
"""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 A_	(unittest.TestCase      ):




  '''simple docstring'''







  def __init__(		self				,   lowercase_				,   lowercase_=7				,   lowercase_=3				,   lowercase_=30				,   lowercase_=400				,   lowercase_=True				,   lowercase_=None				,   lowercase_=True				,   lowercase_=[0.5, 0.5, 0.5]				,   lowercase_=[0.5, 0.5, 0.5]				,   lowercase_=True				,   lowercase_=1 / 255				,   lowercase_=True				,   ):

     """simple docstring"""
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     UpperCAmelCase_					:					Dict			     =    size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
     UpperCAmelCase_					:					List[str]			     =    parent
     UpperCAmelCase_					:					Optional[Any]			     =    batch_size
     UpperCAmelCase_					:					Any			     =    num_channels
     UpperCAmelCase_					:					Tuple			     =    min_resolution
     UpperCAmelCase_					:					Tuple			     =    max_resolution
     UpperCAmelCase_					:					Union[str, Any]			     =    do_resize
     UpperCAmelCase_					:					Union[str, Any]			     =    size
     UpperCAmelCase_					:					str			     =    do_normalize
     UpperCAmelCase_					:					Any			     =    image_mean
     UpperCAmelCase_					:					int			     =    image_std
     UpperCAmelCase_					:					Union[str, Any]			     =    do_rescale
     UpperCAmelCase_					:					str			     =    rescale_factor
     UpperCAmelCase_					:					Tuple			     =    do_pad







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     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				,   lowercase_				,   lowercase_=False  ):

     """simple docstring"""
     if not batched:
        UpperCAmelCase_					:					Dict			     =    image_inputs[0]
        if isinstance(lowercase_				,   Image.Image  ):
           UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    image.size
        else:
           UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    image.shape[1], image.shape[2]
        if w < h:
           UpperCAmelCase_					:					Any			     =    int(self.size["shortest_edge"] * h / w  )
           UpperCAmelCase_					:					Optional[Any]			     =    self.size["shortest_edge"]
        elif w > h:
           UpperCAmelCase_					:					List[Any]			     =    self.size["shortest_edge"]
           UpperCAmelCase_					:					Optional[Any]			     =    int(self.size["shortest_edge"] * w / h  )
        else:
           UpperCAmelCase_					:					Dict			     =    self.size["shortest_edge"]
           UpperCAmelCase_					:					Tuple			     =    self.size["shortest_edge"]

     else:
        UpperCAmelCase_					:					List[str]			     =    []
        for image in image_inputs:
           UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    self.get_expected_values([image]  )
           expected_values.append((expected_height, expected_width)  )
        UpperCAmelCase_					:					Union[str, Any]			     =    max(lowercase_				,   key=lambda lowercase_  : item[0]  )[0]
        UpperCAmelCase_					:					List[Any]			     =    max(lowercase_				,   key=lambda lowercase_  : item[1]  )[1]

     return expected_height, expected_width




@require_torch
@require_vision
class A_	(lowercase__				,unittest.TestCase      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						Optional[int]       =		DetaImageProcessor if is_vision_available() else None







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    DetaImageProcessingTester(self  )







  @property
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    self.image_processing_class(**self.image_processor_dict  )
     self.assertTrue(hasattr(lowercase_				,   "image_mean"  )  )
     self.assertTrue(hasattr(lowercase_				,   "image_std"  )  )
     self.assertTrue(hasattr(lowercase_				,   "do_normalize"  )  )
     self.assertTrue(hasattr(lowercase_				,   "do_resize"  )  )
     self.assertTrue(hasattr(lowercase_				,   "do_rescale"  )  )
     self.assertTrue(hasattr(lowercase_				,   "do_pad"  )  )
     self.assertTrue(hasattr(lowercase_				,   "size"  )  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[str]			     =    self.image_processing_class.from_dict(self.image_processor_dict  )
     self.assertEqual(image_processor.size				,   {"shortest_edge": 18, "longest_edge": 1333}  )
     self.assertEqual(image_processor.do_pad				,   lowercase_  )







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     pass







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # Initialize image_processing
     UpperCAmelCase_					:					Tuple			     =    self.image_processing_class(**self.image_processor_dict  )
     # create random PIL images
     UpperCAmelCase_					:					List[Any]			     =    prepare_image_inputs(self.image_processor_tester				,   equal_resolution=lowercase_  )
     for image in image_inputs:
        self.assertIsInstance(lowercase_				,   Image.Image  )

