



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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()
@skip_mps
class 			_lowerCAmelCase   (							UpperCAmelCase__    ,					UpperCAmelCase__    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Tuple      =			StableDiffusionPanoramaPipeline
			lowercase__      :  List[str]      =			TEXT_TO_IMAGE_PARAMS
			lowercase__      :  Union[str, Any]      =			TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase__      :  Dict      =			TEXT_TO_IMAGE_IMAGE_PARAMS
			lowercase__      :  Union[str, Any]      =			TEXT_TO_IMAGE_IMAGE_PARAMS





			def 		snake_case__			(				self	:       Tuple   )						->					List[str]:
									"""simple docstring"""







									torch.manual_seed(0   )
									__lowercase					=				UNetaDConditionModel(
									    block_out_channels=(32, 64)			,     layers_per_block=1			,     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()
									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-0_5			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1_000			,     )
									__lowercase					=				CLIPTextModel(__lowerCAmelCase   )
									__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 		snake_case__			(				self	:       Any			,     lowercase	:       Optional[int]			,     lowercase	:       Dict=0   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				torch.manual_seed(__lowerCAmelCase   )
									__lowercase					=				{
									    """prompt""": """a photo of the dolomites""",
									    """generator""": generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    """height""": None,
									    """width""": None,
									    """num_inference_steps""": 1,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs





			def 		snake_case__			(				self	:       Optional[int]   )						->					str:
									"""simple docstring"""







									__lowercase					=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowercase					=				self.get_dummy_components()
									__lowercase					=				StableDiffusionPanoramaPipeline(**__lowerCAmelCase   )
									__lowercase					=				sd_pipe.to(__lowerCAmelCase   )
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

									__lowercase					=				self.get_dummy_inputs(__lowerCAmelCase   )
									__lowercase					=				sd_pipe(**__lowerCAmelCase   ).images
									__lowercase					=				image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									__lowercase					=				np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





			def 		snake_case__			(				self	:       str   )						->					Dict:
									"""simple docstring"""







									super().test_inference_batch_consistent(batch_sizes=[1, 2]   )





			def 		snake_case__			(				self	:       Any   )						->					Optional[Any]:
									"""simple docstring"""







									super().test_inference_batch_single_identical(batch_size=2			,     expected_max_diff=3.2_5E-3   )





			def 		snake_case__			(				self	:       Optional[Any]   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowercase					=				self.get_dummy_components()
									__lowercase					=				StableDiffusionPanoramaPipeline(**__lowerCAmelCase   )
									__lowercase					=				sd_pipe.to(__lowerCAmelCase   )
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

									__lowercase					=				self.get_dummy_inputs(__lowerCAmelCase   )
									__lowercase					=				"""french fries"""
									__lowercase					=				sd_pipe(**__lowerCAmelCase			,     negative_prompt=__lowerCAmelCase   )
									__lowercase					=				output.images
									__lowercase					=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__lowercase					=				np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





			def 		snake_case__			(				self	:       List[str]   )						->					str:
									"""simple docstring"""







									__lowercase					=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowercase					=				self.get_dummy_components()
									__lowercase					=				StableDiffusionPanoramaPipeline(**__lowerCAmelCase   )
									__lowercase					=				sd_pipe.to(__lowerCAmelCase   )
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

									__lowercase					=				self.get_dummy_inputs(__lowerCAmelCase   )
									__lowercase					=				sd_pipe(**__lowerCAmelCase			,     view_batch_size=2   )
									__lowercase					=				output.images
									__lowercase					=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__lowercase					=				np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





			def 		snake_case__			(				self	:       str   )						->					Any:
									"""simple docstring"""







									__lowercase					=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowercase					=				self.get_dummy_components()
									__lowercase					=				EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule="""scaled_linear"""   )
									__lowercase					=				StableDiffusionPanoramaPipeline(**__lowerCAmelCase   )
									__lowercase					=				sd_pipe.to(__lowerCAmelCase   )
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

									__lowercase					=				self.get_dummy_inputs(__lowerCAmelCase   )
									__lowercase					=				sd_pipe(**__lowerCAmelCase   ).images
									__lowercase					=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__lowercase					=				np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2







			def 		snake_case__			(				self	:       List[str]   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				"""cpu"""  # ensure determinism for the device-dependent torch.Generator
									__lowercase					=				self.get_dummy_components()
									__lowercase					=				PNDMScheduler(
									    beta_start=0.0_0085			,     beta_end=0.012			,     beta_schedule="""scaled_linear"""			,     skip_prk_steps=__lowerCAmelCase   )
									__lowercase					=				StableDiffusionPanoramaPipeline(**__lowerCAmelCase   )
									__lowercase					=				sd_pipe.to(__lowerCAmelCase   )
									sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

									__lowercase					=				self.get_dummy_inputs(__lowerCAmelCase   )
									__lowercase					=				sd_pipe(**__lowerCAmelCase   ).images
									__lowercase					=				image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									__lowercase					=				np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]   )

									assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2





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






			"""simple docstring"""





			def 		snake_case__			(				self	:       List[str]   )						->					Union[str, Any]:
									"""simple docstring"""







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





			def 		snake_case__			(				self	:       Optional[Any]			,     lowercase	:       List[str]=0   )						->					Tuple:
									"""simple docstring"""







									__lowercase					=				torch.manual_seed(__lowerCAmelCase   )
									__lowercase					=				{
									    """prompt""": """a photo of the dolomites""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs





			def 		snake_case__			(				self	:       Tuple   )						->					Union[str, Any]:
									"""simple docstring"""







