

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class     lowerCAmelCase				(    unittest.TestCase				):


       '''simple docstring'''







       @property
       def   lowerCamelCase__						(	self					:  List[Any]     )   -> Optional[int]:







        '''simple docstring'''

        lowerCamelCase      						=    1
        lowerCamelCase      						=    3
        lowerCamelCase      						=    8
        lowerCamelCase      						=    8

        lowerCamelCase      						=    torch.rand((batch_size, num_channels, height, width)     )

        return sample

       def   lowerCamelCase__						(	self					:  Any						,	__snake_case					:  Union[str, Any]     )   -> Tuple:







        '''simple docstring'''

        if isinstance(image[0]						,	PIL.Image.Image     ):
         return np.stack([np.array(__snake_case     ) for i in image]						,	axis=0     )
        elif isinstance(__snake_case						,	torch.Tensor     ):
         return image.cpu().numpy().transpose(0						,	2						,	3						,	1     )
        return image

       def   lowerCamelCase__						(	self					:  Dict     )   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase      						=    VaeImageProcessor(do_resize=__snake_case						,	do_normalize=__snake_case     )

        lowerCamelCase      						=    self.dummy_sample
        lowerCamelCase      						=    self.to_np(__snake_case     )

        for output_type in ["pt", "np", "pil"]:
         lowerCamelCase      						=    image_processor.postprocess(
             image_processor.preprocess(__snake_case     )						,	output_type=__snake_case						,	)
         lowerCamelCase      						=    self.to_np(__snake_case     )
         lowerCamelCase      						=    (input_np * 255).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   lowerCamelCase__						(	self					:  str     )   -> Dict:







        '''simple docstring'''

        lowerCamelCase      						=    VaeImageProcessor(do_resize=__snake_case						,	do_normalize=__snake_case     )
        lowerCamelCase      						=    self.dummy_sample.cpu().numpy().transpose(0						,	2						,	3						,	1     )

        for output_type in ["pt", "np", "pil"]:
         lowerCamelCase      						=    image_processor.postprocess(image_processor.preprocess(__snake_case     )						,	output_type=__snake_case     )

         lowerCamelCase      						=    self.to_np(__snake_case     )
         lowerCamelCase      						=    (input_np * 255).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   lowerCamelCase__						(	self					:  List[str]     )   -> Any:







        '''simple docstring'''

        lowerCamelCase      						=    VaeImageProcessor(do_resize=__snake_case						,	do_normalize=__snake_case     )

        lowerCamelCase      						=    self.dummy_sample.cpu().numpy().transpose(0						,	2						,	3						,	1     )
        lowerCamelCase      						=    image_processor.numpy_to_pil(__snake_case     )

        for output_type in ["pt", "np", "pil"]:
         lowerCamelCase      						=    image_processor.postprocess(image_processor.preprocess(__snake_case     )						,	output_type=__snake_case     )
         for i, o in zip(__snake_case						,	__snake_case     ):
          lowerCamelCase      						=    np.array(__snake_case     )
          lowerCamelCase      						=    self.to_np(__snake_case     ) if output_type == 'pil' else (self.to_np(__snake_case     ) * 255).round()
          assert (
              np.abs(in_np - out_np     ).max() < 1e-6
          ), F'''decoded output does not match input for output_type {output_type}'''

       def   lowerCamelCase__						(	self					:  Optional[int]     )   -> Union[str, Any]:







        '''simple docstring'''

        lowerCamelCase      						=    VaeImageProcessor(do_resize=__snake_case						,	do_normalize=__snake_case     )

        lowerCamelCase      						=    self.dummy_sample
        lowerCamelCase      						=    input_pt_ad.squeeze(0     )

        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	output_type='np'						,	)
        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	output_type='np'						,	)

        lowerCamelCase      						=    self.to_np(self.dummy_sample     )
        lowerCamelCase      						=    input_np_ad.squeeze(0     )

        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	output_type='np'						,	)
        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	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   lowerCamelCase__						(	self					:  List[Any]     )   -> Optional[int]:







        '''simple docstring'''

        lowerCamelCase      						=    VaeImageProcessor(do_resize=__snake_case						,	do_normalize=__snake_case     )

        lowerCamelCase      						=    self.dummy_sample
        lowerCamelCase      						=    list(__snake_case     )

        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	output_type='np'						,	)

        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	output_type='np'						,	)

        lowerCamelCase      						=    self.to_np(self.dummy_sample     )
        list(__snake_case     )

        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	output_type='np'						,	)

        lowerCamelCase      						=    image_processor.postprocess(
            image_processor.preprocess(__snake_case     )						,	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







def a_		(       UpperCamelCase_   :							int   ,					UpperCamelCase_   :							list  )							->   list:







 """simple docstring"""

 lowerCamelCase      						=    sorted(UpperCamelCase_   ,					key=lambda UpperCamelCase_  : value[2]   ,					reverse=UpperCamelCase_  )

 # Create a list of size equal to the maximum deadline
 # and initialize it with -1
 lowerCamelCase      						=    max(UpperCamelCase_   ,					key=lambda UpperCamelCase_  : value[1]  )[1]
 lowerCamelCase      						=    [-1] * max_deadline

 # Finding the maximum profit and the count of jobs
 lowerCamelCase      						=    0
 lowerCamelCase      						=    0
 for job in jobs:
  # Find a free time slot for this job
  # (Note that we start from the last possible slot)
  for i in range(job[1] - 1   ,					-1   ,					-1  ):
   if time_slots[i] == -1:
    lowerCamelCase      						=    job[0]
    count += 1
    max_profit += job[2]
    break
 return [count, max_profit]


if __name__ == "__main__":
     import doctest

     doctest.testmod()




