





"""simple docstring"""
import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
_snake_case     :		Any       						= logging.get_logger('transformers.models.encodec')

_snake_case     :		int       						= {
    'quantizer.vq.layers.*._codebook.inited': 'quantizer.layers.*.codebook.inited',
    'quantizer.vq.layers.*._codebook.cluster_size': 'quantizer.layers.*.codebook.cluster_size',
    'quantizer.vq.layers.*._codebook.embed': 'quantizer.layers.*.codebook.embed',
    'quantizer.vq.layers.*._codebook.embed_avg': 'quantizer.layers.*.codebook.embed_avg',
}
_snake_case     :		Tuple       						= {
    'encoder.model.0.conv.conv': 'encoder.layers.0.conv',
    'encoder.model.1.block.1.conv.conv': 'encoder.layers.1.block.1.conv',
    'encoder.model.1.block.3.conv.conv': 'encoder.layers.1.block.3.conv',
    'encoder.model.1.shortcut.conv.conv': 'encoder.layers.1.shortcut.conv',
    'encoder.model.3.conv.conv': 'encoder.layers.3.conv',
    'encoder.model.4.block.1.conv.conv': 'encoder.layers.4.block.1.conv',
    'encoder.model.4.block.3.conv.conv': 'encoder.layers.4.block.3.conv',
    'encoder.model.4.shortcut.conv.conv': 'encoder.layers.4.shortcut.conv',
    'encoder.model.6.conv.conv': 'encoder.layers.6.conv',
    'encoder.model.7.block.1.conv.conv': 'encoder.layers.7.block.1.conv',
    'encoder.model.7.block.3.conv.conv': 'encoder.layers.7.block.3.conv',
    'encoder.model.7.shortcut.conv.conv': 'encoder.layers.7.shortcut.conv',
    'encoder.model.9.conv.conv': 'encoder.layers.9.conv',
    'encoder.model.10.block.1.conv.conv': 'encoder.layers.10.block.1.conv',
    'encoder.model.10.block.3.conv.conv': 'encoder.layers.10.block.3.conv',
    'encoder.model.10.shortcut.conv.conv': 'encoder.layers.10.shortcut.conv',
    'encoder.model.12.conv.conv': 'encoder.layers.12.conv',
    'encoder.model.13.lstm': 'encoder.layers.13.lstm',
    'encoder.model.15.conv.conv': 'encoder.layers.15.conv',
}
_snake_case     :		List[str]       						= {
    'encoder.model.0.conv.norm': 'encoder.layers.0.norm',
    'encoder.model.1.block.1.conv.norm': 'encoder.layers.1.block.1.norm',
    'encoder.model.1.block.3.conv.norm': 'encoder.layers.1.block.3.norm',
    'encoder.model.1.shortcut.conv.norm': 'encoder.layers.1.shortcut.norm',
    'encoder.model.3.conv.norm': 'encoder.layers.3.norm',
    'encoder.model.4.block.1.conv.norm': 'encoder.layers.4.block.1.norm',
    'encoder.model.4.block.3.conv.norm': 'encoder.layers.4.block.3.norm',
    'encoder.model.4.shortcut.conv.norm': 'encoder.layers.4.shortcut.norm',
    'encoder.model.6.conv.norm': 'encoder.layers.6.norm',
    'encoder.model.7.block.1.conv.norm': 'encoder.layers.7.block.1.norm',
    'encoder.model.7.block.3.conv.norm': 'encoder.layers.7.block.3.norm',
    'encoder.model.7.shortcut.conv.norm': 'encoder.layers.7.shortcut.norm',
    'encoder.model.9.conv.norm': 'encoder.layers.9.norm',
    'encoder.model.10.block.1.conv.norm': 'encoder.layers.10.block.1.norm',
    'encoder.model.10.block.3.conv.norm': 'encoder.layers.10.block.3.norm',
    'encoder.model.10.shortcut.conv.norm': 'encoder.layers.10.shortcut.norm',
    'encoder.model.12.conv.norm': 'encoder.layers.12.norm',
    'encoder.model.15.conv.norm': 'encoder.layers.15.norm',
}
_snake_case     :		Optional[int]       						= {
    'decoder.model.0.conv.conv': 'decoder.layers.0.conv',
    'decoder.model.1.lstm': 'decoder.layers.1.lstm',
    'decoder.model.3.convtr.convtr': 'decoder.layers.3.conv',
    'decoder.model.4.block.1.conv.conv': 'decoder.layers.4.block.1.conv',
    'decoder.model.4.block.3.conv.conv': 'decoder.layers.4.block.3.conv',
    'decoder.model.4.shortcut.conv.conv': 'decoder.layers.4.shortcut.conv',
    'decoder.model.6.convtr.convtr': 'decoder.layers.6.conv',
    'decoder.model.7.block.1.conv.conv': 'decoder.layers.7.block.1.conv',
    'decoder.model.7.block.3.conv.conv': 'decoder.layers.7.block.3.conv',
    'decoder.model.7.shortcut.conv.conv': 'decoder.layers.7.shortcut.conv',
    'decoder.model.9.convtr.convtr': 'decoder.layers.9.conv',
    'decoder.model.10.block.1.conv.conv': 'decoder.layers.10.block.1.conv',
    'decoder.model.10.block.3.conv.conv': 'decoder.layers.10.block.3.conv',
    'decoder.model.10.shortcut.conv.conv': 'decoder.layers.10.shortcut.conv',
    'decoder.model.12.convtr.convtr': 'decoder.layers.12.conv',
    'decoder.model.13.block.1.conv.conv': 'decoder.layers.13.block.1.conv',
    'decoder.model.13.block.3.conv.conv': 'decoder.layers.13.block.3.conv',
    'decoder.model.13.shortcut.conv.conv': 'decoder.layers.13.shortcut.conv',
    'decoder.model.15.conv.conv': 'decoder.layers.15.conv',
}
_snake_case     :		int       						= {
    'decoder.model.0.conv.norm': 'decoder.layers.0.norm',
    'decoder.model.3.convtr.norm': 'decoder.layers.3.norm',
    'decoder.model.4.block.1.conv.norm': 'decoder.layers.4.block.1.norm',
    'decoder.model.4.block.3.conv.norm': 'decoder.layers.4.block.3.norm',
    'decoder.model.4.shortcut.conv.norm': 'decoder.layers.4.shortcut.norm',
    'decoder.model.6.convtr.norm': 'decoder.layers.6.norm',
    'decoder.model.7.block.1.conv.norm': 'decoder.layers.7.block.1.norm',
    'decoder.model.7.block.3.conv.norm': 'decoder.layers.7.block.3.norm',
    'decoder.model.7.shortcut.conv.norm': 'decoder.layers.7.shortcut.norm',
    'decoder.model.9.convtr.norm': 'decoder.layers.9.norm',
    'decoder.model.10.block.1.conv.norm': 'decoder.layers.10.block.1.norm',
    'decoder.model.10.block.3.conv.norm': 'decoder.layers.10.block.3.norm',
    'decoder.model.10.shortcut.conv.norm': 'decoder.layers.10.shortcut.norm',
    'decoder.model.12.convtr.norm': 'decoder.layers.12.norm',
    'decoder.model.13.block.1.conv.norm': 'decoder.layers.13.block.1.norm',
    'decoder.model.13.block.3.conv.norm': 'decoder.layers.13.block.3.norm',
    'decoder.model.13.shortcut.conv.norm': 'decoder.layers.13.shortcut.norm',
    'decoder.model.15.conv.norm': 'decoder.layers.15.norm',
}
_snake_case     :		List[Any]       						= {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
_snake_case     :		int       						= {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
_snake_case     :		Dict       						= []
_snake_case     :		str       						= []






