




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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


snake_case__			:    str			     =      logging.get_logger(__name__)





class      snake_case	(						_snake_case				):


   '''simple docstring'''







   UpperCamelCase__						:							str 	=   ["pixel_values"]




   def __init__(       self  :						Any    ,     lowerCamelCase_  :						bool = True    ,     lowerCamelCase_  :						Dict[str, int] = None    ,     lowerCamelCase_  :						PILImageResampling = PIL.Image.BICUBIC    ,     lowerCamelCase_  :						bool = True    ,     lowerCamelCase_  :						Dict[str, int] = None    ,     lowerCamelCase_  :						Union[int, float] = 1 / 255    ,     lowerCamelCase_  :						bool = True    ,     lowerCamelCase_  :						bool = True    ,     lowerCamelCase_  :						Optional[Union[float, List[float]]] = None    ,     lowerCamelCase_  :						Optional[Union[float, List[float]]] = None    ,     **lowerCamelCase_  :						Optional[Any]    ,     )       ->None:
     '''simple docstring'''







     super().__init__(**lowerCamelCase_      )
     UpperCAmelCase__         = size if size is not None else {"""height""": 256, """width""": 256}
     UpperCAmelCase__         = get_size_dict(lowerCamelCase_      )
     UpperCAmelCase__         = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
     UpperCAmelCase__         = get_size_dict(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 IMAGENET_STANDARD_MEAN
     UpperCAmelCase__         = image_std if image_std is not None else IMAGENET_STANDARD_STD




   def 						UpperCAmelCase							(       self  :						Dict    ,     lowerCamelCase_  :						np.ndarray    ,     lowerCamelCase_  :						Dict[str, int]    ,     lowerCamelCase_  :						PILImageResampling = PIL.Image.BICUBIC    ,     lowerCamelCase_  :						Optional[Union[str, ChannelDimension]] = None    ,     **lowerCamelCase_  :						List[str]    ,     )       ->np.ndarray:
     '''simple docstring'''







     UpperCAmelCase__         = get_size_dict(lowerCamelCase_      )
     if "height" not in size or "width" not in size:
       raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
     return resize(
         lowerCamelCase_    ,     size=(size["""height"""], size["""width"""])    ,     resample=lowerCamelCase_    ,     data_format=lowerCamelCase_    ,     **lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						Optional[Any]    ,     lowerCamelCase_  :						np.ndarray    ,     lowerCamelCase_  :						Dict[str, int]    ,     lowerCamelCase_  :						Optional[Union[str, ChannelDimension]] = None    ,     **lowerCamelCase_  :						Optional[int]    ,     )       ->np.ndarray:
     '''simple docstring'''







     UpperCAmelCase__         = get_size_dict(lowerCamelCase_      )
     if "height" not in size or "width" not in size:
       raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''      )
     return center_crop(lowerCamelCase_    ,     size=(size["""height"""], size["""width"""])    ,     data_format=lowerCamelCase_    ,     **lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						Optional[int]    ,     lowerCamelCase_  :						np.ndarray    ,     lowerCamelCase_  :						Union[int, float]    ,     lowerCamelCase_  :						Optional[Union[str, ChannelDimension]] = None    ,     **lowerCamelCase_  :						Optional[Any]    ,     )       ->int:
     '''simple docstring'''







     return rescale(lowerCamelCase_    ,     scale=lowerCamelCase_    ,     data_format=lowerCamelCase_    ,     **lowerCamelCase_      )




   def 						UpperCAmelCase							(       self  :						Union[str, Any]    ,     lowerCamelCase_  :						np.ndarray    ,     lowerCamelCase_  :						Union[float, List[float]]    ,     lowerCamelCase_  :						Union[float, List[float]]    ,     lowerCamelCase_  :						Optional[Union[str, ChannelDimension]] = None    ,     **lowerCamelCase_  :						int    ,     )       ->np.ndarray:
     '''simple docstring'''







     return normalize(lowerCamelCase_    ,     mean=lowerCamelCase_    ,     std=lowerCamelCase_    ,     data_format=lowerCamelCase_    ,     **lowerCamelCase_      )







   def 						UpperCAmelCase							(       self  :						Tuple    ,     lowerCamelCase_  :						ImageInput    ,     lowerCamelCase_  :						bool = None    ,     lowerCamelCase_  :						Dict[str, int] = None    ,     lowerCamelCase_  :						Any=None    ,     lowerCamelCase_  :						bool = None    ,     lowerCamelCase_  :						Dict[str, 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_  :						Optional[Union[str, TensorType]] = None    ,     lowerCamelCase_  :						ChannelDimension = ChannelDimension.FIRST    ,     **lowerCamelCase_  :						Union[str, Any]    ,     )       ->PIL.Image.Image:
     '''simple docstring'''







     UpperCAmelCase__         = do_resize if do_resize is not None else self.do_resize
     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__         = 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__         = size if size is not None else self.size
     UpperCAmelCase__         = get_size_dict(lowerCamelCase_      )
     UpperCAmelCase__         = crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase__         = get_size_dict(lowerCamelCase_    ,     param_name="""crop_size"""      )

     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 or resample is None:
       raise ValueError("""Size and resample 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."""      )

     # 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""" )