									__lowercase					=				"""stabilityai/stable-diffusion-2-base"""
									__lowercase					=				DDIMScheduler.from_pretrained(__lowerCAmelCase			,     subfolder="""scheduler"""   )
									__lowercase					=				StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     safety_checker=__lowerCAmelCase   )
									pipe.to(__lowerCAmelCase   )
									pipe.set_progress_bar_config(disable=__lowerCAmelCase   )
									pipe.enable_attention_slicing()

									__lowercase					=				self.get_inputs()
									__lowercase					=				pipe(**__lowerCAmelCase   ).images
									__lowercase					=				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 2_048, 3)

									__lowercase					=				np.array(
									    [
									        0.3696_8392,
									        0.2702_5372,
									        0.3244_6766,
									        0.2837_9387,
									        0.3636_3274,
									        0.3073_3347,
									        0.2710_0027,
									        0.2705_4125,
									        0.2553_6096,
									    ]   )

									assert np.abs(expected_slice - image_slice   ).max() < 1E-2





			def 		snake_case__			(				self	:       Optional[int]   )						->					List[Any]:
									"""simple docstring"""







									__lowercase					=				StableDiffusionPanoramaPipeline.from_pretrained(
									    """stabilityai/stable-diffusion-2-base"""			,     safety_checker=__lowerCAmelCase   )
									__lowercase					=				LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.to(__lowerCAmelCase   )
									pipe.set_progress_bar_config(disable=__lowerCAmelCase   )
									pipe.enable_attention_slicing()

									__lowercase					=				self.get_inputs()
									__lowercase					=				pipe(**__lowerCAmelCase   ).images
									__lowercase					=				image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 2_048, 3)

									__lowercase					=				np.array(
									    [
									        [
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									        ]
									    ]   )

									assert np.abs(expected_slice - image_slice   ).max() < 1E-3





			def 		snake_case__			(				self	:       Dict   )						->					int:
									"""simple docstring"""







									__lowercase					=				0

									def callback_fn(lowercase	:       int			,     lowercase	:       int			,     lowercase	:       torch.FloatTensor   ) -> None:
															__lowercase					=				True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					__lowercase					=				latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					__lowercase					=				latents[0, -3:, -3:, -1]

																					__lowercase					=				np.array(
																					    [
																					        0.1868_1869,
																					        0.3390_7816,
																					        0.536_1276,
																					        0.1443_2865,
																					        -0.0285_6611,
																					        -0.7394_1123,
																					        0.2339_7987,
																					        0.4732_2682,
																					        -0.3782_3164,
																					    ]   )
																					assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2
															elif step == 2:
																					__lowercase					=				latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					__lowercase					=				latents[0, -3:, -3:, -1]

																					__lowercase					=				np.array(
																					    [
																					        0.1853_9645,
																					        0.3398_7248,
																					        0.537_8559,
																					        0.1443_7142,
																					        -0.0245_5261,
																					        -0.733_8317,
																					        0.2399_0755,
																					        0.4735_6272,
																					        -0.378_6505,
																					    ]   )

																					assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5E-2

									__lowercase					=				False

									__lowercase					=				"""stabilityai/stable-diffusion-2-base"""
									__lowercase					=				DDIMScheduler.from_pretrained(__lowerCAmelCase			,     subfolder="""scheduler"""   )
									__lowercase					=				StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     safety_checker=__lowerCAmelCase   )
									__lowercase					=				pipe.to(__lowerCAmelCase   )
									pipe.set_progress_bar_config(disable=__lowerCAmelCase   )
									pipe.enable_attention_slicing()

									__lowercase					=				self.get_inputs()
									pipe(**__lowerCAmelCase			,     callback=__lowerCAmelCase			,     callback_steps=1   )
									assert callback_fn.has_been_called
									assert number_of_steps == 3







			def 		snake_case__			(				self	:       List[str]   )						->					Optional[Any]:
									"""simple docstring"""







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

									__lowercase					=				"""stabilityai/stable-diffusion-2-base"""
									__lowercase					=				DDIMScheduler.from_pretrained(__lowerCAmelCase			,     subfolder="""scheduler"""   )
									__lowercase					=				StableDiffusionPanoramaPipeline.from_pretrained(__lowerCAmelCase			,     scheduler=__lowerCAmelCase			,     safety_checker=__lowerCAmelCase   )
									__lowercase					=				pipe.to(__lowerCAmelCase   )
									pipe.set_progress_bar_config(disable=__lowerCAmelCase   )
									pipe.enable_attention_slicing(1   )
									pipe.enable_sequential_cpu_offload()

									__lowercase					=				self.get_inputs()
									__lowercase					=				pipe(**__lowerCAmelCase   )

									__lowercase					=				torch.cuda.max_memory_allocated()
									# make sure that less than 5.2 GB is allocated
									assert mem_bytes < 5.5 * 10**9










import random






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__ = False   ) ->      dict:
						__lowercase					=				{i: [] for i in range(lowercase__   )}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(lowercase__   )
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(lowercase__   ):
												for j in range(i + 1						,       lowercase__   ):
																		if random.random() < probability:
																								graph[i].append(lowercase__   )
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(lowercase__   )
						return graph






def  UpperCAmelCase__    (    lowercase__   ) ->      dict:
						return {
						    i: [j for j in range(lowercase__   ) if i != j] for i in range(lowercase__   )
						}


if __name__ == "__main__":
			import doctest

			doctest.testmod()





