



"""simple docstring"""





import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin




@skip_mps
class 			lowercase__			(      A,					A,					unittest.TestCase    ):
       '''simple docstring'''







       _UpperCAmelCase      				=					IFPipeline
       _UpperCAmelCase      				=					TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
       _UpperCAmelCase      				=					TEXT_TO_IMAGE_BATCH_PARAMS
       _UpperCAmelCase      				=					PipelineTesterMixin.required_optional_params - {'''latents'''}






       def        lowerCamelCase_   (  self						)			->							int:
         return self._get_dummy_components()






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case=0						)			->							int:
         if str(snake_case						).startswith('mps'						):
           _UpperCAmelCase							 =		torch.manual_seed(snake_case						)
         else:
           _UpperCAmelCase							 =		torch.Generator(device=snake_case						).manual_seed(snake_case						)

         _UpperCAmelCase							 =		{
             'prompt': 'A painting of a squirrel eating a burger',
             'generator': generator,
             'num_inference_steps': 2,
             'output_type': 'numpy',
         }

         return inputs






       def        lowerCamelCase_   (  self						)			->							Any:
         self._test_save_load_optional_components()






       @unittest.skipIf(torch_device != 'cuda'	,       reason='float16 requires CUDA'						)
       def        lowerCamelCase_   (  self						)			->							Optional[int]:
         # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
         super().test_save_load_floataa(expected_max_diff=1E-1						)






       def        lowerCamelCase_   (  self						)			->							Any:
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)






       def        lowerCamelCase_   (  self						)			->							Any:
         self._test_save_load_local()






       def        lowerCamelCase_   (  self						)			->							str:
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2	,       )






       @unittest.skipIf(
           torch_device != 'cuda' or not is_xformers_available()	,       reason='XFormers attention is only available with CUDA and `xformers` installed'	,       )
       def        lowerCamelCase_   (  self						)			->							List[str]:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)







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







       def        lowerCamelCase_   (  self						)			->							Dict:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






       def        lowerCamelCase_   (  self						)			->							Dict:
         # if

         _UpperCAmelCase							 =		IFPipeline.from_pretrained('DeepFloyd/IF-I-XL-v1.0'	,       variant='fp16'	,       torch_dtype=torch.floataa						)

         _UpperCAmelCase							 =		IFSuperResolutionPipeline.from_pretrained(
             'DeepFloyd/IF-II-L-v1.0'	,       variant='fp16'	,       torch_dtype=torch.floataa	,       text_encoder=snake_case	,       tokenizer=snake_case						)

         # pre compute text embeddings and remove T5 to save memory

         pipe_a.text_encoder.to('cuda'						)

         _UpperCAmelCase ,					_UpperCAmelCase							 =		pipe_a.encode_prompt('anime turtle'	,       device='cuda'						)

         del pipe_a.tokenizer
         del pipe_a.text_encoder
         gc.collect()

         _UpperCAmelCase							 =		None
         _UpperCAmelCase							 =		None

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

         self._test_if(snake_case	,       snake_case	,       snake_case	,       snake_case						)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # img2img

         _UpperCAmelCase							 =		IFImgaImgPipeline(**pipe_a.components						)
         _UpperCAmelCase							 =		IFImgaImgSuperResolutionPipeline(**pipe_a.components						)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

         self._test_if_imgaimg(snake_case	,       snake_case	,       snake_case	,       snake_case						)

         pipe_a.remove_all_hooks()
         pipe_a.remove_all_hooks()

         # inpainting

         _UpperCAmelCase							 =		IFInpaintingPipeline(**pipe_a.components						)
         _UpperCAmelCase							 =		IFInpaintingSuperResolutionPipeline(**pipe_a.components						)

         pipe_a.enable_model_cpu_offload()
         pipe_a.enable_model_cpu_offload()

         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)
         pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()						)

         self._test_if_inpainting(snake_case	,       snake_case	,       snake_case	,       snake_case						)






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Union[str, Any]:
         # pipeline 1

         _start_torch_memory_measurement()

         _UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
         _UpperCAmelCase							 =		pipe_a(
             prompt_embeds=snake_case	,       negative_prompt_embeds=snake_case	,       num_inference_steps=2	,       generator=snake_case	,       output_type='np'	,       )

