


'''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 convert_to_rgb, 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


if is_vision_available():
		import PIL


__A	=logging.get_logger(__name__)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :Optional[int]   =					['''pixel_values''']




					def __init__(      self							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = True							,							_lowerCamelCase = 1 / 255							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							**_lowerCamelCase							,							):
								super().__init__(**_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     size if size is not None else {"""height""": 384, """width""": 384}
								UpperCAmelCase__  :					int        =     get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     do_resize
								UpperCAmelCase__  :					int        =     size
								UpperCAmelCase__  :					Union[str, Any]        =     resample
								UpperCAmelCase__  :					Union[str, Any]        =     do_rescale
								UpperCAmelCase__  :					Any        =     rescale_factor
								UpperCAmelCase__  :					Optional[int]        =     do_normalize
								UpperCAmelCase__  :					str        =     image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								UpperCAmelCase__  :					Any        =     image_std if image_std is not None else OPENAI_CLIP_STD
								UpperCAmelCase__  :					int        =     do_convert_rgb




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					List[str]        =     get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)
								if "height" not in size or "width" not in size:
											raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}''')
								UpperCAmelCase__  :					List[Any]        =     (size["""height"""], size["""width"""])
								return resize(_lowerCamelCase							,							size=_lowerCamelCase							,							resample=_lowerCamelCase							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								return rescale(_lowerCamelCase							,							scale=_lowerCamelCase							,							data_format=_lowerCamelCase							,							**_lowerCamelCase)




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




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = ChannelDimension.FIRST							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					List[Any]        =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase__  :					List[str]        =     resample if resample is not None else self.resample
								UpperCAmelCase__  :					List[Any]        =     do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase__  :					List[Any]        =     rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase__  :					int        =     do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase__  :					Optional[int]        =     image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase__  :					Union[str, Any]        =     image_std if image_std is not None else self.image_std
								UpperCAmelCase__  :					Dict        =     do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								UpperCAmelCase__  :					Optional[int]        =     size if size is not None else self.size
								UpperCAmelCase__  :					Optional[Any]        =     get_size_dict(_lowerCamelCase							,							default_to_square=_lowerCamelCase)

								UpperCAmelCase__  :					Dict        =     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_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:
											UpperCAmelCase__  :					List[Any]        =     [convert_to_rgb(_lowerCamelCase) for image in images]

								# All transformations expect numpy arrays.
								UpperCAmelCase__  :					int        =     [to_numpy_array(_lowerCamelCase) for image in images]

								if do_resize:
											UpperCAmelCase__  :					Tuple        =     [self.resize(image=_lowerCamelCase							,							size=_lowerCamelCase							,							resample=_lowerCamelCase) for image in images]

								if do_rescale:
											UpperCAmelCase__  :					Tuple        =     [self.rescale(image=_lowerCamelCase							,							scale=_lowerCamelCase) for image in images]

								if do_normalize:
											UpperCAmelCase__  :					List[str]        =     [self.normalize(image=_lowerCamelCase							,							mean=_lowerCamelCase							,							std=_lowerCamelCase) for image in images]

								UpperCAmelCase__  :					Tuple        =     [to_channel_dimension_format(_lowerCamelCase							,							_lowerCamelCase) for image in images]

								UpperCAmelCase__  :					Dict        =     BatchFeature(data={"""pixel_values""": images}							,							tensor_type=_lowerCamelCase)

								return encoded_outputs



'''simple docstring'''



import warnings
from functools import wraps
from typing import Callable







def     _UpperCamelCase      (		UpperCamelCase__					):

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

			return _inner_fn