





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 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :int		) -> str:



									"""simple docstring"""



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


							@property
							def   _snake_case	(				self     :Any		) -> Tuple:



									"""simple docstring"""



									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(__A		)
									return image


							@property
							def   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									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   _snake_case	(				self     :List[Any]		) -> int:



									"""simple docstring"""



									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   _snake_case	(				self     :Union[str, Any]		) -> str:



									"""simple docstring"""



									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=1000				,					)
									return CLIPTextModel(__A		)


							@property
							def   _snake_case	(				self     :Any		) -> Dict:



									"""simple docstring"""



									def extract(*__A     :Tuple				,					**__A     :str		):




											class 	UpperCamelCase_							:


																		def __init__(				self     :Union[str, Any]		) -> Optional[int]:



																				"""simple docstring"""



																				SCREAMING_SNAKE_CASE__								=   torch.ones([0]		)


																		def   _snake_case	(				self     :Dict				,					__A     :Tuple		) -> Any:



																				"""simple docstring"""



																				self.pixel_values.to(__A		)
																				return self



											return Out()

									return extract


							def   _snake_case	(				self     :Optional[int]		) -> int:



									"""simple docstring"""



									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_0_0_8_5				,					beta_end=0.0_1_2				,					beta_schedule="""scaled_linear"""				,					clip_sample=__A				,					set_alpha_to_one=__A				,					)

									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=__A				,					scheduler=__A				,					vae=__A				,					text_encoder=__A				,					tokenizer=__A				,					safety_checker=__A				,					feature_extractor=self.dummy_extractor				,					)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

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

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

									SCREAMING_SNAKE_CASE__								=   torch.Generator(device=__A		).manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=6.0				,					num_inference_steps=2				,					output_type="""np"""				,					return_dict=__A				,					)[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([0.5_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   _snake_case	(				self     :int		) -> str:



									"""simple docstring"""



									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=__A		)
									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=__A				,					scheduler=__A				,					vae=__A				,					text_encoder=__A				,					tokenizer=__A				,					safety_checker=__A				,					feature_extractor=self.dummy_extractor				,					)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

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

									SCREAMING_SNAKE_CASE__								=   output.images

									SCREAMING_SNAKE_CASE__								=   torch.Generator(device=__A		).manual_seed(0		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=6.0				,					num_inference_steps=2				,					output_type="""np"""				,					return_dict=__A				,					)[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([0.5_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   _snake_case	(				self     :Tuple		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionPipeline.from_pretrained(
									    """hf-internal-testing/tiny-stable-diffusion-lms-pipe"""				,					safety_checker=__A		)
									assert isinstance(__A				,					__A		)
									assert isinstance(pipe.scheduler				,					__A		)
									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(__A		)
											SCREAMING_SNAKE_CASE__								=   StableDiffusionPipeline.from_pretrained(__A		)

									# 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   _snake_case	(				self     :Any		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.dummy_cond_unet
									SCREAMING_SNAKE_CASE__								=   PNDMScheduler(skip_prk_steps=__A		)
									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=__A				,					scheduler=__A				,					vae=__A				,					text_encoder=__A				,					tokenizer=__A				,					safety_checker=__A				,					feature_extractor=self.dummy_extractor				,					)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									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 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :int		) -> Optional[int]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Dict		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,					safety_checker=__A		)
									SCREAMING_SNAKE_CASE__								=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									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__								=   40_0366_0346
									SCREAMING_SNAKE_CASE__								=   7

									# without safety guidance (sld_guidance_scale = 0)
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(__A		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=__A				,					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__								=   [0.2_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)
									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(__A		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=__A				,					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				,					)

									SCREAMING_SNAKE_CASE__								=   output.images
									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE__								=   [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   _snake_case	(				self     :Any		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				,					safety_checker=__A		)
									SCREAMING_SNAKE_CASE__								=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__								=   """padme amidala taking a bath artwork, safe for work, no nudity"""
									SCREAMING_SNAKE_CASE__								=   27_3497_1755
									SCREAMING_SNAKE_CASE__								=   7

									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(__A		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=__A				,					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__								=   [0.3_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

									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(__A		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=__A				,					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				,					)