         _UpperCAmelCase							 =		output.images[0]

         assert image.shape == (64, 64, 3)

         _UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
         assert mem_bytes < 13 * 10**9

         _UpperCAmelCase							 =		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'						)
         assert_mean_pixel_difference(snake_case	,       snake_case						)

         # pipeline 2

         _start_torch_memory_measurement()

         _UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)

         _UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)

         _UpperCAmelCase							 =		pipe_a(
             prompt_embeds=snake_case	,       negative_prompt_embeds=snake_case	,       image=snake_case	,       generator=snake_case	,       num_inference_steps=2	,       output_type='np'	,       )

         _UpperCAmelCase							 =		output.images[0]

         assert image.shape == (256, 256, 3)

         _UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         _UpperCAmelCase							 =		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'						)
         assert_mean_pixel_difference(snake_case	,       snake_case						)






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							List[Any]:
         # pipeline 1

         _start_torch_memory_measurement()

         _UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)

         _UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)

         _UpperCAmelCase							 =		pipe_a(
             prompt_embeds=snake_case	,       negative_prompt_embeds=snake_case	,       image=snake_case	,       num_inference_steps=2	,       generator=snake_case	,       output_type='np'	,       )

         _UpperCAmelCase							 =		output.images[0]

         assert image.shape == (64, 64, 3)

         _UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         _UpperCAmelCase							 =		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'						)
         assert_mean_pixel_difference(snake_case	,       snake_case						)

         # pipeline 2

         _start_torch_memory_measurement()

         _UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)

         _UpperCAmelCase							 =		floats_tensor((1, 3, 256, 256)	,       rng=random.Random(0						)						).to(snake_case						)
         _UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)

         _UpperCAmelCase							 =		pipe_a(
             prompt_embeds=snake_case	,       negative_prompt_embeds=snake_case	,       image=snake_case	,       original_image=snake_case	,       generator=snake_case	,       num_inference_steps=2	,       output_type='np'	,       )

         _UpperCAmelCase							 =		output.images[0]

         assert image.shape == (256, 256, 3)

         _UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         _UpperCAmelCase							 =		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'						)
         assert_mean_pixel_difference(snake_case	,       snake_case						)






       def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[Any]:
         # pipeline 1

         _start_torch_memory_measurement()

         _UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)
         _UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(1						)						).to(snake_case						)

         _UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)
         _UpperCAmelCase							 =		pipe_a(
             prompt_embeds=snake_case	,       negative_prompt_embeds=snake_case	,       image=snake_case	,       mask_image=snake_case	,       num_inference_steps=2	,       generator=snake_case	,       output_type='np'	,       )

         _UpperCAmelCase							 =		output.images[0]

         assert image.shape == (64, 64, 3)

         _UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
         assert mem_bytes < 10 * 10**9

         _UpperCAmelCase							 =		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'						)
         assert_mean_pixel_difference(snake_case	,       snake_case						)

         # pipeline 2

         _start_torch_memory_measurement()

         _UpperCAmelCase							 =		torch.Generator(device='cpu'						).manual_seed(0						)

         _UpperCAmelCase							 =		floats_tensor((1, 3, 64, 64)	,       rng=random.Random(0						)						).to(snake_case						)
         _UpperCAmelCase							 =		floats_tensor((1, 3, 256, 256)	,       rng=random.Random(0						)						).to(snake_case						)
         _UpperCAmelCase							 =		floats_tensor((1, 3, 256, 256)	,       rng=random.Random(1						)						).to(snake_case						)

         _UpperCAmelCase							 =		pipe_a(
             prompt_embeds=snake_case	,       negative_prompt_embeds=snake_case	,       image=snake_case	,       mask_image=snake_case	,       original_image=snake_case	,       generator=snake_case	,       num_inference_steps=2	,       output_type='np'	,       )

         _UpperCAmelCase							 =		output.images[0]

         assert image.shape == (256, 256, 3)

         _UpperCAmelCase							 =		torch.cuda.max_memory_allocated()
         assert mem_bytes < 4 * 10**9

         _UpperCAmelCase							 =		load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'						)
         assert_mean_pixel_difference(snake_case	,       snake_case						)







def 	UpperCAmelCase     (		):

  '''simple docstring'''


  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()







"""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


