




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_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor




class 						__lowercase		(unittest.TestCase							):




      """simple docstring"""





      def __init__(				self    ,				A    ,				A=7    ,				A=3    ,				A=1_8    ,				A=3_0    ,				A=4_0_0    ,				A=True    ,				A=None    ,				A=True    ,				A=None    ,				A=True    ,				)		->    Union[str, Any]:
         snake_case				:    Optional[Any]           =	size if size is not None else {"shortest_edge": 2_0}
         snake_case				:    Dict           =	crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
         snake_case				:    str           =	parent
         snake_case				:    Dict           =	batch_size
         snake_case				:    Optional[Any]           =	num_channels
         snake_case				:    int           =	image_size
         snake_case				:    List[str]           =	min_resolution
         snake_case				:    Dict           =	max_resolution
         snake_case				:    str           =	do_resize
         snake_case				:    Union[str, Any]           =	size
         snake_case				:    Any           =	do_center_crop
         snake_case				:    str           =	crop_size
         snake_case				:    Union[str, Any]           =	do_flip_channel_order




      def      UpperCAmelCase				(				self     )		->    int:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "do_flip_channel_order": self.do_flip_channel_order,
         }




@require_torch
@require_vision
class 						__lowercase		(a__					,		unittest.TestCase							):




      """simple docstring"""





      _snake_case												=						MobileViTImageProcessor if is_vision_available() else None




      def      UpperCAmelCase				(				self     )		->    Dict:
         snake_case				:    Dict           =	MobileViTImageProcessingTester(self     )




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




      def      UpperCAmelCase				(				self     )		->    Optional[int]:
         snake_case				:    str           =	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_flip_channel_order"""     )     )




      def      UpperCAmelCase				(				self     )		->    int:
         snake_case				:    Tuple           =	self.image_processing_class.from_dict(self.image_processor_dict     )
         self.assertEqual(image_processor.size    ,				{"""shortest_edge""": 2_0}     )
         self.assertEqual(image_processor.crop_size    ,				{"""height""": 1_8, """width""": 1_8}     )

         snake_case				:    int           =	self.image_processing_class.from_dict(self.image_processor_dict    ,				size=4_2    ,				crop_size=8_4     )
         self.assertEqual(image_processor.size    ,				{"""shortest_edge""": 4_2}     )
         self.assertEqual(image_processor.crop_size    ,				{"""height""": 8_4, """width""": 8_4}     )




      def      UpperCAmelCase				(				self     )		->    Any:
         pass




      def      UpperCAmelCase				(				self     )		->    List[str]:
         # Initialize image_processing
         snake_case				:    List[str]           =	self.image_processing_class(**self.image_processor_dict     )
         # create random PIL images
         snake_case				:    Union[str, Any]           =	prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=A     )
         for image in image_inputs:
            self.assertIsInstance(A    ,				Image.Image     )

         # Test not batched input
         snake_case				:    Optional[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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )    ,				)

         # Test batched
         snake_case				:    List[str]           =	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      UpperCAmelCase				(				self     )		->    str:
         # Initialize image_processing
         snake_case				:    Tuple           =	self.image_processing_class(**self.image_processor_dict     )
         # create random numpy tensors
         snake_case				:    List[Any]           =	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
         snake_case				:    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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )    ,				)

         # Test batched
         snake_case				:    Optional[int]           =	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      UpperCAmelCase				(				self     )		->    Optional[int]:
         # Initialize image_processing
         snake_case				:    str           =	self.image_processing_class(**self.image_processor_dict     )
         # create random PyTorch tensors
         snake_case				:    List[str]           =	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
         snake_case				:    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.crop_size["""height"""],
                 self.image_processor_tester.crop_size["""width"""],
             )    ,				)

         # Test batched
         snake_case				:    Tuple           =	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"""],
             )    ,				)











import functools




def 						SCREAMING_SNAKE_CASE__ (   lowercase						,lowercase					)  ->       int:

   # Validation
   if not isinstance(lowercase						,lowercase					) or not all(isinstance(lowercase						,lowercase					) for day in days					):
      raise ValueError("""The parameter days should be a list of integers"""					)

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

   if len(lowercase					) == 0:
      return 0

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

   if max(lowercase					) >= 366:
      raise ValueError("""All days elements should be less than 366"""					)

   snake_case				:    List[str]           =	set(lowercase					)

   @functools.cache
   def dynamic_programming(lowercase					) -> int:
      if index > 365:
         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 + 30					)						,)

   return dynamic_programming(1					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()





