


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			):






				'''simple docstring'''



				def 						__magic_name__(			self     :List[Any]      )						->     str:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


				@property
				def 						__magic_name__(			self     :List[str]      )						->     List[Any]:
										__SCREAMING_SNAKE_CASE		:	Dict						     =						1
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						3
										__SCREAMING_SNAKE_CASE		:	int						     =						(32, 32)

										__SCREAMING_SNAKE_CASE		:	List[str]						     =						floats_tensor((batch_size, num_channels) + sizes     ,     rng=random.Random(0      )      ).to(snake_case_      )
										return image


				@property
				def 						__magic_name__(			self     :Union[str, Any]      )						->     List[str]:
										torch.manual_seed(0      )
										__SCREAMING_SNAKE_CASE		:	str						     =						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 						__magic_name__(			self     :Optional[Any]      )						->     str:
										torch.manual_seed(0      )
										__SCREAMING_SNAKE_CASE		:	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 						__magic_name__(			self     :Dict      )						->     Dict:
										torch.manual_seed(0      )
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						CLIPTextConfig(
										    bos_token_id=0     ,     eos_token_id=2     ,     hidden_size=32     ,     intermediate_size=37     ,     layer_norm_eps=1E-0_5     ,     num_attention_heads=4     ,     num_hidden_layers=5     ,     pad_token_id=1     ,     vocab_size=1_000     ,     )
										return CLIPTextModel(snake_case_      )


				@property
				def 						__magic_name__(			self     :str      )						->     Union[str, Any]:
										def extract(*lowerCAmelCase__     :List[Any]     ,     **lowerCAmelCase__     :Optional[int]      ):
																class 			_lowercase :






																				'''simple docstring'''



																				def __init__(			self     :int      )						->     int:
																										__SCREAMING_SNAKE_CASE		:	Tuple						     =						torch.ones([0]      )



																				def 						__magic_name__(			self     :Optional[Any]     ,     lowerCAmelCase__     :Optional[Any]      )						->     int:
																										self.pixel_values.to(snake_case_      )
																										return self




																return Out()

										return extract


				def 						__magic_name__(			self     :Dict      )						->     Any:
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE		:	List[str]						     =						self.dummy_cond_unet
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						DDIMScheduler(
										    beta_start=0.0_0085     ,     beta_end=0.012     ,     beta_schedule='''scaled_linear'''     ,     clip_sample=snake_case_     ,     set_alpha_to_one=snake_case_     ,     )

										__SCREAMING_SNAKE_CASE		:	Tuple						     =						self.dummy_vae
										__SCREAMING_SNAKE_CASE		:	str						     =						self.dummy_text_encoder
										__SCREAMING_SNAKE_CASE		:	List[str]						     =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

										# make sure here that pndm scheduler skips prk
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						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     ,     )
										__SCREAMING_SNAKE_CASE		:	str						     =						sd_pipe.to(snake_case_      )
										sd_pipe.set_progress_bar_config(disable=snake_case_      )

										__SCREAMING_SNAKE_CASE		:	int						     =						'''A painting of a squirrel eating a burger'''

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						torch.Generator(device=snake_case_      ).manual_seed(0      )
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						sd_pipe([prompt]     ,     generator=snake_case_     ,     guidance_scale=6.0     ,     num_inference_steps=2     ,     output_type='''np'''      )
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						output.images

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

										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										__SCREAMING_SNAKE_CASE		:	List[str]						     =						np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]      )

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

										# make sure here that pndm scheduler skips prk
										__SCREAMING_SNAKE_CASE		:	Any						     =						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     ,     )
										__SCREAMING_SNAKE_CASE		:	Dict						     =						sd_pipe.to(snake_case_      )
										sd_pipe.set_progress_bar_config(disable=snake_case_      )

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

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						output.images

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

										__SCREAMING_SNAKE_CASE		:	Any						     =						image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE		:	Dict						     =						image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]      )

										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 						__magic_name__(			self     :Union[str, Any]      )						->     Optional[int]:
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						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

										__SCREAMING_SNAKE_CASE		:	Dict						     =						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_      )
																__SCREAMING_SNAKE_CASE		:	int						     =						StableDiffusionPipeline.from_pretrained(snake_case_      )