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							for attribute in key.split("."       ):
														A		=  getattr(UpperCamelCase					,  UpperCamelCase       )

							if weight_type is not None:
														A		=  getattr(UpperCamelCase					,  UpperCamelCase       ).shape
							else:
														A		=  hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    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":
														A		=  value
							elif weight_type == "weight_g":
														A		=  value
							elif weight_type == "weight_v":
														A		=  value
							elif weight_type == "bias":
														A		=  value
							elif weight_type == "running_mean":
														A		=  value
							elif weight_type == "running_var":
														A		=  value
							elif weight_type == "num_batches_tracked":
														A		=  value
							elif weight_type == "weight_ih_l0":
														A		=  value
							elif weight_type == "weight_hh_l0":
														A		=  value
							elif weight_type == "bias_ih_l0":
														A		=  value
							elif weight_type == "bias_hh_l0":
														A		=  value
							elif weight_type == "weight_ih_l1":
														A		=  value
							elif weight_type == "weight_hh_l1":
														A		=  value
							elif weight_type == "bias_ih_l1":
														A		=  value
							elif weight_type == "bias_hh_l1":
														A		=  value
							else:
														A		=  value

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






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							for key in ignore_keys:
														if key.endswith(".*"       ):
																					if name.startswith(key[:-1]       ):
																												return True
														elif ".*." in key:
																					A,	A		=  key.split(".*."       )
																					if prefix in name and suffix in name:
																												return True
														elif key in name:
																					return True
							return False






def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  []

							if model_name == "encodec_24khz" or "encodec_32khz":
														A		=  MAPPING_24K
							elif model_name == "encodec_48khz":
														A		=  MAPPING_48K
							else:
														raise ValueError(F"Unsupported model: {model_name}"       )

							for name, value in orig_dict.items():
														if should_ignore(UpperCamelCase					,  UpperCamelCase       ):
																					logger.info(F"{name} was ignored"       )
																					continue

