





from ..utils import DummyObject, requires_backends





class 				a			(      metaclass=__lowercase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     ['''torch''', '''scipy''']






				def __init__(  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(self			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)






				@classmethod
				def 			snake_case_    (  cls			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										requires_backends(cls			, ['''torch''', '''scipy''']					)










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


lowerCAmelCase							:       str					 =	logging.get_logger(__name__)





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''pixel_values''']






				def __init__(  self			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, _lowerCAmelCase = PILImageResampling.BILINEAR			, _lowerCAmelCase = True			, _lowerCAmelCase = 1 / 255			, _lowerCAmelCase = True			, _lowerCAmelCase = None			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size if size is not None else {'''shortest_edge''': 384}
										__SCREAMING_SNAKE_CASE:				List[str]           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   size
										# Default value set here for backwards compatibility where the value in config is None
										__SCREAMING_SNAKE_CASE:				List[str]           =   crop_pct if crop_pct is not None else 224 / 256
										__SCREAMING_SNAKE_CASE:				List[Any]           =   resample
										__SCREAMING_SNAKE_CASE:				Any           =   do_rescale
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   rescale_factor
										__SCREAMING_SNAKE_CASE:				Any           =   do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_std if image_std is not None else IMAGENET_STANDARD_STD






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = PILImageResampling.BICUBIC			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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

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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase = None			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										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			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   do_resize if do_resize is not None else self.do_resize
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   crop_pct if crop_pct is not None else self.crop_pct
										__SCREAMING_SNAKE_CASE:				Tuple           =   resample if resample is not None else self.resample
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   do_rescale if do_rescale is not None else self.do_rescale
										__SCREAMING_SNAKE_CASE:				Any           =   rescale_factor if rescale_factor is not None else self.rescale_factor
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   do_normalize if do_normalize is not None else self.do_normalize
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   image_mean if image_mean is not None else self.image_mean
										__SCREAMING_SNAKE_CASE:				str           =   image_std if image_std is not None else self.image_std

										__SCREAMING_SNAKE_CASE:				List[Any]           =   size if size is not None else self.size
										__SCREAMING_SNAKE_CASE:				Dict           =   get_size_dict(_lowerCAmelCase			, default_to_square=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   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_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.
										__SCREAMING_SNAKE_CASE:				Tuple           =   [to_numpy_array(_lowerCAmelCase					) for image in images]

										if do_resize:
																__SCREAMING_SNAKE_CASE:				str           =   [self.resize(image=_lowerCAmelCase			, size=_lowerCAmelCase			, crop_pct=_lowerCAmelCase			, resample=_lowerCAmelCase					) for image in images]

										if do_rescale:
																__SCREAMING_SNAKE_CASE:				Any           =   [self.rescale(image=_lowerCAmelCase			, scale=_lowerCAmelCase					) for image in images]

										if do_normalize:
																__SCREAMING_SNAKE_CASE:				int           =   [self.normalize(image=_lowerCAmelCase			, mean=_lowerCAmelCase			, std=_lowerCAmelCase					) for image in images]

										__SCREAMING_SNAKE_CASE:				str           =   [to_channel_dimension_format(_lowerCAmelCase			, _lowerCAmelCase					) for image in images]

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



