



'''simple docstring'''







import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
		import torch
		from torch import nn

		from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
		from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from transformers import VivitImageProcessor

class      lowerCamelCase__     :






	'''simple docstring'''

	def __init__(  self		:  List[Any]   ,		__A		:  List[str]   ,		__A		:  Union[str, Any]=2   ,		__A		:  int=True   ,		__A		:  Optional[int]=True   ,		__A		:  Tuple=10   ,		__A		:  Any=10   ,		__A		:  Dict=8   ,		__A		:  Tuple=[2, 4, 4]   ,		__A		:  List[str]=3   ,		__A		:  Dict=768   ,		__A		:  Optional[Any]=5   ,		__A		:  str=4   ,		__A		:  str=37   ,		__A		:  Union[str, Any]="gelu_fast"   ,		__A		:  List[str]=0.0   ,		__A		:  List[str]=0.0   ,		__A		:  str=0.0_2   ,		__A		:  int=1E-06   ,		__A		:  str=True   ,		__A		:  Any=None   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   parent
								lowerCAmelCase__  =   batch_size
								lowerCAmelCase__  =   is_training
								lowerCAmelCase__  =   use_labels
								lowerCAmelCase__  =   num_labels
								lowerCAmelCase__  =   image_size
								lowerCAmelCase__  =   num_frames
								lowerCAmelCase__  =   tubelet_size
								lowerCAmelCase__  =   num_channels
								lowerCAmelCase__  =   hidden_size
								lowerCAmelCase__  =   num_hidden_layers
								lowerCAmelCase__  =   num_attention_heads
								lowerCAmelCase__  =   intermediate_size
								lowerCAmelCase__  =   hidden_act
								lowerCAmelCase__  =   hidden_dropout_prob
								lowerCAmelCase__  =   attention_probs_dropout_prob
								lowerCAmelCase__  =   initializer_range
								lowerCAmelCase__  =   layer_norm_eps
								lowerCAmelCase__  =   qkv_bias
								lowerCAmelCase__  =   scope

								lowerCAmelCase__  =   (
								    (self.image_size // self.tubelet_size[2])
								    * (self.image_size // self.tubelet_size[1])
								    * (self.num_frames // self.tubelet_size[0])
								) + 1  # CLS token




	def      lowercase__							(  self		:  str					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]					)

								lowerCAmelCase__  =   None
								if self.use_labels:
															lowerCAmelCase__  =   ids_tensor([self.batch_size]   ,		self.num_labels					)

								lowerCAmelCase__  =   self.get_config()

								return config, pixel_values, labels




	def      lowercase__							(  self		:  str					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   VivitConfig(
								    num_frames=self.num_frames   ,		image_size=self.image_size   ,		tubelet_size=self.tubelet_size   ,		num_channels=self.num_channels   ,		hidden_size=self.hidden_size   ,		num_hidden_layers=self.num_hidden_layers   ,		num_attention_heads=self.num_attention_heads   ,		intermediate_size=self.intermediate_size   ,		hidden_act=self.hidden_act   ,		hidden_dropout_prob=self.hidden_dropout_prob   ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,		initializer_range=self.initializer_range   ,		layer_norm_eps=self.layer_norm_eps   ,		qkv_bias=self.qkv_bias   ,		)
								lowerCAmelCase__  =   self.num_labels
								return config




	def      lowercase__							(  self		:  Dict   ,		__A		:  Optional[Any]   ,		__A		:  Dict   ,		__A		:  str					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   VivitModel(config=__A					)
								model.to(__A					)
								model.eval()
								lowerCAmelCase__  =   model(__A					)
								self.parent.assertEqual(result.last_hidden_state.shape   ,		(self.batch_size, self.seq_length, self.hidden_size)					)




	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  Union[str, Any]   ,		__A		:  str   ,		__A		:  Dict					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   VivitForVideoClassification(__A					)
								model.to(__A					)
								model.eval()

								lowerCAmelCase__  =   model(__A					)

								# verify the logits shape
								lowerCAmelCase__  =   torch.Size((self.batch_size, self.num_labels)					)
								self.parent.assertEqual(result.logits.shape   ,		__A					)




	def      lowercase__							(  self		:  Dict					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.prepare_config_and_inputs()
								lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  =   config_and_inputs
								lowerCAmelCase__  =   {"""pixel_values""": pixel_values}
								return config, inputs_dict





@require_torch
class      lowerCamelCase__     (     _A,     unittest.TestCase				):






	'''simple docstring'''

	A__		=	(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

	A__		=	False
	A__		=	False
	A__		=	False
	A__		=	False




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




								'''simple docstring'''





								lowerCAmelCase__  =   VivitModelTester(self					)
								lowerCAmelCase__  =   ConfigTester(self   ,		config_class=__A   ,		has_text_modality=__A   ,		hidden_size=37					)




	def      lowercase__							(  self		:  str   ,		__A		:  Union[str, Any]   ,		__A		:  Any   ,		__A		:  str=False					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   copy.deepcopy(__A					)

								if return_labels:
															if model_class in get_values(__A					):
																						lowerCAmelCase__  =   torch.zeros(
																						    self.model_tester.batch_size   ,		dtype=torch.long   ,		device=__A					)

								return inputs_dict




	def      lowercase__							(  self		:  Any					)     ->			List[str]:




								'''simple docstring'''





								self.config_tester.run_common_tests()




	@unittest.skip(reason="""Vivit does not use inputs_embeds"""					)
	def      lowercase__							(  self		:  int					)     ->			Union[str, Any]:




								'''simple docstring'''





								pass




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




								'''simple docstring'''





								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowerCAmelCase__  =   model_class(__A					)
															self.assertIsInstance(model.get_input_embeddings()   ,		(nn.Module)					)
															lowerCAmelCase__  =   model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(__A   ,		nn.Linear					)					)




	def      lowercase__							(  self		:  List[str]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowerCAmelCase__  =   model_class(__A					)
															lowerCAmelCase__  =   inspect.signature(model.forward					)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															lowerCAmelCase__  =   [*signature.parameters.keys()]

															lowerCAmelCase__  =   ["""pixel_values""", """head_mask"""]
															self.assertListEqual(arg_names[:2]   ,		__A					)




	def      lowercase__							(  self		:  Union[str, Any]					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__A					)




	def      lowercase__							(  self		:  Optional[int]					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_video_classification(*__A					)




	@slow
	def      lowercase__							(  self		:  int					)     ->			Dict:




								'''simple docstring'''





								for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase__  =   VivitModel.from_pretrained(__A					)
															self.assertIsNotNone(__A					)




	def      lowercase__							(  self		:  List[Any]					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()
								lowerCAmelCase__  =   True

								for model_class in self.all_model_classes:
															lowerCAmelCase__  =   self.model_tester.seq_length

															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   False
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()
															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)
															lowerCAmelCase__  =   outputs.attentions
															self.assertEqual(len(__A					)   ,		self.model_tester.num_hidden_layers					)

															# check that output_attentions also work using config
															del inputs_dict["output_attentions"]
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()
															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)
															lowerCAmelCase__  =   outputs.attentions
															self.assertEqual(len(__A					)   ,		self.model_tester.num_hidden_layers					)

															self.assertListEqual(
															    list(attentions[0].shape[-3:]					)   ,		[self.model_tester.num_attention_heads, seq_len, seq_len]   ,		)
															lowerCAmelCase__  =   len(__A					)

															# Check attention is always last and order is fine
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   True
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()
															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)

															self.assertEqual(out_len + 1   ,		len(__A					)					)

															lowerCAmelCase__  =   outputs.attentions

															self.assertEqual(len(__A					)   ,		self.model_tester.num_hidden_layers					)

															self.assertListEqual(
															    list(self_attentions[0].shape[-3:]					)   ,		[self.model_tester.num_attention_heads, seq_len, seq_len]   ,		)




	def      lowercase__							(  self		:  int					)     ->			Tuple:




								'''simple docstring'''





								def check_hidden_states_output(__A		:  str   ,		__A		:  List[str]   ,		__A		:  Optional[int]					):
															lowerCAmelCase__  =   model_class(__A					)
															model.to(__A					)
															model.eval()

															with torch.no_grad():
																						lowerCAmelCase__  =   model(**self._prepare_for_class(__A   ,		__A					)					)

															lowerCAmelCase__  =   outputs.hidden_states
															lowerCAmelCase__  =   self.model_tester.num_hidden_layers + 1
															self.assertEqual(len(__A					)   ,		__A					)

															lowerCAmelCase__  =   self.model_tester.seq_length

															self.assertListEqual(
															    list(hidden_states[0].shape[-2:]					)   ,		[seq_length, self.model_tester.hidden_size]   ,		)

								lowerCAmelCase__  ,lowerCAmelCase__  =   self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowerCAmelCase__  =   True
															check_hidden_states_output(__A   ,		__A   ,		__A					)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowerCAmelCase__  =   True

															check_hidden_states_output(__A   ,		__A   ,		__A					)




	@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
	def      lowercase__							(  self		:  Tuple					)     ->			Tuple:




								'''simple docstring'''





								pass





def 			_lowerCAmelCase(      ) ->							Optional[int]:
							lowerCAmelCase__  =   hf_hub_download(
							    repo_id="""hf-internal-testing/spaghetti-video"""					, filename="""eating_spaghetti_32_frames.npy"""					, repo_type="""dataset"""						)
							lowerCAmelCase__  =   np.load(UpperCAmelCase_						)
							return list(UpperCAmelCase_						)






@require_torch
@require_vision
class      lowerCamelCase__     (     unittest.TestCase				):






	'''simple docstring'''

	@cached_property
	def      lowercase__							(  self		:  Tuple					)     ->			List[str]:




								'''simple docstring'''





								return VivitImageProcessor() if is_vision_available() else None




	@slow
	def      lowercase__							(  self		:  int					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   VivitForVideoClassification.from_pretrained("""google/vivit-b-16x2-kinetics400"""					).to(__A					)

								lowerCAmelCase__  =   self.default_image_processor
								lowerCAmelCase__  =   prepare_video()
								lowerCAmelCase__  =   image_processor(__A   ,		return_tensors="""pt"""					).to(__A					)

