




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import ImageGPTImageProcessor







class   A(						unittest.TestCase   ):



 '''simple docstring'''


 def __init__(				self					:  int						,							A_					:  List[str]						,							A_					:  Union[str, Any]=7						,							A_					:  List[Any]=3						,							A_					:  Any=18						,							A_					:  str=30						,							A_					:  Union[str, Any]=400						,							A_					:  Dict=True						,							A_					:  Optional[int]=None						,							A_					:  int=True						,							)   ->	int:

       """simple docstring"""






       lowerCamelCase_		     =    size if size is not None else {'height': 18, 'width': 18}
       lowerCamelCase_		     =    parent
       lowerCamelCase_		     =    batch_size
       lowerCamelCase_		     =    num_channels
       lowerCamelCase_		     =    image_size
       lowerCamelCase_		     =    min_resolution
       lowerCamelCase_		     =    max_resolution
       lowerCamelCase_		     =    do_resize
       lowerCamelCase_		     =    size
       lowerCamelCase_		     =    do_normalize





 def 				a__ (				self					:  List[Any]  )   ->	Tuple:

       """simple docstring"""






       return {
           # here we create 2 clusters for the sake of simplicity
           "clusters": np.asarray(
               [
                   [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
                   [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
               ]  ),
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
       }







@require_torch
@require_vision
class   A(						UpperCamelCase							,      unittest.TestCase   ):



 '''simple docstring'''


 UpperCamelCase						   =						ImageGPTImageProcessor if is_vision_available() else None

 def 				a__ (				self					:  Any  )   ->	Dict:

       """simple docstring"""






       lowerCamelCase_		     =    ImageGPTImageProcessingTester(self  )

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

       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()

 def 				a__ (				self					:  Dict  )   ->	str:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(A_						,							'clusters'  )  )
       self.assertTrue(hasattr(A_						,							'do_resize'  )  )
       self.assertTrue(hasattr(A_						,							'size'  )  )
       self.assertTrue(hasattr(A_						,							'do_normalize'  )  )

 def 				a__ (				self					:  Optional[Any]  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class.from_dict(self.image_processor_dict  )
       self.assertEqual(image_processor.size						,							{'height': 18, 'width': 18}  )

       lowerCamelCase_		     =    self.image_processing_class.from_dict(self.image_processor_dict						,							size=42  )
       self.assertEqual(image_processor.size						,							{'height': 42, 'width': 42}  )

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

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )
       lowerCamelCase_		     =    json.loads(image_processor.to_json_string()  )
       for key, value in self.image_processor_dict.items():
             if key == "clusters":
                   self.assertTrue(np.array_equal(A_						,							obj[key]  )  )
             else:
                   self.assertEqual(obj[key]						,							A_  )

 def 				a__ (				self					:  Tuple  )   ->	List[Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
             lowerCamelCase_		     =    os.path.join(A_						,							'image_processor.json'  )
             image_processor_first.to_json_file(A_  )
             lowerCamelCase_		     =    self.image_processing_class.from_json_file(A_  ).to_dict()

       lowerCamelCase_		     =    image_processor_first.to_dict()
       for key, value in image_processor_first.items():
             if key == "clusters":
                   self.assertTrue(np.array_equal(A_						,							image_processor_second[key]  )  )
             else:
                   self.assertEqual(image_processor_first[key]						,							A_  )

 def 				a__ (				self					:  Optional[int]  )   ->	Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_		     =    self.image_processing_class(**self.image_processor_dict  )

       with tempfile.TemporaryDirectory() as tmpdirname:
             image_processor_first.save_pretrained(A_  )
             lowerCamelCase_		     =    self.image_processing_class.from_pretrained(A_  ).to_dict()

       lowerCamelCase_		     =    image_processor_first.to_dict()
       for key, value in image_processor_first.items():
             if key == "clusters":
                   self.assertTrue(np.array_equal(A_						,							image_processor_second[key]  )  )
             else:
                   self.assertEqual(image_processor_first[key]						,							A_  )





 @unittest.skip('ImageGPT requires clusters at initialization'  )
 def 				a__ (				self					:  List[Any]  )   ->	Optional[int]:

       """simple docstring"""






       pass

def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      lowerCamelCase_		     =    load_dataset('hf-internal-testing/fixtures_image_utils'	,							split='test'     )

      lowerCamelCase_		     =    Image.open(dataset[4]['file']     )
      lowerCamelCase_		     =    Image.open(dataset[5]['file']     )

      lowerCamelCase_		     =    [imagea, imagea]

      return images







@require_vision
@require_torch
class   A(						unittest.TestCase   ):



 '''simple docstring'''


 @slow
 def 				a__ (				self					:  List[Any]  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'  )

       lowerCamelCase_		     =    prepare_images()

       # test non-batched
       lowerCamelCase_		     =    image_processing(images[0]						,							return_tensors='pt'  )

       self.assertIsInstance(encoding.input_ids						,							torch.LongTensor  )
       self.assertEqual(encoding.input_ids.shape						,							(1, 1024)  )

       lowerCamelCase_		     =    [306, 191, 191]
       self.assertEqual(encoding.input_ids[0, :3].tolist()						,							A_  )

       # test batched
       lowerCamelCase_		     =    image_processing(A_						,							return_tensors='pt'  )

       self.assertIsInstance(encoding.input_ids						,							torch.LongTensor  )
       self.assertEqual(encoding.input_ids.shape						,							(2, 1024)  )

       lowerCamelCase_		     =    [303, 13, 13]
       self.assertEqual(encoding.input_ids[1, -3:].tolist()						,							A_  )









lowerCamelCase				:		Tuple     =   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       int	,							lowercase	:       Optional[Any]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [False] * len(lowercase     )
      lowerCamelCase_		     =    [s]
      lowerCamelCase_		     =    True

      while queue:
            lowerCamelCase_		     =    queue.pop(0     )
            for ind in range(len(graph[u]     )     ):
                  if visited[ind] is False and graph[u][ind] > 0:
                        queue.append(lowercase     )
                        lowerCamelCase_		     =    True
                        lowerCamelCase_		     =    u

      return visited[t]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       Tuple	,							lowercase	:       Tuple     ):


      '''simple docstring'''



      lowerCamelCase_		     =    [-1] * (len(lowercase     ))
      lowerCamelCase_		     =    0
      lowerCamelCase_		     =    []
      lowerCamelCase_		     =    [i[:] for i in graph]  # Record original cut, copy.
      while bfs(lowercase	,							lowercase	,							lowercase	,							lowercase     ):
            lowerCamelCase_		     =    float('Inf'     )
            lowerCamelCase_		     =    sink

            while s != source:
                  # Find the minimum value in select path
                  lowerCamelCase_		     =    min(lowercase	,							graph[parent[s]][s]     )
                  lowerCamelCase_		     =    parent[s]

            max_flow += path_flow
            lowerCamelCase_		     =    sink

            while v != source:
                  lowerCamelCase_		     =    parent[v]
                  graph[u][v] -= path_flow
                  graph[v][u] += path_flow
                  lowerCamelCase_		     =    parent[v]

      for i in range(len(lowercase     )     ):
            for j in range(len(graph[0]     )     ):
                  if graph[i][j] == 0 and temp[i][j] > 0:
                        res.append((i, j)     )

      return res


if __name__ == "__main__":
 print(mincut(test_graph, source=0, sink=5))



