import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  @property
  def       __A   (							self       :				List[str]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     1
        SCREAMING_SNAKE_CASE_      =     3
        SCREAMING_SNAKE_CASE_      =     (32, 32)

        SCREAMING_SNAKE_CASE_      =     floats_tensor((batch_size, num_channels) + sizes     ,   rng=random.Random(0      )      ).to(__magic_name__      )
        return image







  @property
  def       __A   (							self       :				List[Any]      )     ->		Dict:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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     ,   )
        return model







  @property
  def       __A   (							self       :				Dict      )     ->		List[Any]:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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     ,   )
        return model







  @property
  def       __A   (							self       :				int      )     ->		Optional[int]:
        torch.manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     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     ,   )
        return CLIPTextModel(__magic_name__      )







  @property
  def       __A   (							self       :				str      )     ->		List[Any]:
        def extract(*__magic_name__       :				List[Any]     ,   **__magic_name__       :				List[Any]      ):


              class      lowerCamelCase    :
                """simple docstring"""







                def __init__(							self       :				Dict      )     ->		List[str]:
                      SCREAMING_SNAKE_CASE_      =     torch.ones([0]      )







                def       __A   (							self       :				Tuple     ,   __magic_name__       :				Dict      )     ->		Union[str, Any]:
                      self.pixel_values.to(__magic_name__      )
                      return self



              return Out()

        return extract







  def       __A   (							self       :				int      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_      =     self.dummy_cond_unet
        SCREAMING_SNAKE_CASE_      =     DDIMScheduler(
            beta_start=0.0_0085     ,   beta_end=0.012     ,   beta_schedule="scaled_linear"     ,   clip_sample=__magic_name__     ,   set_alpha_to_one=__magic_name__     ,   )

        SCREAMING_SNAKE_CASE_      =     self.dummy_vae
        SCREAMING_SNAKE_CASE_      =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_      =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline(
            unet=__magic_name__     ,   scheduler=__magic_name__     ,   vae=__magic_name__     ,   text_encoder=__magic_name__     ,   tokenizer=__magic_name__     ,   safety_checker=__magic_name__     ,   feature_extractor=self.dummy_extractor     ,   )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     '''A painting of a squirrel eating a burger'''

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe([prompt]     ,   generator=__magic_name__     ,   guidance_scale=6.0     ,   num_inference_steps=2     ,   output_type="np"      )
        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=6.0     ,   num_inference_steps=2     ,   output_type="np"     ,   return_dict=__magic_name__     ,   )[0]

        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        SCREAMING_SNAKE_CASE_      =     np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]      )

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







  def       __A   (							self       :				Optional[int]      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_      =     self.dummy_cond_unet
        SCREAMING_SNAKE_CASE_      =     PNDMScheduler(skip_prk_steps=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.dummy_vae
        SCREAMING_SNAKE_CASE_      =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_      =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline(
            unet=__magic_name__     ,   scheduler=__magic_name__     ,   vae=__magic_name__     ,   text_encoder=__magic_name__     ,   tokenizer=__magic_name__     ,   safety_checker=__magic_name__     ,   feature_extractor=self.dummy_extractor     ,   )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     '''A painting of a squirrel eating a burger'''
        SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe([prompt]     ,   generator=__magic_name__     ,   guidance_scale=6.0     ,   num_inference_steps=2     ,   output_type="np"      )

        SCREAMING_SNAKE_CASE_      =     output.images

        SCREAMING_SNAKE_CASE_      =     torch.Generator(device=__magic_name__      ).manual_seed(0      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=6.0     ,   num_inference_steps=2     ,   output_type="np"     ,   return_dict=__magic_name__     ,   )[0]

        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        SCREAMING_SNAKE_CASE_      =     np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]      )

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







  def       __A   (							self       :				Tuple      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-lms-pipe"     ,   safety_checker=__magic_name__      )
        assert isinstance(__magic_name__     ,   __magic_name__      )
        assert isinstance(pipe.scheduler     ,   __magic_name__      )
        assert pipe.safety_checker is None

        SCREAMING_SNAKE_CASE_      =     pipe("example prompt"     ,   num_inference_steps=2      ).images[0]
        assert image is not None

        # check that there's no error when saving a pipeline with one of the models being None
        with tempfile.TemporaryDirectory() as tmpdirname:
              pipe.save_pretrained(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline.from_pretrained(__magic_name__      )

        # sanity check that the pipeline still works
        assert pipe.safety_checker is None
        SCREAMING_SNAKE_CASE_      =     pipe("example prompt"     ,   num_inference_steps=2      ).images[0]
        assert image is not None







  @unittest.skipIf(torch_device != "cuda"     ,   "This test requires a GPU"      )
  def       __A   (							self       :				int      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.dummy_cond_unet
        SCREAMING_SNAKE_CASE_      =     PNDMScheduler(skip_prk_steps=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     self.dummy_vae
        SCREAMING_SNAKE_CASE_      =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_      =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"      )

