





"""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    _lowerCAmelCase  (	a    ,  unittest.TestCase			):
       """simple docstring"""




       __magic_name__	:Optional[Any]						    =   DiTPipeline
       __magic_name__	:Optional[Any]						    =   CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
       __magic_name__	:Dict						    =   PipelineTesterMixin.required_optional_params - {
           """latents""",
           """num_images_per_prompt""",
           """callback""",
           """callback_steps""",
       }
       __magic_name__	:List[Any]						    =   CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
       __magic_name__	:Dict						    =   False






       def 	snake_case		(       self ):



         '''simple docstring'''

         torch.manual_seed(0 )
         lowerCAmelCase__     :str											=				TransformeraDModel(
             sample_size=1_6       ,					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=1_0_0_0       ,					norm_type='ada_norm_zero'       ,					norm_elementwise_affine=__UpperCAmelCase       ,					)
         lowerCAmelCase__     :int											=				AutoencoderKL()
         lowerCAmelCase__     :Union[str, Any]											=				DDIMScheduler()
         lowerCAmelCase__     :List[Any]											=				{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
         return components






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=0 ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Union[str, Any]											=				'cpu'

         lowerCAmelCase__     :str											=				self.get_dummy_components()
         lowerCAmelCase__     :Any											=				self.pipeline_class(**__UpperCAmelCase )
         pipe.to(__UpperCAmelCase )
         pipe.set_progress_bar_config(disable=__UpperCAmelCase )

         lowerCAmelCase__     :Tuple											=				self.get_dummy_inputs(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				pipe(**__UpperCAmelCase ).images
         lowerCAmelCase__     :Optional[int]											=				image[0, -3:, -3:, -1]

         self.assertEqual(image.shape       ,					(1, 1_6, 1_6, 3) )
         lowerCAmelCase__     :List[str]											=				np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
         lowerCAmelCase__     :Optional[int]											=				np.abs(image_slice.flatten() - expected_slice ).max()
         self.assertLessEqual(__UpperCAmelCase       ,					1E-3 )






       def 	snake_case		(       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 	snake_case		(       self ):



         '''simple docstring'''

         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )






@require_torch_gpu
@slow
class    _lowerCAmelCase  (	unittest.TestCase			):
       """simple docstring"""




       def 	snake_case		(       self ):



         '''simple docstring'''

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






       def 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :Any											=				torch.manual_seed(0 )

         lowerCAmelCase__     :Optional[int]											=				DiTPipeline.from_pretrained('facebook/DiT-XL-2-256' )
         pipe.to('cuda' )

         lowerCAmelCase__     :Union[str, Any]											=				['vase', 'umbrella', 'white shark', 'white wolf']
         lowerCAmelCase__     :Tuple											=				pipe.get_label_ids(__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				pipe(__UpperCAmelCase       ,					generator=__UpperCAmelCase       ,					num_inference_steps=4_0       ,					output_type='np' ).images

         for word, image in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Optional[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 	snake_case		(       self ):



         '''simple docstring'''

         lowerCAmelCase__     :int											=				DiTPipeline.from_pretrained('facebook/DiT-XL-2-512' )
         lowerCAmelCase__     :Optional[Any]											=				DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
         pipe.to('cuda' )

         lowerCAmelCase__     :Optional[int]											=				['vase', 'umbrella']
         lowerCAmelCase__     :Optional[Any]											=				pipe.get_label_ids(__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				torch.manual_seed(0 )
         lowerCAmelCase__     :int											=				pipe(__UpperCAmelCase       ,					generator=__UpperCAmelCase       ,					num_inference_steps=2_5       ,					output_type='np' ).images

         for word, image in zip(__UpperCAmelCase       ,					__UpperCAmelCase ):
           lowerCAmelCase__     :Optional[int]											=				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"""





from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			42
					lowercase__												=			42







					def __init__(			self     :							int  ,lowercase_     :							UNetaDModel  ,lowercase_     :							ScoreSdeVeScheduler						):
						super().__init__()
						self.register_modules(unet=lowercase_  ,scheduler=lowercase_						)







					@torch.no_grad()
					def __call__(			self     :							List[str]  ,lowercase_     :							int = 1  ,lowercase_     :							int = 2_0_0_0  ,lowercase_     :							Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,lowercase_     :							Optional[str] = "pil"  ,lowercase_     :							bool = True  ,**lowercase_     :							Dict  ,):
						lowerCAmelCase__      :		str    							=      self.unet.config.sample_size
						lowerCAmelCase__      :		int    							=      (batch_size, 3, img_size, img_size)

						lowerCAmelCase__      :		List[Any]    							=      self.unet

						lowerCAmelCase__      :		Tuple    							=      randn_tensor(lowercase_  ,generator=lowercase_						) * self.scheduler.init_noise_sigma
						lowerCAmelCase__      :		List[Any]    							=      sample.to(self.device						)

						self.scheduler.set_timesteps(lowercase_						)
						self.scheduler.set_sigmas(lowercase_						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
							lowerCAmelCase__      :		Tuple    							=      self.scheduler.sigmas[i] * torch.ones(shape[0]  ,device=self.device						)

							# correction step
							for _ in range(self.scheduler.config.correct_steps						):
								lowerCAmelCase__      :		str    							=      self.unet(lowercase_  ,lowercase_						).sample
								lowerCAmelCase__      :		List[str]    							=      self.scheduler.step_correct(lowercase_  ,lowercase_  ,generator=lowercase_						).prev_sample

							# prediction step
							lowerCAmelCase__      :		Dict    							=      model(lowercase_  ,lowercase_						).sample
							lowerCAmelCase__      :		Optional[int]    							=      self.scheduler.step_pred(lowercase_  ,lowercase_  ,lowercase_  ,generator=lowercase_						)

							lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      output.prev_sample, output.prev_sample_mean

						lowerCAmelCase__      :		List[str]    							=      sample_mean.clamp(0  ,1						)
						lowerCAmelCase__      :		Union[str, Any]    							=      sample.cpu().permute(0  ,2  ,3  ,1						).numpy()
						if output_type == "pil":
							lowerCAmelCase__      :		Union[str, Any]    							=      self.numpy_to_pil(lowercase_						)

						if not return_dict:
							return (sample,)

						return ImagePipelineOutput(images=lowercase_						)




