




import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__a  :		Optional[Any]			=    logging.get_logger(__name__)

__a  :		List[str]			=    {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__a  :		Union[str, Any]			=    [
    '''lm_head''',
]
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> Dict:
							for attribute in key.split("."				):
														lowercase__  :       Any   					=							getattr(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)

							if weight_type is not None:
														lowercase__  :       Dict   					=							getattr(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				).shape
							else:
														lowercase__  :       Any   					=							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":
														lowercase__  :       Tuple   					=							value
							elif weight_type == "weight_g":
														lowercase__  :       str   					=							value
							elif weight_type == "weight_v":
														lowercase__  :       List[str]   					=							value
							elif weight_type == "bias":
														lowercase__  :       Any   					=							value
							else:
														lowercase__  :       Any   					=							value

							logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""				)
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> List[Any]:
							lowercase__  :       Dict   					=							[]
							lowercase__  :       str   					=							fairseq_model.state_dict()

							if not is_headless:
														lowercase__  :       Dict   					=							hf_model.dataavec_audio.feature_extractor
														lowercase__  :       List[str]   					=							hf_model.dataavec_audio.encoder.pos_conv_embed

							else:
														lowercase__  :       str   					=							hf_model.feature_extractor
														lowercase__  :       Any   					=							hf_model.encoder.pos_conv_embed

							for name, value in fairseq_dict.items():
														lowercase__  :       Optional[int]   					=							False
														if "conv_layers" in name:
																					load_conv_layer(
																					    SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,)
																					lowercase__  :       Any   					=							True
														elif "pos_conv" in name:
																					load_pos_conv_layer(
																					    SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,)
																					lowercase__  :       Optional[int]   					=							True
														else:
																					for key, mapped_key in MAPPING.items():
																												if not is_headless:
																																			lowercase__  :       Union[str, Any]   					=							"data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split("w2v_model."				)[-1] == name.split("."				)[0]:
																																			lowercase__  :       Dict   					=							True
																																			if "*" in mapped_key:
																																										lowercase__  :       Optional[Any]   					=							name.split(SCREAMING_SNAKE_CASE_				)[0].split("."				)[-2]
																																										lowercase__  :       Union[str, Any]   					=							mapped_key.replace("*"				,SCREAMING_SNAKE_CASE_				)
																																			if "weight_g" in name:
																																										lowercase__  :       List[Any]   					=							"weight_g"
																																			elif "weight_v" in name:
																																										lowercase__  :       Optional[Any]   					=							"weight_v"
																																			elif "bias" in name:
																																										lowercase__  :       int   					=							"bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowercase__  :       Union[str, Any]   					=							"weight"
																																			else:
																																										lowercase__  :       Dict   					=							None
																																			set_recursively(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)
																												continue
														if not is_used:
																					unused_weights.append(SCREAMING_SNAKE_CASE_				)

							logger.warning(F"""Unused weights: {unused_weights}"""				)
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> List[Any]:
							lowercase__  :       Union[str, Any]   					=							path.split("."				)
							return reduce(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> Any:
							lowercase__  :       Dict   					=							access_by_string(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)
							lowercase__  :       Tuple   					=							hf_weight.data

							if fsq_value.shape != hf_value.shape:
														raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""				)
							lowercase__  :       int   					=							fsq_value
							logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""				)
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> Union[str, Any]:
							lowercase__  :       Optional[Any]   					=							full_name.split("conv_layers."				)[-1]
							lowercase__  :       Any   					=							name.split("."				)
							lowercase__  :       Tuple   					=							int(items[0]				)
							lowercase__  :       Optional[Any]   					=							int(items[1]				)

							lowercase__  :       List[Any]   					=							name.split("."				)[-1]
							if type_id == 0:
														lowercase__  :       Optional[int]   					=							"conv"
							elif type_id == 2:
														lowercase__  :       List[str]   					=							"layer_norm"
							else:
														unused_weights.append(SCREAMING_SNAKE_CASE_				)
														return

