



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__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



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



		def 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						torch.manual_seed(0			)
						lowerCAmelCase__					:    Union[str, Any]												=					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=__lowerCamelCase		,activation_fn='''gelu-approximate'''		,num_embeds_ada_norm=10_00		,norm_type='''ada_norm_zero'''		,norm_elementwise_affine=__lowerCamelCase		,)
						lowerCAmelCase__					:    Any												=					AutoencoderKL()
						lowerCAmelCase__					:    Optional[Any]												=					DDIMScheduler()
						lowerCAmelCase__					:    Optional[Any]												=					{'''transformer''': transformer.eval(), '''vae''': vae.eval(), '''scheduler''': scheduler}
						return components



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=0			)					->      Dict:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					'''cpu'''

						lowerCAmelCase__					:    List[Any]												=					self.get_dummy_components()
						lowerCAmelCase__					:    Tuple												=					self.pipeline_class(**__lowerCamelCase			)
						pipe.to(__lowerCamelCase			)
						pipe.set_progress_bar_config(disable=__lowerCamelCase			)

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

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



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						self._test_inference_batch_single_identical(relax_max_difference=__lowerCamelCase		,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			)					->      str:

						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3			)



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




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					torch.manual_seed(0			)

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

						lowerCAmelCase__					:    List[str]												=					['''vase''', '''umbrella''', '''white shark''', '''white wolf''']
						lowerCAmelCase__					:    str												=					pipe.get_label_ids(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					pipe(__lowerCamelCase		,generator=__lowerCamelCase		,num_inference_steps=40		,output_type='''np'''			).images

						for word, image in zip(__lowerCamelCase		,__lowerCamelCase			):
										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 		lowerCAmelCase__				(self			)					->      Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    str												=					DiTPipeline.from_pretrained('''facebook/DiT-XL-2-512'''			)
						lowerCAmelCase__					:    Dict												=					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
						pipe.to('''cuda'''			)

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

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

						for word, image in zip(__lowerCamelCase		,__lowerCamelCase			):
										lowerCAmelCase__					:    Optional[Any]												=					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






def      lowerCAmelCase__		(   lowerCamelCase_		:		str	,lowerCamelCase_		:		str):





				'''simple docstring'''

				lowerCAmelCase__					:    Optional[Any]												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    str												=					len(lowerCamelCase_)
				lowerCAmelCase__					:    Union[str, Any]												=					[[False for _ in range(m + 1)] for _ in range(n + 1)]
				lowerCAmelCase__					:    List[Any]												=					True
				for i in range(lowerCamelCase_):
								for j in range(m + 1):
												if dp[i][j]:
																if j < m and a[i].upper() == b[j]:
																				lowerCAmelCase__					:    int												=					True
																if a[i].islower():
																				lowerCAmelCase__					:    int												=					True
				return dp[n][m]


if __name__ == "__main__":
							import doctest

							doctest.testmod()

