



"""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:
     __lowerCAmelCase       :					Tuple      					=					False

if is_vision_available():
     from PIL import Image

     from transformers import PixaStructImageProcessor



class    _lowerCAmelCase    (				unittest.TestCase		):





  """simple docstring"""






  def __init__(					self							,    _lowercase							,    _lowercase=7							,    _lowercase=3							,    _lowercase=1_8							,    _lowercase=3_0							,    _lowercase=4_0_0							,    _lowercase=None							,    _lowercase=True							,    _lowercase=True							,    _lowercase=None							,    ) ->						List[Any]:







        '''simple docstring'''

        snake_case_   : Dict						=			size if size is not None else {"""height""": 2_0, """width""": 2_0}
        snake_case_   : List[str]						=			parent
        snake_case_   : Dict						=			batch_size
        snake_case_   : Any						=			num_channels
        snake_case_   : Dict						=			image_size
        snake_case_   : List[str]						=			min_resolution
        snake_case_   : str						=			max_resolution
        snake_case_   : List[Any]						=			size
        snake_case_   : Optional[int]						=			do_normalize
        snake_case_   : Optional[Any]						=			do_convert_rgb
        snake_case_   : Optional[int]						=			[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
        snake_case_   : List[str]						=			patch_size if patch_size is not None else {"""height""": 1_6, """width""": 1_6}





  def    UpperCAmelCase__							(					self   ) ->						List[str]:







        '''simple docstring'''

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





  def    UpperCAmelCase__							(					self   ) ->						List[str]:







        '''simple docstring'''

        snake_case_   : Optional[Any]						=			"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
        snake_case_   : int						=			Image.open(requests.get(_lowercase							,    stream=_lowercase   ).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    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





  """simple docstring"""






  _lowerCamelCase			  =       PixaStructImageProcessor if is_vision_available() else None





  def    UpperCAmelCase__							(					self   ) ->						Tuple:







        '''simple docstring'''

        snake_case_   : List[str]						=			PixaStructImageProcessingTester(self   )





  @property
  def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







        '''simple docstring'''

        return self.image_processor_tester.prepare_image_processor_dict()





  def    UpperCAmelCase__							(					self   ) ->						Any:







        '''simple docstring'''

        snake_case_   : Optional[Any]						=			self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(_lowercase							,    """do_normalize"""   )   )
        self.assertTrue(hasattr(_lowercase							,    """do_convert_rgb"""   )   )





  def    UpperCAmelCase__							(					self   ) ->						List[Any]:







        '''simple docstring'''

        snake_case_   : str						=			self.image_processor_tester.prepare_dummy_image()

        snake_case_   : str						=			self.image_processing_class(**self.image_processor_dict   )
        snake_case_   : Dict						=			2_0_4_8

        snake_case_   : Any						=			image_processor(_lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   )
        self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,    torch.tensor(0.0606   )							,    atol=1E-3							,    rtol=1E-3   )   )





  def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







        '''simple docstring'''

        snake_case_   : Any						=			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=_lowercase   )
        for image in image_inputs:
              self.assertIsInstance(_lowercase							,    Image.Image   )

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

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





  def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







        '''simple docstring'''

        snake_case_   : Optional[Any]						=			self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        snake_case_   : 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
        snake_case_   : str						=			(
            (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
            * self.image_processor_tester.num_channels
        ) + 2

        snake_case_   : Dict						=			True

        for max_patch in self.image_processor_tester.max_patches:
              # Test not batched input
              with self.assertRaises(_lowercase   ):
                    snake_case_   : Dict						=			image_processor(
                        image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).flattened_patches

              snake_case_   : Dict						=			"""Hello"""

              snake_case_   : Tuple						=			image_processor(
                  image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=_lowercase							,    header_text=_lowercase   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

              # Test batched
              snake_case_   : Tuple						=			image_processor(
                  _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase							,    header_text=_lowercase   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





  def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







        '''simple docstring'''

        snake_case_   : str						=			self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        snake_case_   : Optional[Any]						=			prepare_image_inputs(self.image_processor_tester							,    equal_resolution=_lowercase							,    numpify=_lowercase   )
        for image in image_inputs:
              self.assertIsInstance(_lowercase							,    np.ndarray   )

        snake_case_   : int						=			(
            (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=_lowercase   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

              # Test batched
              snake_case_   : Tuple						=			image_processor(
                  _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





  def    UpperCAmelCase__							(					self   ) ->						str:







        '''simple docstring'''

        snake_case_   : Optional[int]						=			self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        snake_case_   : 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
        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=_lowercase   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

              # Test batched
              snake_case_   : Optional[Any]						=			image_processor(
                  _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).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    (				SCREAMING_SNAKE_CASE__  ,				unittest.TestCase		):





  """simple docstring"""






  _lowerCamelCase			  =       PixaStructImageProcessor if is_vision_available() else None





  def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







        '''simple docstring'''

        snake_case_   : Tuple						=			PixaStructImageProcessingTester(self							,    num_channels=4   )
        snake_case_   : Tuple						=			3





  @property
  def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







        '''simple docstring'''

        return self.image_processor_tester.prepare_image_processor_dict()





  def    UpperCAmelCase__							(					self   ) ->						int:







        '''simple docstring'''

        snake_case_   : str						=			self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(_lowercase							,    """do_normalize"""   )   )
        self.assertTrue(hasattr(_lowercase							,    """do_convert_rgb"""   )   )





  def    UpperCAmelCase__							(					self   ) ->						Union[str, Any]:







        '''simple docstring'''

        snake_case_   : Any						=			self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        snake_case_   : Tuple						=			prepare_image_inputs(self.image_processor_tester							,    equal_resolution=_lowercase   )
        for image in image_inputs:
              self.assertIsInstance(_lowercase							,    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=_lowercase   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

              # Test batched
              snake_case_   : Any						=			image_processor(
                  _lowercase							,    return_tensors="""pt"""							,    max_patches=_lowercase   ).flattened_patches
              self.assertEqual(
                  encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )






"""simple docstring"""





def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			[0] * len(__UpperCamelCase    )
						snake_case_   : List[str]						=			[]
						snake_case_   : Any						=			[1] * len(__UpperCamelCase    )

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCamelCase    )    ):
												if indegree[i] == 0:
																		queue.append(__UpperCamelCase    )

						while queue:
												snake_case_   : Optional[int]						=			queue.pop(0    )
												for x in graph[vertex]:
																		indegree[x] -= 1

																		if long_dist[vertex] + 1 > long_dist[x]:
																								snake_case_   : Union[str, Any]						=			long_dist[vertex] + 1

																		if indegree[x] == 0:
																								queue.append(__UpperCamelCase    )

						print(max(__UpperCamelCase    )    )


# Adjacency list of Graph
__lowerCAmelCase       :					str      					=					{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)

