






"""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, 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, is_vision_available, logging


if is_vision_available():
					import PIL


UpperCamelCase							       =    logging.get_logger(__name__)




class    lowercase_    (_UpperCAmelCase     ):
						A__	:		Optional[Any]       			=				['''pixel_values''']






						def __init__(    self     ,     a_ = True     ,     a_ = None     ,     a_ = PIL.Image.BICUBIC     ,     a_ = True     ,     a_ = None     ,     a_ = 1 / 2_5_5     ,     a_ = True     ,     a_ = True     ,     a_ = None     ,     a_ = None     ,     **a_     ,     )	->None:







								'''simple docstring'''


								super().__init__(**a_	)
								_a							=	size if size is not None else {"height": 2_5_6, "width": 2_5_6}
								_a							=	get_size_dict(a_	)
								_a							=	crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
								_a							=	get_size_dict(a_     ,     param_name="crop_size"	)

								_a							=	do_resize
								_a							=	size
								_a							=	resample
								_a							=	do_center_crop
								_a							=	crop_size
								_a							=	do_rescale
								_a							=	rescale_factor
								_a							=	do_normalize
								_a							=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_a							=	image_std if image_std is not None else IMAGENET_STANDARD_STD






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_ = PIL.Image.BICUBIC     ,     a_ = None     ,     **a_     ,     )	->np.ndarray:







								'''simple docstring'''


								_a							=	get_size_dict(a_	)
								if "height" not in size or "width" not in size:
										raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
								return resize(
								    a_     ,     size=(size["height"], size["width"])     ,     resample=a_     ,     data_format=a_     ,     **a_	)






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_ = None     ,     **a_     ,     )	->np.ndarray:







								'''simple docstring'''


								_a							=	get_size_dict(a_	)
								if "height" not in size or "width" not in size:
										raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
								return center_crop(a_     ,     size=(size["height"], size["width"])     ,     data_format=a_     ,     **a_	)






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_ = None     ,     **a_     ,     )	->int:







								'''simple docstring'''


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






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_     ,     a_ = None     ,     **a_     ,     )	->np.ndarray:







								'''simple docstring'''


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






						def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None     ,     a_ = None     ,     a_=None     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = None     ,     a_ = ChannelDimension.FIRST     ,     **a_     ,     )	->PIL.Image.Image:







								'''simple docstring'''


								_a							=	do_resize if do_resize is not None else self.do_resize
								_a							=	resample if resample is not None else self.resample
								_a							=	do_center_crop if do_center_crop is not None else self.do_center_crop
								_a							=	do_rescale if do_rescale is not None else self.do_rescale
								_a							=	rescale_factor if rescale_factor is not None else self.rescale_factor
								_a							=	do_normalize if do_normalize is not None else self.do_normalize
								_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(a_	)
								_a							=	crop_size if crop_size is not None else self.crop_size
								_a							=	get_size_dict(a_     ,     param_name="crop_size"	)

								_a							=	make_list_of_images(a_	)

								if not valid_images(a_	):
										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_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.
								_a							=	[to_numpy_array(a_	) for image in images]

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

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

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

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

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

								_a							=	{"pixel_values": images}
								return BatchFeature(data=a_     ,     tensor_type=a_	)










"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


UpperCamelCase							       =    logging.get_logger(__name__)




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     *a_     ,     **a_	)	->None:







        '''simple docstring'''


        warnings.warn(
            "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
            " use YolosImageProcessor instead."     ,     a_     ,     )
        super().__init__(*a_     ,     **a_	)


