

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 						__magic_name__      (							lowerCamelCase__		,	unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__			:     str    =       UnCLIPPipeline
   lowerCamelCase__			:     Optional[Any]    =       TEXT_TO_IMAGE_PARAMS - {
       'negative_prompt',
       'height',
       'width',
       'negative_prompt_embeds',
       'guidance_scale',
       'prompt_embeds',
       'cross_attention_kwargs',
   }
   lowerCamelCase__			:     List[Any]    =       TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCamelCase__			:     List[Any]    =       [
       'generator',
       'return_dict',
       'prior_num_inference_steps',
       'decoder_num_inference_steps',
       'super_res_num_inference_steps',
   ]
   lowerCamelCase__			:     List[Any]    =       False



   @property
   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






     return 32



   @property
   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     return 32



   @property
   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     return self.time_input_dim



   @property
   def 			_UpperCAmelCase			(    self	)							->   int:






     """simple docstring"""






     return self.time_input_dim * 4



   @property
   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     return 100



   @property
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     a__  =CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)
     return tokenizer



   @property
   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     torch.manual_seed(0	)
     a__  =CLIPTextConfig(
         bos_token_id=0,   eos_token_id=2,   hidden_size=self.text_embedder_hidden_size,   projection_dim=self.text_embedder_hidden_size,   intermediate_size=37,   layer_norm_eps=1E-05,   num_attention_heads=4,   num_hidden_layers=5,   pad_token_id=1,   vocab_size=1000,   )
     return CLIPTextModelWithProjection(lowercase_	)



   @property
   def 			_UpperCAmelCase			(    self	)							->   Optional[Any]:






     """simple docstring"""






     torch.manual_seed(0	)

     a__  ={
         '''num_attention_heads''': 2,
         '''attention_head_dim''': 12,
         '''embedding_dim''': self.text_embedder_hidden_size,
         '''num_layers''': 1,
     }

     a__  =PriorTransformer(**lowercase_	)
     return model



   @property
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     torch.manual_seed(0	)

     a__  ={
         '''clip_embeddings_dim''': self.text_embedder_hidden_size,
         '''time_embed_dim''': self.time_embed_dim,
         '''cross_attention_dim''': self.cross_attention_dim,
     }

     a__  =UnCLIPTextProjModel(**lowercase_	)
     return model



   @property
   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     torch.manual_seed(0	)

     a__  ={
         '''sample_size''': 32,
         # RGB in channels
         '''in_channels''': 3,
         # Out channels is double in channels because predicts mean and variance
         '''out_channels''': 6,
         '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
         '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
         '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
         '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
         '''layers_per_block''': 1,
         '''cross_attention_dim''': self.cross_attention_dim,
         '''attention_head_dim''': 4,
         '''resnet_time_scale_shift''': '''scale_shift''',
         '''class_embed_type''': '''identity''',
     }

     a__  =UNetaDConditionModel(**lowercase_	)
     return model



   @property
   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     return {
         "sample_size": 64,
         "layers_per_block": 1,
         "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
         "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
         "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
         "in_channels": 6,
         "out_channels": 3,
     }



   @property
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     torch.manual_seed(0	)

     a__  =UNetaDModel(**self.dummy_super_res_kwargs	)
     return model



   @property
   def 			_UpperCAmelCase			(    self	)							->   str:






     """simple docstring"""






     torch.manual_seed(1	)

     a__  =UNetaDModel(**self.dummy_super_res_kwargs	)
     return model



   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     a__  =self.dummy_prior
     a__  =self.dummy_decoder
     a__  =self.dummy_text_proj
     a__  =self.dummy_text_encoder
     a__  =self.dummy_tokenizer
     a__  =self.dummy_super_res_first
     a__  =self.dummy_super_res_last

     a__  =UnCLIPScheduler(
         variance_type='''fixed_small_log''',   prediction_type='''sample''',   num_train_timesteps=1000,   clip_sample_range=5.0,   )

     a__  =UnCLIPScheduler(
         variance_type='''learned_range''',   prediction_type='''epsilon''',   num_train_timesteps=1000,   )

     a__  =UnCLIPScheduler(
         variance_type='''fixed_small_log''',   prediction_type='''epsilon''',   num_train_timesteps=1000,   )

     a__  ={
         '''prior''': prior,
         '''decoder''': decoder,
         '''text_proj''': text_proj,
         '''text_encoder''': text_encoder,
         '''tokenizer''': tokenizer,
         '''super_res_first''': super_res_first,
         '''super_res_last''': super_res_last,
         '''prior_scheduler''': prior_scheduler,
         '''decoder_scheduler''': decoder_scheduler,
         '''super_res_scheduler''': super_res_scheduler,
     }

     return components



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_=0	)							->   Optional[int]:






     """simple docstring"""






     if str(lowercase_	).startswith('''mps'''	):
       a__  =torch.manual_seed(lowercase_	)
     else:
       a__  =torch.Generator(device=lowercase_	).manual_seed(lowercase_	)
     a__  ={
         '''prompt''': '''horse''',
         '''generator''': generator,
         '''prior_num_inference_steps''': 2,
         '''decoder_num_inference_steps''': 2,
         '''super_res_num_inference_steps''': 2,
         '''output_type''': '''numpy''',
     }
     return inputs



   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     a__  ='''cpu'''

     a__  =self.get_dummy_components()

     a__  =self.pipeline_class(**lowercase_	)
     a__  =pipe.to(lowercase_	)

     pipe.set_progress_bar_config(disable=lowercase_	)

     a__  =pipe(**self.get_dummy_inputs(lowercase_	)	)
     a__  =output.images

     a__  =pipe(
         **self.get_dummy_inputs(lowercase_	),   return_dict=lowercase_,   )[0]

     a__  =image[0, -3:, -3:, -1]
     a__  =image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     a__  =np.array(
         [
             0.9997,
             0.9988,
             0.0028,
             0.9997,
             0.9984,
             0.9965,
             0.0029,
             0.9986,
             0.0025,
         ]	)

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



   def 			_UpperCAmelCase			(    self	)							->   List[Any]:






     """simple docstring"""






     a__  =torch.device('''cpu'''	)







     class 						__magic_name__      :

        '''simple docstring'''




        lowerCamelCase__			:     str    =       1





     a__  =self.get_dummy_components()

     a__  =self.pipeline_class(**lowercase_	)
     a__  =pipe.to(lowercase_	)

     a__  =components['''prior''']
     a__  =components['''decoder''']
     a__  =components['''super_res_first''']
     a__  =components['''tokenizer''']
     a__  =components['''text_encoder''']

     a__  =torch.Generator(device=lowercase_	).manual_seed(0	)
     a__  =prior.dtype
     a__  =1

     a__  =(batch_size, prior.config.embedding_dim)
     a__  =pipe.prepare_latents(
         lowercase_,   dtype=lowercase_,   device=lowercase_,   generator=lowercase_,   latents=lowercase_,   scheduler=DummyScheduler()	)
     a__  =(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
     a__  =pipe.prepare_latents(
         lowercase_,   dtype=lowercase_,   device=lowercase_,   generator=lowercase_,   latents=lowercase_,   scheduler=DummyScheduler()	)

     a__  =(
         batch_size,
         super_res_first.config.in_channels // 2,
         super_res_first.config.sample_size,
         super_res_first.config.sample_size,
     )
     a__  =pipe.prepare_latents(
         lowercase_,   dtype=lowercase_,   device=lowercase_,   generator=lowercase_,   latents=lowercase_,   scheduler=DummyScheduler()	)

     pipe.set_progress_bar_config(disable=lowercase_	)

     a__  ='''this is a prompt example'''

     a__  =torch.Generator(device=lowercase_	).manual_seed(0	)
     a__  =pipe(
         [prompt],   generator=lowercase_,   prior_num_inference_steps=2,   decoder_num_inference_steps=2,   super_res_num_inference_steps=2,   prior_latents=lowercase_,   decoder_latents=lowercase_,   super_res_latents=lowercase_,   output_type='''np''',   )
     a__  =output.images

     a__  =tokenizer(
         lowercase_,   padding='''max_length''',   max_length=tokenizer.model_max_length,   return_tensors='''pt''',   )
     a__  =text_encoder(text_inputs.input_ids	)
     a__  =text_inputs.attention_mask

     a__  =torch.Generator(device=lowercase_	).manual_seed(0	)
     a__  =pipe(
         generator=lowercase_,   prior_num_inference_steps=2,   decoder_num_inference_steps=2,   super_res_num_inference_steps=2,   prior_latents=lowercase_,   decoder_latents=lowercase_,   super_res_latents=lowercase_,   text_model_output=lowercase_,   text_attention_mask=lowercase_,   output_type='''np''',   )[0]

     # make sure passing text embeddings manually is identical
     assert np.abs(image - image_from_text	).max() < 1E-4



   @skip_mps
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     a__  =torch_device == '''cpu'''

     self._test_attention_slicing_forward_pass(test_max_difference=lowercase_,   expected_max_diff=0.01	)



   @skip_mps
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     a__  =torch_device == '''cpu'''
     a__  =True
     a__  =[
         '''prior_num_inference_steps''',
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]

     self._test_inference_batch_single_identical(
         test_max_difference=lowercase_,   relax_max_difference=lowercase_,   additional_params_copy_to_batched_inputs=lowercase_,   )



   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






     a__  =[
         '''prior_num_inference_steps''',
         '''decoder_num_inference_steps''',
         '''super_res_num_inference_steps''',
     ]

     if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       a__  =[2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=lowercase_,   additional_params_copy_to_batched_inputs=lowercase_,   )
     else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=lowercase_	)



   @skip_mps
   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     return super().test_dict_tuple_outputs_equivalent()



   @skip_mps
   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     return super().test_save_load_local()



   @skip_mps
   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     return super().test_save_load_optional_components()







