



"""simple docstring"""







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:
     lowercase__		:    Any				   =     False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor







class 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=7   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_8   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=3_0   ,      SCREAMING_SNAKE_CASE_    :				Dict=4_0_0   ,      SCREAMING_SNAKE_CASE_    :				int=None   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=True   ,      SCREAMING_SNAKE_CASE_    :				int=True   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=None   ,      ):
        lowerCAmelCase_       :    List[str]								=			size if size is not None else {'height': 2_0, 'width': 2_0}
        lowerCAmelCase_       :    Optional[Any]								=			parent
        lowerCAmelCase_       :    Any								=			batch_size
        lowerCAmelCase_       :    Any								=			num_channels
        lowerCAmelCase_       :    Tuple								=			image_size
        lowerCAmelCase_       :    Dict								=			min_resolution
        lowerCAmelCase_       :    Optional[Any]								=			max_resolution
        lowerCAmelCase_       :    Any								=			size
        lowerCAmelCase_       :    int								=			do_normalize
        lowerCAmelCase_       :    Dict								=			do_convert_rgb
        lowerCAmelCase_       :    Dict								=			[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
        lowerCAmelCase_       :    str								=			patch_size if patch_size is not None else {'height': 1_6, 'width': 1_6}



      def 					SCREAMING_SNAKE_CASE__			(				self    :				int	):
        return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg'
        lowerCAmelCase_       :    Dict								=			Image.open(requests.get(SCREAMING_SNAKE_CASE_   ,      stream=SCREAMING_SNAKE_CASE_	).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 				UpperCamelCase__       (     lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					PixaStructImageProcessor if is_vision_available() else None



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    str								=			PixaStructImageProcessingTester(self	)



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        return self.image_processor_tester.prepare_image_processor_dict()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    Any								=			self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'do_normalize'	)	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'do_convert_rgb'	)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    int								=			self.image_processor_tester.prepare_dummy_image()

        lowerCAmelCase_       :    int								=			self.image_processing_class(**self.image_processor_dict	)
        lowerCAmelCase_       :    Optional[Any]								=			2_0_4_8

        lowerCAmelCase_       :    List[Any]								=			image_processor(SCREAMING_SNAKE_CASE_   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_	)
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()   ,      torch.tensor(0.06_06	)   ,      atol=1E-3   ,      rtol=1E-3	)	)



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        # Initialize image_processor
        lowerCAmelCase_       :    Dict								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowerCAmelCase_       :    int								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      Image.Image	)

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

          # Test batched
          lowerCAmelCase_       :    Optional[int]								=			image_processor(
              SCREAMING_SNAKE_CASE_   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
          self.assertEqual(
              encoded_images.shape   ,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        # Initialize image_processor
        lowerCAmelCase_       :    List[str]								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowerCAmelCase_       :    int								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      Image.Image	)

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

        lowerCAmelCase_       :    int								=			True

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

          lowerCAmelCase_       :    List[str]								=			'Hello'

          lowerCAmelCase_       :    Optional[Any]								=			image_processor(
              image_inputs[0]   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_   ,      header_text=SCREAMING_SNAKE_CASE_	).flattened_patches
          self.assertEqual(
              encoded_images.shape   ,      (1, max_patch, expected_hidden_dim)   ,      )

          # Test batched
          lowerCAmelCase_       :    Optional[int]								=			image_processor(
              SCREAMING_SNAKE_CASE_   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_   ,      header_text=SCREAMING_SNAKE_CASE_	).flattened_patches
          self.assertEqual(
              encoded_images.shape   ,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,      )



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        # Initialize image_processor
        lowerCAmelCase_       :    List[Any]								=			self.image_processing_class(**self.image_processor_dict	)
        # create random numpy tensors
        lowerCAmelCase_       :    str								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_   ,      numpify=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      np.ndarray	)

        lowerCAmelCase_       :    Dict								=			(
            (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
          lowerCAmelCase_       :    Tuple								=			image_processor(
              image_inputs[0]   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
          self.assertEqual(
              encoded_images.shape   ,      (1, max_patch, expected_hidden_dim)   ,      )

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





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        # Initialize image_processor
        lowerCAmelCase_       :    str								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PyTorch tensors
        lowerCAmelCase_       :    int								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_   ,      torchify=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      torch.Tensor	)

        # Test not batched input
        lowerCAmelCase_       :    Dict								=			(
            (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
          lowerCAmelCase_       :    str								=			image_processor(
              image_inputs[0]   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
          self.assertEqual(
              encoded_images.shape   ,      (1, max_patch, expected_hidden_dim)   ,      )

          # Test batched
          lowerCAmelCase_       :    Any								=			image_processor(
              SCREAMING_SNAKE_CASE_   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_	).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 				UpperCamelCase__       (     lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					PixaStructImageProcessor if is_vision_available() else None



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        lowerCAmelCase_       :    str								=			PixaStructImageProcessingTester(self   ,      num_channels=4	)
        lowerCAmelCase_       :    str								=			3



