





'''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.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 							lowercase__     (							unittest.TestCase       ):





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	@property
	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       1
							_UpperCamelCase :		List[Any]			    =       3
							_UpperCamelCase :		str			    =       (32, 32)

							_UpperCamelCase :		Tuple			    =       floats_tensor((batch_size, num_channels) + sizes			,rng=random.Random(0			)			).to(lowerCamelCase__			)
							return image





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







							'''simple docstring'''
							torch.manual_seed(0			)
							_UpperCamelCase :		List[Any]			    =       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      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							torch.manual_seed(0			)
							_UpperCamelCase :		Optional[int]			    =       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      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							torch.manual_seed(0			)
							_UpperCamelCase :		Tuple			    =       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(lowerCamelCase__			)





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







							'''simple docstring'''
							def extract(*lowerCamelCase__ :    Optional[int]			,**lowerCamelCase__ :    List[str]			):






													class 							lowercase__     :





														def __init__(			self :    List[Any]			):







																				'''simple docstring'''
																				_UpperCamelCase :		List[Any]			    =       torch.ones([0]			)





														def      UpperCamelCase_  (			self :    int			,lowerCamelCase__ :    Optional[int]			):







																				'''simple docstring'''
																				self.pixel_values.to(lowerCamelCase__			)
																				return self
													return Out()

							return extract





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase :		List[Any]			    =       self.dummy_cond_unet
							_UpperCamelCase :		Union[str, Any]			    =       DDIMScheduler(
							    beta_start=0.0_0_0_8_5			,beta_end=0.0_1_2			,beta_schedule='scaled_linear'			,clip_sample=lowerCamelCase__			,set_alpha_to_one=lowerCamelCase__			,)

							_UpperCamelCase :		Union[str, Any]			    =       self.dummy_vae
							_UpperCamelCase :		Optional[Any]			    =       self.dummy_text_encoder
							_UpperCamelCase :		List[str]			    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

							# make sure here that pndm scheduler skips prk
							_UpperCamelCase :		Optional[Any]			    =       StableDiffusionPipeline(
							    unet=lowerCamelCase__			,scheduler=lowerCamelCase__			,vae=lowerCamelCase__			,text_encoder=lowerCamelCase__			,tokenizer=lowerCamelCase__			,safety_checker=lowerCamelCase__			,feature_extractor=self.dummy_extractor			,)
							_UpperCamelCase :		Any			    =       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		List[Any]			    =       'A painting of a squirrel eating a burger'

							_UpperCamelCase :		Tuple			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(0			)
							_UpperCamelCase :		Tuple			    =       sd_pipe([prompt]			,generator=lowerCamelCase__			,guidance_scale=6.0			,num_inference_steps=2			,output_type='np'			)
							_UpperCamelCase :		Optional[Any]			    =       output.images

							_UpperCamelCase :		int			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(0			)
							_UpperCamelCase :		List[str]			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=6.0			,num_inference_steps=2			,output_type='np'			,return_dict=lowerCamelCase__			,)[0]

							_UpperCamelCase :		str			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		Optional[int]			    =       image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							_UpperCamelCase :		str			    =       np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_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      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCamelCase :		List[str]			    =       self.dummy_cond_unet
							_UpperCamelCase :		Dict			    =       PNDMScheduler(skip_prk_steps=lowerCamelCase__			)
							_UpperCamelCase :		Any			    =       self.dummy_vae
							_UpperCamelCase :		List[Any]			    =       self.dummy_text_encoder
							_UpperCamelCase :		List[str]			    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

							# make sure here that pndm scheduler skips prk
							_UpperCamelCase :		List[str]			    =       StableDiffusionPipeline(
							    unet=lowerCamelCase__			,scheduler=lowerCamelCase__			,vae=lowerCamelCase__			,text_encoder=lowerCamelCase__			,tokenizer=lowerCamelCase__			,safety_checker=lowerCamelCase__			,feature_extractor=self.dummy_extractor			,)
							_UpperCamelCase :		int			    =       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Tuple			    =       'A painting of a squirrel eating a burger'
							_UpperCamelCase :		List[Any]			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(0			)
							_UpperCamelCase :		Union[str, Any]			    =       sd_pipe([prompt]			,generator=lowerCamelCase__			,guidance_scale=6.0			,num_inference_steps=2			,output_type='np'			)

							_UpperCamelCase :		Optional[Any]			    =       output.images

							_UpperCamelCase :		Optional[Any]			    =       torch.Generator(device=lowerCamelCase__			).manual_seed(0			)
							_UpperCamelCase :		int			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=6.0			,num_inference_steps=2			,output_type='np'			,return_dict=lowerCamelCase__			,)[0]

							_UpperCamelCase :		Optional[Any]			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		Any			    =       image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							_UpperCamelCase :		str			    =       np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]			)

							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      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       StableDiffusionPipeline.from_pretrained(
							    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'			,safety_checker=lowerCamelCase__			)
							assert isinstance(lowerCamelCase__			,lowerCamelCase__			)
							assert isinstance(pipe.scheduler			,lowerCamelCase__			)
							assert pipe.safety_checker is None

							_UpperCamelCase :		int			    =       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(lowerCamelCase__			)
													_UpperCamelCase :		Optional[int]			    =       StableDiffusionPipeline.from_pretrained(lowerCamelCase__			)

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							_UpperCamelCase :		List[Any]			    =       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      UpperCamelCase_  (			self :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       self.dummy_cond_unet
							_UpperCamelCase :		Optional[Any]			    =       PNDMScheduler(skip_prk_steps=lowerCamelCase__			)
							_UpperCamelCase :		str			    =       self.dummy_vae
							_UpperCamelCase :		Tuple			    =       self.dummy_text_encoder
							_UpperCamelCase :		Tuple			    =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

