
"""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


lowerCamelCase__        =		logging.get_logger(__name__)




class 			A__	(     _lowerCamelCase):
		A_						:	Optional[Any]           =     ['pixel_values']

		def __init__(				self	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = 1 / 2_55	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			super().__init__(**_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						List[Any]						      =				size if size is not None else {'shortest_edge': 2_56}
			__lowerCAmelCase      :						int						      =				get_size_dict(_SCREAMING_SNAKE_CASE	,		default_to_square=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
			__lowerCAmelCase      :						str						      =				get_size_dict(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[int]						      =				do_resize
			__lowerCAmelCase      :						str						      =				size
			__lowerCAmelCase      :						int						      =				resample
			__lowerCAmelCase      :						str						      =				do_center_crop
			__lowerCAmelCase      :						Union[str, Any]						      =				crop_size
			__lowerCAmelCase      :						Dict						      =				do_rescale
			__lowerCAmelCase      :						str						      =				rescale_factor
			__lowerCAmelCase      :						str						      =				do_normalize
			__lowerCAmelCase      :						str						      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__lowerCAmelCase      :						Union[str, Any]						      =				image_std if image_std is not None else IMAGENET_STANDARD_STD

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						Optional[Any]						      =				get_size_dict(_SCREAMING_SNAKE_CASE	,		default_to_square=_SCREAMING_SNAKE_CASE			)
			if "shortest_edge" not in size:
				raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
			__lowerCAmelCase      :						Dict						      =				get_resize_output_image_size(_SCREAMING_SNAKE_CASE	,		size=size['shortest_edge']	,		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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						int						      =				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 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE			):
			return rescale(_SCREAMING_SNAKE_CASE	,		scale=_SCREAMING_SNAKE_CASE	,		data_format=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
			return normalize(_SCREAMING_SNAKE_CASE	,		mean=_SCREAMING_SNAKE_CASE	,		std=_SCREAMING_SNAKE_CASE	,		data_format=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				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 = ChannelDimension.FIRST	,		**_SCREAMING_SNAKE_CASE	,		):
			__lowerCAmelCase      :						str						      =				do_resize if do_resize is not None else self.do_resize
			__lowerCAmelCase      :						Optional[Any]						      =				size if size is not None else self.size
			__lowerCAmelCase      :						List[str]						      =				get_size_dict(_SCREAMING_SNAKE_CASE	,		default_to_square=_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Dict						      =				resample if resample is not None else self.resample
			__lowerCAmelCase      :						Optional[int]						      =				do_center_crop if do_center_crop is not None else self.do_center_crop
			__lowerCAmelCase      :						int						      =				crop_size if crop_size is not None else self.crop_size
			__lowerCAmelCase      :						List[str]						      =				get_size_dict(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Union[str, Any]						      =				do_rescale if do_rescale is not None else self.do_rescale
			__lowerCAmelCase      :						Optional[int]						      =				rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowerCAmelCase      :						List[str]						      =				do_normalize if do_normalize is not None else self.do_normalize
			__lowerCAmelCase      :						int						      =				image_mean if image_mean is not None else self.image_mean
			__lowerCAmelCase      :						Optional[int]						      =				image_std if image_std is not None else self.image_std

			__lowerCAmelCase      :						List[str]						      =				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.'			)

			# All transformations expect numpy arrays.
			__lowerCAmelCase      :						Optional[Any]						      =				[to_numpy_array(_SCREAMING_SNAKE_CASE			) for image in images]

			if do_resize:
				__lowerCAmelCase      :						List[str]						      =				[self.resize(image=_SCREAMING_SNAKE_CASE	,		size=_SCREAMING_SNAKE_CASE	,		resample=_SCREAMING_SNAKE_CASE			) for image in images]

			if do_center_crop:
				__lowerCAmelCase      :						Dict						      =				[self.center_crop(image=_SCREAMING_SNAKE_CASE	,		size=_SCREAMING_SNAKE_CASE			) for image in images]

			if do_rescale:
				__lowerCAmelCase      :						Union[str, Any]						      =				[self.rescale(image=_SCREAMING_SNAKE_CASE	,		scale=_SCREAMING_SNAKE_CASE			) for image in images]

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

			__lowerCAmelCase      :						Optional[int]						      =				[to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) for image in images]

			__lowerCAmelCase      :						List[str]						      =				{'pixel_values': images}
			return BatchFeature(data=_SCREAMING_SNAKE_CASE	,		tensor_type=_SCREAMING_SNAKE_CASE			)

"""simple docstring"""







import warnings
from functools import wraps
from typing import Callable
def    __lowerCAmelCase    (_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