        # put models in fp16
        SCREAMING_SNAKE_CASE_      =     unet.half()
        SCREAMING_SNAKE_CASE_      =     vae.half()
        SCREAMING_SNAKE_CASE_      =     bert.half()

        # make sure here that pndm scheduler skips prk
        SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline(
            unet=__magic_name__     ,   scheduler=__magic_name__     ,   vae=__magic_name__     ,   text_encoder=__magic_name__     ,   tokenizer=__magic_name__     ,   safety_checker=__magic_name__     ,   feature_extractor=self.dummy_extractor     ,   )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     '''A painting of a squirrel eating a burger'''
        SCREAMING_SNAKE_CASE_      =     sd_pipe([prompt]     ,   num_inference_steps=2     ,   output_type="np"      ).images

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



@nightly
@require_torch_gpu
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Optional[int]      )     ->		int:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







  def       __A   (							self       :				Union[str, Any]      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,   safety_checker=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     (
            '''portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'''
            ''' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'''
            ''' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'''
            ''' children from bahnhof zoo, detailed '''
        )
        SCREAMING_SNAKE_CASE_      =     4_003_660_346
        SCREAMING_SNAKE_CASE_      =     7

        # without safety guidance (sld_guidance_scale = 0)
        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=__magic_name__     ,   num_inference_steps=50     ,   output_type="np"     ,   width=512     ,   height=512     ,   sld_guidance_scale=0     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images
        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     [0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

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

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

        # without safety guidance (strong configuration)
        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=__magic_name__     ,   num_inference_steps=50     ,   output_type="np"     ,   width=512     ,   height=512     ,   sld_guidance_scale=2_000     ,   sld_warmup_steps=7     ,   sld_threshold=0.025     ,   sld_momentum_scale=0.5     ,   sld_mom_beta=0.7     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images
        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     [0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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







  def       __A   (							self       :				List[str]      )     ->		Optional[Any]:
        SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"     ,   safety_checker=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     '''padme amidala taking a bath artwork, safe for work, no nudity'''
        SCREAMING_SNAKE_CASE_      =     2_734_971_755
        SCREAMING_SNAKE_CASE_      =     7

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=__magic_name__     ,   num_inference_steps=50     ,   output_type="np"     ,   width=512     ,   height=512     ,   sld_guidance_scale=0     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images
        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     [0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=__magic_name__     ,   num_inference_steps=50     ,   output_type="np"     ,   width=512     ,   height=512     ,   sld_guidance_scale=2_000     ,   sld_warmup_steps=7     ,   sld_threshold=0.025     ,   sld_momentum_scale=0.5     ,   sld_mom_beta=0.7     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images
        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     [0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

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

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







  def       __A   (							self       :				Tuple      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe.to(__magic_name__      )
        sd_pipe.set_progress_bar_config(disable=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     (
            '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
            ''' leyendecker'''
        )
        SCREAMING_SNAKE_CASE_      =     1_044_355_234
        SCREAMING_SNAKE_CASE_      =     12

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=__magic_name__     ,   num_inference_steps=50     ,   output_type="np"     ,   width=512     ,   height=512     ,   sld_guidance_scale=0     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images
        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]      )

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

        assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-7

        SCREAMING_SNAKE_CASE_      =     torch.manual_seed(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     sd_pipe(
            [prompt]     ,   generator=__magic_name__     ,   guidance_scale=__magic_name__     ,   num_inference_steps=50     ,   output_type="np"     ,   width=512     ,   height=512     ,   sld_guidance_scale=2_000     ,   sld_warmup_steps=7     ,   sld_threshold=0.025     ,   sld_momentum_scale=0.5     ,   sld_mom_beta=0.7     ,   )

        SCREAMING_SNAKE_CASE_      =     output.images
        SCREAMING_SNAKE_CASE_      =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_      =     np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]      )
        assert image.shape == (1, 512, 512, 3)

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









from __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int      )      ->	list[list[int]]:
    UpperCamelCase__							:				list[list[int]]          =      []
    create_all_state(1		,   __UpperCAmelCase		,   __UpperCAmelCase		,   []		,   __UpperCAmelCase      )
    return result




def 		lowerCAmelCase_    (  __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			list[list[int]]		,   )      ->	None:
    if level == 0:
        total_list.append(current_list[:]      )
        return

    for i in range(__UpperCAmelCase		,   total_number - level + 2      ):
        current_list.append(__UpperCAmelCase      )
        create_all_state(i + 1		,   __UpperCAmelCase		,   level - 1		,   __UpperCAmelCase		,   __UpperCAmelCase      )
        current_list.pop()




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[list[int]]      )      ->	None:
    for i in total_list:
        print(*__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 4
  UpperCAmelCase_     							= 2
  UpperCAmelCase_     							= generate_all_combinations(n, k)
  print_all_state(total_list)

