
'''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'''
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       None




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if not self.is_available():
												raise RuntimeError(
												    F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''							)




		@classmethod
		def 					lowerCAmelCase__       (	cls							):

							'''simple docstring'''


							return F'''`pip install {cls.pip_package or cls.name}`'''



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'optuna'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_optuna_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_optuna(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_optuna(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'ray'
		_a									=       '\'ray[tune]\''




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_ray_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_ray(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_ray(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'sigopt'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_sigopt_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_sigopt(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_sigopt(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'wandb'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_wandb_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_wandb(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_wandb(UpperCamelCase_							)



__snake_case							   =  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def     a   (    )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(__a     ) > 0:
										UpperCamelCase__     :Tuple							      =				available_backends[0].name
										if len(__a     ) > 1:
															logger.info(
															    f'''{len(__a     )} hyperparameter search backends available. Using {name} as the default.'''     )
										return name
					raise RuntimeError(
					    '''No hyperparameter search backend available.\n'''
					    + '''\n'''.join(
					        f''' - To install {backend.name} run {backend.pip_install()}'''
					        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()     )     )