"""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  _lowerCamelCase			(unittest.TestCase     ):







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

					"""simple docstring"""






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







				def 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					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			(__lowerCamelCase						,							unittest.TestCase     ):
				_snake_case     =  ImageGPTImageProcessor if is_vision_available() else None







				def 							__UpperCAmelCase					( self    :       Dict						):

					"""simple docstring"""






					_lowercase :    Tuple							=							ImageGPTImageProcessingTester(self						)







				@property
				def 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					return self.image_processor_tester.prepare_image_processor_dict()







				def 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							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 							__UpperCAmelCase					( self    :       Any						):

					"""simple docstring"""






					_lowercase :    Tuple							=							self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size			,					{'height': 1_8, 'width': 1_8}						)

					_lowercase :    str							=							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 							__UpperCAmelCase					( self    :       Tuple						):

					"""simple docstring"""






					_lowercase :    Optional[int]							=							self.image_processing_class(**self.image_processor_dict						)
					_lowercase :    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(lowerCamelCase_			,					obj[key]						)						)
						else:
							self.assertEqual(obj[key]			,					lowerCamelCase_						)







				def 							__UpperCAmelCase					( self    :       str						):

					"""simple docstring"""






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

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

					_lowercase :    List[str]							=							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 							__UpperCAmelCase					( self    :       List[Any]						):

					"""simple docstring"""






					_lowercase :    List[Any]							=							self.image_processing_class(**self.image_processor_dict						)

					with tempfile.TemporaryDirectory() as tmpdirname:
						image_processor_first.save_pretrained(lowerCamelCase_						)
						_lowercase :    Optional[int]							=							self.image_processing_class.from_pretrained(lowerCamelCase_						).to_dict()

					_lowercase :    List[str]							=							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 							__UpperCAmelCase					( self    :       Optional[int]						):

					"""simple docstring"""






					pass



def      __lowerCAmelCase(		):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							load_dataset('hf-internal-testing/fixtures_image_utils'     ,split='test'			)

	_lowercase :    Optional[Any]							=							Image.open(dataset[4]['file']			)
	_lowercase :    Optional[Any]							=							Image.open(dataset[5]['file']			)

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

	return images







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







				@slow
				def 							__UpperCAmelCase					( self    :       Union[str, Any]						):

					"""simple docstring"""






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

					_lowercase :    int							=							prepare_images()

					# test non-batched
					_lowercase :    Dict							=							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)						)

					_lowercase :    str							=							[3_0_6, 1_9_1, 1_9_1]
					self.assertEqual(encoding.input_ids[0, :3].tolist()			,					lowerCamelCase_						)

					# test batched
					_lowercase :    str							=							image_processing(lowerCamelCase_			,					return_tensors='pt'						)

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

					_lowercase :    Tuple							=							[3_0_3, 1_3, 1_3]
					self.assertEqual(encoding.input_ids[1, -3:].tolist()			,					lowerCamelCase_						)




"""simple docstring"""







import math


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if not isinstance(__UpperCAmelCase     ,__UpperCAmelCase			):
		_lowercase :    List[Any]							=							F'''Input value of [number={number}] must be an integer'''
		raise TypeError(__UpperCAmelCase			)

	if number < 1:
		_lowercase :    List[Any]							=							F'''Input value of [number={number}] must be > 0'''
		raise ValueError(__UpperCAmelCase			)
	elif number == 1:
		return 3
	elif number == 2:
		return 5
	else:
		_lowercase :    str							=							int(math.log(number // 3     ,2			)			) + 2

		_lowercase :    Union[str, Any]							=							[3, 5]
		_lowercase :    Optional[int]							=							2
		_lowercase :    List[Any]							=							3
		for block in range(1     ,__UpperCAmelCase			):
			for _ in range(__UpperCAmelCase			):
				proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]			)
				proth_index += 1
			increment *= 2

	return proth_list[number - 1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						for number in range(11):
												SCREAMING_SNAKE_CASE									=						0
												try:
																		SCREAMING_SNAKE_CASE									=						proth(number)
												except ValueError:
																		print(f"""ValueError: there is no {number}th Proth number""")
																		continue

												print(f"""The {number}th Proth number: {value}""")