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

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    self.image_processor_tester.get_expected_values(lowercase_  )

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

     # Test batched
     UpperCAmelCase_     ,   UpperCAmelCase_					:					Union[str, Any]			     =    self.image_processor_tester.get_expected_values(lowercase_				,   batched=lowercase_  )

     UpperCAmelCase_					:					Optional[int]			     =    image_processing(lowercase_				,   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  ):

     """simple docstring"""
     # Initialize image_processing
     UpperCAmelCase_					:					Tuple			     =    self.image_processing_class(**self.image_processor_dict  )
     # create random numpy tensors
     UpperCAmelCase_					:					Optional[Any]			     =    prepare_image_inputs(self.image_processor_tester				,   equal_resolution=lowercase_				,   numpify=lowercase_  )
     for image in image_inputs:
        self.assertIsInstance(lowercase_				,   np.ndarray  )

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

     UpperCAmelCase_     ,   UpperCAmelCase_					:					List[Any]			     =    self.image_processor_tester.get_expected_values(lowercase_  )

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

     # Test batched
     UpperCAmelCase_					:					Optional[Any]			     =    image_processing(lowercase_				,   return_tensors="pt"  ).pixel_values

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Any			     =    self.image_processor_tester.get_expected_values(lowercase_				,   batched=lowercase_  )

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







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # Initialize image_processing
     UpperCAmelCase_					:					Dict			     =    self.image_processing_class(**self.image_processor_dict  )
     # create random PyTorch tensors
     UpperCAmelCase_					:					Union[str, Any]			     =    prepare_image_inputs(self.image_processor_tester				,   equal_resolution=lowercase_				,   torchify=lowercase_  )
     for image in image_inputs:
        self.assertIsInstance(lowercase_				,   torch.Tensor  )

     # Test not batched input
     UpperCAmelCase_					:					Optional[Any]			     =    image_processing(image_inputs[0]				,   return_tensors="pt"  ).pixel_values

     UpperCAmelCase_     ,   UpperCAmelCase_					:					int			     =    self.image_processor_tester.get_expected_values(lowercase_  )

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

     # Test batched
     UpperCAmelCase_					:					Optional[int]			     =    image_processing(lowercase_				,   return_tensors="pt"  ).pixel_values

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Tuple			     =    self.image_processor_tester.get_expected_values(lowercase_				,   batched=lowercase_  )

     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  ):

     """simple docstring"""
     # prepare image and target
     UpperCAmelCase_					:					Optional[int]			     =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"				,   "r"  ) as f:
        UpperCAmelCase_					:					Optional[Any]			     =    json.loads(f.read()  )

     UpperCAmelCase_					:					int			     =    {"image_id": 3_9769, "annotations": target}

     # encode them
     UpperCAmelCase_					:					Tuple			     =    DetaImageProcessor()
     UpperCAmelCase_					:					Dict			     =    image_processing(images=lowercase_				,   annotations=lowercase_				,   return_tensors="pt"  )

     # verify pixel values
     UpperCAmelCase_					:					Tuple			     =    torch.Size([1, 3, 800, 1066]  )
     self.assertEqual(encoding["pixel_values"].shape				,   lowercase_  )

     UpperCAmelCase_					:					int			     =    torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,   lowercase_				,   atol=1E-4  )  )

     # verify area
     UpperCAmelCase_					:					Optional[Any]			     =    torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,   lowercase_  )  )
     # verify boxes
     UpperCAmelCase_					:					Optional[Any]			     =    torch.Size([6, 4]  )
     self.assertEqual(encoding["labels"][0]["boxes"].shape				,   lowercase_  )
     UpperCAmelCase_					:					Dict			     =    torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,   lowercase_				,   atol=1E-3  )  )
     # verify image_id
     UpperCAmelCase_					:					str			     =    torch.tensor([3_9769]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,   lowercase_  )  )
     # verify is_crowd
     UpperCAmelCase_					:					str			     =    torch.tensor([0, 0, 0, 0, 0, 0]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,   lowercase_  )  )
     # verify class_labels
     UpperCAmelCase_					:					str			     =    torch.tensor([75, 75, 63, 65, 17, 17]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,   lowercase_  )  )
     # verify orig_size
     UpperCAmelCase_					:					Union[str, Any]			     =    torch.tensor([480, 640]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,   lowercase_  )  )
     # verify size
     UpperCAmelCase_					:					int			     =    torch.tensor([800, 1066]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,   lowercase_  )  )







