





'''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 (
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase					:		List[str]		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	str       					=				["""pixel_values"""]







  def __init__(		self     ,     lowercase__ = True     ,     lowercase__ = None     ,     lowercase__ = PILImageResampling.BILINEAR     ,     lowercase__ = True     ,     lowercase__ = None     ,     lowercase__ = True     ,     lowercase__ = 1 / 255     ,     lowercase__ = True     ,     lowercase__ = None     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						None:






        """simple docstring"""





        super().__init__(**lowercase__		)
        _snake_case		:	Optional[int]					     =     size if size is not None else {'''shortest_edge''': 256}
        _snake_case		:	Union[str, Any]					     =     get_size_dict(lowercase__     ,     default_to_square=lowercase__		)
        _snake_case		:	List[Any]					     =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
        _snake_case		:	Union[str, Any]					     =     get_size_dict(lowercase__		)
        _snake_case		:	int					     =     do_resize
        _snake_case		:	Dict					     =     size
        _snake_case		:	Optional[int]					     =     resample
        _snake_case		:	Dict					     =     do_center_crop
        _snake_case		:	Any					     =     crop_size
        _snake_case		:	List[Any]					     =     do_rescale
        _snake_case		:	List[str]					     =     rescale_factor
        _snake_case		:	Union[str, Any]					     =     do_normalize
        _snake_case		:	Any					     =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _snake_case		:	Any					     =     image_std if image_std is not None else IMAGENET_STANDARD_STD







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = PILImageResampling.BICUBIC     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						np.ndarray:






        """simple docstring"""





        _snake_case		:	Dict					     =     get_size_dict(lowercase__     ,     default_to_square=lowercase__		)
        if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
        _snake_case		:	Optional[int]					     =     get_resize_output_image_size(lowercase__     ,     size=size['''shortest_edge''']     ,     default_to_square=lowercase__		)
        return resize(lowercase__     ,     size=lowercase__     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						np.ndarray:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     get_size_dict(lowercase__		)
        return center_crop(lowercase__     ,     size=(size['''height'''], size['''width'''])     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__		)		->						np.ndarray:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						np.ndarray:






        """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						Union[str, Any]:






        """simple docstring"""





        _snake_case		:	List[str]					     =     do_resize if do_resize is not None else self.do_resize
        _snake_case		:	int					     =     size if size is not None else self.size
        _snake_case		:	Optional[int]					     =     get_size_dict(lowercase__     ,     default_to_square=lowercase__		)
        _snake_case		:	List[str]					     =     resample if resample is not None else self.resample
        _snake_case		:	Tuple					     =     do_center_crop if do_center_crop is not None else self.do_center_crop
        _snake_case		:	Union[str, Any]					     =     crop_size if crop_size is not None else self.crop_size
        _snake_case		:	Dict					     =     get_size_dict(lowercase__		)
        _snake_case		:	Optional[Any]					     =     do_rescale if do_rescale is not None else self.do_rescale
        _snake_case		:	Dict					     =     rescale_factor if rescale_factor is not None else self.rescale_factor
        _snake_case		:	Optional[int]					     =     do_normalize if do_normalize is not None else self.do_normalize
        _snake_case		:	Optional[Any]					     =     image_mean if image_mean is not None else self.image_mean
        _snake_case		:	Optional[int]					     =     image_std if image_std is not None else self.image_std

        _snake_case		:	Optional[int]					     =     make_list_of_images(lowercase__		)

        if not valid_images(lowercase__		):
              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.'''		)

        # All transformations expect numpy arrays.
        _snake_case		:	Union[str, Any]					     =     [to_numpy_array(lowercase__		) for image in images]

        if do_resize:
              _snake_case		:	Dict					     =     [self.resize(image=lowercase__     ,     size=lowercase__     ,     resample=lowercase__		) for image in images]

        if do_center_crop:
              _snake_case		:	Any					     =     [self.center_crop(image=lowercase__     ,     size=lowercase__		) for image in images]

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

        if do_normalize:
              _snake_case		:	Any					     =     [self.normalize(image=lowercase__     ,     mean=lowercase__     ,     std=lowercase__		) for image in images]

        _snake_case		:	List[Any]					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

        _snake_case		:	int					     =     {'''pixel_values''': images}
        return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)









'''simple docstring'''

import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class lowerCamelCase						(unittest.TestCase					):







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






        """simple docstring"""





        _snake_case		:	Any					     =     torch.nn.Linear(10     ,     10		)
        _snake_case		:	Optional[int]					     =     torch.optim.SGD(model.parameters()     ,     0.1		)
        _snake_case		:	List[str]					     =     Accelerator()
        _snake_case		:	Optional[Any]					     =     accelerator.prepare(lowercase__		)
        try:
              pickle.loads(pickle.dumps(lowercase__		)		)
        except Exception as e:
              self.fail(F'''Accelerated optimizer pickling failed with {e}'''		)
        AcceleratorState._reset_state()


