





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


_lowerCAmelCase							:      List[str]						=	logging.get_logger(__name__)


class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     ['''pixel_values''']



     def __init__(							self       :Optional[int]			,      snake_case       :bool = True			,      snake_case       :Dict[str, int] = None			,      snake_case       :PILImageResampling = PIL.Image.NEAREST			,      snake_case       :bool = False			,      snake_case       :Dict[str, int] = None			,      snake_case       :Union[int, float] = 1 / 255			,      snake_case       :bool = False			,      snake_case       :bool = True			,      snake_case       :bool = True			,      snake_case       :Optional[Union[float, List[float]]] = None			,      snake_case       :Optional[Union[float, List[float]]] = None			,      snake_case       :bool = True			,      **snake_case       :Optional[int]			,      ):





        '''simple docstring'''




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

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



     def 						SCREAMING_SNAKE_CASE		(							self       :Any			,      snake_case       :np.ndarray			,      snake_case       :Dict[str, int]			,      snake_case       :PILImageResampling = PIL.Image.NEAREST			,      snake_case       :Optional[Union[str, ChannelDimension]] = None			,      **snake_case       :Tuple			,      ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			get_size_dict(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(
            snake_case			,      size=(size["height"], size["width"])			,      resample=snake_case			,      data_format=snake_case			,      **snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[Any]			,      snake_case       :np.ndarray			,      snake_case       :Dict[str, int]			,      snake_case       :Optional[Union[str, ChannelDimension]] = None			,      **snake_case       :int			,      ):





        '''simple docstring'''




        A_					:				Optional[int]          =			get_size_dict(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(snake_case			,      size=(size["height"], size["width"])			,      data_format=snake_case			,      **snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :np.ndarray			,      snake_case       :Union[int, float]			,      snake_case       :bool = True			,      snake_case       :Optional[Union[str, ChannelDimension]] = None			,      **snake_case       :Union[str, Any]			,      ):





        '''simple docstring'''




        if offset:
           A_					:				str          =			(image - 127.5) * scale
           if data_format is not None:
              A_					:				Union[str, Any]          =			to_channel_dimension_format(snake_case			,      snake_case  )
           A_					:				Tuple          =			rescaled_image.astype(np.floataa  )
        else:
           A_					:				Optional[int]          =			rescale(snake_case			,      scale=snake_case			,      data_format=snake_case			,      **snake_case  )
        return rescaled_image



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :np.ndarray			,      snake_case       :Union[float, List[float]]			,      snake_case       :Union[float, List[float]]			,      snake_case       :Optional[Union[str, ChannelDimension]] = None			,      **snake_case       :Union[str, Any]			,      ):





        '''simple docstring'''




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





     def 						SCREAMING_SNAKE_CASE		(							self       :str			,      snake_case       :ImageInput			,      snake_case       :bool = None			,      snake_case       :Dict[str, int] = None			,      snake_case       :Union[str, Any]=None			,      snake_case       :bool = None			,      snake_case       :Dict[str, int] = None			,      snake_case       :bool = None			,      snake_case       :float = None			,      snake_case       :bool = None			,      snake_case       :bool = None			,      snake_case       :Optional[Union[float, List[float]]] = None			,      snake_case       :Optional[Union[float, List[float]]] = None			,      snake_case       :bool = None			,      snake_case       :Optional[Union[str, TensorType]] = None			,      snake_case       :ChannelDimension = ChannelDimension.FIRST			,      **snake_case       :List[str]			,      ):





        '''simple docstring'''




        A_					:				Optional[Any]          =			do_resize if do_resize is not None else self.do_resize
        A_					:				Any          =			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_					:				Tuple          =			do_rescale if do_rescale is not None else self.do_rescale
        A_					:				Union[str, Any]          =			rescale_factor if rescale_factor is not None else self.rescale_factor
        A_					:				List[str]          =			rescale_offset if rescale_offset is not None else self.rescale_offset
        A_					:				List[str]          =			do_normalize if do_normalize is not None else self.do_normalize
        A_					:				int          =			image_mean if image_mean is not None else self.image_mean
        A_					:				Dict          =			image_std if image_std is not None else self.image_std
        A_					:				int          =			include_top if include_top is not None else self.include_top

        A_					:				Union[str, Any]          =			size if size is not None else self.size
        A_					:				Union[str, Any]          =			get_size_dict(snake_case  )
        A_					:				Tuple          =			crop_size if crop_size is not None else self.crop_size
        A_					:				int          =			get_size_dict(snake_case			,      param_name="crop_size"  )

        A_					:				int          =			make_list_of_images(snake_case  )

        if not valid_images(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_					:				Dict          =			[to_numpy_array(snake_case  ) for image in images]

        if do_resize:
           A_					:				Union[str, Any]          =			[self.resize(image=snake_case			,      size=snake_case			,      resample=snake_case  ) for image in images]

        if do_center_crop:
           A_					:				str          =			[self.center_crop(image=snake_case			,      size=snake_case  ) for image in images]

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

        if do_normalize:
           A_					:				Union[str, Any]          =			[self.normalize(image=snake_case			,      mean=snake_case			,      std=snake_case  ) for image in images]

        if include_top:
           A_					:				Optional[int]          =			[self.normalize(image=snake_case			,      mean=[0, 0, 0]			,      std=snake_case  ) for image in images]

        A_					:				Optional[Any]          =			[to_channel_dimension_format(snake_case			,      snake_case  ) for image in images]

        A_					:				Optional[Any]          =			{"pixel_values": images}
        return BatchFeature(data=snake_case			,      tensor_type=snake_case  )







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