														A		=  False
														for key, mapped_key in MAPPING.items():
																					if "*" in key:
																												A,	A		=  key.split(".*."       )
																												if prefix in name and suffix in name:
																																			A		=  suffix

																					if key in name:
																												# HACK otherwise .embed gets initialized with .embed_avg too
																												if key.endswith("embed"       ) and name.endswith("embed_avg"       ):
																																			continue

																												A		=  True
																												if "*" in mapped_key:
																																			A		=  name.split(UpperCamelCase       )[0].split("."       )[-2]
																																			A		=  mapped_key.replace("*"					,  UpperCamelCase       )
																												if "weight_g" in name:
																																			A		=  "weight_g"
																												elif "weight_v" in name:
																																			A		=  "weight_v"
																												elif "weight_ih_l0" in name:
																																			A		=  "weight_ih_l0"
																												elif "weight_hh_l0" in name:
																																			A		=  "weight_hh_l0"
																												elif "bias_ih_l0" in name:
																																			A		=  "bias_ih_l0"
																												elif "bias_hh_l0" in name:
																																			A		=  "bias_hh_l0"
																												elif "weight_ih_l1" in name:
																																			A		=  "weight_ih_l1"
																												elif "weight_hh_l1" in name:
																																			A		=  "weight_hh_l1"
																												elif "bias_ih_l1" in name:
																																			A		=  "bias_ih_l1"
																												elif "bias_hh_l1" in name:
																																			A		=  "bias_hh_l1"
																												elif "bias" in name:
																																			A		=  "bias"
																												elif "weight" in name:
																																			A		=  "weight"
																												elif "running_mean" in name:
																																			A		=  "running_mean"
																												elif "running_var" in name:
																																			A		=  "running_var"
																												elif "num_batches_tracked" in name:
																																			A		=  "num_batches_tracked"
																												else:
																																			A		=  None
																												set_recursively(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )
																					continue
														if not is_used:
																					unused_weights.append(UpperCamelCase       )

							logger.warning(F"Unused weights: {unused_weights}"       )






@torch.no_grad()
def 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase=None					,  UpperCamelCase=None					,  ):
							if config_path is not None:
														A		=  EncodecConfig.from_pretrained(UpperCamelCase       )
							else:
														A		=  EncodecConfig()

							if model_name == "encodec_24khz":
														pass  # config is already correct
							elif model_name == "encodec_32khz":
														A		=  [8, 5, 4, 4]
														A		=  [2.2]
														A		=  64
														A		=  32_000
														A		=  2_048
														A		=  False
														A		=  False
														A		=  False
							elif model_name == "encodec_48khz":
														A		=  [8, 5, 4, 2]
														A		=  [3.0, 6.0, 12.0, 24.0]
														A		=  48_000
														A		=  2
														A		=  False
														A		=  "time_group_norm"
														A		=  True
														A		=  1.0
														A		=  0.01
							else:
														raise ValueError(F"Unknown model name: {model_name}"       )

							A		=  EncodecModel(UpperCamelCase       )

							A		=  EncodecFeatureExtractor(
							    feature_size=config.audio_channels					,  sampling_rate=config.sampling_rate					,  chunk_length_s=config.chunk_length_s					,  overlap=config.overlap					,  )
							feature_extractor.save_pretrained(UpperCamelCase       )

							A		=  torch.load(UpperCamelCase       )
							if "best_state" in original_checkpoint:
														# we might have a training state saved, in which case discard the yaml results and just retain the weights
														A		=  original_checkpoint["best_state"]
							recursively_load_weights(UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       )
							model.save_pretrained(UpperCamelCase       )

							if repo_id:
														print("Pushing to the hub..."       )
														feature_extractor.push_to_hub(UpperCamelCase       )
														model.push_to_hub(UpperCamelCase       )


if __name__ == "__main__":
		_snake_case     :		Optional[Any]       						= argparse.ArgumentParser()
		parser.add_argument(
		    '--model',
		    default='encodec_24khz',
		    type=str,
		    help='The model to convert. Should be one of \'encodec_24khz\', \'encodec_32khz\', \'encodec_48khz\'.',
		)
		parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
		parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
		parser.add_argument(
		    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
		)

		_snake_case     :		List[str]       						= parser.parse_args()
		convert_checkpoint(
		    args.model,
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.push_to_hub,
		)









