


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    UpperCAmelCase__	( a__       ,  a__       ,  unittest.TestCase		):



				"""simple docstring"""






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






				def _a (      self   )  ->	int:
									torch.manual_seed(0   )
									__UpperCamelCase   							=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 ,       )
									__UpperCamelCase   							=DDIMScheduler()
									torch.manual_seed(0   )
									__UpperCamelCase   							=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   )
									__UpperCamelCase   							=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=1000 ,       )
									__UpperCamelCase   							=CLIPTextModel(lowerCAmelCase__   )
									__UpperCamelCase   							=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )

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






				def _a (      self ,       A_ ,       A_=0   )  ->	Optional[Any]:
									__UpperCamelCase   							=torch.manual_seed(lowerCAmelCase__   )
									__UpperCamelCase   							={
									    "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   )  ->	int:
									__UpperCamelCase   							="cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase   							=self.get_dummy_components()
									__UpperCamelCase   							=StableDiffusionPanoramaPipeline(**lowerCAmelCase__   )
									__UpperCamelCase   							=sd_pipe.to(lowerCAmelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

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

									__UpperCamelCase   							=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 _a (      self   )  ->	Optional[int]:
									super().test_inference_batch_consistent(batch_sizes=[1, 2]   )






				def _a (      self   )  ->	Optional[int]:
									super().test_inference_batch_single_identical(batch_size=2 ,       expected_max_diff=3.25E-3   )






				def _a (      self   )  ->	List[Any]:
									__UpperCamelCase   							="cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase   							=self.get_dummy_components()
									__UpperCamelCase   							=StableDiffusionPanoramaPipeline(**lowerCAmelCase__   )
									__UpperCamelCase   							=sd_pipe.to(lowerCAmelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

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

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

									__UpperCamelCase   							=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 _a (      self   )  ->	Any:
									__UpperCamelCase   							="cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase   							=self.get_dummy_components()
									__UpperCamelCase   							=StableDiffusionPanoramaPipeline(**lowerCAmelCase__   )
									__UpperCamelCase   							=sd_pipe.to(lowerCAmelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

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

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

									__UpperCamelCase   							=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 _a (      self   )  ->	Union[str, Any]:
									__UpperCamelCase   							="cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase   							=self.get_dummy_components()
									__UpperCamelCase   							=EulerAncestralDiscreteScheduler(
									    beta_start=0.0_0085 ,       beta_end=0.012 ,       beta_schedule='scaled_linear'   )
									__UpperCamelCase   							=StableDiffusionPanoramaPipeline(**lowerCAmelCase__   )
									__UpperCamelCase   							=sd_pipe.to(lowerCAmelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

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

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

									__UpperCamelCase   							=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 _a (      self   )  ->	Any:
									__UpperCamelCase   							="cpu"  # ensure determinism for the device-dependent torch.Generator
									__UpperCamelCase   							=self.get_dummy_components()
									__UpperCamelCase   							=PNDMScheduler(
									    beta_start=0.0_0085 ,       beta_end=0.012 ,       beta_schedule='scaled_linear' ,       skip_prk_steps=lowerCAmelCase__   )
									__UpperCamelCase   							=StableDiffusionPanoramaPipeline(**lowerCAmelCase__   )
									__UpperCamelCase   							=sd_pipe.to(lowerCAmelCase__   )
									sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__   )

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

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

									__UpperCamelCase   							=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    UpperCAmelCase__	( unittest.TestCase		):



				"""simple docstring"""






				def _a (      self   )  ->	str:
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






				def _a (      self ,       A_=0   )  ->	int:
									__UpperCamelCase   							=torch.manual_seed(lowerCAmelCase__   )
									__UpperCamelCase   							={
									    "prompt": "a photo of the dolomites",
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs






				def _a (      self   )  ->	List[str]:
									__UpperCamelCase   							="stabilityai/stable-diffusion-2-base"
									__UpperCamelCase   							=DDIMScheduler.from_pretrained(lowerCAmelCase__ ,       subfolder='scheduler'   )
									__UpperCamelCase   							=StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__ ,       scheduler=lowerCAmelCase__ ,       safety_checker=lowerCAmelCase__   )
									pipe.to(lowerCAmelCase__   )
									pipe.set_progress_bar_config(disable=lowerCAmelCase__   )
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 2048, 3)

									__UpperCamelCase   							=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 _a (      self   )  ->	List[Any]:
									__UpperCamelCase   							=StableDiffusionPanoramaPipeline.from_pretrained(
									    'stabilityai/stable-diffusion-2-base' ,       safety_checker=lowerCAmelCase__   )
									__UpperCamelCase   							=LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
									pipe.to(lowerCAmelCase__   )
									pipe.set_progress_bar_config(disable=lowerCAmelCase__   )
									pipe.enable_attention_slicing()

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

									assert image.shape == (1, 512, 2048, 3)

									__UpperCamelCase   							=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   )  ->	Dict:
									__UpperCamelCase   							=0

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

																			__UpperCamelCase   							=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:
																			__UpperCamelCase   							=latents.detach().cpu().numpy()
																			assert latents.shape == (1, 4, 64, 256)
																			__UpperCamelCase   							=latents[0, -3:, -3:, -1]

																			__UpperCamelCase   							=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

									__UpperCamelCase   							=False

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

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






				def _a (      self   )  ->	int:
									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									__UpperCamelCase   							=self.get_inputs()
									__UpperCamelCase   							=pipe(**lowerCAmelCase__   )

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









from __future__ import annotations

from typing import Any

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]    ):
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					[]      ,					0    )

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : list[Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):

     if index == len(SCREAMING_SNAKE_CASE__    ):
          print(SCREAMING_SNAKE_CASE__    )
          return

     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.append(sequence[index]    )
     create_state_space_tree(SCREAMING_SNAKE_CASE__      ,					SCREAMING_SNAKE_CASE__      ,					index + 1    )
     current_subsequence.pop()


if __name__ == "__main__":
   _A			=						[3, 1, 2, 4]
   generate_all_subsequences(seq)

   seq.clear()
   seq.extend(['A', 'B', 'C'])
   generate_all_subsequences(seq)





