




'''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       ):






       def   lowercase__  (							self						):



              """simple docstring"""







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






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=3
              lowerCamelCase_							=(32, 32)

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






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







              torch.manual_seed(0						)
              lowerCamelCase_							=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(snake_case_						)






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              def extract(*lowerCAmelCase,	**lowerCAmelCase						):


                     class 						__UpperCamelCase :






                            def __init__(							self						):



                                   """simple docstring"""







                                   lowerCamelCase_							=torch.ones([0]						)






                            def   lowercase__  (							self,	lowerCAmelCase						):



                                   """simple docstring"""







                                   self.pixel_values.to(snake_case_						)
                                   return self





                     return Out()

              return extract






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cpu'''  # ensure determinism for the device-dependent torch.Generator
              lowerCamelCase_							=self.dummy_cond_unet
              lowerCamelCase_							=DDIMScheduler(
                  beta_start=0.0_0_0_8_5,	beta_end=0.0_1_2,	beta_schedule='''scaled_linear''',	clip_sample=snake_case_,	set_alpha_to_one=snake_case_,	)

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

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

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

              lowerCamelCase_							=torch.Generator(device=snake_case_						).manual_seed(0						)
              lowerCamelCase_							=sd_pipe([prompt],	generator=snake_case_,	guidance_scale=6.0,	num_inference_steps=2,	output_type='''np'''						)
              lowerCamelCase_							=output.images

              lowerCamelCase_							=torch.Generator(device=snake_case_						).manual_seed(0						)
              lowerCamelCase_							=sd_pipe(
                  [prompt],	generator=snake_case_,	guidance_scale=6.0,	num_inference_steps=2,	output_type='''np''',	return_dict=snake_case_,	)[0]

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

              assert image.shape == (1, 64, 64, 3)
              lowerCamelCase_							=np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]						)

              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   lowercase__  (							self						):



              """simple docstring"""







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

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

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

              lowerCamelCase_							=output.images

              lowerCamelCase_							=torch.Generator(device=snake_case_						).manual_seed(0						)
              lowerCamelCase_							=sd_pipe(
                  [prompt],	generator=snake_case_,	guidance_scale=6.0,	num_inference_steps=2,	output_type='''np''',	return_dict=snake_case_,	)[0]

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

              assert image.shape == (1, 64, 64, 3)
              lowerCamelCase_							=np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]						)

              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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=StableDiffusionPipeline.from_pretrained(
                  '''hf-internal-testing/tiny-stable-diffusion-lms-pipe''',	safety_checker=snake_case_						)
              assert isinstance(snake_case_,	snake_case_						)
              assert isinstance(pipe.scheduler,	snake_case_						)
              assert pipe.safety_checker is None

              lowerCamelCase_							=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(snake_case_						)
                     lowerCamelCase_							=StableDiffusionPipeline.from_pretrained(snake_case_						)

              # sanity check that the pipeline still works
              assert pipe.safety_checker is None
              lowerCamelCase_							=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   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.dummy_cond_unet
              lowerCamelCase_							=PNDMScheduler(skip_prk_steps=snake_case_						)
              lowerCamelCase_							=self.dummy_vae
              lowerCamelCase_							=self.dummy_text_encoder
              lowerCamelCase_							=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''						)

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

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

              lowerCamelCase_							='''A painting of a squirrel eating a burger'''
              lowerCamelCase_							=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       ):






       def   lowercase__  (							self						):



              """simple docstring"""







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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cuda'''
              lowerCamelCase_							=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''						)
              lowerCamelCase_							=pipe.to(snake_case_						)
              pipe.set_progress_bar_config(disable=snake_case_						)

              lowerCamelCase_							='''a photo of a cat'''
              lowerCamelCase_							={
                  '''editing_prompt''': ['''sunglasses'''],
                  '''reverse_editing_direction''': [False],
                  '''edit_warmup_steps''': 10,
                  '''edit_guidance_scale''': 6,
                  '''edit_threshold''': 0.9_5,
                  '''edit_momentum_scale''': 0.5,
                  '''edit_mom_beta''': 0.6,
              }

              lowerCamelCase_							=3
              lowerCamelCase_							=7

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

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.3_4_6_7_3_1_1_3,
                  0.3_8_4_9_2_7_3_3,
                  0.3_7_5_9_7_3_5_2,
                  0.3_4_0_8_6_3_3_5,
                  0.3_5_6_5_0_7_4_8,
                  0.3_5_5_7_9_2_0_5,
                  0.3_3_8_4_7_6_3,
                  0.3_4_3_4_0_2_3_6,
                  0.3_5_7_3_2_7_1,
              ]

              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(snake_case_						)
              lowerCamelCase_							=pipe(
                  [prompt],	generator=snake_case_,	guidance_scale=snake_case_,	num_inference_steps=50,	output_type='''np''',	width=512,	height=512,	**snake_case_,	)

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.4_1_8_8_7_8_2_6,
                  0.3_7_7_2_8_7_6_6,
                  0.3_0_1_3_8_2_7_2,
                  0.4_1_4_1_6_3_3_5,
                  0.4_1_6_6_4_9_8_5,
                  0.3_6_2_8_3_3_9_2,
                  0.3_6_1_9_1_2_4_6,
                  0.4_3_3_6_4_4_6_5,
                  0.4_3_0_0_1_7_3_2,
              ]

