






"""simple docstring"""







import unittest

import numpy as np

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

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 DPTImageProcessor


class      UpperCamelCase		(unittest.TestCase     ):






						def __init__(		self  :Optional[Any]			, __magic_name__  :List[Any]			, __magic_name__  :int=7			, __magic_name__  :Any=3			, __magic_name__  :str=18			, __magic_name__  :Dict=30			, __magic_name__  :Optional[Any]=400			, __magic_name__  :List[Any]=True			, __magic_name__  :int=None			, __magic_name__  :Any=True			, __magic_name__  :Union[str, Any]=[0.5, 0.5, 0.5]			, __magic_name__  :str=[0.5, 0.5, 0.5]			, )      ->Any:
										lowercase    :      Dict       = size if size is not None else {"""height""": 18, """width""": 18}
										lowercase    :      Tuple       = parent
										lowercase    :      Optional[int]       = batch_size
										lowercase    :      str       = num_channels
										lowercase    :      List[str]       = image_size
										lowercase    :      Dict       = min_resolution
										lowercase    :      Any       = max_resolution
										lowercase    :      List[str]       = do_resize
										lowercase    :      List[Any]       = size
										lowercase    :      str       = do_normalize
										lowercase    :      str       = image_mean
										lowercase    :      str       = image_std






						def      __snake_case      (		self  :List[Any]				)      ->List[Any]:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}


@require_torch
@require_vision
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Tuple												=		DPTImageProcessor if is_vision_available() else None






						def      __snake_case      (		self  :Optional[Any]				)      ->List[str]:
										lowercase    :      Any       = DPTImageProcessingTester(self				)






						@property
						def      __snake_case      (		self  :List[str]				)      ->Any:
										return self.image_processor_tester.prepare_image_processor_dict()






						def      __snake_case      (		self  :Any				)      ->List[str]:
										lowercase    :      Optional[Any]       = self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__magic_name__			, """image_mean"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """image_std"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """do_normalize"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """do_resize"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """size"""				)				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->int:
										lowercase    :      List[Any]       = self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size			, {"""height""": 18, """width""": 18}				)

										lowercase    :      Dict       = self.image_processing_class.from_dict(self.image_processor_dict			, size=42				)
										self.assertEqual(image_processor.size			, {"""height""": 42, """width""": 42}				)






						def      __snake_case      (		self  :Dict				)      ->str:
										# Initialize image_processing
										lowercase    :      List[str]       = self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										lowercase    :      Tuple       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, Image.Image				)

										# Test not batched input
										lowercase    :      Union[str, Any]       = 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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )			, )

										# Test batched
										lowercase    :      List[str]       = image_processing(__magic_name__			, 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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )			, )






						def      __snake_case      (		self  :List[Any]				)      ->Union[str, Any]:
										# Initialize image_processing
										lowercase    :      List[str]       = self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										lowercase    :      str       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__			, numpify=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, np.ndarray				)

										# Test not batched input
										lowercase    :      Tuple       = 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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )			, )

										# Test batched
										lowercase    :      Dict       = image_processing(__magic_name__			, 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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )			, )






						def      __snake_case      (		self  :Any				)      ->Tuple:
										# Initialize image_processing
										lowercase    :      Any       = self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										lowercase    :      int       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__			, torchify=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, torch.Tensor				)

										# Test not batched input
										lowercase    :      Tuple       = 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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )			, )

										# Test batched
										lowercase    :      List[Any]       = image_processing(__magic_name__			, 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.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )			, )








"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       None:
				lowercase    :      List[Any]       = len(_A )
				print("""The following activities are selected:""" )

				# The first activity is always selected
				lowercase    :      Optional[int]       = 0
				print(_A  ,				end=""",""" )

				# Consider rest of the activities
				for j in range(_A ):
								# 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(_A  ,				end=""",""" )
												lowercase    :      str       = j


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_lowerCAmelCase            =	[1, 3, 0, 5, 8, 5]
				_lowerCAmelCase            =	[2, 4, 6, 7, 9, 9]
				print_max_activities(start, finish)
