import unittest

import numpy as np

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

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 ViTImageProcessor




class    _UpperCamelCase      (						unittest.TestCase ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__=13      ,   lowerCAmelCase__=3      ,   lowerCAmelCase__=2_24      ,   lowerCAmelCase__=30      ,   lowerCAmelCase__=4_00      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=None      ,   lowerCAmelCase__=True      ,   lowerCAmelCase__=[0.5, 0.5, 0.5]      ,   lowerCAmelCase__=[0.5, 0.5, 0.5]      ,   )     ->					Any:

							'''simple docstring'''


							__lowercase 			=							size if size is not None else {'''height''': 18, '''width''': 18}
							__lowercase 			=							parent
							__lowercase 			=							batch_size
							__lowercase 			=							num_channels
							__lowercase 			=							image_size
							__lowercase 			=							min_resolution
							__lowercase 			=							max_resolution
							__lowercase 			=							do_resize
							__lowercase 			=							size
							__lowercase 			=							do_normalize
							__lowercase 			=							image_mean
							__lowercase 			=							image_std





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							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    _UpperCamelCase      (						_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : Optional[int]			 =						ViTImageProcessor if is_vision_available() else None





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

							'''simple docstring'''


							__lowercase 			=							EfficientFormerImageProcessorTester(self						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[int]:

							'''simple docstring'''


							return self.image_proc_tester.prepare_image_processor_dict()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''image_mean'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''image_std'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''do_normalize'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''do_resize'''						)						)
							self.assertTrue(hasattr(lowerCAmelCase__      ,   '''size'''						)						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							pass





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random PIL images
							__lowercase 			=							prepare_image_inputs(self.image_proc_tester      ,   equal_resolution=lowerCAmelCase__						)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__      ,   Image.Image						)

							# Test not batched input
							__lowercase 			=							image_processor(image_inputs[0]      ,   return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )      ,   )

							# Test batched
							__lowercase 			=							image_processor(lowerCAmelCase__      ,   return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random numpy tensors
							__lowercase 			=							prepare_image_inputs(self.image_proc_tester      ,   equal_resolution=lowerCAmelCase__      ,   numpify=lowerCAmelCase__						)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__      ,   np.ndarray						)

							# Test not batched input
							__lowercase 			=							image_processor(image_inputs[0]      ,   return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )      ,   )

							# Test batched
							__lowercase 			=							image_processor(lowerCAmelCase__      ,   return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )      ,   )





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[Any]:

							'''simple docstring'''


							__lowercase 			=							self.image_processing_class(**self.image_processor_dict						)
							# create random PyTorch tensors
							__lowercase 			=							prepare_image_inputs(self.image_proc_tester      ,   equal_resolution=lowerCAmelCase__      ,   torchify=lowerCAmelCase__						)
							for image in image_inputs:
											self.assertIsInstance(lowerCAmelCase__      ,   torch.Tensor						)

							# Test not batched input
							__lowercase 			=							image_processor(image_inputs[0]      ,   return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )      ,   )

							# Test batched
							__lowercase 			=							image_processor(lowerCAmelCase__      ,   return_tensors='''pt'''						).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,   (
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )      ,   )
import gc
import threading
import time

import psutil
import torch




class    _UpperCamelCase      :


			"""simple docstring"""






			def __init__(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							psutil.Process()
							__lowercase 			=							False





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							-1

							while True:
											__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							True
							__lowercase 			=							threading.Thread(target=self.peak_monitor						)
							__lowercase 			=							True
							self.thread.start()





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							__lowercase 			=							False
							self.thread.join()
							return self.cpu_memory_peak


__a  :     List[str]       =  PeakCPUMemory()





def 				UpperCAmelCase						(    ):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time()}

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

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

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

				return measures





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							{'''time''': time.time() - start_measures['''time''']}

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

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

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

				return measures





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""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(lowercase							)]:.2f}MiB"							)
								__lowercase 			=							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"							)