






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


__UpperCAmelCase							:   int							=  logging.get_logger(__name__)
class 					_snake_case						(			__lowercase ):
	_A          =   ['''pixel_values''']






	def __init__(		self					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BILINEAR					,UpperCamelCase = True					,UpperCamelCase = 1 / 255					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = None					,**UpperCamelCase					,)						->    None:
					super().__init__(**__a       )
					snake_case__      :Optional[Any]         =   size if size is not None else {"""shortest_edge""": 384}
					snake_case__      :List[str]         =   get_size_dict(__a					,default_to_square=__a       )

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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BICUBIC					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :List[Any]         =   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()}'       )
					snake_case__      :Optional[Any]         =   size["""shortest_edge"""]

					if shortest_edge < 384:
									# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
									snake_case__      :Union[str, Any]         =   int(shortest_edge / crop_pct       )
									snake_case__      :str         =   get_resize_output_image_size(__a					,size=__a					,default_to_square=__a       )
									snake_case__      :Optional[int]         =   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    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    Union[str, Any]:
					return rescale(__a					,scale=__a					,data_format=__a					,**__a       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					return normalize(__a					,mean=__a					,std=__a					,data_format=__a					,**__a       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,**UpperCamelCase					,)						->    PIL.Image.Image:
					snake_case__      :Dict         =   do_resize if do_resize is not None else self.do_resize
					snake_case__      :List[str]         =   crop_pct if crop_pct is not None else self.crop_pct
					snake_case__      :Optional[int]         =   resample if resample is not None else self.resample
					snake_case__      :Union[str, Any]         =   do_rescale if do_rescale is not None else self.do_rescale
					snake_case__      :List[str]         =   rescale_factor if rescale_factor is not None else self.rescale_factor
					snake_case__      :Optional[Any]         =   do_normalize if do_normalize is not None else self.do_normalize
					snake_case__      :int         =   image_mean if image_mean is not None else self.image_mean
					snake_case__      :Tuple         =   image_std if image_std is not None else self.image_std

					snake_case__      :Tuple         =   size if size is not None else self.size
					snake_case__      :Any         =   get_size_dict(__a					,default_to_square=__a       )

					snake_case__      :Any         =   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.
					snake_case__      :int         =   [to_numpy_array(__a       ) for image in images]

					if do_resize:
									snake_case__      :Tuple         =   [self.resize(image=__a					,size=__a					,crop_pct=__a					,resample=__a       ) for image in images]

					if do_rescale:
									snake_case__      :Tuple         =   [self.rescale(image=__a					,scale=__a       ) for image in images]

					if do_normalize:
									snake_case__      :List[str]         =   [self.normalize(image=__a					,mean=__a					,std=__a       ) for image in images]

					snake_case__      :Dict         =   [to_channel_dimension_format(__a					,__a       ) for image in images]

					snake_case__      :int         =   {"""pixel_values""": images}
					return BatchFeature(data=__a					,tensor_type=__a       )







from __future__ import annotations





def 					lowercase_ (    __snake_case     :							list      )     -> float:





				'''simple docstring'''


				if not nums:
								raise ValueError("List is empty"      )
				return sum(__snake_case      ) / len(__snake_case      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()