code: 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
  
  
  lowercase__    :       int						=			logging.get_logger(__name__)
  
  
  if is_vision_available():
  	import PIL
  
  
  
  
  class      lowercase_						(						UpperCamelCase_		):
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	UpperCAmelCase_						:      Dict     =       ["""pixel_values"""]
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = 1 / 255	,    __SCREAMING_SNAKE_CASE = True	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = True	,    **__SCREAMING_SNAKE_CASE	,    )		->None:
  								super().__init__(**__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	size if size is not None else {'''shortest_edge''': 224}
  								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
  								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )
  
  								lowerCAmelCase				=	do_resize
  								lowerCAmelCase				=	size
  								lowerCAmelCase				=	resample
  								lowerCAmelCase				=	do_center_crop
  								lowerCAmelCase				=	crop_size
  								lowerCAmelCase				=	do_rescale
  								lowerCAmelCase				=	rescale_factor
  								lowerCAmelCase				=	do_normalize
  								lowerCAmelCase				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
  								lowerCAmelCase				=	image_std if image_std is not None else OPENAI_CLIP_STD
  								lowerCAmelCase				=	do_convert_rgb
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
  								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
  								lowerCAmelCase				=	get_resize_output_image_size(
  								    __SCREAMING_SNAKE_CASE	,    size=(size['''height'''], size['''width'''])	,    default_to_square=__SCREAMING_SNAKE_CASE       )
  								return resize(__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE	,    resample=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
  								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )
  								return center_crop(__SCREAMING_SNAKE_CASE	,    size=(size['''height'''], size['''width'''])	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->List[str]:
  								return rescale(__SCREAMING_SNAKE_CASE	,    scale=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->np.ndarray:
  								return normalize(__SCREAMING_SNAKE_CASE	,    mean=__SCREAMING_SNAKE_CASE	,    std=__SCREAMING_SNAKE_CASE	,    data_format=__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,    **__SCREAMING_SNAKE_CASE	,    )		->PIL.Image.Image:
  								lowerCAmelCase				=	do_resize if do_resize is not None else self.do_resize
  								lowerCAmelCase				=	size if size is not None else self.size
  								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE	,    default_to_square=__SCREAMING_SNAKE_CASE       )
  								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				=	crop_size if crop_size is not None else self.crop_size
  								lowerCAmelCase				=	get_size_dict(__SCREAMING_SNAKE_CASE       )
  								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				=	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				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
  								lowerCAmelCase				=	make_list_of_images(__SCREAMING_SNAKE_CASE       )
  
  								if not valid_images(__SCREAMING_SNAKE_CASE       ):
  															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:
  															lowerCAmelCase				=	[convert_to_rgb(__SCREAMING_SNAKE_CASE       ) for image in images]
  
  								# All transformations expect numpy arrays.
  								lowerCAmelCase				=	[to_numpy_array(__SCREAMING_SNAKE_CASE       ) for image in images]
  
  								if do_resize:
  															lowerCAmelCase				=	[self.resize(image=__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE	,    resample=__SCREAMING_SNAKE_CASE       ) for image in images]
  
  								if do_center_crop:
  															lowerCAmelCase				=	[self.center_crop(image=__SCREAMING_SNAKE_CASE	,    size=__SCREAMING_SNAKE_CASE       ) for image in images]
  
  								if do_rescale:
  															lowerCAmelCase				=	[self.rescale(image=__SCREAMING_SNAKE_CASE	,    scale=__SCREAMING_SNAKE_CASE       ) for image in images]
  
  								if do_normalize:
  															lowerCAmelCase				=	[self.normalize(image=__SCREAMING_SNAKE_CASE	,    mean=__SCREAMING_SNAKE_CASE	,    std=__SCREAMING_SNAKE_CASE       ) for image in images]
  
  								lowerCAmelCase				=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) for image in images]
  
  								lowerCAmelCase				=	{'''pixel_values''': images}
  								return BatchFeature(data=__SCREAMING_SNAKE_CASE	,    tensor_type=__SCREAMING_SNAKE_CASE       )
  
  
  
  
style_context: import torch
  
  from accelerate import Accelerator, DistributedType
  
  
  
  
  class      lowercase_						:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  	def __enter__(							self       )		->Dict:
  								if self.enabled:
  															lowerCAmelCase				=	self.model.no_sync()
  															self.model_sync_obj.__enter__()
  
  								return self
  
  
  	def __exit__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Any:
  								if self.enabled:
  															# Average all models on exit
  															self._sync_and_avg_model_params()
  															self.model_sync_obj.__exit__(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
  
  
  	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = True       )		->Tuple:
  								if accelerator.distributed_type not in [
  								    DistributedType.NO,
  								    DistributedType.MULTI_CPU,
  								    DistributedType.MULTI_GPU,
  								]:
  															raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'''       )
  								lowerCAmelCase				=	enabled and accelerator.distributed_type != DistributedType.NO
  								lowerCAmelCase				=	0
  								if self.enabled:
  															lowerCAmelCase				=	accelerator
  															lowerCAmelCase				=	model
  															lowerCAmelCase				=	local_sgd_steps
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->List[Any]:
  								self.num_steps += 1
  								if not self.enabled:
  															return
  
  								if self.num_steps % self.local_sgd_steps == 0:
  															self._sync_and_avg_model_params()
  
  
  
  
  	def     SCREAMING_SNAKE_CASE_       (							self       )		->Optional[int]:
  								self.accelerator.wait_for_everyone()
  								with self.accelerator.autocast():
  															for param in self.model.parameters():
  																						lowerCAmelCase				=	self.accelerator.reduce(param.data	,    reduction='''mean'''       )
  
  
  
  