							set_weights(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""				)
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)			-> Optional[int]:
							lowercase__  :       Optional[Any]   					=							full_name.split("pos_conv."				)[-1]
							lowercase__  :       Union[str, Any]   					=							name.split("."				)
							lowercase__  :       List[Any]   					=							int(items[0]				)
							lowercase__  :       Tuple   					=							int(items[1]				)

							lowercase__  :       str   					=							name.split("."				)[-1]
							if type_id != 0:
														unused_weights.append(SCREAMING_SNAKE_CASE_				)
														return
							else:
														lowercase__  :       Dict   					=							"conv"

							set_weights(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,F"""layers.{layer_id}.{layer_type}.{weight_type}"""				)
@torch.no_grad()
def 					snake_case_     (		SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_=None				,SCREAMING_SNAKE_CASE_=None				,SCREAMING_SNAKE_CASE_=True				)			-> Union[str, Any]:
							if config_path is not None:
														lowercase__  :       Dict   					=							DataaVecAudioConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
							else:
														lowercase__  :       str   					=							DataaVecAudioConfig()

							if not is_finetuned:
														# Modify final_proj layer name
														lowercase__  :       Tuple   					=							DataaVecAudioModel(SCREAMING_SNAKE_CASE_				)
														lowercase__  :       Tuple   					=							os.path.dirname(SCREAMING_SNAKE_CASE_				)

														lowercase__  :       str   					=							torch.load(SCREAMING_SNAKE_CASE_				)
														lowercase__  :       Union[str, Any]   					=							state_dict["model"].pop("final_proj.0.weight"				)
														lowercase__  :       Dict   					=							state_dict["model"].pop("final_proj.0.bias"				)
														lowercase__  :       List[Any]   					=							os.path.join(SCREAMING_SNAKE_CASE_				,"converted.pt"				)
														torch.save(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				)
							else:
														lowercase__  :       int   					=							DataaVecAudioForCTC(SCREAMING_SNAKE_CASE_				)
														lowercase__  :       List[Any]   					=							checkpoint_path

							def load_dataavec(SCREAMING_SNAKE_CASE_				):
														lowercase__					,   lowercase__					,   lowercase__  :       Dict   					=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]				)
														return model[0].eval()

							lowercase__  :       Union[str, Any]   					=							load_dataavec(SCREAMING_SNAKE_CASE_				)

							recursively_load_weights(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,not is_finetuned				)

							lowercase__  :       List[str]   					=							WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"				)

							lowercase__  :       Dict   					=							load_dataset("patrickvonplaten/librispeech_asr_dummy"				,"clean"				,split="validation"				)
							lowercase__  :       Union[str, Any]   					=							[x["array"] for x in ds[:4]["audio"]]

							lowercase__  :       Optional[int]   					=							processor(SCREAMING_SNAKE_CASE_				,return_tensors="pt"				,padding=SCREAMING_SNAKE_CASE_				)

							lowercase__  :       str   					=							inputs.input_values
							lowercase__  :       Union[str, Any]   					=							inputs.attention_mask
							#    input_values = inputs.input_values[:, :-1]
							#    attention_mask = inputs.attention_mask[:, :-1]

							hf_wavavec.eval()
							model.eval()
							if is_finetuned:
														lowercase__  :       int   					=							model(source=SCREAMING_SNAKE_CASE_				,padding_mask=(1 - attention_mask)				,mask=SCREAMING_SNAKE_CASE_				,features_only=SCREAMING_SNAKE_CASE_				)[
														    "encoder_out"
														].transpose(0				,1				)
														lowercase__  :       str   					=							hf_wavavec(SCREAMING_SNAKE_CASE_				,attention_mask=SCREAMING_SNAKE_CASE_				)["logits"]

														lowercase__  :       Union[str, Any]   					=							torch.argmax(SCREAMING_SNAKE_CASE_				,dim=-1				)
														lowercase__  :       Union[str, Any]   					=							processor.batch_decode(SCREAMING_SNAKE_CASE_				)

