


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


__lowerCamelCase   	=  torch.cuda.is_available()
__lowerCamelCase   	=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowerCamelCase   	=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowerCamelCase   	=  {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  str				      =     """\n""".join(__UpperCamelCase			)
     Path(__UpperCamelCase			).open("""w"""			).writelines(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase			):
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__UpperCamelCase	,	F'''{split}.source'''			)	,	__UpperCamelCase			)
          _dump_articles(os.path.join(__UpperCamelCase	,	F'''{split}.target'''			)	,	__UpperCamelCase			)
     return tmp_dir


class 	a__						(    lowerCAmelCase_     ):

       @classmethod
       def     UpperCAmelCase( cls   :  Any     ):
            return cls

       @require_torch_multi_gpu
       def     UpperCAmelCase( self   :  Any     ):
            a_     :  Any				      =     {
                """no_teacher""": True,
                """freeze_encoder""": True,
                """gpus""": 2,
                """overwrite_output_dir""": True,
                """sortish_sampler""": True,
            }
            self._test_distiller_cli_fork(lowerCamelCase_   ,				check_contents=lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str=True     ):
            a_     :  Union[str, Any]				      =     {
                """label_smoothing""": 0.0,
                """early_stopping_patience""": -1,
                """train_batch_size""": 1,
                """eval_batch_size""": 2,
                """max_epochs""": 2,
                """alpha_mlm""": 0.2,
                """alpha_ce""": 0.8,
                """do_predict""": True,
                """model_name_or_path""": """sshleifer/tinier_bart""",
                """teacher""": CHEAP_ARGS["""model_name_or_path"""],
                """val_check_interval""": 0.5,
            }
            default_updates.update(lowerCamelCase_     )
            a_     :  dict				      =     CHEAP_ARGS.copy()
            a_     :  Union[str, Any]				      =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
            a_     :  Union[str, Any]				      =     self.get_auto_remove_tmp_dir()
            args_d.update(data_dir=lowerCamelCase_   ,				output_dir=lowerCamelCase_   ,				**lowerCamelCase_     )

            def convert(lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Dict     ):
                 if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                      return ""
                 if v is False or v is None:
                      return ""
                 if v is True:  # or len(str(v))==0:
                      return F'''--{k}'''
                 return F'''--{k}={v}'''

            a_     :  Optional[int]				      =     [x for x in (convert(lowerCamelCase_   ,				lowerCamelCase_     ) for k, v in args_d.items()) if len(lowerCamelCase_     )]
            a_     :  str				      =     [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
            execute_subprocess_async(lowerCamelCase_   ,				env=self.get_env()     )

            a_     :  Any				      =     os.listdir(lowerCamelCase_     )
            a_     :  List[Any]				      =     {os.path.basename(lowerCamelCase_     ) for p in contents}
            a_     :  Dict				      =     [p for p in contents if p.endswith("""ckpt"""     )]
            assert len(lowerCamelCase_     ) > 0

            self.assertIn("""test_generations.txt"""   ,				lowerCamelCase_     )
            self.assertIn("""test_results.txt"""   ,				lowerCamelCase_     )

            # get the following from the module, (we don't have access to `model` here)
            a_     :  List[str]				      =     os.path.join(lowerCamelCase_   ,				"""metrics.json"""     )
            a_     :  Union[str, Any]				      =     """rouge2"""

            a_     :  Tuple				      =     load_json(lowerCamelCase_     )
            # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
            print(lowerCamelCase_     )
            a_     :  Any				      =     metrics["""val"""][-1]
            self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]   ,				0.0_1     )
            self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']   ,				lowerCamelCase_     )
            self.assertEqual(len(metrics["""test"""]     )   ,				1     )
            a_     :  str				      =     int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
            self.assertEqual(len(metrics["""val"""]     )   ,				lowerCamelCase_     )








import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput






class 	_a			(      nn.Module  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_=1.0 )	->					Optional[int]:
									super().__init__()

									lowerCAmelCase	:   Tuple					     =					cut_size
									lowerCAmelCase	:   str					     =					cut_power






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					str:
									lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					pixel_values.shape[2:4]
									lowerCAmelCase	:   str					     =					min(lowercase_    ,  lowercase_ )
									lowerCAmelCase	:   Union[str, Any]					     =					min(lowercase_    ,  lowercase_    ,  self.cut_size )
									lowerCAmelCase	:   Union[str, Any]					     =					[]
									for _ in range(lowercase_ ):
															lowerCAmelCase	:   List[Any]					     =					int(torch.rand([] ) ** self.cut_power * (max_size - min_size) + min_size )
															lowerCAmelCase	:   List[Any]					     =					torch.randint(0    ,  sideX - size + 1    ,  () )
															lowerCAmelCase	:   List[Any]					     =					torch.randint(0    ,  sideY - size + 1    ,  () )
															lowerCAmelCase	:   Any					     =					pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(lowercase_    ,  self.cut_size ) )
									return torch.cat(lowercase_ )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   str					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						lowerCAmelCase	:   Tuple					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						for param in model.parameters():
												lowerCAmelCase	:   Optional[int]					     =					value






