





'''simple docstring'''



def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				int(SCREAMING_SNAKE_CASE__       )

  # Initialize Result
  _snake_case     		=				[]

  # Traverse through all denomination
  for denomination in reversed(SCREAMING_SNAKE_CASE__       ):
    # Find denominations
    while int(SCREAMING_SNAKE_CASE__       ) >= int(SCREAMING_SNAKE_CASE__       ):
      total_value -= int(SCREAMING_SNAKE_CASE__       )
      answer.append(SCREAMING_SNAKE_CASE__       )  # Append the "answers" array

  return answer


# Driver Code
if __name__ == "__main__":
 __magic_name__   :						Optional[Any]    =			[]
 __magic_name__   :						int    =			"""0"""

 if (
     input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
     == "y"
 ):
  __magic_name__   :						Dict    =			int(input("""Enter the number of denominations you want to add: """).strip())

  for i in range(0, n):
   denominations.append(int(input(F'Denomination {i}: ').strip()))
  __magic_name__   :						str    =			input("""Enter the change you want to make in Indian Currency: """).strip()
 else:
  # All denominations of Indian Currency if user does not enter
  __magic_name__   :						Any    =			[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
  __magic_name__   :						Tuple    =			input("""Enter the change you want to make: """).strip()

 if int(value) == 0 or int(value) < 0:
  print("""The total value cannot be zero or negative.""")

 else:
  print(F'Following is minimal change for {value}: ')
  __magic_name__   :						List[Any]    =			find_minimum_change(denominations, value)
  # Print result
  for i in range(len(answer)):
   print(answer[i], end=""" """)









'''simple docstring'''



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 DetaImageProcessor


class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=7						,   lowerCamelCase=3						,   lowerCamelCase=30						,   lowerCamelCase=400						,   lowerCamelCase=True						,   lowerCamelCase=None						,   lowerCamelCase=True						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=[0.5, 0.5, 0.5]						,   lowerCamelCase=True						,   lowerCamelCase=1 / 255						,   lowerCamelCase=True						,   ):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _snake_case     		=				size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				num_channels
       _snake_case     		=				min_resolution
       _snake_case     		=				max_resolution
       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean
       _snake_case     		=				image_std
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_pad

     def 	UpperCamelCase(					self	):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=False	):
       if not batched:
         _snake_case     		=				image_inputs[0]
         if isinstance(lowerCamelCase						,   Image.Image	):
           _snake_case		,		_snake_case     		=				image.size
         else:
           _snake_case		,		_snake_case     		=				image.shape[1], image.shape[2]
         if w < h:
           _snake_case     		=				int(self.size["shortest_edge"] * h / w	)
           _snake_case     		=				self.size["shortest_edge"]
         elif w > h:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				int(self.size["shortest_edge"] * w / h	)
         else:
           _snake_case     		=				self.size["shortest_edge"]
           _snake_case     		=				self.size["shortest_edge"]

       else:
         _snake_case     		=				[]
         for image in image_inputs:
           _snake_case		,		_snake_case     		=				self.get_expected_values([image]	)
           expected_values.append((expected_height, expected_width)	)
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[0]	)[0]
         _snake_case     		=				max(lowerCamelCase						,   key=lambda lowerCamelCase	: item[1]	)[1]

       return expected_height, expected_width







@require_torch
@require_vision
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Optional[Any]      =   DetaImageProcessor if is_vision_available() else None

     def 	UpperCamelCase(					self	):
       _snake_case     		=				DetaImageProcessingTester(self	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class(**self.image_processor_dict	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_mean"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "image_std"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_normalize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_resize"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_rescale"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "do_pad"	)	)
       self.assertTrue(hasattr(lowerCamelCase						,   "size"	)	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.image_processing_class.from_dict(self.image_processor_dict	)
       self.assertEqual(image_processor.size						,   {"shortest_edge": 18, "longest_edge": 1_333}	)
       self.assertEqual(image_processor.do_pad						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       pass

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

       # Test not batched input
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values
       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

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

       # Test not batched input
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

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

       # Test not batched input
       _snake_case     		=				image_processing(image_inputs[0]						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (1, self.image_processor_tester.num_channels, expected_height, expected_width)						,   )

       # Test batched
       _snake_case     		=				image_processing(lowerCamelCase						,   return_tensors="pt"	).pixel_values

       _snake_case		,		_snake_case     		=				self.image_processor_tester.get_expected_values(lowerCamelCase						,   batched=lowerCamelCase	)

       self.assertEqual(
           encoded_images.shape						,   (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )						,   )

     @slow
     def 	UpperCamelCase(					self	):
       # prepare image and target
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"image_id": 39_769, "annotations": target}

       # encode them
       _snake_case     		=				DetaImageProcessor()
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([75, 75, 63, 65, 17, 17]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)






     @slow
     def 	UpperCamelCase(					self	):
       # prepare image, target and masks_path
       _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
       with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"						,   "r"	) as f:
         _snake_case     		=				json.loads(f.read()	)

       _snake_case     		=				{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

       _snake_case     		=				pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"	)

       # encode them
       _snake_case     		=				DetaImageProcessor(format="coco_panoptic"	)
       _snake_case     		=				image_processing(images=lowerCamelCase						,   annotations=lowerCamelCase						,   masks_path=lowerCamelCase						,   return_tensors="pt"	)

       # verify pixel values
       _snake_case     		=				torch.Size([1, 3, 800, 1_066]	)
       self.assertEqual(encoding["pixel_values"].shape						,   lowerCamelCase	)

       _snake_case     		=				torch.tensor([0.2796, 0.3138, 0.3481]	)
       self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]						,   lowerCamelCase						,   atol=1e-4	)	)

       # verify area
       _snake_case     		=				torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["area"]						,   lowerCamelCase	)	)
       # verify boxes
       _snake_case     		=				torch.Size([6, 4]	)
       self.assertEqual(encoding["labels"][0]["boxes"].shape						,   lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]						,   lowerCamelCase						,   atol=1e-3	)	)
       # verify image_id
       _snake_case     		=				torch.tensor([39_769]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]						,   lowerCamelCase	)	)
       # verify is_crowd
       _snake_case     		=				torch.tensor([0, 0, 0, 0, 0, 0]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]						,   lowerCamelCase	)	)
       # verify class_labels
       _snake_case     		=				torch.tensor([17, 17, 63, 75, 75, 93]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]						,   lowerCamelCase	)	)
       # verify masks
       _snake_case     		=				822_873
       self.assertEqual(encoding["labels"][0]["masks"].sum().item()						,   lowerCamelCase	)
       # verify orig_size
       _snake_case     		=				torch.tensor([480, 640]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]						,   lowerCamelCase	)	)
       # verify size
       _snake_case     		=				torch.tensor([800, 1_066]	)
       self.assertTrue(torch.allclose(encoding["labels"][0]["size"]						,   lowerCamelCase	)	)