@nightly
class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   Any:






     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






     a__  =load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/unclip/karlo_v1_alpha_horse_cpu.npy'''	)

     a__  =UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''	)
     pipeline.set_progress_bar_config(disable=lowercase_	)

     a__  =torch.manual_seed(0	)
     a__  =pipeline(
         '''horse''',   num_images_per_prompt=1,   generator=lowercase_,   output_type='''np''',   )

     a__  =output.images[0]

     assert image.shape == (256, 256, 3)
     assert np.abs(expected_image - image	).max() < 1E-1







@slow
@require_torch_gpu
class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



   def 			_UpperCAmelCase			(    self	)							->   Tuple:






     """simple docstring"""






     a__  =load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
         '''/unclip/karlo_v1_alpha_horse_fp16.npy'''	)

     a__  =UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha''',   torch_dtype=torch.floataa	)
     a__  =pipeline.to(lowercase_	)
     pipeline.set_progress_bar_config(disable=lowercase_	)

     a__  =torch.Generator(device='''cpu'''	).manual_seed(0	)
     a__  =pipeline(
         '''horse''',   generator=lowercase_,   output_type='''np''',   )

     a__  =output.images[0]

     assert image.shape == (256, 256, 3)

     assert_mean_pixel_difference(lowercase_,   lowercase_	)



   def 			_UpperCAmelCase			(    self	)							->   Dict:






     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     a__  =UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha''',   torch_dtype=torch.floataa	)
     a__  =pipe.to(lowercase_	)
     pipe.set_progress_bar_config(disable=lowercase_	)
     pipe.enable_attention_slicing()
     pipe.enable_sequential_cpu_offload()

     a__  =pipe(
         '''horse''',   num_images_per_prompt=1,   prior_num_inference_steps=2,   decoder_num_inference_steps=2,   super_res_num_inference_steps=2,   output_type='''np''',   )

     a__  =torch.cuda.max_memory_allocated()
     # make sure that less than 7 GB is allocated
     assert mem_bytes < 7 * 10**9



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 						__magic_name__      (							lowerCamelCase__							):

   '''simple docstring'''




   def __init__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 0.93,   lowercase_ = True,   )							->   Any:






     """simple docstring"""






     a__  =mixing_coeff
     a__  =leapfrog_steps

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

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_ = None,   lowercase_ = False	)							->   int:






     """simple docstring"""






     a__  =self.tokenizer(
         lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

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

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

     if do_classifier_free_guidance:
       a__  ='''''' if negative_prompt is None else negative_prompt

       a__  =self.tokenizer(
           lowercase_,   padding='''max_length''',   max_length=self.tokenizer.model_max_length,   truncation=lowercase_,   return_tensors='''pt''',   )

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

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

     return prompt_embeds



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Optional[Any]:






     """simple docstring"""






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

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Dict:






     """simple docstring"""






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

     return [x, y]



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =int(lowercase_	)

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

     return alpha_prod, 1 - alpha_prod



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






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

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

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

     a__  =(base - b_t * model_output) / a_t

     return model_input, next_model_input.to(base.dtype	)



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   )							->   Any:






     """simple docstring"""






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

     a__,				a__  =self._get_alpha_and_beta(lowercase_	)
     a__,				a__  =self._get_alpha_and_beta(lowercase_	)

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

     return model_input, next_model_input.to(base.dtype	)



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   int:






     """simple docstring"""






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



   @torch.no_grad()
   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = None,   )							->   str:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =image.to(device=self.device,   dtype=text_embeds.dtype	)
     a__  =self.vae.encode(lowercase_	).latent_dist.sample(lowercase_	)

     a__  =self.vae.config.scaling_factor * latent

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

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

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

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

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

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

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

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

         a__,				a__  =self.noise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

     return coupled_latents



   @torch.no_grad()
   def __call__(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_ = 3.0,   lowercase_ = 50,   lowercase_ = 0.8,   lowercase_ = None,   lowercase_ = None,   lowercase_ = "pil",   )							->   List[Any]:






     """simple docstring"""






     a__  =guidance_scale > 1.0

     a__  =self.image_processor.preprocess(lowercase_	)

     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)
     a__  =self._encode_prompt(lowercase_,   lowercase_,   lowercase_	)

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

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

     a__  =self.prepare_latents(lowercase_,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)

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

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

         a__  =coupled_latents[j]
         a__  =coupled_latents[k]

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

         a__  =self.unet(lowercase_,   lowercase_,   encoder_hidden_states=lowercase_	).sample

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

         a__,				a__  =self.denoise_step(
             base=lowercase_,   model_input=lowercase_,   model_output=lowercase_,   timestep=lowercase_,   )

         a__  =model_input

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

     # either one is fine
     a__  =coupled_latents[0]

     if output_type not in ["latent", "pt", "np", "pil"]:
       a__  =(
           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''',   lowercase_,   standard_warn=lowercase_	)
       a__  ='''np'''

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

     return image
