





"""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


lowerCamelCase__      =      logging.get_logger(__name__)



class    __SCREAMING_SNAKE_CASE			(  _lowerCAmelCase      ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Any		   =    ["pixel_values"]

  def __init__(						self      :				str    ,      __a      :				bool = True    ,      __a      :				Dict[str, int] = None    ,      __a      :				PILImageResampling = PIL.Image.NEAREST    ,      __a      :				bool = False    ,      __a      :				Dict[str, int] = None    ,      __a      :				Union[int, float] = 1 / 255    ,      __a      :				bool = False    ,      __a      :				bool = True    ,      __a      :				bool = True    ,      __a      :				Optional[Union[float, List[float]]] = None    ,      __a      :				Optional[Union[float, List[float]]] = None    ,      __a      :				bool = True    ,      **__a      :				Any    ,      )						-> None:
        super().__init__(**lowerCAmelCase__		)
        _UpperCamelCase					:		List[str]  =				size if size is not None else {'''height''': 346, '''width''': 346}
        _UpperCamelCase					:		List[Any]  =				get_size_dict(lowerCAmelCase__		)
        _UpperCamelCase					:		Optional[Any]  =				crop_size if crop_size is not None else {'''height''': 289, '''width''': 289}
        _UpperCamelCase					:		Optional[Any]  =				get_size_dict(lowerCAmelCase__    ,      param_name="crop_size"		)

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

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]    ,      __a      :				np.ndarray    ,      __a      :				Dict[str, int]    ,      __a      :				PILImageResampling = PIL.Image.NEAREST    ,      __a      :				Optional[Union[str, ChannelDimension]] = None    ,      **__a      :				Tuple    ,      )						-> np.ndarray:
        _UpperCamelCase					:		Union[str, Any]  =				get_size_dict(lowerCAmelCase__		)
        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(
            lowerCAmelCase__    ,      size=(size["height"], size["width"])    ,      resample=lowerCAmelCase__    ,      data_format=lowerCAmelCase__    ,      **lowerCAmelCase__		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				np.ndarray    ,      __a      :				Dict[str, int]    ,      __a      :				Optional[Union[str, ChannelDimension]] = None    ,      **__a      :				Tuple    ,      )						-> np.ndarray:
        _UpperCamelCase					:		Any  =				get_size_dict(lowerCAmelCase__		)
        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(lowerCAmelCase__    ,      size=(size["height"], size["width"])    ,      data_format=lowerCAmelCase__    ,      **lowerCAmelCase__		)

  def       __SCREAMING_SNAKE_CASE (						self      :				int    ,      __a      :				np.ndarray    ,      __a      :				Union[int, float]    ,      __a      :				bool = True    ,      __a      :				Optional[Union[str, ChannelDimension]] = None    ,      **__a      :				Any    ,      )						-> Dict:



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

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]    ,      __a      :				np.ndarray    ,      __a      :				Union[float, List[float]]    ,      __a      :				Union[float, List[float]]    ,      __a      :				Optional[Union[str, ChannelDimension]] = None    ,      **__a      :				Optional[int]    ,      )						-> np.ndarray:
        return normalize(lowerCAmelCase__    ,      mean=lowerCAmelCase__    ,      std=lowerCAmelCase__    ,      data_format=lowerCAmelCase__    ,      **lowerCAmelCase__		)


