






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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


__a:     str   					=					logging.get_logger(__name__)


if is_vision_available():
  import PIL
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  ['''pixel_values''']


  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Union[int, float] = 1 / 255       ,				lowerCamelCase		:			bool = True       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			bool = True       ,				**lowerCamelCase		:			Optional[int]       ,				)						->	None:


         """simple docstring"""
         super().__init__(**lowerCamelCase							)
         _UpperCAmelCase							=    size if size is not None else {"""shortest_edge""": 224}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)
         _UpperCAmelCase							=    crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase       ,				param_name="""crop_size"""							)

         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    resample
         _UpperCAmelCase							=    do_center_crop
         _UpperCAmelCase							=    crop_size
         _UpperCAmelCase							=    do_rescale
         _UpperCAmelCase							=    rescale_factor
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         _UpperCAmelCase							=    image_std if image_std is not None else OPENAI_CLIP_STD
         _UpperCAmelCase							=    do_convert_rgb


  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Dict[str, int]       ,				lowerCamelCase		:			PILImageResampling = PILImageResampling.BICUBIC       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				default_to_square=lowerCamelCase							)
         if "shortest_edge" not in size:
                raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""							)
         _UpperCAmelCase							=    get_resize_output_image_size(lowerCamelCase       ,				size=size["""shortest_edge"""]       ,				default_to_square=lowerCamelCase							)
         return resize(lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Dict[str, int]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	np.ndarray:


         """simple docstring"""
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase							)
         if "height" not in size or "width" not in size:
                raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""							)
         return center_crop(lowerCamelCase       ,				size=(size["""height"""], size["""width"""])       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[int, float]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Dict       ,				)						->	Optional[int]:


         """simple docstring"""
         return rescale(lowerCamelCase       ,				scale=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)


  def  lowerCamelCase       (      self		:			List[Any]       ,				lowerCamelCase		:			np.ndarray       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Union[float, List[float]]       ,				lowerCamelCase		:			Optional[Union[str, ChannelDimension]] = None       ,				**lowerCamelCase		:			Any       ,				)						->	np.ndarray:


         """simple docstring"""
         return normalize(lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase       ,				data_format=lowerCamelCase       ,				**lowerCamelCase							)





  def  lowerCamelCase       (      self		:			List[str]       ,				lowerCamelCase		:			ImageInput       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Dict[str, int] = None       ,				lowerCamelCase		:			PILImageResampling = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			int = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			float = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			Optional[Union[float, List[float]]] = None       ,				lowerCamelCase		:			bool = None       ,				lowerCamelCase		:			Optional[Union[str, TensorType]] = None       ,				lowerCamelCase		:			Optional[ChannelDimension] = ChannelDimension.FIRST       ,				**lowerCamelCase		:			Any       ,				)						->	PIL.Image.Image:


         """simple docstring"""
         _UpperCAmelCase							=    do_resize if do_resize is not None else self.do_resize
         _UpperCAmelCase							=    size if size is not None else self.size
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				param_name="""size"""       ,				default_to_square=lowerCamelCase							)
         _UpperCAmelCase							=    resample if resample is not None else self.resample
         _UpperCAmelCase							=    do_center_crop if do_center_crop is not None else self.do_center_crop
         _UpperCAmelCase							=    crop_size if crop_size is not None else self.crop_size
         _UpperCAmelCase							=    get_size_dict(lowerCamelCase       ,				param_name="""crop_size"""       ,				default_to_square=lowerCamelCase							)
         _UpperCAmelCase							=    do_rescale if do_rescale is not None else self.do_rescale
         _UpperCAmelCase							=    rescale_factor if rescale_factor is not None else self.rescale_factor
         _UpperCAmelCase							=    do_normalize if do_normalize is not None else self.do_normalize
         _UpperCAmelCase							=    image_mean if image_mean is not None else self.image_mean
         _UpperCAmelCase							=    image_std if image_std is not None else self.image_std
         _UpperCAmelCase							=    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         _UpperCAmelCase							=    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:
                raise ValueError("""Size must be specified if do_resize is True."""							)

         if do_center_crop and crop_size is None:
                raise ValueError("""Crop size must be specified if do_center_crop 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:
                _UpperCAmelCase							=    [convert_to_rgb(lowerCamelCase							) for image in images]

         # All transformations expect numpy arrays.
         _UpperCAmelCase							=    [to_numpy_array(lowerCamelCase							) for image in images]

         if do_resize:
                _UpperCAmelCase							=    [self.resize(image=lowerCamelCase       ,				size=lowerCamelCase       ,				resample=lowerCamelCase							) for image in images]

         if do_center_crop:
                _UpperCAmelCase							=    [self.center_crop(image=lowerCamelCase       ,				size=lowerCamelCase							) for image in images]

         if do_rescale:
                _UpperCAmelCase							=    [self.rescale(image=lowerCamelCase       ,				scale=lowerCamelCase							) for image in images]

         if do_normalize:
                _UpperCAmelCase							=    [self.normalize(image=lowerCamelCase       ,				mean=lowerCamelCase       ,				std=lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    [to_channel_dimension_format(lowerCamelCase       ,				lowerCamelCase							) for image in images]

         _UpperCAmelCase							=    {"""pixel_values""": images}
         return BatchFeature(data=lowerCamelCase       ,				tensor_type=lowerCamelCase							)







import gc
import threading
import time

import psutil
import torch
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







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


         """simple docstring"""
         _UpperCAmelCase							=    psutil.Process()
         _UpperCAmelCase							=    False


  def  lowerCamelCase       (      self		:			List[Any]							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    -1

         while True:
                _UpperCAmelCase							=    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		:			Optional[Any]							)						->	List[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    True
         _UpperCAmelCase							=    threading.Thread(target=self.peak_monitor							)
         _UpperCAmelCase							=    True
         self.thread.start()





  def  lowerCamelCase       (      self		:			Optional[int]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    False
         self.thread.join()
         return self.cpu_memory_peak



__a:     Union[str, Any]   					=					PeakCPUMemory()




def       _SCREAMING_SNAKE_CASE (							)				->						List[Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time()}

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

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

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

       return measures




def       _SCREAMING_SNAKE_CASE (							__snake_case )				->						Union[str, Any]:
       # Time
       _UpperCAmelCase							=    {"""time""": time.time() - start_measures["""time"""]}

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

       # CPU mem
       _UpperCAmelCase							=    (psutil.Process().memory_info().rss - start_measures["""cpu"""]) / 2**2_0
       _UpperCAmelCase							=    (cpu_peak_tracker.stop() - start_measures["""cpu"""]) / 2**2_0

       # GPU mem
       for i in range(torch.cuda.device_count() ):
              _UpperCAmelCase							=    (torch.cuda.memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0
              _UpperCAmelCase							=    (torch.cuda.max_memory_allocated(__snake_case ) - start_measures[str(__snake_case )]) / 2**2_0

       return measures





def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						str:
       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(__snake_case )]:.2f}MiB""" )
              _UpperCAmelCase							=    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""" )