


import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
A	:		Optional[int]    =       logging.get_logger(__name__)

A	:		Any    =       {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
A	:		Any    =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			UpperCamelCase (     __magic_name__					:							Union[str, Any]   ,   __magic_name__					:							str   ,   __magic_name__					:							Dict   ,   __magic_name__					:							List[str]   ,   __magic_name__					:							Dict					)       ->  Any:
		"""simple docstring"""




		for attribute in key.split("""."""					):
				lowercase__					      =		getattr(__magic_name__   ,   __magic_name__					)

		if weight_type is not None:
				lowercase__					      =		getattr(__magic_name__   ,   __magic_name__					).shape
		else:
				lowercase__					      =		hf_pointer.shape

		assert hf_shape == value.shape, (
		    f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
		    f''' {value.shape} for {full_name}'''
		)

		if weight_type == "weight":
				lowercase__					      =		value
		elif weight_type == "weight_g":
				lowercase__					      =		value
		elif weight_type == "weight_v":
				lowercase__					      =		value
		elif weight_type == "bias":
				lowercase__					      =		value
		else:
				lowercase__					      =		value

		logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''					)






def 			UpperCamelCase (     __magic_name__					:							str   ,   __magic_name__					:							Optional[int]					)       ->  Tuple:
		"""simple docstring"""




		lowercase__					      =		[]
		lowercase__					      =		fairseq_model.state_dict()

		lowercase__					      =		hf_model.feature_extractor

		# if encoder has different dim to decoder -> use proj_weight
		lowercase__					      =		None

		for name, value in fairseq_dict.items():
				lowercase__					      =		False
				if "conv_layers" in name:
						load_conv_layer(
						    __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   hf_model.config.feat_extract_norm == """group"""   ,   )
						lowercase__					      =		True
				elif name.split("""."""					)[0] == "proj":
						lowercase__					      =		fairseq_model.proj
						lowercase__					      =		True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
										lowercase__					      =		True
										if "*" in mapped_key:
												lowercase__					      =		name.split(__magic_name__					)[0].split("""."""					)[-2]
												lowercase__					      =		mapped_key.replace("""*"""   ,   __magic_name__					)
										if "weight_g" in name:
												lowercase__					      =		"""weight_g"""
										elif "weight_v" in name:
												lowercase__					      =		"""weight_v"""
										elif "bias" in name:
												lowercase__					      =		"""bias"""
										elif "weight" in name:
												lowercase__					      =		"""weight"""
										else:
												lowercase__					      =		None
										set_recursively(__magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__   ,   __magic_name__					)
								continue
				if not is_used:
						unused_weights.append(__magic_name__					)

		logger.warning(f'''Unused weights: {unused_weights}'''					)

		return proj_weight






def 			UpperCamelCase (     __magic_name__					:							Dict   ,   __magic_name__					:							Any   ,   __magic_name__					:							List[Any]   ,   __magic_name__					:							int   ,   __magic_name__					:							Dict					)       ->  str:
		"""simple docstring"""




		lowercase__					      =		full_name.split("""conv_layers."""					)[-1]
		lowercase__					      =		name.split("""."""					)
		lowercase__					      =		int(items[0]					)
		lowercase__					      =		int(items[1]					)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
						)
						lowercase__					      =		value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
						)
						lowercase__					      =		value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''					)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
						    " found."
						)
						lowercase__					      =		value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    f'''{full_name} has size {value.shape}, but'''
						    f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
						)
						lowercase__					      =		value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''					)
		else:
				unused_weights.append(__magic_name__					)






def 			UpperCamelCase (     __magic_name__					:							Any					)       ->  Optional[int]:
		"""simple docstring"""




		lowercase__      ,     lowercase__					      =		emb.weight.shape
		lowercase__					      =		nn.Linear(__magic_name__   ,   __magic_name__   ,   bias=__magic_name__					)
		lowercase__					      =		emb.weight.data
		return lin_layer






def 			UpperCamelCase (     __magic_name__					:							int					)       ->  Union[str, Any]:
		"""simple docstring"""




		with open(__magic_name__   ,   """r"""   ,   encoding="""utf-8"""					) as f:
				lowercase__					      =		f.readlines()
				lowercase__					      =		[line.split(""" """					)[0] for line in lines]

		lowercase__					      =		len(__magic_name__					)

		lowercase__					      =		{
		    """<s>""": 0,
		    """<pad>""": 1,
		    """</s>""": 2,
		    """<unk>""": 3,
		}

		vocab_dict.update(dict(zip(__magic_name__   ,   range(4   ,   num_words + 4					)					)					)					)
		return vocab_dict






