config()

								return config, pixel_values, labels




			def      lowerCamelCase__     (				self						:					Dict	)      -> int:







								"""simple docstring"""
								A_				 =						{
								    "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=_snake_case       ,   initializer_range=self.initializer_range       ,   is_hybrid=self.is_hybrid       ,   backbone_config=_snake_case       ,   backbone_featmap_shape=self.backbone_featmap_shape       ,   )




			def      lowerCamelCase__     (				self						:					Optional[Any]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					str	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						DPTModel(config=_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)
								self.parent.assertEqual(result.last_hidden_state.shape       ,   (self.batch_size, self.seq_length, self.hidden_size)	)




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Dict       ,   _snake_case						:					List[str]	)      -> List[str]:







								"""simple docstring"""
								A_				 =						self.num_labels
								A_				 =						DPTForDepthEstimation(_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case	)
								self.parent.assertEqual(result.predicted_depth.shape       ,   (self.batch_size, self.image_size, self.image_size)	)




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					Optional[int]       ,   _snake_case						:					str       ,   _snake_case						:					Optional[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								A_				 =						self.num_labels
								A_				 =						DPTForSemanticSegmentation(_snake_case	)
								model.to(_snake_case	)
								model.eval()
								A_				 =						model(_snake_case       ,   labels=_snake_case	)
								self.parent.assertEqual(
								    result.logits.shape       ,   (self.batch_size, self.num_labels, self.image_size, self.image_size)	)






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







								"""simple docstring"""
								A_				 =						self.prepare_config_and_inputs()
								A_ ,    A_ ,    A_				 =						config_and_inputs
								A_				 =						{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class __lowerCAmelCase				(					_lowercase     ,				_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
			snake_case	      =				(
			    {
			        "depth-estimation": DPTForDepthEstimation,
			        "feature-extraction": DPTModel,
			        "image-segmentation": DPTForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			snake_case	      =				False
			snake_case	      =				False
			snake_case	      =				False




			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						DPTModelTester(self	)
								A_				 =						ConfigTester(self       ,   config_class=_snake_case       ,   has_text_modality=_snake_case       ,   hidden_size=37	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> str:







								"""simple docstring"""
								self.config_tester.run_common_tests()




			@unittest.skip(reason="DPT does not use inputs_embeds"	)
			def      lowerCamelCase__     (				self						:					str	)      -> Optional[int]:







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					str	)      -> str:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

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




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







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

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

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




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Tuple:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_snake_case	)




			def      lowerCamelCase__     (				self						:					Any	)      -> Any:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_snake_case	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> int:







								"""simple docstring"""
								A_				 =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_snake_case	)




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







								"""simple docstring"""
								for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																		continue

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

													if model_class in get_values(_snake_case	):
																		continue

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




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







								"""simple docstring"""
								for model_class in self.all_model_classes:
													if model_class.__name__ == "DPTForDepthEstimation":
																		continue

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

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




			def      lowerCamelCase__     (				self						:					Dict	)      -> Any:







								"""simple docstring"""
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()

								A_				 =						_config_zero_init(_snake_case	)
								for model_class in self.all_model_classes:
													A_				 =						model_class(config=_snake_case	)
													# Skip the check for the backbone
													A_				 =						[]
													for name, module in model.named_modules():
																		if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																							A_				 =						[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      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[Any]:







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
													A_				 =						DPTModel.from_pretrained(_snake_case	)
													self.assertIsNotNone(_snake_case	)






			def      lowerCamelCase__     (				self						:					int	)      -> str:







								"""simple docstring"""
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								A_ ,    A_				 =						self.model_tester.prepare_config_and_inputs_for_common()
								A_				 =						"add"
								with self.assertRaises(_snake_case	):
													A_				 =						DPTForDepthEstimation(_snake_case	)





def 							A_     ():

					'''simple docstring'''





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




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




			"""simple docstring"""







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







								"""simple docstring"""
								A_				 =						DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
								A_				 =						DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(_snake_case	)

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

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

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

								A_				 =						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(_snake_case	)

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






"""simple docstring"""





from collections import deque



class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					Optional[Any]       ,   _snake_case						:					str       ,   _snake_case						:					int       ,   _snake_case						:					int	)      -> None:







								"""simple docstring"""
								A_				 =						process_name  # process name
								A_				 =						arrival_time  # arrival time of the process
								# completion time of finished process or last interrupted time
								A_				 =						arrival_time
								A_				 =						burst_time  # remaining burst time
								A_				 =						0  # total time of the process wait in ready queue
								A_				 =						0  # time from arrival time to completion time





class __lowerCAmelCase				:




			"""simple docstring"""







			def __init__(				self						:					str       ,   _snake_case						:					int       ,   _snake_case						:					list[int]       ,   _snake_case						:					deque[Process]       ,   _snake_case						:					int       ,   )      -> None:







								"""simple docstring"""
								# total number of mlfq's queues
								A_				 =						number_of_queues
								# time slice of queues that round robin algorithm applied
								A_				 =						time_slices
								# unfinished process is in this ready_queue
								A_				 =						queue
								# current time
								A_				 =						current_time
								# finished process is in this sequence queue
								A_				 =						deque()




			def      lowerCamelCase__     (				self						:					List[str]	)      -> list[str]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(self.finish_queue	)	):
													sequence.append(self.finish_queue[i].process_name	)
								return sequence




			def      lowerCamelCase__     (				self						:					str       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													waiting_times.append(queue[i].waiting_time	)
								return waiting_times




