


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				unittest.TestCase   ):
					__SCREAMING_SNAKE_CASE       :  List[str]						=   StableDiffusionSAGPipeline
					__SCREAMING_SNAKE_CASE       :  int						=   TEXT_TO_IMAGE_PARAMS
					__SCREAMING_SNAKE_CASE       :  List[str]						=   TEXT_TO_IMAGE_BATCH_PARAMS
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   TEXT_TO_IMAGE_IMAGE_PARAMS
					__SCREAMING_SNAKE_CASE       :  Tuple						=   TEXT_TO_IMAGE_IMAGE_PARAMS
					__SCREAMING_SNAKE_CASE       :  int						=   False

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										torch.manual_seed(0       )
										lowercase_  							=			UNetaDConditionModel(
										    block_out_channels=(32, 64)							,					layers_per_block=2							,					sample_size=32							,					in_channels=4							,					out_channels=4							,					down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")							,					up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")							,					cross_attention_dim=32							,					)
										lowercase_  							=			DDIMScheduler(
										    beta_start=0.00_085							,					beta_end=0.012							,					beta_schedule="""scaled_linear"""							,					clip_sample=UpperCamelCase__							,					set_alpha_to_one=UpperCamelCase__							,					)
										torch.manual_seed(0       )
										lowercase_  							=			AutoencoderKL(
										    block_out_channels=[32, 64]							,					in_channels=3							,					out_channels=3							,					down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,					up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,					latent_channels=4							,					)
										torch.manual_seed(0       )
										lowercase_  							=			CLIPTextConfig(
										    bos_token_id=0							,					eos_token_id=2							,					hidden_size=32							,					intermediate_size=37							,					layer_norm_eps=1e-05							,					num_attention_heads=4							,					num_hidden_layers=5							,					pad_token_id=1							,					vocab_size=1_000							,					)
										lowercase_  							=			CLIPTextModel(UpperCamelCase__       )
										lowercase_  							=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )

										lowercase_  							=			{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components

					def   UpperCAmelCase__       (     self       :   Optional[int]							,					UpperCamelCase__       :   Union[str, Any]							,					UpperCamelCase__       :   List[str]=0       ):






										'''simple docstring'''






										if str(UpperCamelCase__       ).startswith("""mps"""       ):
															lowercase_  							=			torch.manual_seed(UpperCamelCase__       )
										else:
															lowercase_  							=			torch.Generator(device=UpperCamelCase__       ).manual_seed(UpperCamelCase__       )
										lowercase_  							=			{
										    """prompt""": """.""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 1.0,
										    """sag_scale""": 1.0,
										    """output_type""": """numpy""",
										}
										return inputs







					def   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






										super().test_inference_batch_single_identical(expected_max_diff=3e-3       )







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

					def   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""       )
										lowercase_  							=			sag_pipe.to(UpperCamelCase__       )
										sag_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			"""."""
										lowercase_  							=			torch.manual_seed(0       )
										lowercase_  							=			sag_pipe(
										    [prompt]							,					generator=UpperCamelCase__							,					guidance_scale=7.5							,					sag_scale=1.0							,					num_inference_steps=20							,					output_type="""np"""       )

										lowercase_  							=			output.images

										lowercase_  							=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										lowercase_  							=			np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-2

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""       )
										lowercase_  							=			sag_pipe.to(UpperCamelCase__       )
										sag_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			"""."""
										lowercase_  							=			torch.manual_seed(0       )
										lowercase_  							=			sag_pipe(
										    [prompt]							,					generator=UpperCamelCase__							,					guidance_scale=7.5							,					sag_scale=1.0							,					num_inference_steps=20							,					output_type="""np"""       )

										lowercase_  							=			output.images

										lowercase_  							=			image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										lowercase_  							=			np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5e-2







					def   UpperCAmelCase__       (     self       :   str       ):






										'''simple docstring'''






										lowercase_  							=			StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""       )
										lowercase_  							=			sag_pipe.to(UpperCamelCase__       )
										sag_pipe.set_progress_bar_config(disable=UpperCamelCase__       )

										lowercase_  							=			"""."""
										lowercase_  							=			torch.manual_seed(0       )
										lowercase_  							=			sag_pipe(
										    [prompt]							,					width=768							,					height=512							,					generator=UpperCamelCase__							,					guidance_scale=7.5							,					sag_scale=1.0							,					num_inference_steps=20							,					output_type="""np"""							,					)

										lowercase_  							=			output.images

										assert image.shape == (1, 512, 768, 3)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .multicontrolnet import MultiControlNetModel
							from .pipeline_controlnet import StableDiffusionControlNetPipeline
							from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
							from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
							from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