  @slow
  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     # prepare image, target and masks_path
     UpperCAmelCase_					:					Any			     =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"				,   "r"  ) as f:
        UpperCAmelCase_					:					str			     =    json.loads(f.read()  )

     UpperCAmelCase_					:					Optional[int]			     =    {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}

     UpperCAmelCase_					:					Optional[int]			     =    pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"  )

     # encode them
     UpperCAmelCase_					:					Dict			     =    DetaImageProcessor(format="coco_panoptic"  )
     UpperCAmelCase_					:					int			     =    image_processing(images=lowercase_				,   annotations=lowercase_				,   masks_path=lowercase_				,   return_tensors="pt"  )

     # verify pixel values
     UpperCAmelCase_					:					List[Any]			     =    torch.Size([1, 3, 800, 1066]  )
     self.assertEqual(encoding["pixel_values"].shape				,   lowercase_  )

     UpperCAmelCase_					:					Dict			     =    torch.tensor([0.27_96, 0.31_38, 0.34_81]  )
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]				,   lowercase_				,   atol=1E-4  )  )

     # verify area
     UpperCAmelCase_					:					Union[str, Any]			     =    torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"]				,   lowercase_  )  )
     # verify boxes
     UpperCAmelCase_					:					Tuple			     =    torch.Size([6, 4]  )
     self.assertEqual(encoding["labels"][0]["boxes"].shape				,   lowercase_  )
     UpperCAmelCase_					:					Tuple			     =    torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]				,   lowercase_				,   atol=1E-3  )  )
     # verify image_id
     UpperCAmelCase_					:					str			     =    torch.tensor([3_9769]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]				,   lowercase_  )  )
     # verify is_crowd
     UpperCAmelCase_					:					Any			     =    torch.tensor([0, 0, 0, 0, 0, 0]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]				,   lowercase_  )  )
     # verify class_labels
     UpperCAmelCase_					:					Tuple			     =    torch.tensor([17, 17, 63, 75, 75, 93]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]				,   lowercase_  )  )
     # verify masks
     UpperCAmelCase_					:					Any			     =    82_2873
     self.assertEqual(encoding["labels"][0]["masks"].sum().item()				,   lowercase_  )
     # verify orig_size
     UpperCAmelCase_					:					List[str]			     =    torch.tensor([480, 640]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]				,   lowercase_  )  )
     # verify size
     UpperCAmelCase_					:					Any			     =    torch.tensor([800, 1066]  )
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"]				,   lowercase_  )  )




"""simple docstring"""





def 	__a     (		__lowerCamelCase  ):
   UpperCAmelCase_					:					List[str]			     =    int(__lowerCamelCase  )
   if n_element < 1:
      UpperCAmelCase_					:					List[Any]			     =    ValueError("a should be a positive number"  )
      raise my_error

   UpperCAmelCase_					:					List[Any]			     =    [1]
   UpperCAmelCase_     ,   UpperCAmelCase_     ,   UpperCAmelCase_					:					Optional[int]			     =    (0, 0, 0)
   UpperCAmelCase_					:					Dict			     =    1
   while index < n_element:
      while hamming_list[i] * 2 <= hamming_list[-1]:
         i += 1
      while hamming_list[j] * 3 <= hamming_list[-1]:
         j += 1
      while hamming_list[k] * 5 <= hamming_list[-1]:
         k += 1
      hamming_list.append(
          min(hamming_list[i] * 2,		hamming_list[j] * 3,		hamming_list[k] * 5  )  )
      index += 1
   return hamming_list


if __name__ == "__main__":
    _a		       =							input('Enter the last number (nth term) of the Hamming Number Series: ')
    print('Formula of Hamming Number Series => 2^i * 3^j * 5^k')
    _a		       =							hamming(int(n))
    print('-----------------------------------------------------')
    print(f"""The list with nth numbers is: {hamming_numbers}""")
    print('-----------------------------------------------------')


