

"""simple docstring"""


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        snake_case      (							unittest.TestCase    ):

				def __init__(      self				:							Optional[Any]	,    UpperCamelCase__				:							Tuple	,    UpperCamelCase__				:							Any=7	,    UpperCamelCase__				:							Union[str, Any]=3	,    UpperCamelCase__				:							Tuple=1_8	,    UpperCamelCase__				:							List[str]=3_0	,    UpperCamelCase__				:							Union[str, Any]=4_0_0	,    UpperCamelCase__				:							Optional[int]=True	,    UpperCamelCase__				:							Any=None	,    UpperCamelCase__				:							Any=True	,    )->  Any:



						'''simple docstring'''


						__lowerCAmelCase:			Union[str, Any]        =     size if size is not None else {'''height''': 1_8, '''width''': 1_8}
						__lowerCAmelCase:			Tuple        =     parent
						__lowerCAmelCase:			str        =     batch_size
						__lowerCAmelCase:			List[str]        =     num_channels
						__lowerCAmelCase:			int        =     image_size
						__lowerCAmelCase:			int        =     min_resolution
						__lowerCAmelCase:			Optional[Any]        =     max_resolution
						__lowerCAmelCase:			Any        =     do_resize
						__lowerCAmelCase:			int        =     size
						__lowerCAmelCase:			int        =     do_normalize




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



						'''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        snake_case      (							lowercase__,				unittest.TestCase    ):
				SCREAMING_SNAKE_CASE_  : Any   =    ImageGPTImageProcessor if is_vision_available() else None

				def 				lowercase_	(      self				:							int)->  int:



						'''simple docstring'''


						__lowerCAmelCase:			Union[str, Any]        =     ImageGPTImageProcessingTester(self)

				@property
				def 				lowercase_	(      self				:							List[Any])->  Tuple:



						'''simple docstring'''


						return self.image_processor_tester.prepare_image_processor_dict()

				def 				lowercase_	(      self				:							int)->  Tuple:



						'''simple docstring'''


						__lowerCAmelCase:			Optional[Any]        =     self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(UpperCAmelCase__	,    "clusters"))
						self.assertTrue(hasattr(UpperCAmelCase__	,    "do_resize"))
						self.assertTrue(hasattr(UpperCAmelCase__	,    "size"))
						self.assertTrue(hasattr(UpperCAmelCase__	,    "do_normalize"))

				def 				lowercase_	(      self				:							Optional[Any])->  Tuple:



						'''simple docstring'''


						__lowerCAmelCase:			Any        =     self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size	,    {"height": 1_8, "width": 1_8})

						__lowerCAmelCase:			Any        =     self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2)
						self.assertEqual(image_processor.size	,    {"height": 4_2, "width": 4_2})

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



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							Tuple)->  Any:



						'''simple docstring'''


						__lowerCAmelCase:			str        =     self.image_processing_class(**self.image_processor_dict)

						with tempfile.TemporaryDirectory() as tmpdirname:
								__lowerCAmelCase:			Any        =     os.path.join(UpperCAmelCase__	,    "image_processor.json")
								image_processor_first.to_json_file(UpperCAmelCase__)
								__lowerCAmelCase:			Optional[Any]        =     self.image_processing_class.from_json_file(UpperCAmelCase__).to_dict()

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

				def 				lowercase_	(      self				:							List[str])->  Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase:			Optional[int]        =     self.image_processing_class(**self.image_processor_dict)

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

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




				@unittest.skip("ImageGPT requires clusters at initialization")
				def 				lowercase_	(      self				:							Optional[Any])->  Dict:



						'''simple docstring'''


						pass






def        a__       (       )      ->      Dict:
		__lowerCAmelCase:			List[Any]        =     load_dataset("hf-internal-testing/fixtures_image_utils"   ,					split="test"					)

		__lowerCAmelCase:			Optional[int]        =     Image.open(dataset[4]["file"]					)
		__lowerCAmelCase:			Optional[Any]        =     Image.open(dataset[5]["file"]					)

		__lowerCAmelCase:			Optional[Any]        =     [imagea, imagea]

		return images





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

				@slow
				def 				lowercase_	(      self				:							Optional[int])->  Dict:



						'''simple docstring'''


						__lowerCAmelCase:			Union[str, Any]        =     ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small")

						__lowerCAmelCase:			int        =     prepare_images()

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

						self.assertIsInstance(encoding.input_ids	,    torch.LongTensor)
						self.assertEqual(encoding.input_ids.shape	,    (1, 1_0_2_4))

						__lowerCAmelCase:			Tuple        =     [3_0_6, 1_9_1, 1_9_1]
						self.assertEqual(encoding.input_ids[0, :3].tolist()	,    UpperCAmelCase__)

						# test batched
						__lowerCAmelCase:			Optional[int]        =     image_processing(UpperCAmelCase__	,    return_tensors="pt")

						self.assertIsInstance(encoding.input_ids	,    torch.LongTensor)
						self.assertEqual(encoding.input_ids.shape	,    (2, 1_0_2_4))

						__lowerCAmelCase:			str        =     [3_0_3, 1_3, 1_3]
						self.assertEqual(encoding.input_ids[1, -3:].tolist()	,    UpperCAmelCase__)





'''simple docstring'''





from collections import defaultdict






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  int:
						lowercase :							Optional[Any]    		=1
						lowercase :							Union[str, Any]    		=True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(__magic_name__						)
						if ret % 2 == 0:
												cuts.append(__magic_name__						)
						return ret






def 				_lowerCAmelCase			(   )				->  int:
						dfs(1						)


if __name__ == "__main__":
							UpperCamelCase_						,   UpperCamelCase_    						=       10, 9
							UpperCamelCase_    						=       defaultdict(list)
							UpperCamelCase_    						=       {}
							UpperCamelCase_    						=       []
							UpperCamelCase_    						=       0
							UpperCamelCase_    						=       [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)





