





import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	@property
	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





				torch.manual_seed(0	)
				__lowercase			  =							UNetaDModel(
				    block_out_channels=(32, 64)       ,layers_per_block=2       ,sample_size=32       ,in_channels=3       ,out_channels=3       ,down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')       ,up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')       ,)
				return model


	@property
	def   _UpperCAmelCase       (self	)							->							str:







				'''simple docstring'''





				torch.manual_seed(0	)
				__lowercase			  =							VQModel(
				    block_out_channels=[32, 64]       ,in_channels=3       ,out_channels=3       ,down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,latent_channels=3       ,)
				return model


	@property
	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				torch.manual_seed(0	)
				__lowercase			  =							CLIPTextConfig(
				    bos_token_id=0       ,eos_token_id=2       ,hidden_size=32       ,intermediate_size=37       ,layer_norm_eps=1E-0_5       ,num_attention_heads=4       ,num_hidden_layers=5       ,pad_token_id=1       ,vocab_size=1000       ,)
				return CLIPTextModel(UpperCamelCase__	)


	def   _UpperCAmelCase       (self	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							self.dummy_uncond_unet
				__lowercase			  =							DDIMScheduler()
				__lowercase			  =							self.dummy_vq_model

				__lowercase			  =							LDMPipeline(unet=UpperCamelCase__       ,vqvae=UpperCamelCase__       ,scheduler=UpperCamelCase__	)
				ldm.to(UpperCamelCase__	)
				ldm.set_progress_bar_config(disable=UpperCamelCase__	)

				__lowercase			  =							torch.manual_seed(0	)
				__lowercase			  =							ldm(generator=UpperCamelCase__       ,num_inference_steps=2       ,output_type='''numpy'''	).images

				__lowercase			  =							torch.manual_seed(0	)
				__lowercase			  =							ldm(generator=UpperCamelCase__       ,num_inference_steps=2       ,output_type='''numpy'''       ,return_dict=UpperCamelCase__	)[0]

				__lowercase			  =							image[0, -3:, -3:, -1]
				__lowercase			  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				__lowercase			  =							np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]	)
				__lowercase			  =							1E-2 if torch_device != '''mps''' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < tolerance






@slow
@require_torch
class 							__lowercase    ( unittest.TestCase  ):







	'''simple docstring'''

	def   _UpperCAmelCase       (self	)							->							Dict:







				'''simple docstring'''





				__lowercase			  =							LDMPipeline.from_pretrained('''CompVis/ldm-celebahq-256'''	)
				ldm.to(UpperCamelCase__	)
				ldm.set_progress_bar_config(disable=UpperCamelCase__	)

				__lowercase			  =							torch.manual_seed(0	)
				__lowercase			  =							ldm(generator=UpperCamelCase__       ,num_inference_steps=5       ,output_type='''numpy'''	).images

				__lowercase			  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 256, 256, 3)
				__lowercase			  =							np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]	)
				__lowercase			  =							1E-2 if torch_device != '''mps''' else 3E-2

				assert np.abs(image_slice.flatten() - expected_slice	).max() < tolerance












'''simple docstring'''



def 				_lowerCAmelCase  (			lowerCamelCase_     :					int = 6_0_0_8_5_1_4_7_5_1_4_3			):

			try:
						__lowercase			  =							int(lowerCamelCase_			)
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.'''			)
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.'''			)
			__lowercase			  =							1
			__lowercase			  =							2
			while i * i <= n:
						while n % i == 0:
									__lowercase			  =							i
									n //= i
						i += 1
			if n > 1:
						__lowercase			  =							n
			return int(lowerCamelCase_			)


if __name__ == "__main__":
	print(f'''{solution() = }''')





