

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import VideoMAEImageProcessor


class     lowerCAmelCase				(    unittest.TestCase				):


       '''simple docstring'''







       def __init__(	self					:  List[str]						,	__snake_case					:  List[str]						,	__snake_case					:  int=7						,	__snake_case					:  List[str]=3						,	__snake_case					:  Union[str, Any]=10						,	__snake_case					:  Tuple=18						,	__snake_case					:  Tuple=30						,	__snake_case					:  Tuple=400						,	__snake_case					:  Any=True						,	__snake_case					:  int=None						,	__snake_case					:  Optional[Any]=True						,	__snake_case					:  Any=[0.5, 0.5, 0.5]						,	__snake_case					:  Any=[0.5, 0.5, 0.5]						,	__snake_case					:  Any=None						,	)   -> Tuple:







        '''simple docstring'''

        lowerCamelCase      						=    size if size is not None else {"shortest_edge": 18}
        lowerCamelCase      						=    crop_size if crop_size is not None else {"height": 18, "width": 18}

        lowerCamelCase      						=    parent
        lowerCamelCase      						=    batch_size
        lowerCamelCase      						=    num_channels
        lowerCamelCase      						=    num_frames
        lowerCamelCase      						=    image_size
        lowerCamelCase      						=    min_resolution
        lowerCamelCase      						=    max_resolution
        lowerCamelCase      						=    do_resize
        lowerCamelCase      						=    size
        lowerCamelCase      						=    do_normalize
        lowerCamelCase      						=    image_mean
        lowerCamelCase      						=    image_std
        lowerCamelCase      						=    crop_size





       def   lowerCamelCase__						(	self					:  Dict     )   -> Optional[Any]:







        '''simple docstring'''

        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "crop_size": self.crop_size,
        }


@require_torch
@require_vision
class     lowerCAmelCase				(    A__   ,				unittest.TestCase				):


       '''simple docstring'''







       snake_case    							=	VideoMAEImageProcessor if is_vision_available() else None

       def   lowerCamelCase__						(	self					:  Any     )   -> str:







        '''simple docstring'''

        lowerCamelCase      						=    VideoMAEImageProcessingTester(self     )

       @property
       def   lowerCamelCase__						(	self					:  str     )   -> Dict:







        '''simple docstring'''

        return self.image_processor_tester.prepare_image_processor_dict()

       def   lowerCamelCase__						(	self					:  Dict     )   -> List[str]:







        '''simple docstring'''

        lowerCamelCase      						=    self.image_processing_class(**self.image_processor_dict     )
        self.assertTrue(hasattr(__snake_case						,	'image_mean'     )     )
        self.assertTrue(hasattr(__snake_case						,	'image_std'     )     )
        self.assertTrue(hasattr(__snake_case						,	'do_normalize'     )     )
        self.assertTrue(hasattr(__snake_case						,	'do_resize'     )     )
        self.assertTrue(hasattr(__snake_case						,	'do_center_crop'     )     )
        self.assertTrue(hasattr(__snake_case						,	'size'     )     )

       def   lowerCamelCase__						(	self					:  str     )   -> Any:







        '''simple docstring'''

        lowerCamelCase      						=    self.image_processing_class.from_dict(self.image_processor_dict     )
        self.assertEqual(image_processor.size						,	{'shortest_edge': 18}     )
        self.assertEqual(image_processor.crop_size						,	{'height': 18, 'width': 18}     )

        lowerCamelCase      						=    self.image_processing_class.from_dict(self.image_processor_dict						,	size=42						,	crop_size=84     )
        self.assertEqual(image_processor.size						,	{'shortest_edge': 42}     )
        self.assertEqual(image_processor.crop_size						,	{'height': 84, 'width': 84}     )

       def   lowerCamelCase__						(	self					:  Any     )   -> Dict:







        '''simple docstring'''

        pass

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







        '''simple docstring'''

        lowerCamelCase      						=    self.image_processing_class(**self.image_processor_dict     )
        # create random PIL videos
        lowerCamelCase      						=    prepare_video_inputs(self.image_processor_tester						,	equal_resolution=__snake_case     )
        for video in video_inputs:
         self.assertIsInstance(__snake_case						,	__snake_case     )
         self.assertIsInstance(video[0]						,	Image.Image     )

        # Test not batched input
        lowerCamelCase      						=    image_processing(video_inputs[0]						,	return_tensors='pt'     ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,	(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )						,	)

        # Test batched
        lowerCamelCase      						=    image_processing(__snake_case						,	return_tensors='pt'     ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )						,	)

       def   lowerCamelCase__						(	self					:  Dict     )   -> List[Any]:







        '''simple docstring'''

        lowerCamelCase      						=    self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        lowerCamelCase      						=    prepare_video_inputs(self.image_processor_tester						,	equal_resolution=__snake_case						,	numpify=__snake_case     )
        for video in video_inputs:
         self.assertIsInstance(__snake_case						,	__snake_case     )
         self.assertIsInstance(video[0]						,	np.ndarray     )

        # Test not batched input
        lowerCamelCase      						=    image_processing(video_inputs[0]						,	return_tensors='pt'     ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,	(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )						,	)

        # Test batched
        lowerCamelCase      						=    image_processing(__snake_case						,	return_tensors='pt'     ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )						,	)





       def   lowerCamelCase__						(	self					:  str     )   -> int:







        '''simple docstring'''

        lowerCamelCase      						=    self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        lowerCamelCase      						=    prepare_video_inputs(self.image_processor_tester						,	equal_resolution=__snake_case						,	torchify=__snake_case     )
        for video in video_inputs:
         self.assertIsInstance(__snake_case						,	__snake_case     )
         self.assertIsInstance(video[0]						,	torch.Tensor     )

        # Test not batched input
        lowerCamelCase      						=    image_processing(video_inputs[0]						,	return_tensors='pt'     ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,	(
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )						,	)

        # Test batched
        lowerCamelCase      						=    image_processing(__snake_case						,	return_tensors='pt'     ).pixel_values
        self.assertEqual(
            encoded_videos.shape						,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['height'],
                self.image_processor_tester.crop_size['width'],
            )						,	)









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()