@torch.no_grad()
def 			UpperCamelCase (     __magic_name__					:							Any   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Any   ,   __magic_name__					:							Dict   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Optional[Any]   ,   __magic_name__					:							Optional[int]   ,   )       ->  Optional[int]:
		"""simple docstring"""




		lowercase__					      =		WavaVecaConfig.from_pretrained(__magic_name__					)
		lowercase__					      =		SpeechaTextaConfig.from_pretrained(
		    __magic_name__   ,   vocab_size=__magic_name__   ,   decoder_layers=__magic_name__   ,   do_stable_layer_norm=__magic_name__					)

		lowercase__					      =		WavaVecaFeatureExtractor(
		    feature_size=1   ,   sampling_rate=1_6000   ,   padding_value=0   ,   do_normalize=__magic_name__   ,   return_attention_mask=__magic_name__   ,   )

		lowercase__      ,     lowercase__      ,     lowercase__					      =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]   ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					)}					)
		lowercase__					      =		model[0].eval()

		# set weights for wav2vec2 encoder
		lowercase__					      =		WavaVecaModel(__magic_name__					)
		lowercase__					      =		recursively_load_weights_wavaveca(model.encoder   ,   __magic_name__					)

		lowercase__					      =		SpeechaTextaForCausalLM(__magic_name__					)
		lowercase__      ,     lowercase__					      =		hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   ,   strict=__magic_name__					)

		# set output linear layer
		unexpected_keys.remove("""embed_out"""					)
		lowercase__					      =		nn.Parameter(model.decoder.embed_out.detach()					)

		# layer norm is init to identity matrix so leaving it is fine
		logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}'''					)
		logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}'''					)

		lowercase__					      =		SpeechEncoderDecoderModel(encoder=__magic_name__   ,   decoder=__magic_name__					)
		lowercase__					      =		False

		# add projection layer
		lowercase__					      =		nn.Parameter(projection_layer.weight					)
		lowercase__					      =		nn.Parameter(projection_layer.bias					)

		lowercase__					      =		create_vocab_dict(__magic_name__					)

		with open(os.path.join(__magic_name__   ,   """vocab.json"""					)   ,   """w"""					) as fp:
				json.dump(__magic_name__   ,   __magic_name__					)

		lowercase__					      =		SpeechaTextaTokenizer(os.path.join(__magic_name__   ,   """vocab.json"""					)					)
		tokenizer.save_pretrained(__magic_name__					)

		lowercase__					      =		hf_wavavec.config.to_dict()
		lowercase__					      =		tokenizer.pad_token_id
		lowercase__					      =		tokenizer.bos_token_id
		lowercase__					      =		tokenizer.eos_token_id
		lowercase__					      =		"""speech_to_text_2"""
		lowercase__					      =		"""wav2vec2"""

		lowercase__					      =		SpeechEncoderDecoderConfig.from_dict(__magic_name__					)

		hf_wavavec.save_pretrained(__magic_name__					)
		feature_extractor.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	A	:		Dict    =       argparse.ArgumentParser()
	parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
	parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
	parser.add_argument(
	    '--encoder_config_path',
	    default='facebook/wav2vec2-large-lv60',
	    type=str,
	    help='Path to hf encoder wav2vec2 checkpoint config',
	)
	parser.add_argument(
	    '--decoder_config_path',
	    default='facebook/s2t-small-mustc-en-fr-st',
	    type=str,
	    help='Path to hf decoder s2t checkpoint config',
	)
	parser.add_argument('--vocab_size', default=1_0_2_2_4, type=int, help='Vocab size of decoder')
	parser.add_argument('--num_decoder_layers', default=7, type=int, help='Number of decoder layers')

	A	:		Union[str, Any]    =       parser.parse_args()
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.dict_path,
	    encoder_config_path=args.encoder_config_path,
	    decoder_config_path=args.decoder_config_path,
	    vocab_size=args.vocab_size,
	    num_decoder_layers=args.num_decoder_layers,
	)





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


A	:		str    =       logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							A   (	UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self   :						List[str]				, _UpperCAmelCase   :						AutoencoderKL				, _UpperCAmelCase   :						CLIPTextModel				, _UpperCAmelCase   :						CLIPTokenizer				, _UpperCAmelCase   :						UNetaDConditionModel				, _UpperCAmelCase   :						Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]				, _UpperCAmelCase   :						StableDiffusionSafetyChecker				, _UpperCAmelCase   :						CLIPImageProcessor				, )						->       Dict:




							"""simple docstring"""


							super().__init__()
							self.register_modules(
							    vae=_UpperCAmelCase				, text_encoder=_UpperCAmelCase				, tokenizer=_UpperCAmelCase				, unet=_UpperCAmelCase				, scheduler=_UpperCAmelCase				, safety_checker=_UpperCAmelCase				, feature_extractor=_UpperCAmelCase				, )

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Optional[Union[str, int]] = "auto"		)						->       Tuple:




							"""simple docstring"""


							if slice_size == "auto":
									# half the attention head size is usually a good trade-off between
									# speed and memory
									lowercase__					      =		self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(_UpperCAmelCase		)

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




							"""simple docstring"""


							self.enable_attention_slicing(_UpperCAmelCase		)


					@torch.no_grad()
					def __call__(self   :						Any				, _UpperCAmelCase   :						Union[str, List[str]]				, _UpperCAmelCase   :						int = 512				, _UpperCAmelCase   :						int = 512				, _UpperCAmelCase   :						int = 50				, _UpperCAmelCase   :						float = 7.5				, _UpperCAmelCase   :						Optional[Union[str, List[str]]] = None				, _UpperCAmelCase   :						Optional[int] = 1				, _UpperCAmelCase   :						float = 0.0				, _UpperCAmelCase   :						Optional[torch.Generator] = None				, _UpperCAmelCase   :						Optional[torch.FloatTensor] = None				, _UpperCAmelCase   :						Optional[str] = "pil"				, _UpperCAmelCase   :						bool = True				, _UpperCAmelCase   :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None				, _UpperCAmelCase   :						int = 1				, _UpperCAmelCase   :						Optional[torch.FloatTensor] = None				, **_UpperCAmelCase   :						Any				, )						->       Tuple:




							"""simple docstring"""


							if isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
									lowercase__					      =		1
							elif isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
									lowercase__					      =		len(_UpperCAmelCase		)
							else:
									raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_UpperCAmelCase		)}'''		)

							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}.'''		)

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(_UpperCAmelCase				, _UpperCAmelCase		) or callback_steps <= 0)
							):
									raise ValueError(
									    f'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
									    f''' {type(_UpperCAmelCase		)}.'''		)

							# get prompt text embeddings
							lowercase__					      =		self.tokenizer(
							    _UpperCAmelCase				, padding="""max_length"""				, max_length=self.tokenizer.model_max_length				, return_tensors="""pt"""				, )
							lowercase__					      =		text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
									lowercase__					      =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
									logger.warning(
									    """The following part of your input was truncated because CLIP can only handle sequences up to"""
									    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''		)
									lowercase__					      =		text_input_ids[:, : self.tokenizer.model_max_length]

							if text_embeddings is None:
									lowercase__					      =		self.text_encoder(text_input_ids.to(self.device		)		)[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							lowercase__      ,     lowercase__      ,     lowercase__					      =		text_embeddings.shape
							lowercase__					      =		text_embeddings.repeat(1				, _UpperCAmelCase				, 1		)
							lowercase__					      =		text_embeddings.view(bs_embed * num_images_per_prompt				, _UpperCAmelCase				, -1		)

							# 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.
							lowercase__					      =		guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
									lowercase__					      =		42
									if negative_prompt is None:
											lowercase__					      =		[""""""]
									elif type(_UpperCAmelCase		) is not type(_UpperCAmelCase		):
											raise TypeError(
											    f'''`negative_prompt` should be the same type to `prompt`, but got {type(_UpperCAmelCase		)} !='''
											    f''' {type(_UpperCAmelCase		)}.'''		)
									elif isinstance(_UpperCAmelCase				, _UpperCAmelCase		):
											lowercase__					      =		[negative_prompt]
									elif batch_size != len(_UpperCAmelCase		):
											raise ValueError(
											    f'''`negative_prompt`: {negative_prompt} has batch size {len(_UpperCAmelCase		)}, but `prompt`:'''
											    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
											    """ the batch size of `prompt`."""		)
									else:
											lowercase__					      =		negative_prompt

									lowercase__					      =		text_input_ids.shape[-1]
									lowercase__					      =		self.tokenizer(
									    _UpperCAmelCase				, padding="""max_length"""				, max_length=_UpperCAmelCase				, truncation=_UpperCAmelCase				, return_tensors="""pt"""				, )
									lowercase__					      =		self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
									lowercase__					      =		uncond_embeddings.shape[1]
									lowercase__					      =		uncond_embeddings.repeat(_UpperCAmelCase				, _UpperCAmelCase				, 1		)
									lowercase__					      =		uncond_embeddings.view(batch_size * num_images_per_prompt				, _UpperCAmelCase				, -1		)