									SCREAMING_SNAKE_CASE__								=   output.images
									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE__								=   [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   _snake_case	(				self     :Dict		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe.to(__A		)
									sd_pipe.set_progress_bar_config(disable=__A		)

									SCREAMING_SNAKE_CASE__								=   (
									    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
									    """ leyendecker"""
									)
									SCREAMING_SNAKE_CASE__								=   10_4435_5234
									SCREAMING_SNAKE_CASE__								=   12

									SCREAMING_SNAKE_CASE__								=   torch.manual_seed(__A		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=__A				,					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(__A		)
									SCREAMING_SNAKE_CASE__								=   sd_pipe(
									    [prompt]				,					generator=__A				,					guidance_scale=__A				,					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				,					)

									SCREAMING_SNAKE_CASE__								=   output.images
									SCREAMING_SNAKE_CASE__								=   image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE__								=   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






import unittest

from diffusers.pipelines.pipeline_utils import is_safetensors_compatible




class 	UpperCamelCase_							(  unittest.TestCase     ):


							def   _snake_case	(				self     :Tuple		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.bin""",
									    """safety_checker/model.safetensors""",
									    """vae/diffusion_pytorch_model.bin""",
									    """vae/diffusion_pytorch_model.safetensors""",
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									self.assertTrue(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :List[str]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									self.assertTrue(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.bin""",
									    """safety_checker/model.safetensors""",
									    """vae/diffusion_pytorch_model.bin""",
									    """vae/diffusion_pytorch_model.safetensors""",
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									    """unet/diffusion_pytorch_model.bin""",
									    # Removed: 'unet/diffusion_pytorch_model.safetensors',
									]
									self.assertFalse(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									]
									self.assertTrue(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Optional[Any]		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.bin""",
									    """safety_checker/model.safetensors""",
									    """vae/diffusion_pytorch_model.bin""",
									    """vae/diffusion_pytorch_model.safetensors""",
									    """text_encoder/pytorch_model.bin""",
									    # Removed: 'text_encoder/model.safetensors',
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									self.assertFalse(is_safetensors_compatible(__A		)		)


							def   _snake_case	(				self     :Tuple		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.fp16.bin""",
									    """safety_checker/model.fp16.safetensors""",
									    """vae/diffusion_pytorch_model.fp16.bin""",
									    """vae/diffusion_pytorch_model.fp16.safetensors""",
									    """text_encoder/pytorch_model.fp16.bin""",
									    """text_encoder/model.fp16.safetensors""",
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    """unet/diffusion_pytorch_model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :Any		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    """unet/diffusion_pytorch_model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :str		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """unet/diffusion_pytorch_model.bin""",
									    """unet/diffusion_pytorch_model.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :List[Any]		) -> List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.fp16.bin""",
									    """safety_checker/model.fp16.safetensors""",
									    """vae/diffusion_pytorch_model.fp16.bin""",
									    """vae/diffusion_pytorch_model.fp16.safetensors""",
									    """text_encoder/pytorch_model.fp16.bin""",
									    """text_encoder/model.fp16.safetensors""",
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    # Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertFalse(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :str		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """text_encoder/pytorch_model.fp16.bin""",
									    """text_encoder/model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :Optional[int]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """text_encoder/pytorch_model.bin""",
									    """text_encoder/model.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertTrue(is_safetensors_compatible(__A				,					variant=__A		)		)


							def   _snake_case	(				self     :Union[str, Any]		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [
									    """safety_checker/pytorch_model.fp16.bin""",
									    """safety_checker/model.fp16.safetensors""",
									    """vae/diffusion_pytorch_model.fp16.bin""",
									    """vae/diffusion_pytorch_model.fp16.safetensors""",
									    """text_encoder/pytorch_model.fp16.bin""",
									    # 'text_encoder/model.fp16.safetensors',
									    """unet/diffusion_pytorch_model.fp16.bin""",
									    """unet/diffusion_pytorch_model.fp16.safetensors""",
									]
									SCREAMING_SNAKE_CASE__								=   """fp16"""
									self.assertFalse(is_safetensors_compatible(__A				,					variant=__A		)		)