






'''simple docstring'''




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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class      _UpperCAmelCase    (  unittest.TestCase		):




   """simple docstring"""

   def 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





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

   @property
   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         _A      =			1
         _A      =			3
         _A      =			(32, 32)

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

   @property
   def 		lowerCAmelCase				(	self				:			Any	):





         '''simple docstring'''





         torch.manual_seed(0	)
         _A      =			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 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         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   ,   )
         return model

   @property
   def 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





         torch.manual_seed(0	)
         _A      =			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   ,   )
         return CLIPTextModel(lowercase_	)

   @property
   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         def extract(*__UpperCAmelCase				:			Dict   ,   **__UpperCAmelCase				:			Any	):

               class      _UpperCAmelCase    :




                  """simple docstring"""

                  def __init__(	self				:			Any	):





                        '''simple docstring'''





                        _A      =			torch.ones([0]	)

                  def 		lowerCAmelCase				(	self				:			Dict   ,   __UpperCAmelCase				:			str	):





                        '''simple docstring'''





                        self.pixel_values.to(lowercase_	)
                        return self





               return Out()

         return extract

   def 		lowerCAmelCase				(	self				:			Dict	):





         '''simple docstring'''





         _A      =			"cpu"  # ensure determinism for the device-dependent torch.Generator
         _A      =			self.dummy_cond_unet
         _A      =			DDIMScheduler(
             beta_start=0.00085   ,   beta_end=0.012   ,   beta_schedule="scaled_linear"   ,   clip_sample=lowercase_   ,   set_alpha_to_one=lowercase_   ,   )

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

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

         _A      =			"A painting of a squirrel eating a burger"

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

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

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

         assert image.shape == (1, 64, 64, 3)
         _A      =			np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]	)

         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 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			"cpu"  # ensure determinism for the device-dependent torch.Generator
         _A      =			self.dummy_cond_unet
         _A      =			PNDMScheduler(skip_prk_steps=lowercase_	)
         _A      =			self.dummy_vae
         _A      =			self.dummy_text_encoder
         _A      =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

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

         _A      =			output.images

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

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

         assert image.shape == (1, 64, 64, 3)
         _A      =			np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]	)

         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 		lowerCAmelCase				(	self				:			List[str]	):





         '''simple docstring'''





         _A      =			StableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-lms-pipe"   ,   safety_checker=lowercase_	)
         assert isinstance(lowercase_   ,   lowercase_	)
         assert isinstance(pipe.scheduler   ,   lowercase_	)
         assert pipe.safety_checker is None

         _A      =			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(lowercase_	)
               _A      =			StableDiffusionPipeline.from_pretrained(lowercase_	)

         # sanity check that the pipeline still works
         assert pipe.safety_checker is None
         _A      =			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 		lowerCAmelCase				(	self				:			Optional[Any]	):





         '''simple docstring'''





         _A      =			self.dummy_cond_unet
         _A      =			PNDMScheduler(skip_prk_steps=lowercase_	)
         _A      =			self.dummy_vae
         _A      =			self.dummy_text_encoder
         _A      =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

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

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

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





@nightly
@require_torch_gpu
class      _UpperCAmelCase    (  unittest.TestCase		):




   """simple docstring"""

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





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

   def 		lowerCAmelCase				(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			"cuda"
         _A      =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
         _A      =			pipe.to(lowercase_	)
         pipe.set_progress_bar_config(disable=lowercase_	)

         _A      =			"a photo of a cat"
         _A      =			{
             "editing_prompt": ["sunglasses"],
             "reverse_editing_direction": [False],
             "edit_warmup_steps": 10,
             "edit_guidance_scale": 6,
             "edit_threshold": 0.95,
             "edit_momentum_scale": 0.5,
             "edit_mom_beta": 0.6,
         }

         _A      =			3
         _A      =			7

         # no sega enabled
         _A      =			torch.Generator(lowercase_	)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.34673113,
             0.38492733,
             0.37597352,
             0.34086335,
             0.35650748,
             0.35579205,
             0.3384763,
             0.34340236,
             0.3573271,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   **lowercase_   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.41887826,
             0.37728766,
             0.30138272,
             0.41416335,
             0.41664985,
             0.36283392,
             0.36191246,
             0.43364465,
             0.43001732,
         ]

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

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

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			"cuda"
         _A      =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
         _A      =			pipe.to(lowercase_	)
         pipe.set_progress_bar_config(disable=lowercase_	)

         _A      =			"an image of a crowded boulevard, realistic, 4k"
         _A      =			{
             "editing_prompt": "crowd, crowded, people",
             "reverse_editing_direction": True,
             "edit_warmup_steps": 10,
             "edit_guidance_scale": 8.3,
             "edit_threshold": 0.9,
             "edit_momentum_scale": 0.5,
             "edit_mom_beta": 0.6,
         }