class 	_a			(      snake_case_  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )	->					Optional[Any]:
									super().__init__()
									self.register_modules(
									    vae=lowercase_    ,  text_encoder=lowercase_    ,  clip_model=lowercase_    ,  tokenizer=lowercase_    ,  unet=lowercase_    ,  scheduler=lowercase_    ,  feature_extractor=lowercase_    ,  )

									lowerCAmelCase	:   Optional[Any]					     =					transforms.Normalize(mean=feature_extractor.image_mean    ,  std=feature_extractor.image_std )
									lowerCAmelCase	:   Dict					     =					(
									    feature_extractor.size
									    if isinstance(feature_extractor.size    ,  lowercase_ )
									    else feature_extractor.size["""shortest_edge"""]
									)
									lowerCAmelCase	:   str					     =					MakeCutouts(self.cut_out_size )

									set_requires_grad(self.text_encoder    ,  lowercase_ )
									set_requires_grad(self.clip_model    ,  lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_ = "auto" )	->					Any:
									if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															lowerCAmelCase	:   Any					     =					self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(lowercase_ )






			def 							_snake_case   (    self )	->					str:
									self.enable_attention_slicing(lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									set_requires_grad(self.vae    ,  lowercase_ )






			def 							_snake_case   (    self )	->					List[Any]:
									set_requires_grad(self.vae    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									set_requires_grad(self.unet    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Dict:
									set_requires_grad(self.unet    ,  lowercase_ )






			@torch.enable_grad()
			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_=True    ,  )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					latents.detach().requires_grad_()

									lowerCAmelCase	:   Optional[int]					     =					self.scheduler.scale_model_input(lowercase_    ,  lowercase_ )

									# predict the noise residual
									lowerCAmelCase	:   Dict					     =					self.unet(lowercase_    ,  lowercase_    ,  encoder_hidden_states=lowercase_ ).sample

									if isinstance(self.scheduler    ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
															lowerCAmelCase	:   Any					     =					self.scheduler.alphas_cumprod[timestep]
															lowerCAmelCase	:   Union[str, Any]					     =					1 - alpha_prod_t
															# compute predicted original sample from predicted noise also called
															# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
															lowerCAmelCase	:   Tuple					     =					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															lowerCAmelCase	:   Any					     =					torch.sqrt(lowercase_ )
															lowerCAmelCase	:   Tuple					     =					pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler    ,  lowercase_ ):
															lowerCAmelCase	:   Optional[int]					     =					self.scheduler.sigmas[index]
															lowerCAmelCase	:   str					     =					latents - sigma * noise_pred
									else:
															raise ValueError(f"""scheduler type {type(self.scheduler )} not supported""" )

									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * sample
									lowerCAmelCase	:   List[str]					     =					self.vae.decode(lowercase_ ).sample
									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )

									if use_cutouts:
															lowerCAmelCase	:   Union[str, Any]					     =					self.make_cutouts(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   Union[str, Any]					     =					transforms.Resize(self.cut_out_size )(lowercase_ )
									lowerCAmelCase	:   Any					     =					self.normalize(lowercase_ ).to(latents.dtype )

									lowerCAmelCase	:   Union[str, Any]					     =					self.clip_model.get_image_features(lowercase_ )
									lowerCAmelCase	:   Dict					     =					image_embeddings_clip / image_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )

									if use_cutouts:
															lowerCAmelCase	:   Optional[int]					     =					spherical_dist_loss(lowercase_    ,  lowercase_ )
															lowerCAmelCase	:   Optional[int]					     =					dists.view([num_cutouts, sample.shape[0], -1] )
															lowerCAmelCase	:   int					     =					dists.sum(2 ).mean(0 ).sum() * clip_guidance_scale
									else:
															lowerCAmelCase	:   List[Any]					     =					spherical_dist_loss(lowercase_    ,  lowercase_ ).mean() * clip_guidance_scale

									lowerCAmelCase	:   Optional[Any]					     =					-torch.autograd.grad(lowercase_    ,  lowercase_ )[0]

									if isinstance(self.scheduler    ,  lowercase_ ):
															lowerCAmelCase	:   int					     =					latents.detach() + grads * (sigma**2)
															lowerCAmelCase	:   Any					     =					noise_pred_original
									else:
															lowerCAmelCase	:   Any					     =					noise_pred_original - torch.sqrt(lowercase_ ) * grads
									return noise_pred, latents






			@torch.no_grad()
			def __call__(    self    ,  lowercase_    ,  lowercase_ = 512    ,  lowercase_ = 512    ,  lowercase_ = 50    ,  lowercase_ = 7.5    ,  lowercase_ = 1    ,  lowercase_ = 0.0    ,  lowercase_ = 100    ,  lowercase_ = None    ,  lowercase_ = 4    ,  lowercase_ = True    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = "pil"    ,  lowercase_ = True    ,  )	->					str:
									if isinstance(lowercase_    ,  lowercase_ ):
															lowerCAmelCase	:   str					     =					1
									elif isinstance(lowercase_    ,  lowercase_ ):
															lowerCAmelCase	:   Optional[Any]					     =					len(lowercase_ )
									else:
															raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_ )}""" )

									if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )

