


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       A    (   A_					,				A_					,				unittest.TestCase ):
							UpperCamelCase_   :							str       =StableDiffusionPanoramaPipeline
							UpperCamelCase_   :							str       =TEXT_TO_IMAGE_PARAMS
							UpperCamelCase_   :							Any       =TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCamelCase_   :							int       =TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase_   :							Optional[int]       =TEXT_TO_IMAGE_IMAGE_PARAMS


							def 			_A       (self					):
									torch.manual_seed(0					)
									__lowercase=			UNetaDConditionModel(
									    block_out_channels=(3_2, 6_4)      ,				layers_per_block=1      ,				sample_size=3_2      ,				in_channels=4      ,				out_channels=4      ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,				cross_attention_dim=3_2      ,				)
									__lowercase=			DDIMScheduler()
									torch.manual_seed(0					)
									__lowercase=			AutoencoderKL(
									    block_out_channels=[3_2, 6_4]      ,				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=3_2      ,				intermediate_size=3_7      ,				layer_norm_eps=1E-05      ,				num_attention_heads=4      ,				num_hidden_layers=5      ,				pad_token_id=1      ,				vocab_size=1_0_0_0      ,				)
									__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 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase=0					):
									__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 			_A       (self					):
									__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, 6_4, 6_4, 3)

									__lowercase=			np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]					)

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


							def 			_A       (self					):
									super().test_inference_batch_consistent(batch_sizes=[1, 2]					)


							def 			_A       (self					):
									super().test_inference_batch_single_identical(batch_size=2      ,				expected_max_diff=3.25E-3					)


							def 			_A       (self					):
									__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, 6_4, 6_4, 3)

									__lowercase=			np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]					)

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


							def 			_A       (self					):
									__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, 6_4, 6_4, 3)

									__lowercase=			np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]					)

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


							def 			_A       (self					):
									__lowercase=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									__lowercase=			self.get_dummy_components()
									__lowercase=			EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85      ,				beta_end=0.0_12      ,				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, 6_4, 6_4, 3)

									__lowercase=			np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]					)

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







							def 			_A       (self					):
									__lowercase=			'cpu'  # ensure determinism for the device-dependent torch.Generator
									__lowercase=			self.get_dummy_components()
									__lowercase=			PNDMScheduler(
									    beta_start=0.0_00_85      ,				beta_end=0.0_12      ,				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, 6_4, 6_4, 3)

									__lowercase=			np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]					)

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





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


							def 			_A       (self					):
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()


							def 			_A       (self      ,				lowerCAmelCase=0					):
									__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 			_A       (self					):
									__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, 5_1_2, 2_0_4_8, 3)

									__lowercase=			np.array(
									    [
									        0.36_96_83_92,
									        0.27_02_53_72,
									        0.32_44_67_66,
									        0.28_37_93_87,
									        0.36_36_32_74,
									        0.30_73_33_47,
									        0.27_10_00_27,
									        0.27_05_41_25,
									        0.25_53_60_96,
									    ]					)

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


							def 			_A       (self					):
									__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, 5_1_2, 2_0_4_8, 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 			_A       (self					):
									__lowercase=			0

									def callback_fn(lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					) -> None:
											__lowercase=			True
											nonlocal number_of_steps
											number_of_steps += 1
											if step == 1:
													__lowercase=			latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 6_4, 2_5_6)
													__lowercase=			latents[0, -3:, -3:, -1]

													__lowercase=			np.array(
													    [
													        0.18_68_18_69,
													        0.33_90_78_16,
													        0.5_36_12_76,
													        0.14_43_28_65,
													        -0.02_85_66_11,
													        -0.73_94_11_23,
													        0.23_39_79_87,
													        0.47_32_26_82,
													        -0.37_82_31_64,
													    ]					)
													assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
											elif step == 2:
													__lowercase=			latents.detach().cpu().numpy()
													assert latents.shape == (1, 4, 6_4, 2_5_6)
													__lowercase=			latents[0, -3:, -3:, -1]

													__lowercase=			np.array(
													    [
													        0.18_53_96_45,
													        0.33_98_72_48,
													        0.5_37_85_59,
													        0.14_43_71_42,
													        -0.02_45_52_61,
													        -0.7_33_83_17,
													        0.23_99_07_55,
													        0.47_35_62_72,
													        -0.3_78_65_05,
													    ]					)

													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 			_A       (self					):
									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 * 1_0**9










from __future__ import annotations







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			2
		__lowercase=			[]
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.append(lowercase__	)
		if n > 1:
				factors.append(lowercase__	)
		return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()






