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 SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def __init__(     self						:						Dict   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						str=7   ,   lowerCamelCase_						:						List[str]=3   ,   lowerCamelCase_						:						str=18   ,   lowerCamelCase_						:						Optional[int]=30   ,   lowerCamelCase_						:						Tuple=400   ,   lowerCamelCase_						:						List[str]=True   ,   lowerCamelCase_						:						Dict=None   ,   lowerCamelCase_						:						int=True   ,   ):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8_8_6_6_4_4_3_6_3_4_0_3_3_2_0_3, 0.6_6_1_8_8_2_9_3_6_9_5_4_4_9_8_3, 0.3_8_9_1_7_4_6_4_0_1_7_8_6_8_0_4],
                [-0.6_0_4_2_5_5_9_1_4_6_8_8_1_1_0_4, -0.0_2_2_9_5_0_0_8_8_6_0_5_2_8_4_6_9, 0.5_4_2_3_7_9_7_3_6_9_0_0_3_2_9_6],
            ]				),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }





@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     unittest.TestCase		):
   __lowerCAmelCase           =				ImageGPTImageProcessor if is_vision_available() else None







   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    UpperCamelCase			  =      ImageGPTImageProcessingTester(self				)







   @property
   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    return self.image_processor_tester.prepare_image_processor_dict()







   def 						lowerCamelCase_	(     self						:						List[str]				):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


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

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







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


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







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


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

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

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







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


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

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

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







   @unittest.skip("""ImageGPT requires clusters at initialization"""				)
   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    pass





def 			lowercase(	)		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      load_dataset("""hf-internal-testing/fixtures_image_utils"""							,					split="""test"""       )

 UpperCamelCase			  =      Image.open(dataset[4]["""file"""]       )
 UpperCamelCase			  =      Image.open(dataset[5]["""file"""]       )

 UpperCamelCase			  =      [imagea, imagea]

 return images


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







   @slow
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""				)

    UpperCamelCase			  =      prepare_images()

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

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

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

    # test batched
    UpperCamelCase			  =      image_processing(lowerCamelCase_   ,   return_tensors="""pt"""				)

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

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






'''simple docstring'''



from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst




def   lowercase__(			):

      """simple docstring"""






      SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE   :      str    					=						9, 14  # noqa: F841
      SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						[
          [0, 1, 4],
          [0, 7, 8],
          [1, 2, 8],
          [7, 8, 7],
          [7, 6, 1],
          [2, 8, 2],
          [8, 6, 6],
          [2, 3, 7],
          [2, 5, 4],
          [6, 5, 2],
          [3, 5, 14],
          [3, 4, 9],
          [5, 4, 10],
          [1, 7, 11],
      ]

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						defaultdict(__UpperCamelCase    )
      for nodea, nodea, cost in edges:
            adjancency[nodea].append([nodea, cost]    )
            adjancency[nodea].append([nodea, cost]    )

      SCREAMING_SNAKE_CASE   :      Dict    					=						mst(__UpperCamelCase    )

      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[
          [7, 6, 1],
          [2, 8, 2],
          [6, 5, 2],
          [0, 1, 4],
          [2, 5, 4],
          [2, 3, 7],
          [0, 7, 8],
          [3, 4, 9],
      ]

      for answer in expected:
            SCREAMING_SNAKE_CASE   :      Any    					=						tuple(answer[:2]    )
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						tuple(edge[::-1]    )
            assert edge in result or reverse in result