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

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

										# make sure here that pndm scheduler skips prk
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						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     ,     )
										__SCREAMING_SNAKE_CASE		:	str						     =						sd_pipe.to(snake_case_      )
										sd_pipe.set_progress_bar_config(disable=snake_case_      )

										__SCREAMING_SNAKE_CASE		:	str						     =						'''A painting of a squirrel eating a burger'''
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						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			):






				'''simple docstring'''



				def 						__magic_name__(			self     :List[str]      )						->     Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


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

										__SCREAMING_SNAKE_CASE		:	Any						     =						(
										    '''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		:	Optional[Any]						     =						4_003_660_346
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						7

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

										__SCREAMING_SNAKE_CASE		:	List[str]						     =						output.images
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE		:	Dict						     =						[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

										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		:	Dict						     =						torch.manual_seed(snake_case_      )
										__SCREAMING_SNAKE_CASE		:	Dict						     =						sd_pipe(
										    [prompt]     ,     generator=snake_case_     ,     guidance_scale=snake_case_     ,     num_inference_steps=50     ,     output_type='''np'''     ,     width=512     ,     height=512     ,     sld_guidance_scale=2_000     ,     sld_warmup_steps=7     ,     sld_threshold=0.025     ,     sld_momentum_scale=0.5     ,     sld_mom_beta=0.7     ,     )

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						output.images
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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


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

										__SCREAMING_SNAKE_CASE		:	Tuple						     =						'''padme amidala taking a bath artwork, safe for work, no nudity'''
										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						2_734_971_755
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						7

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						torch.manual_seed(snake_case_      )
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						sd_pipe(
										    [prompt]     ,     generator=snake_case_     ,     guidance_scale=snake_case_     ,     num_inference_steps=50     ,     output_type='''np'''     ,     width=512     ,     height=512     ,     sld_guidance_scale=0     ,     )

										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						output.images
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE		:	Any						     =						[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

										__SCREAMING_SNAKE_CASE		:	List[str]						     =						torch.manual_seed(snake_case_      )
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						sd_pipe(
										    [prompt]     ,     generator=snake_case_     ,     guidance_scale=snake_case_     ,     num_inference_steps=50     ,     output_type='''np'''     ,     width=512     ,     height=512     ,     sld_guidance_scale=2_000     ,     sld_warmup_steps=7     ,     sld_threshold=0.025     ,     sld_momentum_scale=0.5     ,     sld_mom_beta=0.7     ,     )

										__SCREAMING_SNAKE_CASE		:	int						     =						output.images
										__SCREAMING_SNAKE_CASE		:	List[Any]						     =						image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE		:	str						     =						[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

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

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



				def 						__magic_name__(			self     :Dict      )						->     List[Any]:
										__SCREAMING_SNAKE_CASE		:	Dict						     =						StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''      )
										__SCREAMING_SNAKE_CASE		:	List[str]						     =						sd_pipe.to(snake_case_      )
										sd_pipe.set_progress_bar_config(disable=snake_case_      )

										__SCREAMING_SNAKE_CASE		:	Tuple						     =						(
										    '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
										    ''' leyendecker'''
										)
										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						1_044_355_234
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						12

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

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

										__SCREAMING_SNAKE_CASE		:	Union[str, Any]						     =						torch.manual_seed(snake_case_      )
										__SCREAMING_SNAKE_CASE		:	Tuple						     =						sd_pipe(
										    [prompt]     ,     generator=snake_case_     ,     guidance_scale=snake_case_     ,     num_inference_steps=50     ,     output_type='''np'''     ,     width=512     ,     height=512     ,     sld_guidance_scale=2_000     ,     sld_warmup_steps=7     ,     sld_threshold=0.025     ,     sld_momentum_scale=0.5     ,     sld_mom_beta=0.7     ,     )

										__SCREAMING_SNAKE_CASE		:	Optional[int]						     =						output.images
										__SCREAMING_SNAKE_CASE		:	Any						     =						image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE		:	Optional[Any]						     =						np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]      )
										assert image.shape == (1, 512, 512, 3)

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


"""simple docstring"""
import warnings

from ..trainer import Trainer
from ..utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							def __init__( self    ,				snake_case_=None    ,				**snake_case_				)       ->  Optional[Any]:
													warnings.warn(
													    """`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
													    """instead."""    ,				snake_case_    ,				)
													super().__init__(args=snake_case_    ,				**snake_case_				)
