





'''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, is_vision_available, logging


if is_vision_available():
			import PIL


lowercase__						:      Tuple	=    logging.get_logger(__name__)
class     __lowerCAmelCase					(   __a			):






							"""simple docstring"""
							_snake_case     :					Any						    =   ["""pixel_values"""]





							def __init__(							self       :		str	,			lowerCAmelCase__       :		bool = True	,			lowerCAmelCase__       :		Dict[str, int] = None	,			lowerCAmelCase__       :		float = None	,			lowerCAmelCase__       :		PILImageResampling = PILImageResampling.BILINEAR	,			lowerCAmelCase__       :		bool = True	,			lowerCAmelCase__       :		Union[int, float] = 1 / 255	,			lowerCAmelCase__       :		bool = True	,			lowerCAmelCase__       :		Optional[Union[float, List[float]]] = None	,			lowerCAmelCase__       :		Optional[Union[float, List[float]]] = None	,			**lowerCAmelCase__       :		int	,			)   ->					List[Any]:

												'''simple docstring'''




												super().__init__(**a__					)
												_UpperCamelCase       =	size if size is not None else {'''shortest_edge''': 384}
												_UpperCamelCase       =	get_size_dict(a__	,			default_to_square=a__					)

												_UpperCamelCase       =	do_resize
												_UpperCamelCase       =	size
												# Default value set here for backwards compatibility where the value in config is None
												_UpperCamelCase       =	crop_pct if crop_pct is not None else 224 / 256
												_UpperCamelCase       =	resample
												_UpperCamelCase       =	do_rescale
												_UpperCamelCase       =	rescale_factor
												_UpperCamelCase       =	do_normalize
												_UpperCamelCase       =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												_UpperCamelCase       =	image_std if image_std is not None else IMAGENET_STANDARD_STD





							def snake_case__						(							self       :		List[str]	,			lowerCAmelCase__       :		np.ndarray	,			lowerCAmelCase__       :		Dict[str, int]	,			lowerCAmelCase__       :		float	,			lowerCAmelCase__       :		PILImageResampling = PILImageResampling.BICUBIC	,			lowerCAmelCase__       :		Optional[Union[str, ChannelDimension]] = None	,			**lowerCAmelCase__       :		List[Any]	,			)   ->					Tuple:

												'''simple docstring'''




												_UpperCamelCase       =	get_size_dict(a__	,			default_to_square=a__					)
												if "shortest_edge" not in size:
																	raise ValueError(f"""Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"""					)
												_UpperCamelCase       =	size['''shortest_edge''']

												if shortest_edge < 384:
																	# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																	_UpperCamelCase       =	int(shortest_edge / crop_pct					)
																	_UpperCamelCase       =	get_resize_output_image_size(a__	,			size=a__	,			default_to_square=a__					)
																	_UpperCamelCase       =	resize(image=a__	,			size=a__	,			resample=a__	,			data_format=a__	,			**a__					)
																	# then crop to (shortest_edge, shortest_edge)
																	return center_crop(image=a__	,			size=(shortest_edge, shortest_edge)	,			data_format=a__	,			**a__					)
												else:
																	# warping (no cropping) when evaluated at 384 or larger
																	return resize(
																	    a__	,			size=(shortest_edge, shortest_edge)	,			resample=a__	,			data_format=a__	,			**a__					)





							def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		np.ndarray	,			lowerCAmelCase__       :		Union[int, float]	,			lowerCAmelCase__       :		Optional[Union[str, ChannelDimension]] = None	,			**lowerCAmelCase__       :		Tuple	,			)   ->					int:

												'''simple docstring'''




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





							def snake_case__						(							self       :		Optional[int]	,			lowerCAmelCase__       :		np.ndarray	,			lowerCAmelCase__       :		Union[float, List[float]]	,			lowerCAmelCase__       :		Union[float, List[float]]	,			lowerCAmelCase__       :		Optional[Union[str, ChannelDimension]] = None	,			**lowerCAmelCase__       :		int	,			)   ->					Dict:

												'''simple docstring'''




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







							def snake_case__						(							self       :		List[Any]	,			lowerCAmelCase__       :		ImageInput	,			lowerCAmelCase__       :		bool = None	,			lowerCAmelCase__       :		Dict[str, int] = None	,			lowerCAmelCase__       :		float = None	,			lowerCAmelCase__       :		PILImageResampling = None	,			lowerCAmelCase__       :		bool = None	,			lowerCAmelCase__       :		float = None	,			lowerCAmelCase__       :		bool = None	,			lowerCAmelCase__       :		Optional[Union[float, List[float]]] = None	,			lowerCAmelCase__       :		Optional[Union[float, List[float]]] = None	,			lowerCAmelCase__       :		Optional[Union[str, TensorType]] = None	,			lowerCAmelCase__       :		ChannelDimension = ChannelDimension.FIRST	,			**lowerCAmelCase__       :		Optional[int]	,			)   ->					Tuple:

												'''simple docstring'''




												_UpperCamelCase       =	do_resize if do_resize is not None else self.do_resize
												_UpperCamelCase       =	crop_pct if crop_pct is not None else self.crop_pct
												_UpperCamelCase       =	resample if resample is not None else self.resample
												_UpperCamelCase       =	do_rescale if do_rescale is not None else self.do_rescale
												_UpperCamelCase       =	rescale_factor if rescale_factor is not None else self.rescale_factor
												_UpperCamelCase       =	do_normalize if do_normalize is not None else self.do_normalize
												_UpperCamelCase       =	image_mean if image_mean is not None else self.image_mean
												_UpperCamelCase       =	image_std if image_std is not None else self.image_std

												_UpperCamelCase       =	size if size is not None else self.size
												_UpperCamelCase       =	get_size_dict(a__	,			default_to_square=a__					)

												_UpperCamelCase       =	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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																	raise ValueError('''crop_pct must be specified if size < 384.'''					)

												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.
												_UpperCamelCase       =	[to_numpy_array(a__					) for image in images]

												if do_resize:
																	_UpperCamelCase       =	[self.resize(image=a__	,			size=a__	,			crop_pct=a__	,			resample=a__					) for image in images]

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

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

												_UpperCamelCase       =	[to_channel_dimension_format(a__	,			a__					) for image in images]

												_UpperCamelCase       =	{'''pixel_values''': images}
												return BatchFeature(data=a__	,			tensor_type=a__					)










'''simple docstring'''

import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


lowercase__						:      Optional[Any]	=    logging.get_logger(__name__)
class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       def __init__(							self       :		List[Any]	,			*lowerCAmelCase__       :		List[str]	,			**lowerCAmelCase__       :		List[str]					)   ->					None:

            '''simple docstring'''




            warnings.warn(
                '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                ''' use OwlViTImageProcessor instead.'''	,			lowerCAmelCase__	,			)
            super().__init__(*lowerCAmelCase__	,			**lowerCAmelCase__					)