"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_snake_case     :		Any       						= logging.get_logger(__name__)  # pylint: disable=invalid-name






class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:WhisperForConditionalGeneration     ,       __UpperCamelCase			:WhisperProcessor     ,       __UpperCamelCase			:AutoencoderKL     ,       __UpperCamelCase			:CLIPTextModel     ,       __UpperCamelCase			:CLIPTokenizer     ,       __UpperCamelCase			:UNetaDConditionModel     ,       __UpperCamelCase			:Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,       __UpperCamelCase			:StableDiffusionSafetyChecker     ,       __UpperCamelCase			:CLIPImageProcessor     ,       ):
												super().__init__()

												if safety_checker is None:
																			logger.warning(
																			    f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
																			    " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
																			    " results in services or applications open to the public. Both the diffusers team and Hugging Face"
																			    " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
																			    " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
																			    " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."		)

												self.register_modules(
												    speech_model=__UpperCamelCase     ,       speech_processor=__UpperCamelCase     ,       vae=__UpperCamelCase     ,       text_encoder=__UpperCamelCase     ,       tokenizer=__UpperCamelCase     ,       unet=__UpperCamelCase     ,       scheduler=__UpperCamelCase     ,       feature_extractor=__UpperCamelCase     ,       )


					def 	lowerCamelCase       (					self			:Any     ,       __UpperCamelCase			:Optional[Union[str, int]] = "auto"		):
												if slice_size == "auto":
																			A		=  self.unet.config.attention_head_dim // 2
												self.unet.set_attention_slice(__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple		):
												self.enable_attention_slicing(__UpperCamelCase		)


					@torch.no_grad()
					def __call__(					self			:Optional[Any]     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:Dict=1_60_00     ,       __UpperCamelCase			:int = 5_12     ,       __UpperCamelCase			:int = 5_12     ,       __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			:Dict     ,       ):
												A		=  self.speech_processor.feature_extractor(
												    __UpperCamelCase     ,       return_tensors="pt"     ,       sampling_rate=__UpperCamelCase		).input_features.to(self.device		)
												A		=  self.speech_model.generate(__UpperCamelCase     ,       max_length=48_00_00		)

												A		=  self.speech_processor.tokenizer.batch_decode(__UpperCamelCase     ,       skip_special_tokens=__UpperCamelCase     ,       normalize=__UpperCamelCase		)[
												    0
												]

												if isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  1
												elif isinstance(__UpperCamelCase     ,       __UpperCamelCase		):
																			A		=  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
												A		=  self.tokenizer(
												    __UpperCamelCase     ,       padding="max_length"     ,       max_length=self.tokenizer.model_max_length     ,       return_tensors="pt"     ,       )
												A		=  text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			A		=  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}"		)
																			A		=  text_input_ids[:, : self.tokenizer.model_max_length]
												A		=  self.text_encoder(text_input_ids.to(self.device		)		)[0]

												# duplicate text embeddings for each generation per prompt, using mps friendly method
												A,	A,	A		=  text_embeddings.shape
												A		=  text_embeddings.repeat(1     ,       __UpperCamelCase     ,       1		)
												A		=  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.
												A		=  guidance_scale > 1.0
												# get unconditional embeddings for classifier free guidance
												if do_classifier_free_guidance:
																			A		=  42
																			if negative_prompt is None:
																										A		=  [""] * batch_size
																			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		):
																										A		=  [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:
																										A		=  negative_prompt

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

																			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																			A		=  uncond_embeddings.shape[1]
																			A		=  uncond_embeddings.repeat(1     ,       __UpperCamelCase     ,       1		)
																			A		=  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
																			A		=  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`.
												A		=  (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
												A		=  text_embeddings.dtype
												if latents is None:
																			if self.device.type == "mps":
																										# randn does not exist on mps
																										A		=  torch.randn(__UpperCamelCase     ,       generator=__UpperCamelCase     ,       device="cpu"     ,       dtype=__UpperCamelCase		).to(
																										    self.device		)
																			else:
																										A		=  torch.randn(__UpperCamelCase     ,       generator=__UpperCamelCase     ,       device=self.device     ,       dtype=__UpperCamelCase		)
												else:
																			if latents.shape != latents_shape:
																										raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"		)
																			A		=  latents.to(self.device		)

												# 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
												A		=  self.scheduler.timesteps.to(self.device		)

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

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

																			# predict the noise residual
																			A		=  self.unet(__UpperCamelCase     ,       __UpperCamelCase     ,       encoder_hidden_states=__UpperCamelCase		).sample

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

																			# compute the previous noisy sample x_t -> x_t-1
																			A		=  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		)

												A		=  1 / 0.18_215 * latents
												A		=  self.vae.decode(__UpperCamelCase		).sample

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

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

												if output_type == "pil":
																			A		=  self.numpy_to_pil(__UpperCamelCase		)

												if not return_dict:
																			return image

												return StableDiffusionPipelineOutput(images=__UpperCamelCase     ,       nsfw_content_detected=__UpperCamelCase		)


