

'''simple docstring'''





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		list[int] ,   _SCREAMING_SNAKE_CASE					:		list[int]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						len(_SCREAMING_SNAKE_CASE       )
    print('The following activities are selected:'       )

    # The first activity is always selected
    SCREAMING_SNAKE_CASE_									=						0
    print(_SCREAMING_SNAKE_CASE ,   end=','       )

    # Consider rest of the activities
    for j in range(_SCREAMING_SNAKE_CASE       ):
        # If this activity has start time greater than
        # or equal to the finish time of previously
        # selected activity, then select it
        if start[j] >= finish[i]:
            print(_SCREAMING_SNAKE_CASE ,   end=','       )
            SCREAMING_SNAKE_CASE_									=						j


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCamelCase__  :      List[str]			=    [1, 3, 0, 5, 8, 5]
       UpperCamelCase__  :      Dict			=    [2, 4, 6, 7, 9, 9]
       print_max_activities(start, finish)





import unittest

import numpy as np
from datasets import load_dataset

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_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import BeitImageProcessor



class       __snake_case  (    unittest.TestCase       ):

  def __init__(					self						,		_A						,		_A=7						,		_A=3						,		_A=18						,		_A=30						,		_A=400						,		_A=True						,		_A=None						,		_A=True						,		_A=None						,		_A=True						,		_A=[0.5, 0.5, 0.5]						,		_A=[0.5, 0.5, 0.5]						,		_A=False						,		):
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'height': 20, 'width': 20}
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						num_channels
      SCREAMING_SNAKE_CASE_									=						image_size
      SCREAMING_SNAKE_CASE_									=						min_resolution
      SCREAMING_SNAKE_CASE_									=						max_resolution
      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size
      SCREAMING_SNAKE_CASE_									=						do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean
      SCREAMING_SNAKE_CASE_									=						image_std
      SCREAMING_SNAKE_CASE_									=						do_reduce_labels


  def 						lowerCAmelCase__     (					self):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }






def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[1]['file']       )

    return image, map


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(ds[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[1]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[2]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[3]['file']       )

    return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Union[str, Any]						 =  BeitImageProcessor if is_vision_available() else None

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						BeitImageProcessingTester(self)

  @property
  def 						lowerCAmelCase__     (					self):
      return self.image_processor_tester.prepare_image_processor_dict()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(_A						,		'do_resize'))
      self.assertTrue(hasattr(_A						,		'size'))
      self.assertTrue(hasattr(_A						,		'do_center_crop'))
      self.assertTrue(hasattr(_A						,		'center_crop'))
      self.assertTrue(hasattr(_A						,		'do_normalize'))
      self.assertTrue(hasattr(_A						,		'image_mean'))
      self.assertTrue(hasattr(_A						,		'image_std'))

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size						,		{'height': 20, 'width': 20})
      self.assertEqual(image_processor.crop_size						,		{'height': 18, 'width': 18})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(
          self.image_processor_dict						,		size=42						,		crop_size=84						,		reduce_labels=_A)
      self.assertEqual(image_processor.size						,		{'height': 42, 'width': 42})
      self.assertEqual(image_processor.crop_size						,		{'height': 84, 'width': 84})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		numpify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)
          maps.append(torch.zeros(image.shape[-2:]).long())

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		maps[0]						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test not batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_batch_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              2,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 150)

      SCREAMING_SNAKE_CASE_									=						True
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


