



"""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  A_     :



						"""simple docstring"""






						def __init__(   self					:List[Any]  ,    lowercase_					:List[str]  ,    lowercase_					:Union[str, Any]=2  ,    lowercase_					:int=True  ,    lowercase_					:Optional[int]=True  ,    lowercase_					:Tuple=10  ,    lowercase_					:Any=10  ,    lowercase_					:Dict=8  ,    lowercase_					:Tuple=[2, 4, 4]  ,    lowercase_					:List[str]=3  ,    lowercase_					:Dict=7_68  ,    lowercase_					:Optional[Any]=5  ,    lowercase_					:str=4  ,    lowercase_					:str=37  ,    lowercase_					:Union[str, Any]="gelu_fast"  ,    lowercase_					:List[str]=0.0  ,    lowercase_					:List[str]=0.0  ,    lowercase_					:str=0.02  ,    lowercase_					:int=1E-06  ,    lowercase_					:str=True  ,    lowercase_					:Any=None  ,    )							->     int:
												UpperCAmelCase			 =					parent
												UpperCAmelCase			 =					batch_size
												UpperCAmelCase			 =					is_training
												UpperCAmelCase			 =					use_labels
												UpperCAmelCase			 =					num_labels
												UpperCAmelCase			 =					image_size
												UpperCAmelCase			 =					num_frames
												UpperCAmelCase			 =					tubelet_size
												UpperCAmelCase			 =					num_channels
												UpperCAmelCase			 =					hidden_size
												UpperCAmelCase			 =					num_hidden_layers
												UpperCAmelCase			 =					num_attention_heads
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					hidden_dropout_prob
												UpperCAmelCase			 =					attention_probs_dropout_prob
												UpperCAmelCase			 =					initializer_range
												UpperCAmelCase			 =					layer_norm_eps
												UpperCAmelCase			 =					qkv_bias
												UpperCAmelCase			 =					scope

												UpperCAmelCase			 =					(
												    (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       UpperCAmelCase__	(   self					:str   )							->     Any:
												UpperCAmelCase			 =					floats_tensor(
												    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]   )

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

												UpperCAmelCase			 =					self.get_config()

												return config, pixel_values, labels






						def       UpperCAmelCase__	(   self					:str   )							->     Dict:
												UpperCAmelCase			 =					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  ,    )
												UpperCAmelCase			 =					self.num_labels
												return config






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:Optional[Any]  ,    lowercase_					:Dict  ,    lowercase_					:str   )							->     Tuple:
												UpperCAmelCase			 =					VivitModel(config=__A   )
												model.to(__A   )
												model.eval()
												UpperCAmelCase			 =					model(__A   )
												self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)   )






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:str  ,    lowercase_					:Dict   )							->     Optional[Any]:
												UpperCAmelCase			 =					VivitForVideoClassification(__A   )
												model.to(__A   )
												model.eval()

												UpperCAmelCase			 =					model(__A   )

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






						def       UpperCAmelCase__	(   self					:Dict   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.prepare_config_and_inputs()
												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					config_and_inputs
												UpperCAmelCase			 =					{'pixel_values': pixel_values}
												return config, inputs_dict







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



						"""simple docstring"""
						__UpperCamelCase													=				(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False
						__UpperCamelCase													=				False






						def       UpperCAmelCase__	(   self					:str   )							->     List[str]:
												UpperCAmelCase			 =					VivitModelTester(self   )
												UpperCAmelCase			 =					ConfigTester(self  ,    config_class=__A  ,    has_text_modality=__A  ,    hidden_size=37   )






						def       UpperCAmelCase__	(   self					:str  ,    lowercase_					:Union[str, Any]  ,    lowercase_					:Any  ,    lowercase_					:str=False   )							->     Dict:
												UpperCAmelCase			 =					copy.deepcopy(__A   )

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

												return inputs_dict






						def       UpperCAmelCase__	(   self					:Any   )							->     List[str]:
												self.config_tester.run_common_tests()






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






						def       UpperCAmelCase__	(   self					:Optional[Any]   )							->     Any:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

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






						def       UpperCAmelCase__	(   self					:List[str]   )							->     Optional[int]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

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

																		UpperCAmelCase			 =					['pixel_values', 'head_mask']
																		self.assertListEqual(arg_names[:2]  ,    __A   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A   )






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Optional[Any]:
												UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_video_classification(*__A   )






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





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






						def       UpperCAmelCase__	(   self					:List[Any]   )							->     Optional[int]:
												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()
												UpperCAmelCase			 =					True

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

																		UpperCAmelCase			 =					True
																		UpperCAmelCase			 =					False
																		UpperCAmelCase			 =					True
																		UpperCAmelCase			 =					model_class(__A   )
																		model.to(__A   )
																		model.eval()
																		with torch.no_grad():
																								UpperCAmelCase			 =					model(**self._prepare_for_class(__A  ,    __A   )   )
																		UpperCAmelCase			 =					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"]
																		UpperCAmelCase			 =					True
																		UpperCAmelCase			 =					model_class(__A   )
																		model.to(__A   )
																		model.eval()
																		with torch.no_grad():
																								UpperCAmelCase			 =					model(**self._prepare_for_class(__A  ,    __A   )   )
																		UpperCAmelCase			 =					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]  ,    )
																		UpperCAmelCase			 =					len(__A   )

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

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

																		UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:int   )							->     Tuple:





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

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

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

																		UpperCAmelCase			 =					self.model_tester.seq_length

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

												UpperCAmelCase ,       UpperCAmelCase			 =					self.model_tester.prepare_config_and_inputs_for_common()

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

																		# check that output_hidden_states also work using config
																		del inputs_dict["output_hidden_states"]
																		UpperCAmelCase			 =					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       UpperCAmelCase__	(   self					:Tuple   )							->     Tuple:
												pass


def 					_lowerCAmelCase (  ):
						UpperCAmelCase			 =					hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'						,      filename='eating_spaghetti_32_frames.npy'						,      repo_type='dataset'		)
						UpperCAmelCase			 =					np.load(UpperCAmelCase_		)
						return list(UpperCAmelCase_		)







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



						"""simple docstring"""






						@cached_property
						def       UpperCAmelCase__	(   self					:Tuple   )							->     List[str]:
												return VivitImageProcessor() if is_vision_available() else None






						@slow
						def       UpperCAmelCase__	(   self					:int   )							->     Optional[Any]:
												UpperCAmelCase			 =					VivitForVideoClassification.from_pretrained('google/vivit-b-16x2-kinetics400'   ).to(__A   )

												UpperCAmelCase			 =					self.default_image_processor
												UpperCAmelCase			 =					prepare_video()
												UpperCAmelCase			 =					image_processor(__A  ,    return_tensors='pt'   ).to(__A   )

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

												# verify the logits
												UpperCAmelCase			 =					torch.Size((1, 4_00)   )
												self.assertEqual(outputs.logits.shape  ,    __A   )

												# taken from original model
												UpperCAmelCase			 =					torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]   ).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