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

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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cuda'''
              lowerCamelCase_							=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''						)
              lowerCamelCase_							=pipe.to(snake_case_						)
              pipe.set_progress_bar_config(disable=snake_case_						)

              lowerCamelCase_							='''an image of a crowded boulevard, realistic, 4k'''
              lowerCamelCase_							={
                  '''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,
              }

              lowerCamelCase_							=9
              lowerCamelCase_							=7

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

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.4_3_4_9_7_9_9_8,
                  0.9_1_8_1_4_0_6_5,
                  0.7_5_4_0_7_3_9,
                  0.5_5_5_8_0_2_0_5,
                  0.8_4_6_7_2_6_5,
                  0.5_3_8_9_6_9_1,
                  0.6_2_5_7_4_5_0_6,
                  0.5_8_8_9_7_7_6_3,
                  0.5_0_9_2_6_7_5_7,
              ]

              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(snake_case_						)
              lowerCamelCase_							=pipe(
                  [prompt],	generator=snake_case_,	guidance_scale=snake_case_,	num_inference_steps=50,	output_type='''np''',	width=512,	height=512,	**snake_case_,	)

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.3_0_8_9_7_1_9,
                  0.3_0_5_0_0_1_4_4,
                  0.2_9_0_1_6_0_4_2,
                  0.3_0_6_3_0_9_6_4,
                  0.3_2_5_6_8_7,
                  0.2_9_4_1_9_2_2_5,
                  0.2_9_0_8_0_9_1,
                  0.2_8_7_2_3_5_9_8,
                  0.2_7_6_9_6_2_9_4,
              ]

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

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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cuda'''
              lowerCamelCase_							=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''						)
              lowerCamelCase_							=pipe.to(snake_case_						)
              pipe.set_progress_bar_config(disable=snake_case_						)

              lowerCamelCase_							='''a castle next to a river'''
              lowerCamelCase_							={
                  '''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,
              }

              lowerCamelCase_							=48
              lowerCamelCase_							=7

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

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.7_5_1_6_3_5_5_5,
                  0.7_6_0_3_7_1_4_5,
                  0.6_1_7_8_5,
                  0.9_1_8_9_6_7_3,
                  0.8_6_2_7_7_0_1,
                  0.8_5_1_8_9_6_9_4,
                  0.8_5_1_2_8_1_3,
                  0.8_7_0_1_2_0_7_6,
                  0.8_3_1_2_8_5_7,
              ]

              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(snake_case_						)
              lowerCamelCase_							=pipe(
                  [prompt],	generator=snake_case_,	guidance_scale=snake_case_,	num_inference_steps=50,	output_type='''np''',	width=512,	height=512,	**snake_case_,	)

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.7_3_5_5_3_3_6_5,
                  0.7_5_3_7_2_7_1,
                  0.7_4_3_4_1_9_0_5,
                  0.6_6_4_8_0_3_5_6,
                  0.6_4_7_2_9_2_5,
                  0.6_3_0_3_9_4_1_6,
                  0.6_4_8_1_2_9_0_5,
                  0.6_7_4_9_7_1_7,
                  0.6_5_1_7_1_0_2,
              ]

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

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






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							='''cuda'''
              lowerCamelCase_							=StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''',	torch_dtype=torch.floataa						)
              lowerCamelCase_							=pipe.to(snake_case_						)
              pipe.set_progress_bar_config(disable=snake_case_						)

              lowerCamelCase_							='''a photo of a cat'''
              lowerCamelCase_							={
                  '''editing_prompt''': ['''sunglasses'''],
                  '''reverse_editing_direction''': [False],
                  '''edit_warmup_steps''': 10,
                  '''edit_guidance_scale''': 6,
                  '''edit_threshold''': 0.9_5,
                  '''edit_momentum_scale''': 0.5,
                  '''edit_mom_beta''': 0.6,
              }

              lowerCamelCase_							=3
              lowerCamelCase_							=7

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

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.3_4_8_8_7_6_9_5,
                  0.3_8_7_6_9_5_3,
                  0.3_7_5,
                  0.3_4_4_2_3_8_2_8,
                  0.3_5_8_1_5_4_3,
                  0.3_5_7_1_7_7_7_3,
                  0.3_3_8_3_7_8_9,
                  0.3_4_5_7_0_3_1_2,
                  0.3_5_9_3_7_5,
              ]

              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(snake_case_						)
              lowerCamelCase_							=pipe(
                  [prompt],	generator=snake_case_,	guidance_scale=snake_case_,	num_inference_steps=50,	output_type='''np''',	width=512,	height=512,	**snake_case_,	)

              lowerCamelCase_							=output.images
              lowerCamelCase_							=image[0, -3:, -3:, -1]
              lowerCamelCase_							=[
                  0.4_2_2_8_5_1_5_6,
                  0.3_6_9_1_4_0_6_2,
                  0.2_9_0_7_7_1_4_8,
                  0.4_2_0_4_1_0_1_6,
                  0.4_1_9_1_8_9_4_5,
                  0.3_5_4_9_8_0_4_7,
                  0.3_6_1_8_1_6_4,
                  0.4_4_2_3_8_2_8,
                  0.4_3_1_1_5_2_3_4,
              ]

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

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










'''simple docstring'''




from .hash_table import HashTable


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	*lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              super().__init__(*lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=None						):



              """simple docstring"""







              lowerCamelCase_							=1
              lowerCamelCase_							=self.hash_function(key + i * i						)

              while self.values[new_key] is not None and self.values[new_key] != key:
                     i += 1
                     lowerCamelCase_							=(
                         self.hash_function(key + i * i						)
                         if not self.balanced_factor() >= self.lim_charge
                         else None
                     )

                     if new_key is None:
                            break

              return new_key