							# put models in fp16
							_UpperCamelCase :		List[Any]			    =       unet.half()
							_UpperCamelCase :		Optional[Any]			    =       vae.half()
							_UpperCamelCase :		int			    =       bert.half()

							# make sure here that pndm scheduler skips prk
							_UpperCamelCase :		str			    =       StableDiffusionPipeline(
							    unet=lowerCamelCase__			,scheduler=lowerCamelCase__			,vae=lowerCamelCase__			,text_encoder=lowerCamelCase__			,tokenizer=lowerCamelCase__			,safety_checker=lowerCamelCase__			,feature_extractor=self.dummy_extractor			,)
							_UpperCamelCase :		List[str]			    =       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		int			    =       'A painting of a squirrel eating a burger'
							_UpperCamelCase :		Union[str, Any]			    =       sd_pipe([prompt]			,num_inference_steps=2			,output_type='np'			).images

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






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





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'			,safety_checker=lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config			)
							_UpperCamelCase :		Dict			    =       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		List[str]			    =       (
							    '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 '
							)
							_UpperCamelCase :		Optional[Any]			    =       4003660346
							_UpperCamelCase :		Dict			    =       7

							# without safety guidance (sld_guidance_scale = 0)
							_UpperCamelCase :		List[Any]			    =       torch.manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		Dict			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=lowerCamelCase__			,num_inference_steps=50			,output_type='np'			,width=512			,height=512			,sld_guidance_scale=0			,)

							_UpperCamelCase :		Optional[int]			    =       output.images
							_UpperCamelCase :		Union[str, Any]			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		Tuple			    =       [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

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

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

							# without safety guidance (strong configuration)
							_UpperCamelCase :		List[Any]			    =       torch.manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=lowerCamelCase__			,num_inference_steps=50			,output_type='np'			,width=512			,height=512			,sld_guidance_scale=2000			,sld_warmup_steps=7			,sld_threshold=0.0_2_5			,sld_momentum_scale=0.5			,sld_mom_beta=0.7			,)

							_UpperCamelCase :		int			    =       output.images
							_UpperCamelCase :		Optional[int]			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		Tuple			    =       [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

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

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





	def      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'			,safety_checker=lowerCamelCase__			)
							_UpperCamelCase :		Optional[int]			    =       LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config			)
							_UpperCamelCase :		List[str]			    =       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       'padme amidala taking a bath artwork, safe for work, no nudity'
							_UpperCamelCase :		int			    =       2734971755
							_UpperCamelCase :		Tuple			    =       7

							_UpperCamelCase :		List[Any]			    =       torch.manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=lowerCamelCase__			,num_inference_steps=50			,output_type='np'			,width=512			,height=512			,sld_guidance_scale=0			,)

							_UpperCamelCase :		List[str]			    =       output.images
							_UpperCamelCase :		Tuple			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		List[Any]			    =       [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

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

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

							_UpperCamelCase :		str			    =       torch.manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		int			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=lowerCamelCase__			,num_inference_steps=50			,output_type='np'			,width=512			,height=512			,sld_guidance_scale=2000			,sld_warmup_steps=7			,sld_threshold=0.0_2_5			,sld_momentum_scale=0.5			,sld_mom_beta=0.7			,)

							_UpperCamelCase :		Union[str, Any]			    =       output.images
							_UpperCamelCase :		Any			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		List[str]			    =       [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

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

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





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		List[Any]			    =       StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'			)
							_UpperCamelCase :		Optional[Any]			    =       sd_pipe.to(lowerCamelCase__			)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__			)

							_UpperCamelCase :		List[Any]			    =       (
							    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
							    ' leyendecker'
							)
							_UpperCamelCase :		Dict			    =       1044355234
							_UpperCamelCase :		List[Any]			    =       12

							_UpperCamelCase :		List[Any]			    =       torch.manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		Union[str, Any]			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=lowerCamelCase__			,num_inference_steps=50			,output_type='np'			,width=512			,height=512			,sld_guidance_scale=0			,)

							_UpperCamelCase :		List[str]			    =       output.images
							_UpperCamelCase :		Tuple			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		Any			    =       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

							_UpperCamelCase :		int			    =       torch.manual_seed(lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       sd_pipe(
							    [prompt]			,generator=lowerCamelCase__			,guidance_scale=lowerCamelCase__			,num_inference_steps=50			,output_type='np'			,width=512			,height=512			,sld_guidance_scale=2000			,sld_warmup_steps=7			,sld_threshold=0.0_2_5			,sld_momentum_scale=0.5			,sld_mom_beta=0.7			,)

							_UpperCamelCase :		int			    =       output.images
							_UpperCamelCase :		Any			    =       image[0, -3:, -3:, -1]
							_UpperCamelCase :		Any			    =       np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]			)
							assert image.shape == (1, 512, 512, 3)

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







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


snake_case_			:     Optional[int]  	=  {
    'configuration_mask2former': [
        'MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Mask2FormerConfig',
    ],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     int  	=  ['Mask2FormerImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_			:     List[Any]  	=  [
					    'MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'Mask2FormerForUniversalSegmentation',
					    'Mask2FormerModel',
					    'Mask2FormerPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_maskaformer import MaskaFormerImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_maskaformer import (
										    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MaskaFormerForUniversalSegmentation,
										    MaskaFormerModel,
										    MaskaFormerPreTrainedModel,
										)


else:
					import sys

					snake_case_			:     Dict  	=  _LazyModule(__name__, globals()['__file__'], _import_structure)


