


'''simple docstring'''
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      lowercase			(							A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       DiTPipeline
		_a									=       CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
		_a									=       PipelineTesterMixin.required_optional_params - {
		    'latents',
		    'num_images_per_prompt',
		    'callback',
		    'callback_steps',
		}
		_a									=       CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :Optional[int]							      =				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=UpperCamelCase_					,     activation_fn='''gelu-approximate'''					,     num_embeds_ada_norm=1000					,     norm_type='''ada_norm_zero'''					,     norm_elementwise_affine=UpperCamelCase_					,     )
							UpperCamelCase__     :List[str]							      =				AutoencoderKL()
							UpperCamelCase__     :Any							      =				DDIMScheduler()
							UpperCamelCase__     :Tuple							      =				{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
							return components




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__     :Optional[Any]							      =				torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__     :List[Any]							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				{
							    '''class_labels''': [1],
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				'''cpu'''

							UpperCamelCase__     :Union[str, Any]							      =				self.get_dummy_components()
							UpperCamelCase__     :Dict							      =				self.pipeline_class(**UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Any							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :Union[str, Any]							      =				image[0, -3:, -3:, -1]

							self.assertEqual(image.shape					,     (1, 16, 16, 3)							)
							UpperCamelCase__     :Union[str, Any]							      =				np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]							)
							UpperCamelCase__     :int							      =				np.abs(image_slice.flatten() - expected_slice							).max()
							self.assertLessEqual(UpperCamelCase_					,     1e-3							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self._test_inference_batch_single_identical(relax_max_difference=UpperCamelCase_					,     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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)



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

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				torch.manual_seed(0							)

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

							UpperCamelCase__     :Dict							      =				['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
							UpperCamelCase__     :int							      =				pipe.get_label_ids(UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				pipe(UpperCamelCase_					,     generator=UpperCamelCase_					,     num_inference_steps=40					,     output_type='''np'''							).images

							for word, image in zip(UpperCamelCase_					,     UpperCamelCase_							):
												UpperCamelCase__     :Union[str, Any]							      =				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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''							)
							UpperCamelCase__     :Union[str, Any]							      =				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
							pipe.to('''cuda'''							)

							UpperCamelCase__     :Union[str, Any]							      =				['''vase''', '''umbrella''']
							UpperCamelCase__     :List[Any]							      =				pipe.get_label_ids(UpperCamelCase_							)

							UpperCamelCase__     :List[Any]							      =				torch.manual_seed(0							)
							UpperCamelCase__     :Any							      =				pipe(UpperCamelCase_					,     generator=UpperCamelCase_					,     num_inference_steps=25					,     output_type='''np'''							).images

							for word, image in zip(UpperCamelCase_					,     UpperCamelCase_							):
												UpperCamelCase__     :Dict							      =				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



'''simple docstring'''
import numpy as np


def     a   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				int(np.ceil((x_end - xa) / h     )     )
					UpperCamelCase__     :Optional[int]							      =				np.zeros((n + 1,)     )
					UpperCamelCase__     :List[str]							      =				ya
					UpperCamelCase__     :Tuple							      =				xa

					for k in range(__a     ):
										UpperCamelCase__     :Dict							      =				f(__a				,      y[k]     )
										UpperCamelCase__     :List[Any]							      =				f(x + 0.5 * h				,      y[k] + 0.5 * h * ka     )
										UpperCamelCase__     :Dict							      =				f(x + 0.5 * h				,      y[k] + 0.5 * h * ka     )
										UpperCamelCase__     :List[Any]							      =				f(x + h				,      y[k] + h * ka     )
										UpperCamelCase__     :List[str]							      =				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
										x += h

					return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()