import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor






class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   @property
   def _lowercase(     self     )						->Tuple:
        a	:     Any						      =    1
        a	:     Union[str, Any]						      =    3
        a	:     Optional[Any]						      =    8
        a	:     int						      =    8

        a	:     Optional[Any]						      =    torch.rand((batch_size, num_channels, height, width)     )

        return sample
   def _lowercase(     self				,  lowerCAmelCase_     )						->Optional[int]:
        if isinstance(image[0]				,  PIL.Image.Image     ):
             return np.stack([np.array(lowerCAmelCase_     ) for i in image]				,  axis=0     )
        elif isinstance(lowerCAmelCase_				,  torch.Tensor     ):
             return image.cpu().numpy().transpose(0				,  2				,  3				,  1     )
        return image
   def _lowercase(     self     )						->List[Any]:
        a	:     Any						      =    VaeImageProcessor(do_resize=lowerCAmelCase_				,  do_normalize=lowerCAmelCase_     )

        a	:     int						      =    self.dummy_sample
        a	:     str						      =    self.to_np(lowerCAmelCase_     )

        for output_type in ["pt", "np", "pil"]:
             a	:     Union[str, Any]						      =    image_processor.postprocess(
                 image_processor.preprocess(lowerCAmelCase_     )				,  output_type=lowerCAmelCase_				,  )
             a	:     List[str]						      =    self.to_np(lowerCAmelCase_     )
             a	:     Optional[Any]						      =    (input_np * 2_55).round() if output_type == '''pil''' else input_np
             assert (
                 np.abs(in_np - out_np     ).max() < 1e-6
             ), F"""decoded output does not match input for output_type {output_type}"""
   def _lowercase(     self     )						->List[Any]:
        a	:     Union[str, Any]						      =    VaeImageProcessor(do_resize=lowerCAmelCase_				,  do_normalize=lowerCAmelCase_     )
        a	:     Optional[Any]						      =    self.dummy_sample.cpu().numpy().transpose(0				,  2				,  3				,  1     )

        for output_type in ["pt", "np", "pil"]:
             a	:     Union[str, Any]						      =    image_processor.postprocess(image_processor.preprocess(lowerCAmelCase_     )				,  output_type=lowerCAmelCase_     )

             a	:     Dict						      =    self.to_np(lowerCAmelCase_     )
             a	:     Optional[Any]						      =    (input_np * 2_55).round() if output_type == '''pil''' else input_np
             assert (
                 np.abs(in_np - out_np     ).max() < 1e-6
             ), F"""decoded output does not match input for output_type {output_type}"""
   def _lowercase(     self     )						->Tuple:
        a	:     Union[str, Any]						      =    VaeImageProcessor(do_resize=lowerCAmelCase_				,  do_normalize=lowerCAmelCase_     )

        a	:     List[Any]						      =    self.dummy_sample.cpu().numpy().transpose(0				,  2				,  3				,  1     )
        a	:     Optional[Any]						      =    image_processor.numpy_to_pil(lowerCAmelCase_     )

        for output_type in ["pt", "np", "pil"]:
             a	:     str						      =    image_processor.postprocess(image_processor.preprocess(lowerCAmelCase_     )				,  output_type=lowerCAmelCase_     )
             for i, o in zip(lowerCAmelCase_				,  lowerCAmelCase_     ):
                  a	:     Tuple						      =    np.array(lowerCAmelCase_     )
                  a	:     Tuple						      =    self.to_np(lowerCAmelCase_     ) if output_type == '''pil''' else (self.to_np(lowerCAmelCase_     ) * 2_55).round()
                  assert (
                      np.abs(in_np - out_np     ).max() < 1e-6
                  ), F"""decoded output does not match input for output_type {output_type}"""
   def _lowercase(     self     )						->Union[str, Any]:
        a	:     Any						      =    VaeImageProcessor(do_resize=lowerCAmelCase_				,  do_normalize=lowerCAmelCase_     )

        a	:     Union[str, Any]						      =    self.dummy_sample
        a	:     Tuple						      =    input_pt_ad.squeeze(0     )

        a	:     Any						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )
        a	:     Optional[Any]						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )

        a	:     List[str]						      =    self.to_np(self.dummy_sample     )
        a	:     str						      =    input_np_ad.squeeze(0     )

        a	:     List[Any]						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )
        a	:     Tuple						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )

        assert np.abs(out_pt_ad - out_pt_ad     ).max() < 1e-6
        assert np.abs(out_np_ad - out_np_ad     ).max() < 1e-6


   def _lowercase(     self     )						->List[str]:
        a	:     str						      =    VaeImageProcessor(do_resize=lowerCAmelCase_				,  do_normalize=lowerCAmelCase_     )

        a	:     Optional[int]						      =    self.dummy_sample
        a	:     List[Any]						      =    list(lowerCAmelCase_     )

        a	:     Union[str, Any]						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )

        a	:     Union[str, Any]						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )

        a	:     Optional[Any]						      =    self.to_np(self.dummy_sample     )
        list(lowerCAmelCase_     )

        a	:     List[str]						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )

        a	:     Optional[int]						      =    image_processor.postprocess(
            image_processor.preprocess(lowerCAmelCase_     )				,  output_type='''np'''				,  )

        assert np.abs(out_pt_ad - out_pt_list     ).max() < 1e-6
        assert np.abs(out_np_ad - out_np_list     ).max() < 1e-6
import torch
from torch import autograd






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[str]:
        a	:     str						      =    inputs.numel()
        a	:     str						      =    int(0.0_05 * nb_elems     ) + 1
        if sigmoid:
             a	:     Optional[Any]						      =    (torch.sigmoid(lowerCAmelCase_     ) > threshold).type(inputs.type()     )
        else:
             a	:     Union[str, 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	:     str						      =    inputs.flatten().kthvalue(max(nb_elems - nb_min				,  1     )     ).values
             a	:     Tuple						      =    (inputs > k_threshold).type(inputs.type()     )
        return mask


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Union[str, Any]:
        return gradOutput, None, None






class 				_SCREAMING_SNAKE_CASE( autograd.Function						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_     )						->List[Any]:
        a	:     List[Any]						      =    inputs.clone()
        a      ,				a	:     Optional[Any]						      =    inputs.flatten().sort(descending=lowerCAmelCase_     )
        a	:     Tuple						      =    int(threshold * inputs.numel()     )

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


   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        return gradOutput, None






class 				_SCREAMING_SNAKE_CASE( snake_case__						):
   @staticmethod
   def _lowercase(     lowerCAmelCase_				,  lowerCAmelCase_     )						->Tuple:
        a	:     Union[str, Any]						      =    inputs.clone()
        a      ,				a	:     List[Any]						      =    inputs.abs().flatten().sort(descending=lowerCAmelCase_     )
        a	:     Optional[int]						      =    int(threshold * inputs.numel()     )

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