

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 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def __init__(				self	,       __snake_case	,       __snake_case=7	,       __snake_case=3	,       __snake_case=18	,       __snake_case=30	,       __snake_case=4_00	,       __snake_case=True	,       __snake_case=None	,       __snake_case=True	,       __snake_case=[0.5, 0.5, 0.5]	,       __snake_case=[0.5, 0.5, 0.5]	,       ):
			_UpperCamelCase   :		Tuple			=						size if size is not None else {'height': 18, 'width': 18}
			_UpperCamelCase   :		int			=						parent
			_UpperCamelCase   :		Dict			=						batch_size
			_UpperCamelCase   :		str			=						num_channels
			_UpperCamelCase   :		Tuple			=						image_size
			_UpperCamelCase   :		Optional[int]			=						min_resolution
			_UpperCamelCase   :		int			=						max_resolution
			_UpperCamelCase   :		Optional[Any]			=						do_resize
			_UpperCamelCase   :		Union[str, Any]			=						size
			_UpperCamelCase   :		Any			=						do_normalize
			_UpperCamelCase   :		Any			=						image_mean
			_UpperCamelCase   :		Dict			=						image_std






		def 		A__					(				self):
			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 	lowercase    (				_lowercase    ,							unittest.TestCase							):


		"""simple docstring"""




		a__					       =  DPTImageProcessor if is_vision_available() else None


		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						DPTImageProcessingTester(self)


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


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						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	,       'size'))


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						self.image_processing_class.from_dict(self.image_processor_dict)
			self.assertEqual(image_processor.size	,       {'height': 18, 'width': 18})

			_UpperCamelCase   :		List[Any]			=						self.image_processing_class.from_dict(self.image_processor_dict	,       size=42)
			self.assertEqual(image_processor.size	,       {'height': 42, 'width': 42})


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

			# Test not batched input
			_UpperCamelCase   :		List[str]			=						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
			_UpperCamelCase   :		str			=						image_processing(__snake_case	,       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 		A__					(				self):
			# Initialize image_processing
			_UpperCamelCase   :		Any			=						self.image_processing_class(**self.image_processor_dict)
			# create random numpy tensors
			_UpperCamelCase   :		Union[str, Any]			=						prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__snake_case	,       numpify=__snake_case)
			for image in image_inputs:
				self.assertIsInstance(__snake_case	,       np.ndarray)

			# Test not batched input
			_UpperCamelCase   :		str			=						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
			_UpperCamelCase   :		Optional[Any]			=						image_processing(__snake_case	,       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 		A__					(				self):
			# Initialize image_processing
			_UpperCamelCase   :		Union[str, Any]			=						self.image_processing_class(**self.image_processor_dict)
			# create random PyTorch tensors
			_UpperCamelCase   :		Optional[int]			=						prepare_image_inputs(self.image_processor_tester	,       equal_resolution=__snake_case	,       torchify=__snake_case)
			for image in image_inputs:
				self.assertIsInstance(__snake_case	,       torch.Tensor)

			# Test not batched input
			_UpperCamelCase   :		List[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
			_UpperCamelCase   :		Tuple			=						image_processing(__snake_case	,       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'],
			    )	,       )







import functools






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[int]							,       UpperCAmelCase_							:     list[int]	)						->				int:




	'''simple docstring'''




	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for day in days	):
		raise ValueError('The parameter days should be a list of integers'	)

	if len(UpperCAmelCase_	) != 3 or not all(isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) for cost in costs	):
		raise ValueError('The parameter costs should be a list of three integers'	)

	if len(UpperCAmelCase_	) == 0:
		return 0

	if min(UpperCAmelCase_	) <= 0:
		raise ValueError('All days elements should be greater than 0'	)

	if max(UpperCAmelCase_	) >= 3_6_6:
		raise ValueError('All days elements should be less than 366'	)

	_UpperCamelCase   :		Union[str, Any]			=						set(UpperCAmelCase_	)

	@functools.cache
	def dynamic_programming(UpperCAmelCase_							:     int	) -> int:
		if index > 3_6_5:
			return 0

		if index not in days_set:
			return dynamic_programming(index + 1	)

		return min(
		    costs[0] + dynamic_programming(index + 1	)							,       costs[1] + dynamic_programming(index + 7	)							,       costs[2] + dynamic_programming(index + 3_0	)							,       )

	return dynamic_programming(1	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