									# get prompt text embeddings
									lowerCAmelCase	:   Tuple					     =					self.tokenizer(
									    lowercase_    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  truncation=lowercase_    ,  return_tensors="""pt"""    ,  )
									lowerCAmelCase	:   str					     =					self.text_encoder(text_input.input_ids.to(self.device ) )[0]
									# duplicate text embeddings for each generation per prompt
									lowerCAmelCase	:   Dict					     =					text_embeddings.repeat_interleave(lowercase_    ,  dim=0 )

									if clip_guidance_scale > 0:
															if clip_prompt is not None:
																					lowerCAmelCase	:   int					     =					self.tokenizer(
																					    lowercase_    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  truncation=lowercase_    ,  return_tensors="""pt"""    ,  ).input_ids.to(self.device )
															else:
																					lowerCAmelCase	:   Tuple					     =					text_input.input_ids.to(self.device )
															lowerCAmelCase	:   Dict					     =					self.clip_model.get_text_features(lowercase_ )
															lowerCAmelCase	:   Tuple					     =					text_embeddings_clip / text_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )
															# duplicate text embeddings clip for each generation per prompt
															lowerCAmelCase	:   Any					     =					text_embeddings_clip.repeat_interleave(lowercase_    ,  dim=0 )

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									lowerCAmelCase	:   Optional[Any]					     =					guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															lowerCAmelCase	:   Optional[Any]					     =					text_input.input_ids.shape[-1]
															lowerCAmelCase	:   Dict					     =					self.tokenizer([""""""]    ,  padding="""max_length"""    ,  max_length=lowercase_    ,  return_tensors="""pt""" )
															lowerCAmelCase	:   str					     =					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
															# duplicate unconditional embeddings for each generation per prompt
															lowerCAmelCase	:   Optional[int]					     =					uncond_embeddings.repeat_interleave(lowercase_    ,  dim=0 )

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															lowerCAmelCase	:   str					     =					torch.cat([uncond_embeddings, text_embeddings] )

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									lowerCAmelCase	:   Optional[int]					     =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									lowerCAmelCase	:   int					     =					text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not work reproducibly on mps
																					lowerCAmelCase	:   str					     =					torch.randn(lowercase_    ,  generator=lowercase_    ,  device="""cpu"""    ,  dtype=lowercase_ ).to(
																					    self.device )
															else:
																					lowerCAmelCase	:   Optional[int]					     =					torch.randn(lowercase_    ,  generator=lowercase_    ,  device=self.device    ,  dtype=lowercase_ )
									else:
															if latents.shape != latents_shape:
																					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
															lowerCAmelCase	:   Any					     =					latents.to(self.device )

									# set timesteps
									lowerCAmelCase	:   List[str]					     =					"""offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_offset:
															lowerCAmelCase	:   List[str]					     =					1

									self.scheduler.set_timesteps(lowercase_    ,  **lowercase_ )

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									lowerCAmelCase	:   Union[str, Any]					     =					self.scheduler.timesteps.to(self.device )

									# scale the initial noise by the standard deviation required by the scheduler
									lowerCAmelCase	:   List[Any]					     =					latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									lowerCAmelCase	:   List[str]					     =					"""eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_eta:
															lowerCAmelCase	:   Any					     =					eta

									# check if the scheduler accepts generator
									lowerCAmelCase	:   Union[str, Any]					     =					"""generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									if accepts_generator:
															lowerCAmelCase	:   Dict					     =					generator

									for i, t in enumerate(self.progress_bar(lowercase_ ) ):
															# expand the latents if we are doing classifier free guidance
															lowerCAmelCase	:   int					     =					torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
															lowerCAmelCase	:   Dict					     =					self.scheduler.scale_model_input(lowercase_    ,  lowercase_ )

															# predict the noise residual
															lowerCAmelCase	:   Optional[Any]					     =					self.unet(lowercase_    ,  lowercase_    ,  encoder_hidden_states=lowercase_ ).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																					lowerCAmelCase     ,	lowerCAmelCase	:   int					     =					noise_pred.chunk(2 )
																					lowerCAmelCase	:   str					     =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																					lowerCAmelCase	:   Any					     =					(
																					    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
																					)
																					lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					self.cond_fn(
																					    lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )

															# compute the previous noisy sample x_t -> x_t-1
															lowerCAmelCase	:   List[Any]					     =					self.scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_    ,  **lowercase_ ).prev_sample

									# scale and decode the image latents with vae
									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * latents
									lowerCAmelCase	:   Optional[int]					     =					self.vae.decode(lowercase_ ).sample

									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )
									lowerCAmelCase	:   str					     =					image.cpu().permute(0    ,  2    ,  3    ,  1 ).numpy()

									if output_type == "pil":
															lowerCAmelCase	:   Tuple					     =					self.numpy_to_pil(lowercase_ )

									if not return_dict:
															return (image, None)

									return StableDiffusionPipelineOutput(images=lowercase_    ,  nsfw_content_detected=lowercase_ )