								# forward pass
								with torch.no_grad():
															lowerCAmelCase__  =   model(**__A					)

								# verify the logits
								lowerCAmelCase__  =   torch.Size((1, 400)					)
								self.assertEqual(outputs.logits.shape   ,		__A					)

								# taken from original model
								lowerCAmelCase__  =   torch.tensor([-1.0_5_4_3, 2.0_7_6_4, -0.2_1_0_4, 0.4_4_3_9, -0.9_6_5_8]					).to(__A					)

								self.assertTrue(torch.allclose(outputs.logits[0, :5]   ,		__A   ,		atol=1E-4					)					)










'''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      lowerCamelCase__     (     _A				):






	'''simple docstring'''

	def __init__(  self		:  Optional[Any]   ,		__A		:  AutoencoderKL   ,		__A		:  CLIPTextModel   ,		__A		:  CLIPTokenizer   ,		__A		:  UNetaDConditionModel   ,		__A		:  DDIMScheduler   ,		__A		:  float = 0.9_3   ,		__A		:  bool = True   ,		)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   mixing_coeff
								lowerCAmelCase__  =   leapfrog_steps

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

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




	def      lowercase__							(  self		:  Optional[int]   ,		__A		:  str   ,		__A		:  Optional[str] = None   ,		__A		:  bool = False					)     ->			List[str]:




								'''simple docstring'''





								lowerCAmelCase__  =   self.tokenizer(
								    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

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

															lowerCAmelCase__  =   self.tokenizer(
															    __A   ,		padding="""max_length"""   ,		max_length=self.tokenizer.model_max_length   ,		truncation=__A   ,		return_tensors="""pt"""   ,		)

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

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

								return prompt_embeds




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Tuple:




								'''simple docstring'''





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

								return [x, y]




	def      lowercase__							(  self		:  Dict   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





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

								return [x, y]




	def      lowercase__							(  self		:  List[str]   ,		__A		:  torch.Tensor					)     ->			Dict:




								'''simple docstring'''





								lowerCAmelCase__  =   int(__A					)

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

								return alpha_prod, 1 - alpha_prod




	def      lowercase__							(  self		:  List[Any]   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			int:




								'''simple docstring'''





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

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								lowerCAmelCase__  =   (base - b_t * model_output) / a_t

								return model_input, next_model_input.to(base.dtype					)




	def      lowercase__							(  self		:  Tuple   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		)     ->			List[str]:




								'''simple docstring'''





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

								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   self._get_alpha_and_beta(__A					)

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

								return model_input, next_model_input.to(base.dtype					)




	@torch.no_grad()
	def      lowercase__							(  self		:  int   ,		__A		:  torch.Tensor					)     ->			Union[str, Any]:




								'''simple docstring'''





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




	@torch.no_grad()
	def      lowercase__							(  self		:  List[str]   ,		__A		:  Image.Image   ,		__A		:  torch.Tensor   ,		__A		:  torch.Tensor   ,		__A		:  float   ,		__A		:  Optional[torch.Generator] = None   ,		)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   image.to(device=self.device   ,		dtype=text_embeds.dtype					)
								lowerCAmelCase__  =   self.vae.encode(__A					).latent_dist.sample(__A					)

								lowerCAmelCase__  =   self.vae.config.scaling_factor * latent

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

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

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

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.noise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

								return coupled_latents




	@torch.no_grad()
	def __call__(  self		:  List[Any]   ,		__A		:  str   ,		__A		:  str   ,		__A		:  Image.Image   ,		__A		:  float = 3.0   ,		__A		:  int = 50   ,		__A		:  float = 0.8   ,		__A		:  Optional[str] = None   ,		__A		:  Optional[torch.Generator] = None   ,		__A		:  Optional[str] = "pil"   ,		)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   guidance_scale > 1.0

								lowerCAmelCase__  =   self.image_processor.preprocess(__A					)

								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)
								lowerCAmelCase__  =   self._encode_prompt(__A   ,		__A   ,		__A					)

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

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

								lowerCAmelCase__  =   self.prepare_latents(__A   ,		__A   ,		__A   ,		__A   ,		__A					)

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

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

																						lowerCAmelCase__  =   coupled_latents[j]
																						lowerCAmelCase__  =   coupled_latents[k]

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

																						lowerCAmelCase__  =   self.unet(__A   ,		__A   ,		encoder_hidden_states=__A					).sample

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

																						lowerCAmelCase__  ,lowerCAmelCase__  =   self.denoise_step(
																						    base=__A   ,		model_input=__A   ,		model_output=__A   ,		timestep=__A   ,		)

																						lowerCAmelCase__  =   model_input

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

								# either one is fine
								lowerCAmelCase__  =   coupled_latents[0]

								if output_type not in ["latent", "pt", "np", "pil"]:
															lowerCAmelCase__  =   (
															    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"""   ,		__A   ,		standard_warn=__A					)
															lowerCAmelCase__  =   """np"""

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

								return image