														print(F"""Expected Output: {ds[:4]["text"]}, Pred: {output_string}"""				)
							else:
														lowercase__  :       Tuple   					=							model(source=SCREAMING_SNAKE_CASE_				,padding_mask=(1 - attention_mask)				,mask=SCREAMING_SNAKE_CASE_				,features_only=SCREAMING_SNAKE_CASE_				)[
														    "layer_results"
														][-1][0].transpose(0				,1				)
														lowercase__  :       Optional[Any]   					=							hf_wavavec(SCREAMING_SNAKE_CASE_				,attention_mask=SCREAMING_SNAKE_CASE_				)["last_hidden_state"]

							print(our_output.shape				,their_output.shape				)
							lowercase__  :       Dict   					=							torch.max(torch.abs(our_output - their_output				)				).item()
							print(F"""max_absolute_diff = {max_absolute_diff}"""				)  # ~ 1e-7
							lowercase__  :       Optional[Any]   					=							torch.allclose(SCREAMING_SNAKE_CASE_				,SCREAMING_SNAKE_CASE_				,atol=1E-3				)
							print("Do both models output the same tensors?"				,"🔥" if success else "💩"				)
							if not success:
														raise Exception("Something went wRoNg"				)

							hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_				)

							if is_finetuned:
														processor.save_pretrained(SCREAMING_SNAKE_CASE_				)
							else:
														processor.feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_				)


if __name__ == "__main__":
						__a  :		int			=    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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						parser.add_argument(
						    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
						)
						__a  :		int			=    parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)







"""simple docstring"""







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)





class      UpperCamelCase_       (				a_     ):



  def __init__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 0.93       ,						snake_case__ = True       ,						)	->	Optional[Any]:



         """simple docstring"""





         UpperCAmelCase					=	mixing_coeff
         UpperCAmelCase					=	leapfrog_steps

         super().__init__()
         self.register_modules(
             vae=snake_case__       ,						text_encoder=snake_case__       ,						tokenizer=snake_case__       ,						unet=snake_case__       ,						scheduler=snake_case__       ,						)

         UpperCAmelCase					=	2 ** (len(self.vae.config.block_out_channels ) - 1)
         UpperCAmelCase					=	VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ = None       ,						snake_case__ = False )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	self.tokenizer(
             snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

         UpperCAmelCase					=	self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state

         UpperCAmelCase					=	prompt_embeds.to(dtype=self.text_encoder.dtype       ,						device=self.device )

         if do_classifier_free_guidance:
                UpperCAmelCase					=	"""""" if negative_prompt is None else negative_prompt

                UpperCAmelCase					=	self.tokenizer(
                    snake_case__       ,						padding="""max_length"""       ,						max_length=self.tokenizer.model_max_length       ,						truncation=snake_case__       ,						return_tensors="""pt"""       ,						)

                UpperCAmelCase					=	self.text_encoder(uncond_input.input_ids.to(self.device ) ).last_hidden_state

                UpperCAmelCase					=	torch.cat([negative_prompt_embeds, prompt_embeds] )

         return prompt_embeds



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         UpperCAmelCase					=	self.mixing_coeff * y + (1 - self.mixing_coeff) * x

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         UpperCAmelCase					=	(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

         return [x, y]



  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	Dict:



         """simple docstring"""





         UpperCAmelCase					=	int(snake_case__ )

         UpperCAmelCase					=	self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

         return alpha_prod, 1 - alpha_prod



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	int:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

         UpperCAmelCase					=	(base - b_t * model_output) / a_t

         return model_input, next_model_input.to(base.dtype )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )
         UpperCAmelCase			,				UpperCAmelCase					=	self._get_alpha_and_beta(snake_case__ )

         UpperCAmelCase					=	(alpha_prod_t_prev / alpha_prod_t) ** 0.5
         UpperCAmelCase					=	-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         UpperCAmelCase					=	a_t * base + b_t * model_output

         return model_input, next_model_input.to(base.dtype )



