
"""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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_							=					logging.get_logger(__name__)





class 				snake_case    (			_UpperCAmelCase						):
   a_ :						List[Any]			 =			['''pixel_values''']







   def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = PILImageResampling.BICUBIC    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = 1 / 2_55    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)					->None:
      super().__init__(**lowerCAmelCase__)
      a_							=			size if size is not None else {"height": 2_24, "width": 2_24}
      a_							=			get_size_dict(lowerCAmelCase__)
      a_							=			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
      a_							=			get_size_dict(lowerCAmelCase__    ,				default_to_square=lowerCAmelCase__    ,				param_name="crop_size")

      a_							=			do_resize
      a_							=			do_rescale
      a_							=			do_normalize
      a_							=			do_center_crop
      a_							=			crop_size
      a_							=			size
      a_							=			resample
      a_							=			rescale_factor
      a_							=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      a_							=			image_std if image_std is not None else IMAGENET_DEFAULT_STD







   def 					UpperCAmelCase__     (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)					->np.ndarray:
      a_							=			get_size_dict(lowerCAmelCase__)

      if "shortest_edge" in size:
         a_							=			get_resize_output_image_size(lowerCAmelCase__    ,				size=size["shortest_edge"]    ,				default_to_square=lowerCAmelCase__)
         # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
         a_							=			(size["height"], size["width"])
      else:
         raise ValueError(F'''Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}''')
      return resize(lowerCAmelCase__    ,				size=lowerCAmelCase__    ,				resample=lowerCAmelCase__    ,				data_format=lowerCAmelCase__    ,				**lowerCAmelCase__)







   def 					UpperCAmelCase__     (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)					->np.ndarray:
      a_							=			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 					UpperCAmelCase__     (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase)					->np.ndarray:
      return rescale(lowerCAmelCase__    ,				scale=lowerCAmelCase__    ,				data_format=lowerCAmelCase__    ,				**lowerCAmelCase__)







   def 					UpperCAmelCase__     (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)					->np.ndarray:
      return normalize(lowerCAmelCase__    ,				mean=lowerCAmelCase__    ,				std=lowerCAmelCase__    ,				data_format=lowerCAmelCase__    ,				**lowerCAmelCase__)







   def 					UpperCAmelCase__     (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)					->BatchFeature:
      a_							=			do_resize if do_resize is not None else self.do_resize
      a_							=			do_rescale if do_rescale is not None else self.do_rescale
      a_							=			do_normalize if do_normalize is not None else self.do_normalize
      a_							=			do_center_crop if do_center_crop is not None else self.do_center_crop
      a_							=			crop_size if crop_size is not None else self.crop_size
      a_							=			get_size_dict(lowerCAmelCase__    ,				param_name="crop_size"    ,				default_to_square=lowerCAmelCase__)
      a_							=			resample if resample is not None else self.resample
      a_							=			rescale_factor if rescale_factor is not None else self.rescale_factor
      a_							=			image_mean if image_mean is not None else self.image_mean
      a_							=			image_std if image_std is not None else self.image_std

      a_							=			size if size is not None else self.size
      a_							=			get_size_dict(lowerCAmelCase__)

      if not is_batched(lowerCAmelCase__):
         a_							=			[images]

      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.")

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

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

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

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

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

      a_							=			[to_channel_dimension_format(lowerCAmelCase__    ,				lowerCAmelCase__) for image in images]

      a_							=			{"pixel_values": images}
      return BatchFeature(data=lowerCAmelCase__    ,				tensor_type=lowerCAmelCase__)
import warnings
from functools import wraps
from typing import Callable





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				@wraps(lowercase							)
				def _inner_fn(*lowercase							,			**lowercase							):
								warnings.warn(
								    (F"'{fn.__name__}' is experimental and might be subject to breaking changes in the future.")							,			lowercase							,			)
								return fn(*lowercase							,			**lowercase							)

				return _inner_fn