



'''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      __magic_name__						(   unittest.TestCase       ):







	def 							__lowercase      (							self :	Optional[Any]		):
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







	@property
	def 							__lowercase      (							self :	int		):
			_a					:				Union[str, Any]				=							1
			_a					:				List[str]				=							3
			_a					:				Dict				=							(32, 32)

			_a					:				Optional[int]				=							floats_tensor((batch_size, num_channels) + sizes     ,rng=random.Random(0		)		).to(__UpperCAmelCase		)
			return image







	@property
	def 							__lowercase      (							self :	Dict		):
			torch.manual_seed(0		)
			_a					:				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 							__lowercase      (							self :	List[str]		):
			torch.manual_seed(0		)
			_a					:				List[str]				=							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 :	Optional[int]		):
			torch.manual_seed(0		)
			_a					:				Dict				=							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(__UpperCAmelCase		)







	@property
	def 							__lowercase      (							self :	Optional[Any]		):
			def extract(*_UpperCAmelCase :	Dict     ,**_UpperCAmelCase :	Union[str, Any]		):

					class      __magic_name__						:







						def __init__(							self :	List[str]		):
								_a					:				str				=							torch.ones([0]		)







						def 							__lowercase      (							self :	Dict     ,_UpperCAmelCase :	List[Any]		):
								self.pixel_values.to(__UpperCAmelCase		)
								return self
					return Out()

			return extract







	def 							__lowercase      (							self :	Union[str, Any]		):
			_a					:				Any				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
			_a					:				Union[str, Any]				=							self.dummy_cond_unet
			_a					:				Optional[int]				=							DDIMScheduler(
			    beta_start=0.0_00_85     ,beta_end=0.0_12     ,beta_schedule='scaled_linear'     ,clip_sample=__UpperCAmelCase     ,set_alpha_to_one=__UpperCAmelCase     ,)

			_a					:				int				=							self.dummy_vae
			_a					:				Tuple				=							self.dummy_text_encoder
			_a					:				Optional[Any]				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

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

			_a					:				Optional[Any]				=							"""A painting of a squirrel eating a burger"""

			_a					:				str				=							torch.Generator(device=__UpperCAmelCase		).manual_seed(0		)
			_a					:				List[Any]				=							sd_pipe([prompt]     ,generator=__UpperCAmelCase     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type='np'		)
			_a					:				Union[str, Any]				=							output.images

			_a					:				Tuple				=							torch.Generator(device=__UpperCAmelCase		).manual_seed(0		)
			_a					:				str				=							sd_pipe(
			    [prompt]     ,generator=__UpperCAmelCase     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type='np'     ,return_dict=__UpperCAmelCase     ,)[0]

			_a					:				str				=							image[0, -3:, -3:, -1]
			_a					:				List[Any]				=							image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			_a					:				List[Any]				=							np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64]		)

			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 :	int		):
			_a					:				str				=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
			_a					:				List[str]				=							self.dummy_cond_unet
			_a					:				Union[str, Any]				=							PNDMScheduler(skip_prk_steps=__UpperCAmelCase		)
			_a					:				str				=							self.dummy_vae
			_a					:				List[str]				=							self.dummy_text_encoder
			_a					:				Optional[int]				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

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

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

			_a					:				Any				=							output.images

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

			_a					:				str				=							image[0, -3:, -3:, -1]
			_a					:				str				=							image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			_a					:				List[str]				=							np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93]		)

			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 :	Any		):
			_a					:				List[Any]				=							StableDiffusionPipeline.from_pretrained(
			    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'     ,safety_checker=__UpperCAmelCase		)
			assert isinstance(__UpperCAmelCase     ,__UpperCAmelCase		)
			assert isinstance(pipe.scheduler     ,__UpperCAmelCase		)
			assert pipe.safety_checker is None

			_a					:				List[Any]				=							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(__UpperCAmelCase		)
					_a					:				Dict				=							StableDiffusionPipeline.from_pretrained(__UpperCAmelCase		)

			# sanity check that the pipeline still works
			assert pipe.safety_checker is None
			_a					:				int				=							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 :	Tuple		):
			_a					:				List[str]				=							self.dummy_cond_unet
			_a					:				Optional[int]				=							PNDMScheduler(skip_prk_steps=__UpperCAmelCase		)
			_a					:				List[str]				=							self.dummy_vae
			_a					:				str				=							self.dummy_text_encoder
			_a					:				Union[str, Any]				=							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

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

			# make sure here that pndm scheduler skips prk
			_a					:				int				=							StableDiffusionPipeline(
			    unet=__UpperCAmelCase     ,scheduler=__UpperCAmelCase     ,vae=__UpperCAmelCase     ,text_encoder=__UpperCAmelCase     ,tokenizer=__UpperCAmelCase     ,safety_checker=__UpperCAmelCase     ,feature_extractor=self.dummy_extractor     ,)
			_a					:				Any				=							sd_pipe.to(__UpperCAmelCase		)
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

