
import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						str        =		DiTPipeline
		a__       :						List[Any]        =		CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		a__       :						str        =		PipelineTesterMixin.required_optional_params - {
		    """latents""",
		    """num_images_per_prompt""",
		    """callback""",
		    """callback_steps""",
		}
		a__       :						List[str]        =		CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		a__       :						int        =		False






		def  __A       (		self		:							Any  )     ->							Union[str, Any]:
						torch.manual_seed(0  )
						__lowerCamelCase    					=	TransformeraDModel(
						    sample_size=16							,			num_layers=2							,			patch_size=4							,			attention_head_dim=8							,			num_attention_heads=2							,			in_channels=4							,			out_channels=8							,			attention_bias=SCREAMING_SNAKE_CASE__							,			activation_fn='''gelu-approximate'''							,			num_embeds_ada_norm=10_00							,			norm_type='''ada_norm_zero'''							,			norm_elementwise_affine=SCREAMING_SNAKE_CASE__							,			)
						__lowerCamelCase    					=	AutoencoderKL()
						__lowerCamelCase    					=	DDIMScheduler()
						__lowerCamelCase    					=	{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
						return components






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Any=0  )     ->							Tuple:
						if str(SCREAMING_SNAKE_CASE__  ).startswith('''mps'''  ):
										__lowerCamelCase    					=	torch.manual_seed(SCREAMING_SNAKE_CASE__  )
						else:
										__lowerCamelCase    					=	torch.Generator(device=SCREAMING_SNAKE_CASE__  ).manual_seed(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	{
						    '''class_labels''': [1],
						    '''generator''': generator,
						    '''num_inference_steps''': 2,
						    '''output_type''': '''numpy''',
						}
						return inputs






		def  __A       (		self		:							Tuple  )     ->							Dict:
						__lowerCamelCase    					=	'''cpu'''

						__lowerCamelCase    					=	self.get_dummy_components()
						__lowerCamelCase    					=	self.pipeline_class(**SCREAMING_SNAKE_CASE__  )
						pipe.to(SCREAMING_SNAKE_CASE__  )
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_dummy_inputs(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	pipe(**SCREAMING_SNAKE_CASE__  ).images
						__lowerCamelCase    					=	image[0, -3:, -3:, -1]

						self.assertEqual(image.shape							,			(1, 16, 16, 3)  )
						__lowerCamelCase    					=	np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]  )
						__lowerCamelCase    					=	np.abs(image_slice.flatten() - expected_slice  ).max()
						self.assertLessEqual(SCREAMING_SNAKE_CASE__							,			1e-3  )






		def  __A       (		self		:							Tuple  )     ->							Optional[Any]:
						self._test_inference_batch_single_identical(relax_max_difference=SCREAMING_SNAKE_CASE__							,			expected_max_diff=1e-3  )







		@unittest.skipIf(
		    torch_device != '''cuda''' or not is_xformers_available()							,			reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,			)
		def  __A       (		self		:							Any  )     ->							List[str]:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3  )





@require_torch_gpu
@slow
class    lowerCAmelCase__     (	unittest.TestCase			):






		def  __A       (		self		:							Union[str, Any]  )     ->							Tuple:
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






		def  __A       (		self		:							Dict  )     ->							str:
						__lowerCamelCase    					=	torch.manual_seed(0  )

						__lowerCamelCase    					=	DiTPipeline.from_pretrained('''facebook/DiT-XL-2-256'''  )
						pipe.to('''cuda'''  )

						__lowerCamelCase    					=	['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
						__lowerCamelCase    					=	pipe.get_label_ids(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	pipe(SCREAMING_SNAKE_CASE__							,			generator=SCREAMING_SNAKE_CASE__							,			num_inference_steps=40							,			output_type='''np'''  ).images

						for word, image in zip(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
										__lowerCamelCase    					=	load_numpy(
										    f'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''  )
										assert np.abs((expected_image - image).max()  ) < 1e-2







		def  __A       (		self		:							Dict  )     ->							List[Any]:
						__lowerCamelCase    					=	DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''  )
						__lowerCamelCase    					=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
						pipe.to('''cuda'''  )

						__lowerCamelCase    					=	['''vase''', '''umbrella''']
						__lowerCamelCase    					=	pipe.get_label_ids(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	torch.manual_seed(0  )
						__lowerCamelCase    					=	pipe(SCREAMING_SNAKE_CASE__							,			generator=SCREAMING_SNAKE_CASE__							,			num_inference_steps=25							,			output_type='''np'''  ).images

						for word, image in zip(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
										__lowerCamelCase    					=	load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    f'''/dit/{word}_512.npy'''  )

										assert np.abs((expected_image - image).max()  ) < 1e-1





from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors




def  __magic_name__ (   __lowerCAmelCase	:						int							)			->   int:
				__lowerCamelCase    					=	prime_factors(__lowerCAmelCase							)
				if is_square_free(__lowerCAmelCase							):
								return -1 if len(__lowerCAmelCase							) % 2 else 1
				return 0


if __name__ == "__main__":
					import doctest

					doctest.testmod()



