





"""simple docstring"""




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


a		:					Dict	      =     logging.get_logger(__name__)


class 			__UpperCamelCase							(		a__		):
							lowerCamelCase  :   Optional[int]    =["""pixel_values"""]






							def __init__(  self ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = PILImageResampling.BICUBIC ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = 1 / 255 ,   lowerCAmelCase__ = True ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = True ,   **lowerCAmelCase__ ,   )      ->					None:
								super().__init__(**lowerCAmelCase__ )
								a :						List[str]      =							size if size is not None else {"height": 384, "width": 384}
								a :						List[Any]      =							get_size_dict(lowerCAmelCase__ ,   default_to_square=lowerCAmelCase__ )

								a :						List[str]      =							do_resize
								a :						List[str]      =							size
								a :						int      =							resample
								a :						List[Any]      =							do_rescale
								a :						str      =							rescale_factor
								a :						List[Any]      =							do_normalize
								a :						List[str]      =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								a :						Dict      =							image_std if image_std is not None else OPENAI_CLIP_STD
								a :						str      =							do_convert_rgb






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = PILImageResampling.BICUBIC ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								a :						List[Any]      =							get_size_dict(lowerCAmelCase__ ,   default_to_square=lowerCAmelCase__ )
								if "height" not in size or "width" not in size:
									raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
								a :						Union[str, Any]      =							(size["height"], size["width"])
								return resize(lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					Union[str, Any]:
								return rescale(lowerCAmelCase__ ,   scale=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   **lowerCAmelCase__ ,   )      ->					np.ndarray:
								return normalize(lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ,   data_format=lowerCAmelCase__ ,   **lowerCAmelCase__ )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = None ,   lowerCAmelCase__ = ChannelDimension.FIRST ,   **lowerCAmelCase__ ,   )      ->					PIL.Image.Image:
								a :						Tuple      =							do_resize if do_resize is not None else self.do_resize
								a :						str      =							resample if resample is not None else self.resample
								a :						Dict      =							do_rescale if do_rescale is not None else self.do_rescale
								a :						Any      =							rescale_factor if rescale_factor is not None else self.rescale_factor
								a :						int      =							do_normalize if do_normalize is not None else self.do_normalize
								a :						str      =							image_mean if image_mean is not None else self.image_mean
								a :						List[Any]      =							image_std if image_std is not None else self.image_std
								a :						List[str]      =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								a :						Tuple      =							size if size is not None else self.size
								a :						List[Any]      =							get_size_dict(lowerCAmelCase__ ,   default_to_square=lowerCAmelCase__ )

								a :						Optional[Any]      =							make_list_of_images(lowerCAmelCase__ )

								if not valid_images(lowerCAmelCase__ ):
									raise ValueError(
									    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
									    "torch.Tensor, tf.Tensor or jax.ndarray." )

								if do_resize and size is None or resample is None:
									raise ValueError("Size and resample must be specified if do_resize is True." )

								if do_rescale and rescale_factor is None:
									raise ValueError("Rescale factor must be specified if do_rescale is True." )

								if do_normalize and (image_mean is None or image_std is None):
									raise ValueError("Image mean and std must be specified if do_normalize is True." )

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
									a :						Optional[Any]      =							[convert_to_rgb(lowerCAmelCase__ ) for image in images]

								# All transformations expect numpy arrays.
								a :						List[Any]      =							[to_numpy_array(lowerCAmelCase__ ) for image in images]

								if do_resize:
									a :						List[Any]      =							[self.resize(image=lowerCAmelCase__ ,   size=lowerCAmelCase__ ,   resample=lowerCAmelCase__ ) for image in images]

								if do_rescale:
									a :						int      =							[self.rescale(image=lowerCAmelCase__ ,   scale=lowerCAmelCase__ ) for image in images]

								if do_normalize:
									a :						Optional[Any]      =							[self.normalize(image=lowerCAmelCase__ ,   mean=lowerCAmelCase__ ,   std=lowerCAmelCase__ ) for image in images]

								a :						int      =							[to_channel_dimension_format(lowerCAmelCase__ ,   lowerCAmelCase__ ) for image in images]

								a :						Dict      =							BatchFeature(data={"pixel_values": images} ,   tensor_type=lowerCAmelCase__ )

								return encoded_outputs









"""simple docstring"""




import gc
import threading
import time

import psutil
import torch


class 			__UpperCamelCase							:






							def __init__(  self )      ->					Any:
								a :						str      =							psutil.Process()
								a :						str      =							False






							def        __a  (  self )      ->					str:
								a :						List[Any]      =							-1

								while True:
									a :						str      =							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        __a  (  self )      ->					List[str]:
								a :						Dict      =							True
								a :						Optional[Any]      =							threading.Thread(target=self.peak_monitor )
								a :						List[Any]      =							True
								self.thread.start()






							def        __a  (  self )      ->					List[str]:
								a :						Optional[int]      =							False
								self.thread.join()
								return self.cpu_memory_peak
a		:					Tuple	      =     PeakCPUMemory()






def  _SCREAMING_SNAKE_CASE						(      )					->List[Any]:






	'''simple docstring'''






	a :						List[str]      =							{"time": time.time()}

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

	# CPU mem
	a :						Tuple      =							psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()   ):
		a :						Any      =							torch.cuda.memory_allocated(_lowercase   )
	torch.cuda.reset_peak_memory_stats()

	return measures






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






	'''simple docstring'''






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

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

	# CPU mem
	a :						List[Any]      =							(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
	a :						Union[str, Any]      =							(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

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

	return measures







def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str       ,			_lowercase		:       Union[str, 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(_lowercase   )]:.2f}MiB"""   )
		a :						str      =							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"""   )


