





'''simple docstring'''







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 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_       ,							unittest.TestCase      ):






				"""simple docstring"""







				__a            =StableDiffusionPanoramaPipeline
				__a            =TEXT_TO_IMAGE_PARAMS
				__a            =TEXT_TO_IMAGE_BATCH_PARAMS
				__a            =TEXT_TO_IMAGE_IMAGE_PARAMS
				__a            =TEXT_TO_IMAGE_IMAGE_PARAMS




				def   UpperCamelCase__   (  self     :					List[str]					):
								torch.manual_seed(0					)
								_a								= 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					,				)
								_a								= DDIMScheduler()
								torch.manual_seed(0					)
								_a								= 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					)
								_a								= 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=10_00					,				)
								_a								= CLIPTextModel(__a					)
								_a								= CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

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




				def   UpperCamelCase__   (  self     :					int					,				__a     :					Tuple					,				__a     :					List[str]=0					):
								_a								= torch.manual_seed(__a					)
								_a								= {
								    "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   UpperCamelCase__   (  self     :					Dict					):
								_a								= "cpu"  # ensure determinism for the device-dependent torch.Generator
								_a								= self.get_dummy_components()
								_a								= StableDiffusionPanoramaPipeline(**__a					)
								_a								= sd_pipe.to(__a					)
								sd_pipe.set_progress_bar_config(disable=__a					)

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

								_a								= 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   UpperCamelCase__   (  self     :					Dict					):
								super().test_inference_batch_consistent(batch_sizes=[1, 2]					)




				def   UpperCamelCase__   (  self     :					List[str]					):
								super().test_inference_batch_single_identical(batch_size=2					,				expected_max_diff=3.2_5e-3					)




				def   UpperCamelCase__   (  self     :					Union[str, Any]					):
								_a								= "cpu"  # ensure determinism for the device-dependent torch.Generator
								_a								= self.get_dummy_components()
								_a								= StableDiffusionPanoramaPipeline(**__a					)
								_a								= sd_pipe.to(__a					)
								sd_pipe.set_progress_bar_config(disable=__a					)

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

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

								_a								= 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   UpperCamelCase__   (  self     :					int					):
								_a								= "cpu"  # ensure determinism for the device-dependent torch.Generator
								_a								= self.get_dummy_components()
								_a								= StableDiffusionPanoramaPipeline(**__a					)
								_a								= sd_pipe.to(__a					)
								sd_pipe.set_progress_bar_config(disable=__a					)

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

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

								_a								= 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   UpperCamelCase__   (  self     :					str					):
								_a								= "cpu"  # ensure determinism for the device-dependent torch.Generator
								_a								= self.get_dummy_components()
								_a								= EulerAncestralDiscreteScheduler(
								    beta_start=0.00085					,				beta_end=0.012					,				beta_schedule="scaled_linear"					)
								_a								= StableDiffusionPanoramaPipeline(**__a					)
								_a								= sd_pipe.to(__a					)
								sd_pipe.set_progress_bar_config(disable=__a					)

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

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

								_a								= 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   UpperCamelCase__   (  self     :					int					):
								_a								= "cpu"  # ensure determinism for the device-dependent torch.Generator
								_a								= self.get_dummy_components()
								_a								= PNDMScheduler(
								    beta_start=0.00085					,				beta_end=0.012					,				beta_schedule="scaled_linear"					,				skip_prk_steps=__a					)
								_a								= StableDiffusionPanoramaPipeline(**__a					)
								_a								= sd_pipe.to(__a					)
								sd_pipe.set_progress_bar_config(disable=__a					)

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

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

								_a								= 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 				__SCREAMING_SNAKE_CASE  (unittest.TestCase      ):






				"""simple docstring"""







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




				def   UpperCamelCase__   (  self     :					str					,				__a     :					Optional[int]=0					):
								_a								= torch.manual_seed(__a					)
								_a								= {
								    "prompt": "a photo of the dolomites",
								    "generator": generator,
								    "num_inference_steps": 3,
								    "guidance_scale": 7.5,
								    "output_type": "numpy",
								}
								return inputs




				def   UpperCamelCase__   (  self     :					str					):
								_a								= "stabilityai/stable-diffusion-2-base"
								_a								= DDIMScheduler.from_pretrained(__a					,				subfolder="scheduler"					)
								_a								= StableDiffusionPanoramaPipeline.from_pretrained(__a					,				scheduler=__a					,				safety_checker=__a					)
								pipe.to(__a					)
								pipe.set_progress_bar_config(disable=__a					)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_12, 20_48, 3)

								_a								= np.array(
								    [
								        0.36968392,
								        0.27025372,
								        0.32446766,
								        0.28379387,
								        0.36363274,
								        0.30733347,
								        0.27100027,
								        0.27054125,
								        0.25536096,
								    ]					)

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




				def   UpperCamelCase__   (  self     :					Any					):
								_a								= StableDiffusionPanoramaPipeline.from_pretrained(
								    "stabilityai/stable-diffusion-2-base"					,				safety_checker=__a					)
								_a								= LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
								pipe.to(__a					)
								pipe.set_progress_bar_config(disable=__a					)
								pipe.enable_attention_slicing()

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

								assert image.shape == (1, 5_12, 20_48, 3)

								_a								= 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   UpperCamelCase__   (  self     :					Optional[int]					):
								_a								= 0

								def callback_fn(__a     :					int					,				__a     :					int					,				__a     :					torch.FloatTensor					) -> None:
												_a								= True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																_a								= latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 2_56)
																_a								= latents[0, -3:, -3:, -1]

																_a								= np.array(
																    [
																        0.18681869,
																        0.33907816,
																        0.5361276,
																        0.14432865,
																        -0.02856611,
																        -0.73941123,
																        0.23397987,
																        0.47322682,
																        -0.37823164,
																    ]					)
																assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2
												elif step == 2:
																_a								= latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 2_56)
																_a								= latents[0, -3:, -3:, -1]

																_a								= np.array(
																    [
																        0.18539645,
																        0.33987248,
																        0.5378559,
																        0.14437142,
																        -0.02455261,
																        -0.7338317,
																        0.23990755,
																        0.47356272,
																        -0.3786505,
																    ]					)

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

								_a								= False

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

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




				def   UpperCamelCase__   (  self     :					Tuple					):
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

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

								_a								= self.get_inputs()
								_a								= pipe(**__a					)

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




"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  list[int] )	->						float:


    """simple docstring"""





    if not nums:  # Makes sure that the list is not empty
        raise ValueError("List is empty" )

    snake_case    :      List[str]      					=    sum(lowercase ) / len(lowercase )  # Calculate the average
    return sum(abs(x - average ) for x in nums ) / len(lowercase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()





