



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 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Tuple=7			,						__lowerCAmelCase					:					Any=3			,						__lowerCAmelCase					:					str=18			,						__lowerCAmelCase					:					Union[str, Any]=30			,						__lowerCAmelCase					:					Optional[Any]=4_00			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Optional[Any]=None			,						__lowerCAmelCase					:					str=True			,						)   ->	Dict:
    _A      =       size if size is not None else {'''height''': 18, '''width''': 18}
    _A      =       parent
    _A      =       batch_size
    _A      =       num_channels
    _A      =       image_size
    _A      =       min_resolution
    _A      =       max_resolution
    _A      =       do_resize
    _A      =       size
    _A      =       do_normalize

 def     snake_case_      (    self					:					Dict      )   ->	Union[str, Any]:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]      ),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }


@require_torch
@require_vision
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Union[str, Any] =  ImageGPTImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					List[str]      )   ->	str:
    _A      =       ImageGPTImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					List[Any]      )   ->	int:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Optional[int]:
    _A      =       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     snake_case_      (    self					:					List[Any]      )   ->	Any:
    _A      =       self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size			,						{'''height''': 18, '''width''': 18}      )

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

 def     snake_case_      (    self					:					str      )   ->	Optional[int]:
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    _A      =       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     snake_case_      (    self					:					int      )   ->	Tuple:
    _A      =       self.image_processing_class(**self.image_processor_dict      )

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

    _A      =       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     snake_case_      (    self					:					Dict      )   ->	int:
    _A      =       self.image_processing_class(**self.image_processor_dict      )

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

    _A      =       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     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    pass

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Optional[int]:
   _A      =       load_dataset('''hf-internal-testing/fixtures_image_utils'''	,   split='''test'''				)

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

   _A      =       [imagea, imagea]

   return images


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



 """simple docstring"""

 @slow
 def     snake_case_      (    self					:					Optional[Any]      )   ->	str:
    _A      =       ImageGPTImageProcessor.from_pretrained('''openai/imagegpt-small'''      )

    _A      =       prepare_images()

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

    self.assertIsInstance(encoding.input_ids			,						torch.LongTensor      )
    self.assertEqual(encoding.input_ids.shape			,						(1, 10_24)      )

    _A      =       [3_06, 1_91, 1_91]
    self.assertEqual(encoding.input_ids[0, :3].tolist()			,						__lowerCAmelCase      )

    # test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      )

    self.assertIsInstance(encoding.input_ids			,						torch.LongTensor      )
    self.assertEqual(encoding.input_ids.shape			,						(2, 10_24)      )

    _A      =       [3_03, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist()			,						__lowerCAmelCase      )








from graphs.minimum_spanning_tree_kruskal import kruskal

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   _A      =       9
   _A      =       [
       [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],
   ]

   _A      =       kruskal(_snake_case	,   _snake_case				)

   _A      =       [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(_snake_case				) == sorted(_snake_case				)



