



"""simple docstring"""

import unittest

import numpy as np
from datasets import load_dataset

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

if is_vision_available():
     from PIL import Image

     from transformers import BeitImageProcessor




class 			_UpperCAmelCase			(     unittest.TestCase):

 def __init__(	self						:			List[Any]    ,	lowercase_						:			Any    ,	lowercase_						:			Dict=7    ,	lowercase_						:			int=3    ,	lowercase_						:			str=18    ,	lowercase_						:			Optional[int]=30    ,	lowercase_						:			int=400    ,	lowercase_						:			Tuple=True    ,	lowercase_						:			List[str]=None    ,	lowercase_						:			int=True    ,	lowercase_						:			Any=None    ,	lowercase_						:			Optional[Any]=True    ,	lowercase_						:			Union[str, Any]=[0.5, 0.5, 0.5]    ,	lowercase_						:			List[Any]=[0.5, 0.5, 0.5]    ,	lowercase_						:			Optional[int]=False    ,	):
     snake_case_					:	Dict							=      size if size is not None else {'''height''': 20, '''width''': 20}
     snake_case_					:	Optional[Any]							=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
     snake_case_					:	int							=      parent
     snake_case_					:	str							=      batch_size
     snake_case_					:	List[Any]							=      num_channels
     snake_case_					:	Any							=      image_size
     snake_case_					:	int							=      min_resolution
     snake_case_					:	Dict							=      max_resolution
     snake_case_					:	Optional[int]							=      do_resize
     snake_case_					:	List[Any]							=      size
     snake_case_					:	Optional[int]							=      do_center_crop
     snake_case_					:	Tuple							=      crop_size
     snake_case_					:	str							=      do_normalize
     snake_case_					:	Optional[int]							=      image_mean
     snake_case_					:	List[str]							=      image_std
     snake_case_					:	Optional[int]							=      do_reduce_labels






 def 		_snake_case   (	self						:			Dict							):
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_reduce_labels": self.do_reduce_labels,
     }





def __lowercase     (				):
    snake_case_					:	Optional[int]							=      load_dataset('''hf-internal-testing/fixtures_ade20k'''						,     split='''test'''       )

    snake_case_					:	Optional[int]							=      Image.open(dataset[0]['''file''']       )
    snake_case_					:	Any							=      Image.open(dataset[1]['''file''']       )

    return image, map




def __lowercase     (				):
    snake_case_					:	Union[str, Any]							=      load_dataset('''hf-internal-testing/fixtures_ade20k'''						,     split='''test'''       )

    snake_case_					:	Tuple							=      Image.open(ds[0]['''file''']       )
    snake_case_					:	Optional[int]							=      Image.open(ds[1]['''file''']       )
    snake_case_					:	str							=      Image.open(ds[2]['''file''']       )
    snake_case_					:	Union[str, Any]							=      Image.open(ds[3]['''file''']       )

    return [imagea, imagea], [mapa, mapa]






@require_torch
@require_vision
class 			_UpperCAmelCase			(     lowerCAmelCase__       ,	unittest.TestCase):
 _lowerCAmelCase  :     List[Any]						   =						BeitImageProcessor if is_vision_available() else None

 def 		_snake_case   (	self						:			List[Any]							):
     snake_case_					:	int							=      BeitImageProcessingTester(self							)

 @property
 def 		_snake_case   (	self						:			List[Any]							):
     return self.image_processor_tester.prepare_image_processor_dict()

 def 		_snake_case   (	self						:			str							):
     snake_case_					:	Any							=      self.image_processing_class(**self.image_processor_dict							)
     self.assertTrue(hasattr(lowercase__    ,	'''do_resize'''							)							)
     self.assertTrue(hasattr(lowercase__    ,	'''size'''							)							)
     self.assertTrue(hasattr(lowercase__    ,	'''do_center_crop'''							)							)
     self.assertTrue(hasattr(lowercase__    ,	'''center_crop'''							)							)
     self.assertTrue(hasattr(lowercase__    ,	'''do_normalize'''							)							)
     self.assertTrue(hasattr(lowercase__    ,	'''image_mean'''							)							)
     self.assertTrue(hasattr(lowercase__    ,	'''image_std'''							)							)

 def 		_snake_case   (	self						:			Union[str, Any]							):
     snake_case_					:	Any							=      self.image_processing_class.from_dict(self.image_processor_dict							)
     self.assertEqual(image_processor.size    ,	{'''height''': 20, '''width''': 20}							)
     self.assertEqual(image_processor.crop_size    ,	{'''height''': 18, '''width''': 18}							)
     self.assertEqual(image_processor.do_reduce_labels    ,	lowercase__							)

     snake_case_					:	int							=      self.image_processing_class.from_dict(
         self.image_processor_dict    ,	size=42    ,	crop_size=84    ,	reduce_labels=lowercase__							)
     self.assertEqual(image_processor.size    ,	{'''height''': 42, '''width''': 42}							)
     self.assertEqual(image_processor.crop_size    ,	{'''height''': 84, '''width''': 84}							)
     self.assertEqual(image_processor.do_reduce_labels    ,	lowercase__							)

 def 		_snake_case   (	self						:			Tuple							):
     pass

 def 		_snake_case   (	self						:			List[Any]							):
     snake_case_					:	Union[str, Any]							=      self.image_processing_class(**self.image_processor_dict							)
     # create random PIL images
     snake_case_					:	int							=      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							=      image_processing(image_inputs[0]    ,	return_tensors='''pt'''							).pixel_values
     self.assertEqual(
         encoded_images.shape    ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)

