
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			):






		def __init__(		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							str=7							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=3							,			SCREAMING_SNAKE_CASE__		:							int=18							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=30							,			SCREAMING_SNAKE_CASE__		:							int=4_00							,			SCREAMING_SNAKE_CASE__		:							str=True							,			SCREAMING_SNAKE_CASE__		:							int=None							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			)     ->							Union[str, Any]:
						__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		:							Tuple  )     ->							List[str]:
						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    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Tuple        =		ImageGPTImageProcessor if is_vision_available() else None






		def  __A       (		self		:							int  )     ->							List[str]:
						__lowerCamelCase    					=	ImageGPTImageProcessingTester(self  )






		@property
		def  __A       (		self		:							str  )     ->							Dict:
						return self.image_processor_tester.prepare_image_processor_dict()






		def  __A       (		self		:							Tuple  )     ->							int:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''clusters'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_resize'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''size'''  )  )
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__							,			'''do_normalize'''  )  )






		def  __A       (		self		:							Any  )     ->							int:
						__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		:							Optional[Any]  )     ->							Tuple:
						__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(SCREAMING_SNAKE_CASE__							,			obj[key]  )  )
										else:
														self.assertEqual(obj[key]							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							int  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )

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

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






		def  __A       (		self		:							Tuple  )     ->							Optional[Any]:
						__lowerCamelCase    					=	self.image_processing_class(**self.image_processor_dict  )

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

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







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





def  __magic_name__ (   )			->   Optional[int]:
				__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    lowerCAmelCase__     (	unittest.TestCase			):






		@slow
		def  __A       (		self		:							str  )     ->							Optional[int]:
						__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, 10_24)  )

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

						# test batched
						__lowerCamelCase    					=	image_processing(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  )

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

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





import os




def  __magic_name__ (   )			->   str:
				__lowerCamelCase    					=	os.path.join(os.path.dirname(__lowerCAmelCase							)		,  '''num.txt'''							)
				with open(__lowerCAmelCase							) as file_hand:
								return str(sum(int(__lowerCAmelCase							) for line in file_hand							)							)[:10]


if __name__ == "__main__":
					print(solution())



