'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

A_     :						Optional[Any]    							=logging.get_logger(__name__)



def snake_case_      (					__snake_case  :							Tuple)			->					List[List[ImageInput]]:
   if isinstance(__snake_case	,   (list, tuple)) and isinstance(videos[0]	,   (list, tuple)) and is_valid_image(videos[0][0]):
      return videos

   elif isinstance(__snake_case	,   (list, tuple)) and is_valid_image(videos[0]):
      return [videos]

   elif is_valid_image(__snake_case):
      return [[videos]]

   raise ValueError(F'''Could not make batched video from {videos}''')






class     __UpperCAmelCase       ( __a		):
  __A :				Tuple											= ['pixel_values']





  def __init__(	self    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = PILImageResampling.BILINEAR    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = True    ,     _lowerCamelCase = 1 / 255    ,     _lowerCamelCase = True    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )
     lowerCAmelCase_			=      size if size is not None else {'''shortest_edge''': 256}
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase )
     lowerCAmelCase_			=      crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     param_name='''crop_size''' )

     lowerCAmelCase_			=      do_resize
     lowerCAmelCase_			=      size
     lowerCAmelCase_			=      do_center_crop
     lowerCAmelCase_			=      crop_size
     lowerCAmelCase_			=      resample
     lowerCAmelCase_			=      do_rescale
     lowerCAmelCase_			=      rescale_factor
     lowerCAmelCase_			=      offset
     lowerCAmelCase_			=      do_normalize
     lowerCAmelCase_			=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     lowerCAmelCase_			=      image_std if image_std is not None else IMAGENET_STANDARD_STD





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = PILImageResampling.BILINEAR    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase )
     if "shortest_edge" in size:
        lowerCAmelCase_			=      get_resize_output_image_size(_lowerCamelCase    ,     size['''shortest_edge''']    ,     default_to_square=_lowerCamelCase )
     elif "height" in size and "width" in size:
        lowerCAmelCase_			=      (size['''height'''], size['''width'''])
     else:
        raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
     return resize(_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase )
     if "height" not in size or "width" not in size:
        raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
     return center_crop(_lowerCamelCase    ,     size=(size['''height'''], size['''width'''])    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = True    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      image.astype(np.floataa )
     if offset:
        lowerCAmelCase_			=      image - (scale / 2)
     return rescale(_lowerCamelCase    ,     scale=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     **_lowerCamelCase    ,     ):
     return normalize(_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     **_lowerCamelCase )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = ChannelDimension.FIRST    ,     ):
     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.''' )

     if offset and not do_rescale:
        raise ValueError('''For offset, do_rescale must also be set to True.''' )

     # All transformations expect numpy arrays.
     lowerCAmelCase_			=      to_numpy_array(_lowerCamelCase )

     if do_resize:
        lowerCAmelCase_			=      self.resize(image=_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase )

     if do_center_crop:
        lowerCAmelCase_			=      self.center_crop(_lowerCamelCase    ,     size=_lowerCamelCase )

     if do_rescale:
        lowerCAmelCase_			=      self.rescale(image=_lowerCamelCase    ,     scale=_lowerCamelCase    ,     offset=_lowerCamelCase )

     if do_normalize:
        lowerCAmelCase_			=      self.normalize(image=_lowerCamelCase    ,     mean=_lowerCamelCase    ,     std=_lowerCamelCase )

     lowerCAmelCase_			=      to_channel_dimension_format(_lowerCamelCase    ,     _lowerCamelCase )
     return image







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = None    ,     _lowerCamelCase = ChannelDimension.FIRST    ,     **_lowerCamelCase    ,     ):
     lowerCAmelCase_			=      do_resize if do_resize is not None else self.do_resize
     lowerCAmelCase_			=      resample if resample is not None else self.resample
     lowerCAmelCase_			=      do_center_crop if do_center_crop is not None else self.do_center_crop
     lowerCAmelCase_			=      do_rescale if do_rescale is not None else self.do_rescale
     lowerCAmelCase_			=      rescale_factor if rescale_factor is not None else self.rescale_factor
     lowerCAmelCase_			=      offset if offset is not None else self.offset
     lowerCAmelCase_			=      do_normalize if do_normalize is not None else self.do_normalize
     lowerCAmelCase_			=      image_mean if image_mean is not None else self.image_mean
     lowerCAmelCase_			=      image_std if image_std is not None else self.image_std

     lowerCAmelCase_			=      size if size is not None else self.size
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     default_to_square=_lowerCamelCase )
     lowerCAmelCase_			=      crop_size if crop_size is not None else self.crop_size
     lowerCAmelCase_			=      get_size_dict(_lowerCamelCase    ,     param_name='''crop_size''' )

     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.''' )

     lowerCAmelCase_			=      make_batched(_lowerCamelCase )

     lowerCAmelCase_			=      [
         [
             self._preprocess_image(
                 image=_lowerCamelCase    ,     do_resize=_lowerCamelCase    ,     size=_lowerCamelCase    ,     resample=_lowerCamelCase    ,     do_center_crop=_lowerCamelCase    ,     crop_size=_lowerCamelCase    ,     do_rescale=_lowerCamelCase    ,     rescale_factor=_lowerCamelCase    ,     offset=_lowerCamelCase    ,     do_normalize=_lowerCamelCase    ,     image_mean=_lowerCamelCase    ,     image_std=_lowerCamelCase    ,     data_format=_lowerCamelCase    ,     )
             for img in video
         ]
         for video in videos
     ]

     lowerCAmelCase_			=      {'''pixel_values''': videos}
     return BatchFeature(data=_lowerCamelCase    ,     tensor_type=_lowerCamelCase )






'''simple docstring'''

import gc
import threading
import time

import psutil
import torch


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      psutil.Process()
     lowerCAmelCase_			=      False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      -1

     while True:
        lowerCAmelCase_			=      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 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      True
     lowerCAmelCase_			=      threading.Thread(target=self.peak_monitor )
     lowerCAmelCase_			=      True
     self.thread.start()







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      False
     self.thread.join()
     return self.cpu_memory_peak






A_     :						List[str]    							=PeakCPUMemory()



def snake_case_      (					)			->					Tuple:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time()}

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

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

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

   return measures



def snake_case_      (					__snake_case  :							Any)			->					List[str]:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time() - start_measures['''time''']}

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

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

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

   return measures






def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Optional[int])			->					Dict:
   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''')
      lowerCAmelCase_			=      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''')