     # Test batched
     snake_case_					:	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,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)

 def 		_snake_case   (	self						:			Any							):
     snake_case_					:	Optional[int]							=      self.image_processing_class(**self.image_processor_dict							)
     # create random numpy tensors
     snake_case_					:	Tuple							=      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
     snake_case_					:	Optional[Any]							=      image_processing(image_inputs[0]    ,	return_tensors='''pt'''							).pixel_values
     self.assertEqual(
         encoded_images.shape    ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)

     # Test batched
     snake_case_					:	List[str]							=      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,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)

 def 		_snake_case   (	self						:			Union[str, Any]							):
     snake_case_					:	Optional[int]							=      self.image_processing_class(**self.image_processor_dict							)
     # create random PyTorch tensors
     snake_case_					:	Optional[int]							=      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_					:	str							=      image_processing(image_inputs[0]    ,	return_tensors='''pt'''							).pixel_values
     self.assertEqual(
         encoded_images.shape    ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)

     # Test batched
     snake_case_					:	Tuple							=      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,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)

 def 		_snake_case   (	self						:			str							):
     snake_case_					:	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__							)
     snake_case_					:	Tuple							=      []
     for image in image_inputs:
         self.assertIsInstance(lowercase__    ,	torch.Tensor							)
         maps.append(torch.zeros(image.shape[-2:]							).long()							)

     # Test not batched input
     snake_case_					:	Dict							=      image_processing(image_inputs[0]    ,	maps[0]    ,	return_tensors='''pt'''							)
     self.assertEqual(
         encoding['''pixel_values'''].shape    ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(
         encoding['''labels'''].shape    ,	(
             1,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(encoding['''labels'''].dtype    ,	torch.long							)
     self.assertTrue(encoding['''labels'''].min().item() >= 0							)
     self.assertTrue(encoding['''labels'''].max().item() <= 255							)

     # Test batched
     snake_case_					:	List[str]							=      image_processing(lowercase__    ,	lowercase__    ,	return_tensors='''pt'''							)
     self.assertEqual(
         encoding['''pixel_values'''].shape    ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(
         encoding['''labels'''].shape    ,	(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(encoding['''labels'''].dtype    ,	torch.long							)
     self.assertTrue(encoding['''labels'''].min().item() >= 0							)
     self.assertTrue(encoding['''labels'''].max().item() <= 255							)

     # Test not batched input (PIL images)
     snake_case_,      snake_case_					:	Optional[Any]							=      prepare_semantic_single_inputs()

     snake_case_					:	Tuple							=      image_processing(lowercase__    ,	lowercase__    ,	return_tensors='''pt'''							)
     self.assertEqual(
         encoding['''pixel_values'''].shape    ,	(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(
         encoding['''labels'''].shape    ,	(
             1,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(encoding['''labels'''].dtype    ,	torch.long							)
     self.assertTrue(encoding['''labels'''].min().item() >= 0							)
     self.assertTrue(encoding['''labels'''].max().item() <= 255							)

     # Test batched input (PIL images)
     snake_case_,      snake_case_					:	Any							=      prepare_semantic_batch_inputs()

     snake_case_					:	List[Any]							=      image_processing(lowercase__    ,	lowercase__    ,	return_tensors='''pt'''							)
     self.assertEqual(
         encoding['''pixel_values'''].shape    ,	(
             2,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(
         encoding['''labels'''].shape    ,	(
             2,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )    ,	)
     self.assertEqual(encoding['''labels'''].dtype    ,	torch.long							)
     self.assertTrue(encoding['''labels'''].min().item() >= 0							)
     self.assertTrue(encoding['''labels'''].max().item() <= 255							)






 def 		_snake_case   (	self						:			Dict							):
     snake_case_					:	Dict							=      self.image_processing_class(**self.image_processor_dict							)

     # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
     snake_case_,      snake_case_					:	List[str]							=      prepare_semantic_single_inputs()
     snake_case_					:	List[str]							=      image_processing(lowercase__    ,	lowercase__    ,	return_tensors='''pt'''							)
     self.assertTrue(encoding['''labels'''].min().item() >= 0							)
     self.assertTrue(encoding['''labels'''].max().item() <= 150							)

     snake_case_					:	Any							=      True
     snake_case_					:	Optional[Any]							=      image_processing(lowercase__    ,	lowercase__    ,	return_tensors='''pt'''							)
     self.assertTrue(encoding['''labels'''].min().item() >= 0							)
     self.assertTrue(encoding['''labels'''].max().item() <= 255							)








from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