         _A      =			9
         _A      =			7

         # no sega enabled
         _A      =			torch.Generator(lowercase_	)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.43497998,
             0.91814065,
             0.7540739,
             0.55580205,
             0.8467265,
             0.5389691,
             0.62574506,
             0.58897763,
             0.50926757,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   **lowercase_   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.3089719,
             0.30500144,
             0.29016042,
             0.30630964,
             0.325687,
             0.29419225,
             0.2908091,
             0.28723598,
             0.27696294,
         ]

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

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

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			"cuda"
         _A      =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
         _A      =			pipe.to(lowercase_	)
         pipe.set_progress_bar_config(disable=lowercase_	)

         _A      =			"a castle next to a river"
         _A      =			{
             "editing_prompt": ["boat on a river, boat", "monet, impression, sunrise"],
             "reverse_editing_direction": False,
             "edit_warmup_steps": [15, 18],
             "edit_guidance_scale": 6,
             "edit_threshold": [0.9, 0.8],
             "edit_momentum_scale": 0.5,
             "edit_mom_beta": 0.6,
         }

         _A      =			48
         _A      =			7

         # no sega enabled
         _A      =			torch.Generator(lowercase_	)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.75163555,
             0.76037145,
             0.61785,
             0.9189673,
             0.8627701,
             0.85189694,
             0.8512813,
             0.87012076,
             0.8312857,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   **lowercase_   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.73553365,
             0.7537271,
             0.74341905,
             0.66480356,
             0.6472925,
             0.63039416,
             0.64812905,
             0.6749717,
             0.6517102,
         ]

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

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

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			"cuda"
         _A      =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"   ,   torch_dtype=torch.floataa	)
         _A      =			pipe.to(lowercase_	)
         pipe.set_progress_bar_config(disable=lowercase_	)

         _A      =			"a photo of a cat"
         _A      =			{
             "editing_prompt": ["sunglasses"],
             "reverse_editing_direction": [False],
             "edit_warmup_steps": 10,
             "edit_guidance_scale": 6,
             "edit_threshold": 0.95,
             "edit_momentum_scale": 0.5,
             "edit_mom_beta": 0.6,
         }

         _A      =			3
         _A      =			7

         # no sega enabled
         _A      =			torch.Generator(lowercase_	)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.34887695,
             0.3876953,
             0.375,
             0.34423828,
             0.3581543,
             0.35717773,
             0.3383789,
             0.34570312,
             0.359375,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(lowercase_	)
         _A      =			pipe(
             [prompt]   ,   generator=lowercase_   ,   guidance_scale=lowercase_   ,   num_inference_steps=50   ,   output_type="np"   ,   width=512   ,   height=512   ,   **lowercase_   ,   )

         _A      =			output.images
         _A      =			image[0, -3:, -3:, -1]
         _A      =			[
             0.42285156,
             0.36914062,
             0.29077148,
             0.42041016,
             0.41918945,
             0.35498047,
             0.3618164,
             0.4423828,
             0.43115234,
         ]

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

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














'''simple docstring'''




from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Any   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			data
         _A      =			None

   def __str__(	self				:			str	):





         '''simple docstring'''





         return f'''{self.data}'''





class      _UpperCAmelCase    :




   """simple docstring"""

   def __init__(	self				:			Optional[int]	):





         '''simple docstring'''





         _A      =			None
         _A      =			None

   def __iter__(	self				:			List[Any]	):





         '''simple docstring'''





         _A      =			self.front
         while node:
               yield node.data
               _A      =			node.next

   def __len__(	self				:			Union[str, Any]	):





         '''simple docstring'''





         return len(tuple(iter(self	)	)	)

   def __str__(	self				:			int	):





         '''simple docstring'''





         return " <- ".join(str(__UpperCAmelCase	) for item in self	)

   def 		lowerCAmelCase				(	self				:			List[Any]	):





         '''simple docstring'''





         return len(self	) == 0

   def 		lowerCAmelCase				(	self				:			int   ,   __UpperCAmelCase				:			Any	):





         '''simple docstring'''





         _A      =			Node(__UpperCAmelCase	)
         if self.is_empty():
               _A      =			_A      =			node
         else:
               assert isinstance(self.rear   ,   __UpperCAmelCase	)
               _A      =			node
               _A      =			node

   def 		lowerCAmelCase				(	self				:			str	):





         '''simple docstring'''





         if self.is_empty():
               raise IndexError("dequeue from empty queue"	)
         assert isinstance(self.front   ,   __UpperCAmelCase	)
         _A      =			self.front
         _A      =			self.front.next
         if self.front is None:
               _A      =			None
         return node.data

   def 		lowerCAmelCase				(	self				:			int	):





         '''simple docstring'''





         _A      =			_A      =			None


if __name__ == "__main__":
    from doctest import testmod

    testmod()