									# 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
									lowercase__					      =		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`.
							lowercase__					      =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							lowercase__					      =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
							lowercase__					      =		text_embeddings.dtype
							if latents is None:
									if self.device.type == "mps":
											# randn does not exist on mps
											lowercase__					      =		torch.randn(
											    _UpperCAmelCase				, generator=_UpperCAmelCase				, device="""cpu"""				, dtype=_UpperCAmelCase		).to(self.device		)
											lowercase__					      =		torch.randn(_UpperCAmelCase				, generator=_UpperCAmelCase				, device="""cpu"""				, dtype=_UpperCAmelCase		).to(
											    self.device		)
									else:
											lowercase__					      =		torch.randn(
											    _UpperCAmelCase				, generator=_UpperCAmelCase				, device=self.device				, dtype=_UpperCAmelCase		)
											lowercase__					      =		torch.randn(_UpperCAmelCase				, generator=_UpperCAmelCase				, device=self.device				, dtype=_UpperCAmelCase		)
							else:
									if latents_reference.shape != latents_shape:
											raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''		)
									lowercase__					      =		latents_reference.to(self.device		)
									lowercase__					      =		latents.to(self.device		)

							# This is the key part of the pipeline where we
							# try to ensure that the generated images w/ the same seed
							# but different sizes actually result in similar images
							lowercase__					      =		(latents_shape[3] - latents_shape_reference[3]) // 2
							lowercase__					      =		(latents_shape[2] - latents_shape_reference[2]) // 2
							lowercase__					      =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
							lowercase__					      =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
							lowercase__					      =		0 if dx < 0 else dx
							lowercase__					      =		0 if dy < 0 else dy
							lowercase__					      =		max(-dx				, 0		)
							lowercase__					      =		max(-dy				, 0		)
							# import pdb
							# pdb.set_trace()
							lowercase__					      =		latents_reference[:, :, dy : dy + h, dx : dx + w]

							# set timesteps
							self.scheduler.set_timesteps(_UpperCAmelCase		)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							lowercase__					      =		self.scheduler.timesteps.to(self.device		)

							# scale the initial noise by the standard deviation required by the scheduler
							lowercase__					      =		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]
							lowercase__					      =		"""eta""" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							lowercase__					      =		{}
							if accepts_eta:
									lowercase__					      =		eta

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

									# predict the noise residual
									lowercase__					      =		self.unet(_UpperCAmelCase				, _UpperCAmelCase				, encoder_hidden_states=_UpperCAmelCase		).sample

									# perform guidance
									if do_classifier_free_guidance:
											lowercase__      ,     lowercase__					      =		noise_pred.chunk(2		)
											lowercase__					      =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									# compute the previous noisy sample x_t -> x_t-1
									lowercase__					      =		self.scheduler.step(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase				, **_UpperCAmelCase		).prev_sample

									# call the callback, if provided
									if callback is not None and i % callback_steps == 0:
											callback(_UpperCAmelCase				, _UpperCAmelCase				, _UpperCAmelCase		)

							lowercase__					      =		1 / 0.18_215 * latents
							lowercase__					      =		self.vae.decode(_UpperCAmelCase		).sample

							lowercase__					      =		(image / 2 + 0.5).clamp(0				, 1		)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							lowercase__					      =		image.cpu().permute(0				, 2				, 3				, 1		).float().numpy()

							if self.safety_checker is not None:
									lowercase__					      =		self.feature_extractor(self.numpy_to_pil(_UpperCAmelCase		)				, return_tensors="""pt"""		).to(
									    self.device		)
									lowercase__      ,     lowercase__					      =		self.safety_checker(
									    images=_UpperCAmelCase				, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype		)		)
							else:
									lowercase__					      =		None

							if output_type == "pil":
									lowercase__					      =		self.numpy_to_pil(_UpperCAmelCase		)

							if not return_dict:
									return (image, has_nsfw_concept)

							return StableDiffusionPipelineOutput(images=_UpperCAmelCase				, nsfw_content_detected=_UpperCAmelCase		)