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													turnaround_times.append(queue[i].turnaround_time	)
								return turnaround_times




			def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					list[Process]	)      -> list[int]:







								"""simple docstring"""
								A_				 =						[]
								for i in range(len(_snake_case	)	):
													completion_times.append(queue[i].stop_time	)
								return completion_times




			def      lowerCamelCase__     (				self						:					Optional[int]       ,   _snake_case						:					deque[Process]	)      -> list[int]:







								"""simple docstring"""
								return [q.burst_time for q in queue]




			def      lowerCamelCase__     (				self						:					int       ,   _snake_case						:					Process	)      -> int:







								"""simple docstring"""
								process.waiting_time += self.current_time - process.stop_time
								return process.waiting_time




			def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					deque[Process]	)      -> deque[Process]:







								"""simple docstring"""
								A_				 =						deque()  # sequence deque of finished process
								while len(_snake_case	) != 0:
													A_				 =						ready_queue.popleft()  # current process

													# if process's arrival time is later than current time, update current time
													if self.current_time < cp.arrival_time:
																		self.current_time += cp.arrival_time

													# update waiting time of current process
													self.update_waiting_time(_snake_case	)
													# update current time
													self.current_time += cp.burst_time
													# finish the process and set the process's burst-time 0
													A_				 =						0
													# set the process's turnaround time because it is finished
													A_				 =						self.current_time - cp.arrival_time
													# set the completion time
													A_				 =						self.current_time
													# add the process to queue that has finished queue
													finished.append(_snake_case	)

								self.finish_queue.extend(_snake_case	)  # add finished process to finish queue
								# FCFS will finish all remaining processes
								return finished




			def      lowerCamelCase__     (				self						:					List[str]       ,   _snake_case						:					deque[Process]       ,   _snake_case						:					int	)      -> tuple[deque[Process], deque[Process]]:







								"""simple docstring"""
								A_				 =						deque()  # sequence deque of terminated process
								# just for 1 cycle and unfinished processes will go back to queue
								for _ in range(len(_snake_case	)	):
													A_				 =						ready_queue.popleft()  # current process

													# if process's arrival time is later than current time, update current time
													if self.current_time < cp.arrival_time:
																		self.current_time += cp.arrival_time

													# update waiting time of unfinished processes
													self.update_waiting_time(_snake_case	)
													# if the burst time of process is bigger than time-slice
													if cp.burst_time > time_slice:
																		# use CPU for only time-slice
																		self.current_time += time_slice
																		# update remaining burst time
																		cp.burst_time -= time_slice
																		# update end point time
																		A_				 =						self.current_time
																		# locate the process behind the queue because it is not finished
																		ready_queue.append(_snake_case	)
													else:
																		# use CPU for remaining burst time
																		self.current_time += cp.burst_time
																		# set burst time 0 because the process is finished
																		A_				 =						0
																		# set the finish time
																		A_				 =						self.current_time
																		# update the process' turnaround time because it is finished
																		A_				 =						self.current_time - cp.arrival_time
																		# add the process to queue that has finished queue
																		finished.append(_snake_case	)

								self.finish_queue.extend(_snake_case	)  # add finished process to finish queue
								# return finished processes queue and remaining processes queue
								return finished, ready_queue






			def      lowerCamelCase__     (				self						:					Any	)      -> deque[Process]:







								"""simple docstring"""

								#  all queues except last one have round_robin algorithm
								for i in range(self.number_of_queues - 1	):
													A_ ,    A_				 =						self.round_robin(
													    self.ready_queue       ,   self.time_slices[i]	)
								#  the last queue has first_come_first_served algorithm
								self.first_come_first_served(self.ready_queue	)

								return self.finish_queue


if __name__ == "__main__":
		import doctest

		UpperCamelCase_					:						Tuple				= Process('''P1''', 0, 53)
		UpperCamelCase_					:						List[Any]				= Process('''P2''', 0, 17)
		UpperCamelCase_					:						Optional[int]				= Process('''P3''', 0, 68)
		UpperCamelCase_					:						Union[str, Any]				= Process('''P4''', 0, 24)
		UpperCamelCase_					:						Any				= 3
		UpperCamelCase_					:						List[str]				= [17, 25]
		UpperCamelCase_					:						List[str]				= deque([Pa, Pa, Pa, Pa])

		if len(time_slices) != number_of_queues - 1:
				raise SystemExit(0)

		doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

		UpperCamelCase_					:						str				= Process('''P1''', 0, 53)
		UpperCamelCase_					:						Dict				= Process('''P2''', 0, 17)
		UpperCamelCase_					:						List[Any]				= Process('''P3''', 0, 68)
		UpperCamelCase_					:						int				= Process('''P4''', 0, 24)
		UpperCamelCase_					:						Dict				= 3
		UpperCamelCase_					:						str				= [17, 25]
		UpperCamelCase_					:						Optional[int]				= deque([Pa, Pa, Pa, Pa])
		UpperCamelCase_					:						str				= MLFQ(number_of_queues, time_slices, queue, 0)
		UpperCamelCase_					:						int				= mlfq.multi_level_feedback_queue()

		# print total waiting times of processes(P1, P2, P3, P4)
		print(
		    F"""waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}"""
		)
		# print completion times of processes(P1, P2, P3, P4)
		print(
		    F"""completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}"""
		)
		# print total turnaround times of processes(P1, P2, P3, P4)
		print(
		    F"""turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}"""
		)
		# print sequence of finished processes
		print(
		    F"""sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}"""
		)




