"""simple docstring"""





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import DPTImageProcessor
class  _snake_case     (					unittest.TestCase		):






						def __init__(       self      :			Union[str, Any]							,					UpperCAmelCase      :			List[str]							,					UpperCAmelCase      :			int=7							,					UpperCAmelCase      :			Dict=3							,					UpperCAmelCase      :			int=18							,					UpperCAmelCase      :			Tuple=30							,					UpperCAmelCase      :			str=400							,					UpperCAmelCase      :			Dict=True							,					UpperCAmelCase      :			int=None							,					UpperCAmelCase      :			Optional[int]=True							,					UpperCAmelCase      :			List[Any]=[0.5, 0.5, 0.5]							,					UpperCAmelCase      :			List[Any]=[0.5, 0.5, 0.5]							,					):
													__lowerCamelCase					:		Optional[Any]		   =     size if size is not None else {"height": 18, "width": 18}
													__lowerCamelCase					:		Any		   =     parent
													__lowerCamelCase					:		str		   =     batch_size
													__lowerCamelCase					:		Dict		   =     num_channels
													__lowerCamelCase					:		Any		   =     image_size
													__lowerCamelCase					:		List[str]		   =     min_resolution
													__lowerCamelCase					:		int		   =     max_resolution
													__lowerCamelCase					:		Optional[int]		   =     do_resize
													__lowerCamelCase					:		int		   =     size
													__lowerCamelCase					:		Any		   =     do_normalize
													__lowerCamelCase					:		List[Any]		   =     image_mean
													__lowerCamelCase					:		Any		   =     image_std







						def       lowerCamelCase__     (       self      :			Tuple    ):
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													}

@require_torch
@require_vision
class  _snake_case     (					a__				, unittest.TestCase		):
						snake_case__			  =			DPTImageProcessor if is_vision_available() else None






						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													__lowerCamelCase					:		Optional[Any]		   =     DPTImageProcessingTester(self    )






						@property
						def       lowerCamelCase__     (       self      :			Optional[int]    ):
													return self.image_processor_tester.prepare_image_processor_dict()






						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_mean"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"image_std"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_normalize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"do_resize"    )    )
													self.assertTrue(hasattr(UpperCAmelCase							,					"size"    )    )






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		Any		   =     self.image_processing_class.from_dict(self.image_processor_dict    )
													self.assertEqual(image_processor.size							,					{"height": 18, "width": 18}    )

													__lowerCamelCase					:		int		   =     self.image_processing_class.from_dict(self.image_processor_dict							,					size=42    )
													self.assertEqual(image_processor.size							,					{"height": 42, "width": 42}    )






						def       lowerCamelCase__     (       self      :			str    ):
													# Initialize image_processing
													__lowerCamelCase					:		Tuple		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PIL images
													__lowerCamelCase					:		Any		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					Image.Image    )

													# Test not batched input
													__lowerCamelCase					:		str		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size["height"],
													        self.image_processor_tester.size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Tuple		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size["height"],
													        self.image_processor_tester.size["width"],
													    )							,					)






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													# Initialize image_processing
													__lowerCamelCase					:		int		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random numpy tensors
													__lowerCamelCase					:		Union[str, Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					numpify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					np.ndarray    )

													# Test not batched input
													__lowerCamelCase					:		List[Any]		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size["height"],
													        self.image_processor_tester.size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Any		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size["height"],
													        self.image_processor_tester.size["width"],
													    )							,					)







						def       lowerCamelCase__     (       self      :			int    ):
													# Initialize image_processing
													__lowerCamelCase					:		Union[str, Any]		   =     self.image_processing_class(**self.image_processor_dict    )
													# create random PyTorch tensors
													__lowerCamelCase					:		Union[str, Any]		   =     prepare_image_inputs(self.image_processor_tester							,					equal_resolution=UpperCAmelCase							,					torchify=UpperCAmelCase    )
													for image in image_inputs:
																				self.assertIsInstance(UpperCAmelCase							,					torch.Tensor    )

													# Test not batched input
													__lowerCamelCase					:		str		   =     image_processing(image_inputs[0]							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size["height"],
													        self.image_processor_tester.size["width"],
													    )							,					)

													# Test batched
													__lowerCamelCase					:		Any		   =     image_processing(UpperCAmelCase							,					return_tensors="pt"    ).pixel_values
													self.assertEqual(
													    encoded_images.shape							,					(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size["height"],
													        self.image_processor_tester.size["width"],
													    )							,					)
"""simple docstring"""





import gc
import threading
import time

import psutil
import torch
class  _snake_case     :






						def __init__(       self      :			str    ):
													__lowerCamelCase					:		Optional[Any]		   =     psutil.Process()
													__lowerCamelCase					:		List[Any]		   =     False






						def       lowerCamelCase__     (       self      :			str    ):
													__lowerCamelCase					:		List[Any]		   =     -1

													while True:
																				__lowerCamelCase					:		Union[str, Any]		   =     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       lowerCamelCase__     (       self      :			Any    ):
													__lowerCamelCase					:		Optional[Any]		   =     True
													__lowerCamelCase					:		Union[str, Any]		   =     threading.Thread(target=self.peak_monitor    )
													__lowerCamelCase					:		Optional[Any]		   =     True
													self.thread.start()







						def       lowerCamelCase__     (       self      :			Dict    ):
													__lowerCamelCase					:		str		   =     False
													self.thread.join()
													return self.cpu_memory_peak

__A  =				PeakCPUMemory()


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



							'''simple docstring'''

							__lowerCamelCase					:		Any		   =     {"time": time.time()}

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

							# CPU mem
							__lowerCamelCase					:		Optional[int]		   =     psutil.Process().memory_info().rss
							cpu_peak_tracker.start()

							# GPU mem
							for i in range(torch.cuda.device_count()      ):
														__lowerCamelCase					:		int		   =     torch.cuda.memory_allocated(_lowerCamelCase      )
							torch.cuda.reset_peak_memory_stats()

							return measures


def    lowercase_   (   _lowerCamelCase:	Optional[Any]      ) ->      Tuple:



							'''simple docstring'''

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

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

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

							# GPU mem
							for i in range(torch.cuda.device_count()      ):
														__lowerCamelCase					:		Any		   =     (torch.cuda.memory_allocated(_lowerCamelCase      ) - start_measures[str(_lowerCamelCase      )]) / 2**20
														__lowerCamelCase					:		Optional[int]		   =     (torch.cuda.max_memory_allocated(_lowerCamelCase      ) - start_measures[str(_lowerCamelCase      )]) / 2**20

							return measures







def    lowercase_   (   _lowerCamelCase:	int						,   _lowerCamelCase:	List[Any]      ) ->      Optional[int]:



							'''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(_lowerCamelCase      )]:.2f}MiB"""      )
														__lowerCamelCase					:		List[Any]		   =     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"""      )