



"""simple docstring"""





import warnings

from ..trainer import Trainer
from ..utils import logging


lowercase  							=					logging.get_logger(__name__)




class 			lowercase__			(      A    ):
       '''simple docstring'''







       def __init__(  self	,       snake_case=None	,       **snake_case						)			->							List[Any]:
         warnings.warn(
             '`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` '
             'instead.'	,       snake_case	,       )
         super().__init__(args=snake_case	,       **snake_case						)







"""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'''







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






							def        lowerCamelCase_   (  self						)			->							str:
									torch.manual_seed(0						)
									_UpperCAmelCase							 =		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=snake_case	,       activation_fn='gelu-approximate'	,       num_embeds_ada_norm=1000	,       norm_type='ada_norm_zero'	,       norm_elementwise_affine=snake_case	,       )
									_UpperCAmelCase							 =		AutoencoderKL()
									_UpperCAmelCase							 =		DDIMScheduler()
									_UpperCAmelCase							 =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
									return components






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






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		'cpu'

									_UpperCAmelCase							 =		self.get_dummy_components()
									_UpperCAmelCase							 =		self.pipeline_class(**snake_case						)
									pipe.to(snake_case						)
									pipe.set_progress_bar_config(disable=snake_case						)

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

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






							def        lowerCamelCase_   (  self						)			->							Any:
									self._test_inference_batch_single_identical(relax_max_difference=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        lowerCamelCase_   (  self						)			->							Optional[int]:
									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







@require_torch_gpu
@slow
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		torch.manual_seed(0						)

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

									_UpperCAmelCase							 =		['vase', 'umbrella', 'white shark', 'white wolf']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

									_UpperCAmelCase							 =		pipe(snake_case	,       generator=snake_case	,       num_inference_steps=40	,       output_type='np'						).images

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		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						)			->							int:
									_UpperCAmelCase							 =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'						)
									_UpperCAmelCase							 =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config						)
									pipe.to('cuda'						)

									_UpperCAmelCase							 =		['vase', 'umbrella']
									_UpperCAmelCase							 =		pipe.get_label_ids(snake_case						)

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

									for word, image in zip(snake_case	,       snake_case						):
											_UpperCAmelCase							 =		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


