"""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


_A         =		logging.get_logger(__name__)
class 	__UpperCAmelCase						(lowerCamelCase__    ):







			'''simple docstring'''
			_UpperCamelCase     :	Tuple				    =							['pixel_values']





			def __init__( self      ,			snake_case_ = True      ,			snake_case_ = None      ,			snake_case_ = PIL.Image.NEAREST      ,			snake_case_ = False      ,			snake_case_ = None      ,			snake_case_ = 1 / 255      ,			snake_case_ = False      ,			snake_case_ = True      ,			snake_case_ = True      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = True      ,			**snake_case_      ,			):







										'''simple docstring'''


										super().__init__(**SCREAMING_SNAKE_CASE_       )
										A__					:    List[str]														= size if size is not None else {"""height""": 346, """width""": 346}
										A__					:    Any														= get_size_dict(SCREAMING_SNAKE_CASE_       )
										A__					:    Tuple														= crop_size if crop_size is not None else {"""height""": 289, """width""": 289}
										A__					:    Tuple														= get_size_dict(SCREAMING_SNAKE_CASE_      ,			param_name="""crop_size"""       )

										A__					:    str														= do_resize
										A__					:    int														= size
										A__					:    Optional[int]														= resample
										A__					:    Any														= do_center_crop
										A__					:    Any														= crop_size
										A__					:    Any														= do_rescale
										A__					:    Dict														= rescale_factor
										A__					:    List[str]														= rescale_offset
										A__					:    List[Any]														= do_normalize
										A__					:    List[str]														= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										A__					:    Dict														= image_std if image_std is not None else IMAGENET_STANDARD_STD
										A__					:    int														= include_top





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_ = PIL.Image.NEAREST      ,			snake_case_ = None      ,			**snake_case_      ,			):







										'''simple docstring'''


										A__					:    Any														= get_size_dict(SCREAMING_SNAKE_CASE_       )
										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(
										    SCREAMING_SNAKE_CASE_      ,			size=(size["""height"""], size["""width"""])      ,			resample=SCREAMING_SNAKE_CASE_      ,			data_format=SCREAMING_SNAKE_CASE_      ,			**SCREAMING_SNAKE_CASE_       )





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







										'''simple docstring'''


										A__					:    Tuple														= get_size_dict(SCREAMING_SNAKE_CASE_       )
										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(SCREAMING_SNAKE_CASE_      ,			size=(size["""height"""], size["""width"""])      ,			data_format=SCREAMING_SNAKE_CASE_      ,			**SCREAMING_SNAKE_CASE_       )





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_      ,			snake_case_ = True      ,			snake_case_ = None      ,			**snake_case_      ,			):







										'''simple docstring'''


										if offset:
																	A__					:    List[str]														= (image - 1_27.5) * scale
																	if data_format is not None:
																								A__					:    Tuple														= to_channel_dimension_format(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_       )
																	A__					:    Dict														= rescaled_image.astype(np.floataa       )
										else:
																	A__					:    Any														= rescale(SCREAMING_SNAKE_CASE_      ,			scale=SCREAMING_SNAKE_CASE_      ,			data_format=SCREAMING_SNAKE_CASE_      ,			**SCREAMING_SNAKE_CASE_       )
										return rescaled_image





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







										'''simple docstring'''


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





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_=None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = None      ,			snake_case_ = ChannelDimension.FIRST      ,			**snake_case_      ,			):







										'''simple docstring'''


										A__					:    Optional[Any]														= do_resize if do_resize is not None else self.do_resize
										A__					:    List[str]														= resample if resample is not None else self.resample
										A__					:    Dict														= do_center_crop if do_center_crop is not None else self.do_center_crop
										A__					:    Optional[Any]														= do_rescale if do_rescale is not None else self.do_rescale
										A__					:    str														= rescale_factor if rescale_factor is not None else self.rescale_factor
										A__					:    str														= rescale_offset if rescale_offset is not None else self.rescale_offset
										A__					:    str														= do_normalize if do_normalize is not None else self.do_normalize
										A__					:    List[Any]														= image_mean if image_mean is not None else self.image_mean
										A__					:    Optional[Any]														= image_std if image_std is not None else self.image_std
										A__					:    List[Any]														= include_top if include_top is not None else self.include_top

										A__					:    Dict														= size if size is not None else self.size
										A__					:    str														= get_size_dict(SCREAMING_SNAKE_CASE_       )
										A__					:    Optional[Any]														= crop_size if crop_size is not None else self.crop_size
										A__					:    int														= get_size_dict(SCREAMING_SNAKE_CASE_      ,			param_name="""crop_size"""       )

										A__					:    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 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__					:    Tuple														= [to_numpy_array(SCREAMING_SNAKE_CASE_       ) for image in images]

										if do_resize:
																	A__					:    Dict														= [self.resize(image=SCREAMING_SNAKE_CASE_      ,			size=SCREAMING_SNAKE_CASE_      ,			resample=SCREAMING_SNAKE_CASE_       ) for image in images]

										if do_center_crop:
																	A__					:    Any														= [self.center_crop(image=SCREAMING_SNAKE_CASE_      ,			size=SCREAMING_SNAKE_CASE_       ) for image in images]

										if do_rescale:
																	A__					:    Optional[int]														= [self.rescale(image=SCREAMING_SNAKE_CASE_      ,			scale=SCREAMING_SNAKE_CASE_      ,			offset=SCREAMING_SNAKE_CASE_       ) for image in images]

										if do_normalize:
																	A__					:    Optional[Any]														= [self.normalize(image=SCREAMING_SNAKE_CASE_      ,			mean=SCREAMING_SNAKE_CASE_      ,			std=SCREAMING_SNAKE_CASE_       ) for image in images]

										if include_top:
																	A__					:    str														= [self.normalize(image=SCREAMING_SNAKE_CASE_      ,			mean=[0, 0, 0]      ,			std=SCREAMING_SNAKE_CASE_       ) for image in images]

										A__					:    Union[str, Any]														= [to_channel_dimension_format(SCREAMING_SNAKE_CASE_      ,			SCREAMING_SNAKE_CASE_       ) for image in images]

										A__					:    List[Any]														= {"""pixel_values""": images}
										return BatchFeature(data=SCREAMING_SNAKE_CASE_      ,			tensor_type=SCREAMING_SNAKE_CASE_       )






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							if not isinstance(lowerCAmelCase ,     lowerCAmelCase       ):
														A__					:    int														= F'''Input value of [number={number}] must be an integer'''
														raise TypeError(lowerCAmelCase       )
							if number < 0 or number % 2 == 1:
														return False
							A__					:    List[Any]														= int(number**0.5       )
							return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