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__ )	->	str:



         """simple docstring"""





         UpperCAmelCase					=	1 / self.vae.config.scaling_factor * latents
         UpperCAmelCase					=	self.vae.decode(snake_case__ ).sample
         UpperCAmelCase					=	(image / 2 + 0.5).clamp(0       ,						1 )
         return image



  @torch.no_grad()
  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						)	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	image.to(device=self.device       ,						dtype=text_embeds.dtype )
         UpperCAmelCase					=	self.vae.encode(snake_case__ ).latent_dist.sample(snake_case__ )

         UpperCAmelCase					=	self.vae.config.scaling_factor * latent

         UpperCAmelCase					=	[latent.clone(), latent.clone()]

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                UpperCAmelCase					=	self.noise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

                # j - model_input index, k - base index
                for j in range(2 ):
                       UpperCAmelCase					=	j ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 0:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.noise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

         return coupled_latents



  @torch.no_grad()
  def __call__(					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = 3.0       ,						snake_case__ = 50       ,						snake_case__ = 0.8       ,						snake_case__ = None       ,						snake_case__ = None       ,						snake_case__ = "pil"       ,						)	->	str:



         """simple docstring"""





         UpperCAmelCase					=	guidance_scale > 1.0

         UpperCAmelCase					=	self.image_processor.preprocess(snake_case__ )

         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )
         UpperCAmelCase					=	self._encode_prompt(snake_case__       ,						snake_case__       ,						snake_case__ )

         self.scheduler.set_timesteps(snake_case__       ,						self.device )

         UpperCAmelCase					=	num_inference_steps - int(num_inference_steps * strength )
         UpperCAmelCase					=	self.scheduler.timesteps[t_limit:]
         UpperCAmelCase					=	fwd_timesteps.flip(0 )

         UpperCAmelCase					=	self.prepare_latents(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

         for i, t in tqdm(enumerate(snake_case__ )       ,						total=len(snake_case__ ) ):
                # j - model_input index, k - base index
                for k in range(2 ):
                       UpperCAmelCase					=	k ^ 1

                       if self.leapfrog_steps:
                              if i % 2 == 1:
                                     UpperCAmelCase			,				UpperCAmelCase					=	j, k

                       UpperCAmelCase					=	coupled_latents[j]
                       UpperCAmelCase					=	coupled_latents[k]

                       UpperCAmelCase					=	torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input

                       UpperCAmelCase					=	self.unet(snake_case__       ,						snake_case__       ,						encoder_hidden_states=snake_case__ ).sample

                       if do_classifier_free_guidance:
                              UpperCAmelCase			,				UpperCAmelCase					=	noise_pred.chunk(2 )
                              UpperCAmelCase					=	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                       UpperCAmelCase			,				UpperCAmelCase					=	self.denoise_step(
                           base=snake_case__       ,						model_input=snake_case__       ,						model_output=snake_case__       ,						timestep=snake_case__       ,						)

                       UpperCAmelCase					=	model_input

                UpperCAmelCase					=	self.denoise_mixing_layer(x=coupled_latents[0]       ,						y=coupled_latents[1] )

         # either one is fine
         UpperCAmelCase					=	coupled_latents[0]

         if output_type not in ["latent", "pt", "np", "pil"]:
                UpperCAmelCase					=	(
                    f'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
                    """`pil`, `np`, `pt`, `latent`"""
                )
                deprecate("""Unsupported output_type"""       ,						"""1.0.0"""       ,						snake_case__       ,						standard_warn=snake_case__ )
                UpperCAmelCase					=	"""np"""

         if output_type == "latent":
                UpperCAmelCase					=	final_latent
         else:
                UpperCAmelCase					=	self.decode_latents(snake_case__ )
                UpperCAmelCase					=	self.image_processor.postprocess(snake_case__       ,						output_type=snake_case__ )

         return image