  def       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				ImageInput    ,      __a      :				bool = None    ,      __a      :				Dict[str, int] = None    ,      __a      :				Tuple=None    ,      __a      :				bool = None    ,      __a      :				Dict[str, int] = None    ,      __a      :				bool = None    ,      __a      :				float = None    ,      __a      :				bool = None    ,      __a      :				bool = None    ,      __a      :				Optional[Union[float, List[float]]] = None    ,      __a      :				Optional[Union[float, List[float]]] = None    ,      __a      :				bool = None    ,      __a      :				Optional[Union[str, TensorType]] = None    ,      __a      :				ChannelDimension = ChannelDimension.FIRST    ,      **__a      :				Optional[int]    ,      )						-> PIL.Image.Image:
        _UpperCamelCase					:		Any  =				do_resize if do_resize is not None else self.do_resize
        _UpperCamelCase					:		int  =				resample if resample is not None else self.resample
        _UpperCamelCase					:		Dict  =				do_center_crop if do_center_crop is not None else self.do_center_crop
        _UpperCamelCase					:		str  =				do_rescale if do_rescale is not None else self.do_rescale
        _UpperCamelCase					:		Any  =				rescale_factor if rescale_factor is not None else self.rescale_factor
        _UpperCamelCase					:		Tuple  =				rescale_offset if rescale_offset is not None else self.rescale_offset
        _UpperCamelCase					:		Tuple  =				do_normalize if do_normalize is not None else self.do_normalize
        _UpperCamelCase					:		Any  =				image_mean if image_mean is not None else self.image_mean
        _UpperCamelCase					:		str  =				image_std if image_std is not None else self.image_std
        _UpperCamelCase					:		Dict  =				include_top if include_top is not None else self.include_top

        _UpperCamelCase					:		List[Any]  =				size if size is not None else self.size
        _UpperCamelCase					:		List[Any]  =				get_size_dict(lowerCAmelCase__		)
        _UpperCamelCase					:		int  =				crop_size if crop_size is not None else self.crop_size
        _UpperCamelCase					:		str  =				get_size_dict(lowerCAmelCase__    ,      param_name="crop_size"		)

        _UpperCamelCase					:		Any  =				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_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.
        _UpperCamelCase					:		str  =				[to_numpy_array(lowerCAmelCase__		) for image in images]

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

        if do_center_crop:
              _UpperCamelCase					:		Dict  =				[self.center_crop(image=lowerCAmelCase__    ,      size=lowerCAmelCase__		) for image in images]

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

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

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

        _UpperCamelCase					:		Tuple  =				[to_channel_dimension_format(lowerCAmelCase__    ,      lowerCAmelCase__		) for image in images]

        _UpperCamelCase					:		Optional[int]  =				{'''pixel_values''': images}
        return BatchFeature(data=lowerCAmelCase__    ,      tensor_type=lowerCAmelCase__		)







"""simple docstring"""




import torch
from torch import autograd




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float, lowerCAmelCase__		:    bool							)	->      List[Any]:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[Any]     =    inputs.numel()
										_UpperCamelCase			:					int     =    int(0.005 * nb_elems							) + 1
										if sigmoid:
																_UpperCamelCase			:					Dict     =    (torch.sigmoid(lowerCAmelCase__							) > threshold).type(inputs.type()							)
										else:
																_UpperCamelCase			:					Optional[int]     =    (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
																_UpperCamelCase			:					int     =    inputs.flatten().kthvalue(max(nb_elems - nb_min, 1							)							).values
																_UpperCamelCase			:					List[str]     =    (inputs > k_threshold).type(inputs.type()							)
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    str, lowerCAmelCase__		:    Optional[int]							)	->      Union[str, Any]:




										'''simple docstring'''



										return gradOutput, None, None




class  _a						(      autograd.Function				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					str     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Union[str, Any]     =    inputs.flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					Optional[Any]     =    int(threshold * inputs.numel()							)

										# flat_out and mask access the same memory.
										_UpperCamelCase			:					Tuple     =    mask.flatten()
										_UpperCamelCase			:					int     =    0
										_UpperCamelCase			:					Tuple     =    1
										return mask






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    Any, lowerCAmelCase__		:    Union[str, Any]							)	->      int:




										'''simple docstring'''



										return gradOutput, None




class  _a						(      _lowerCAmelCase				):






				@staticmethod
				def 				snake_case	( lowerCAmelCase__		:    torch.tensor, lowerCAmelCase__		:    float							)	->      Dict:




										'''simple docstring'''



										_UpperCamelCase			:					List[Any]     =    inputs.clone()
										_UpperCamelCase   ,							_UpperCamelCase			:					Optional[int]     =    inputs.abs().flatten().sort(descending=lowerCAmelCase__							)
										_UpperCamelCase			:					List[Any]     =    int(threshold * inputs.numel()							)

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