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

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

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







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







	def 							__lowercase      (							self :	Optional[int]		):
			_a					:				Union[str, Any]				=							StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'     ,safety_checker=__UpperCAmelCase		)
			_a					:				List[str]				=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
			_a					:				List[Any]				=							sd_pipe.to(__UpperCAmelCase		)
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

			_a					:				Tuple				=							(
			    """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 """
			)
			_a					:				int				=							4003660346
			_a					:				List[str]				=							7

			# without safety guidance (sld_guidance_scale = 0)
			_a					:				List[str]				=							torch.manual_seed(__UpperCAmelCase		)
			_a					:				Tuple				=							sd_pipe(
			    [prompt]     ,generator=__UpperCAmelCase     ,guidance_scale=__UpperCAmelCase     ,num_inference_steps=50     ,output_type='np'     ,width=512     ,height=512     ,sld_guidance_scale=0     ,)

			_a					:				Union[str, Any]				=							output.images
			_a					:				Dict				=							image[0, -3:, -3:, -1]
			_a					:				Union[str, Any]				=							[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

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

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

			# without safety guidance (strong configuration)
			_a					:				str				=							torch.manual_seed(__UpperCAmelCase		)
			_a					:				Tuple				=							sd_pipe(
			    [prompt]     ,generator=__UpperCAmelCase     ,guidance_scale=__UpperCAmelCase     ,num_inference_steps=50     ,output_type='np'     ,width=512     ,height=512     ,sld_guidance_scale=2000     ,sld_warmup_steps=7     ,sld_threshold=0.0_25     ,sld_momentum_scale=0.5     ,sld_mom_beta=0.7     ,)

			_a					:				Optional[int]				=							output.images
			_a					:				Dict				=							image[0, -3:, -3:, -1]
			_a					:				List[str]				=							[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

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

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







	def 							__lowercase      (							self :	List[str]		):
			_a					:				List[str]				=							StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'     ,safety_checker=__UpperCAmelCase		)
			_a					:				Any				=							LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
			_a					:				int				=							sd_pipe.to(__UpperCAmelCase		)
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

			_a					:				Dict				=							"""padme amidala taking a bath artwork, safe for work, no nudity"""
			_a					:				Optional[Any]				=							2734971755
			_a					:				Any				=							7

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

			_a					:				Optional[Any]				=							output.images
			_a					:				Dict				=							image[0, -3:, -3:, -1]
			_a					:				Optional[Any]				=							[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

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

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

			_a					:				Any				=							torch.manual_seed(__UpperCAmelCase		)
			_a					:				str				=							sd_pipe(
			    [prompt]     ,generator=__UpperCAmelCase     ,guidance_scale=__UpperCAmelCase     ,num_inference_steps=50     ,output_type='np'     ,width=512     ,height=512     ,sld_guidance_scale=2000     ,sld_warmup_steps=7     ,sld_threshold=0.0_25     ,sld_momentum_scale=0.5     ,sld_mom_beta=0.7     ,)

			_a					:				str				=							output.images
			_a					:				Union[str, Any]				=							image[0, -3:, -3:, -1]
			_a					:				List[Any]				=							[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

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

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







	def 							__lowercase      (							self :	Union[str, Any]		):
			_a					:				Union[str, Any]				=							StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'		)
			_a					:				List[Any]				=							sd_pipe.to(__UpperCAmelCase		)
			sd_pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

			_a					:				Optional[Any]				=							(
			    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
			    """ leyendecker"""
			)
			_a					:				Optional[int]				=							1044355234
			_a					:				int				=							12

			_a					:				Any				=							torch.manual_seed(__UpperCAmelCase		)
			_a					:				Optional[Any]				=							sd_pipe(
			    [prompt]     ,generator=__UpperCAmelCase     ,guidance_scale=__UpperCAmelCase     ,num_inference_steps=50     ,output_type='np'     ,width=512     ,height=512     ,sld_guidance_scale=0     ,)

			_a					:				int				=							output.images
			_a					:				Union[str, Any]				=							image[0, -3:, -3:, -1]
			_a					:				List[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

			_a					:				List[str]				=							torch.manual_seed(__UpperCAmelCase		)
			_a					:				Union[str, Any]				=							sd_pipe(
			    [prompt]     ,generator=__UpperCAmelCase     ,guidance_scale=__UpperCAmelCase     ,num_inference_steps=50     ,output_type='np'     ,width=512     ,height=512     ,sld_guidance_scale=2000     ,sld_warmup_steps=7     ,sld_threshold=0.0_25     ,sld_momentum_scale=0.5     ,sld_mom_beta=0.7     ,)

			_a					:				Tuple				=							output.images
			_a					:				int				=							image[0, -3:, -3:, -1]
			_a					:				List[str]				=							np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61]		)
			assert image.shape == (1, 512, 512, 3)

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









'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


__lowerCAmelCase						      =       Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
__lowerCAmelCase						      =       sorted({word.strip().lower() for word in data.splitlines()})

__lowerCAmelCase						      =       collections.defaultdict(list)
for word in word_list:
							word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
							__lowerCAmelCase						      =       {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

							with open('''anagrams.txt''', '''w''') as file:
														file.write('''all_anagrams = \n ''')
														file.write(pprint.pformat(all_anagrams))