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        return self.image_processor_tester.prepare_image_processor_dict()



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        lowerCAmelCase_       :    Dict								=			self.image_processing_class(**self.image_processor_dict	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'do_normalize'	)	)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,      'do_convert_rgb'	)	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        # Initialize image_processor
        lowerCAmelCase_       :    Any								=			self.image_processing_class(**self.image_processor_dict	)
        # create random PIL images
        lowerCAmelCase_       :    str								=			prepare_image_inputs(self.image_processor_tester   ,      equal_resolution=SCREAMING_SNAKE_CASE_	)
        for image in image_inputs:
          self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,      Image.Image	)

        # Test not batched input
        lowerCAmelCase_       :    str								=			(
            (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
          lowerCAmelCase_       :    Tuple								=			image_processor(
              image_inputs[0]   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
          self.assertEqual(
              encoded_images.shape   ,      (1, max_patch, expected_hidden_dim)   ,      )

          # Test batched
          lowerCAmelCase_       :    Optional[Any]								=			image_processor(
              SCREAMING_SNAKE_CASE_   ,      return_tensors='pt'   ,      max_patches=SCREAMING_SNAKE_CASE_	).flattened_patches
          self.assertEqual(
              encoded_images.shape   ,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)   ,      )










"""simple docstring"""







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt

def     UpperCamelCase_   (   lowerCAmelCase__				:    int						)						->       bool:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			int(number**0.5						)
  return number == sq * sq

def     UpperCamelCase_   (   lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int   ,       lowerCAmelCase__				:    int						)						->       tuple[int, int]:

  """simple docstring"""




  lowerCAmelCase_       :    int								=			x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
  lowerCAmelCase_       :    int								=			x_den * y_den * z_den
  lowerCAmelCase_       :    int								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
  top //= hcf
  bottom //= hcf
  return top, bottom

def     UpperCamelCase_   (   lowerCAmelCase__				:    int = 35						)						->       int:

  """simple docstring"""




  lowerCAmelCase_       :    set								=			set()
  lowerCAmelCase_       :    int
  lowerCAmelCase_       :    Fraction								=			Fraction(0						)
  lowerCAmelCase_       :    tuple[int, int]

  for x_num in range(1   ,       order + 1						):
    for x_den in range(x_num + 1   ,       order + 1						):
      for y_num in range(1   ,       order + 1						):
        for y_den in range(y_num + 1   ,       order + 1						):
          # n=1
          lowerCAmelCase_       :    str								=			x_num * y_den + x_den * y_num
          lowerCAmelCase_       :    int								=			x_den * y_den
          lowerCAmelCase_       :    int								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
          z_num //= hcf
          z_den //= hcf
          if 0 < z_num < z_den <= order:
            lowerCAmelCase_       :    List[str]								=			add_three(
                lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
            unique_s.add(lowerCAmelCase__						)

          # n=2
          lowerCAmelCase_       :    Optional[int]								=			(
              x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
          )
          lowerCAmelCase_       :    Dict								=			x_den * x_den * y_den * y_den
          if is_sq(lowerCAmelCase__						) and is_sq(lowerCAmelCase__						):
            lowerCAmelCase_       :    Optional[int]								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    List[str]								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    Union[str, Any]								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
            z_num //= hcf
            z_den //= hcf
            if 0 < z_num < z_den <= order:
              lowerCAmelCase_       :    Union[str, Any]								=			add_three(
                  lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
              unique_s.add(lowerCAmelCase__						)

                    # n=-1
          lowerCAmelCase_       :    Dict								=			x_num * y_num
          lowerCAmelCase_       :    Optional[int]								=			x_den * y_num + x_num * y_den
          lowerCAmelCase_       :    Any								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
          z_num //= hcf
          z_den //= hcf
          if 0 < z_num < z_den <= order:
            lowerCAmelCase_       :    Tuple								=			add_three(
                lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
            unique_s.add(lowerCAmelCase__						)

          # n=2
          lowerCAmelCase_       :    List[str]								=			x_num * x_num * y_num * y_num
          lowerCAmelCase_       :    Optional[Any]								=			(
              x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
          )
          if is_sq(lowerCAmelCase__						) and is_sq(lowerCAmelCase__						):
            lowerCAmelCase_       :    Tuple								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    Optional[Any]								=			int(sqrt(lowerCAmelCase__						)						)
            lowerCAmelCase_       :    Optional[int]								=			gcd(lowerCAmelCase__   ,       lowerCAmelCase__						)
            z_num //= hcf
            z_den //= hcf
            if 0 < z_num < z_den <= order:
              lowerCAmelCase_       :    Any								=			add_three(
                  lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__   ,       lowerCAmelCase__						)
              unique_s.add(lowerCAmelCase__						)

  for num, den in unique_s:
    total += Fraction(lowerCAmelCase__   ,       lowerCAmelCase__						)

  return total.denominator + total.numerator


if __name__ == "__main__":
     print(f'{solution() = }')





