





import unittest

import numpy as np
import requests

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

      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
      lowerCAmelCase_						   =    False

if is_vision_available():
      from PIL import Image

      from transformers import PixaStructImageProcessor



class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''







  def __init__(      self      :					List[Any]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					Union[str, Any]=7      ,				UpperCamelCase      :					Union[str, Any]=3      ,				UpperCamelCase      :					Optional[Any]=18      ,				UpperCamelCase      :					Any=30      ,				UpperCamelCase      :					Tuple=4_00      ,				UpperCamelCase      :					int=None      ,				UpperCamelCase      :					int=True      ,				UpperCamelCase      :					List[Any]=True      ,				UpperCamelCase      :					Union[str, Any]=None      ,				):


    '''simple docstring'''





    _snake_case   :				Dict														=	size if size is not None else {'height': 20, 'width': 20}
    _snake_case   :				List[Any]														=	parent
    _snake_case   :				int														=	batch_size
    _snake_case   :				int														=	num_channels
    _snake_case   :				Optional[int]														=	image_size
    _snake_case   :				Optional[int]														=	min_resolution
    _snake_case   :				Any														=	max_resolution
    _snake_case   :				Dict														=	size
    _snake_case   :				Any														=	do_normalize
    _snake_case   :				List[Any]														=	do_convert_rgb
    _snake_case   :				Any														=	[5_12, 10_24, 20_48, 40_96]
    _snake_case   :				Optional[int]														=	patch_size if patch_size is not None else {'height': 16, 'width': 16}







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
    _snake_case   :				Any														=	Image.open(requests.get(UpperCamelCase      ,				stream=UpperCamelCase							).raw							).convert('RGB'							)
    return raw_image




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,    reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""							,    )
@require_torch
@require_vision
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       Dict      					=PixaStructImageProcessor if is_vision_available() else None







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	PixaStructImageProcessingTester(self							)







  @property
  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    return self.image_processor_tester.prepare_image_processor_dict()







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				int														=	self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_normalize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_convert_rgb'							)							)







  def UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	self.image_processor_tester.prepare_dummy_image()

    _snake_case   :				Tuple														=	self.image_processing_class(**self.image_processor_dict							)
    _snake_case   :				Optional[int]														=	20_48

    _snake_case   :				str														=	image_processor(UpperCamelCase      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							)
    self.assertTrue(torch.allclose(inputs.flattened_patches.mean()      ,				torch.tensor(0.06_06							)      ,				atol=1e-3      ,				rtol=1e-3							)							)







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    _snake_case   :				Tuple														=	prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				Image.Image							)

    # Test not batched input
    _snake_case   :				List[str]														=	(
        (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
      # Test not batched input
      _snake_case   :				str														=	image_processor(
          image_inputs[0]      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(1, max_patch, expected_hidden_dim)      ,				)

      # Test batched
      _snake_case   :				Tuple														=	image_processor(
          UpperCamelCase      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,				)







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				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=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				Image.Image							)

    # Test not batched input
    _snake_case   :				Optional[Any]														=	(
        (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
        * self.image_processor_tester.num_channels
    ) + 2

    _snake_case   :				str														=	True

    for max_patch in self.image_processor_tester.max_patches:
      # Test not batched input
      with self.assertRaises(UpperCamelCase							):
        _snake_case   :				List[str]														=	image_processor(
            image_inputs[0]      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches

      _snake_case   :				int														=	'Hello'

      _snake_case   :				str														=	image_processor(
          image_inputs[0]      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase      ,				header_text=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(1, max_patch, expected_hidden_dim)      ,				)

      # Test batched
      _snake_case   :				List[Any]														=	image_processor(
          UpperCamelCase      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase      ,				header_text=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,				)







  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				List[str]														=	self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    _snake_case   :				Dict														=	prepare_image_inputs(self.image_processor_tester      ,				equal_resolution=UpperCamelCase      ,				numpify=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				np.ndarray							)

    _snake_case   :				Tuple														=	(
        (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
      # Test not batched input
      _snake_case   :				Tuple														=	image_processor(
          image_inputs[0]      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(1, max_patch, expected_hidden_dim)      ,				)

      # Test batched
      _snake_case   :				Tuple														=	image_processor(
          UpperCamelCase      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,				)







  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





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

    # Test not batched input
    _snake_case   :				List[str]														=	(
        (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
        * self.image_processor_tester.num_channels
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
      # Test not batched input
      _snake_case   :				Union[str, Any]														=	image_processor(
          image_inputs[0]      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(1, max_patch, expected_hidden_dim)      ,				)

      # Test batched
      _snake_case   :				str														=	image_processor(
          UpperCamelCase      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,				)




@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11							,    reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""							,    )
@require_torch
@require_vision
class        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       Tuple      					=PixaStructImageProcessor if is_vision_available() else None







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Any														=	PixaStructImageProcessingTester(self      ,				num_channels=4							)
    _snake_case   :				Union[str, Any]														=	3







  @property
  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    return self.image_processor_tester.prepare_image_processor_dict()







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_normalize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_convert_rgb'							)							)







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





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

    # Test not batched input
    _snake_case   :				int														=	(
        (self.image_processor_tester.patch_size['height'] * self.image_processor_tester.patch_size['width'])
        * (self.image_processor_tester.num_channels - 1)
    ) + 2

    for max_patch in self.image_processor_tester.max_patches:
      # Test not batched input
      _snake_case   :				Dict														=	image_processor(
          image_inputs[0]      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(1, max_patch, expected_hidden_dim)      ,				)

      # Test batched
      _snake_case   :				Dict														=	image_processor(
          UpperCamelCase      ,				return_tensors='pt'      ,				max_patches=UpperCamelCase							).flattened_patches
      self.assertEqual(
          encoded_images.shape      ,				(self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)      ,				)











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	int(lowerCAmelCase				)
  if n_element < 1:
    _snake_case   :				int														=	ValueError('a should be a positive number'				)
    raise my_error

  _snake_case   :				Union[str, Any]														=	[1]
  _snake_case							,					_snake_case							,					_snake_case   :				Any														=	(0, 0, 0)
  _snake_case   :				str														=	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__":
      lowerCAmelCase_						   =    input("""Enter the last number (nth term) of the Hamming Number Series: """)
      print("""Formula of Hamming Number Series => 2^i * 3^j * 5^k""")
      lowerCAmelCase_						   =    hamming(int(n))
      print("""-----------------------------------------------------""")
      print(F"""The list with nth numbers is: {hamming_numbers}""")
      print("""-----------------------------------------------------""")




