




import inspect
import unittest

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

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


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
				from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import GLPNImageProcessor




class     A					(				_A       ):







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

										"""simple docstring"""



										UpperCamelCase_     				= self.config_class(**self.inputs_dict				)
										self.parent.assertTrue(hasattr(__A   ,     'hidden_sizes'				)				)
										self.parent.assertTrue(hasattr(__A   ,     'num_attention_heads'				)				)
										self.parent.assertTrue(hasattr(__A   ,     'num_encoder_blocks'				)				)







class     A					:







							def __init__(    self   :	List[str]   ,     __UpperCAmelCase   :	Dict   ,     __UpperCAmelCase   :	Any=13   ,     __UpperCAmelCase   :	str=64   ,     __UpperCAmelCase   :	Any=3   ,     __UpperCAmelCase   :	Any=4   ,     __UpperCAmelCase   :	str=[2, 2, 2, 2]   ,     __UpperCAmelCase   :	str=[8, 4, 2, 1]   ,     __UpperCAmelCase   :	Union[str, Any]=[16, 32, 64, 128]   ,     __UpperCAmelCase   :	List[str]=[1, 4, 8, 16]   ,     __UpperCAmelCase   :	Tuple=[1, 2, 4, 8]   ,     __UpperCAmelCase   :	Tuple=True   ,     __UpperCAmelCase   :	List[str]=True   ,     __UpperCAmelCase   :	Optional[int]="gelu"   ,     __UpperCAmelCase   :	List[str]=0.1   ,     __UpperCAmelCase   :	List[str]=0.1   ,     __UpperCAmelCase   :	int=0.02   ,     __UpperCAmelCase   :	Optional[Any]=16   ,     __UpperCAmelCase   :	int=3   ,     __UpperCAmelCase   :	str=None   ,     )		->					Optional[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= parent
										UpperCamelCase_     				= batch_size
										UpperCamelCase_     				= image_size
										UpperCamelCase_     				= num_channels
										UpperCamelCase_     				= num_encoder_blocks
										UpperCamelCase_     				= sr_ratios
										UpperCamelCase_     				= depths
										UpperCamelCase_     				= hidden_sizes
										UpperCamelCase_     				= downsampling_rates
										UpperCamelCase_     				= num_attention_heads
										UpperCamelCase_     				= is_training
										UpperCamelCase_     				= use_labels
										UpperCamelCase_     				= hidden_act
										UpperCamelCase_     				= hidden_dropout_prob
										UpperCamelCase_     				= attention_probs_dropout_prob
										UpperCamelCase_     				= initializer_range
										UpperCamelCase_     				= decoder_hidden_size
										UpperCamelCase_     				= num_labels
										UpperCamelCase_     				= scope







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

										"""simple docstring"""



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

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

										UpperCamelCase_     				= self.get_config()
										return config, pixel_values, labels







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

										"""simple docstring"""



										return GLPNConfig(
										    image_size=self.image_size   ,     num_channels=self.num_channels   ,     num_encoder_blocks=self.num_encoder_blocks   ,     depths=self.depths   ,     hidden_sizes=self.hidden_sizes   ,     num_attention_heads=self.num_attention_heads   ,     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   ,     decoder_hidden_size=self.decoder_hidden_size   ,     )







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

										"""simple docstring"""



										UpperCamelCase_     				= GLPNModel(config=__A				)
										model.to(__A				)
										model.eval()
										UpperCamelCase_     				= model(__A				)
										UpperCamelCase_     				= UpperCamelCase_     				= self.image_size // (self.downsampling_rates[-1] * 2)
										self.parent.assertEqual(
										    result.last_hidden_state.shape   ,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)				)







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

										"""simple docstring"""



										UpperCamelCase_     				= self.num_labels
										UpperCamelCase_     				= GLPNForDepthEstimation(__A				)
										model.to(__A				)
										model.eval()
										UpperCamelCase_     				= model(__A				)
										self.parent.assertEqual(result.predicted_depth.shape   ,     (self.batch_size, self.image_size, self.image_size)				)
										UpperCamelCase_     				= model(__A   ,     labels=__A				)
										self.parent.assertEqual(result.predicted_depth.shape   ,     (self.batch_size, self.image_size, self.image_size)				)







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

										"""simple docstring"""



										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  ,						_A  ,						unittest.TestCase       ):
							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
							_SCREAMING_SNAKE_CASE  :		Union[str, Any]			  =       (
							    {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
							)

							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       False
							_SCREAMING_SNAKE_CASE  :		Any			  =       False
							_SCREAMING_SNAKE_CASE  :		Dict			  =       False







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

										"""simple docstring"""



										UpperCamelCase_     				= GLPNModelTester(self				)
										UpperCamelCase_     				= GLPNConfigTester(self   ,     config_class=__A				)







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

										"""simple docstring"""



										self.config_tester.run_common_tests()







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

										"""simple docstring"""



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







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

										"""simple docstring"""



										UpperCamelCase_     				= self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*__A				)







							@unittest.skip('GLPN does not use inputs_embeds'				)
							def        lowercase__							(    self   :	Optional[int]				)		->					List[Any]:

										"""simple docstring"""



										pass







							@unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'				)
							def        lowercase__							(    self   :	List[Any]				)		->					List[str]:

										"""simple docstring"""



										pass







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

										"""simple docstring"""



										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']
													self.assertListEqual(arg_names[:1]   ,     __A				)







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

										"""simple docstring"""



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

										for model_class in self.all_model_classes:
													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

													UpperCamelCase_     				= sum(self.model_tester.depths				)
													self.assertEqual(len(__A				)   ,     __A				)

													# 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				)   ,     __A				)

													# verify the first attentions (first block, first layer)
													UpperCamelCase_     				= (self.model_tester.image_size // 4) ** 2
													UpperCamelCase_     				= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(attentions[0].shape[-3:]				)   ,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,     )

													# verify the last attentions (last block, last layer)
													UpperCamelCase_     				= (self.model_tester.image_size // 32) ** 2
													UpperCamelCase_     				= (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
													self.assertListEqual(
													    list(attentions[-1].shape[-3:]				)   ,     [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_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				)   ,     __A				)
													# verify the first attentions (first block, first layer)
													UpperCamelCase_     				= (self.model_tester.image_size // 4) ** 2
													UpperCamelCase_     				= (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
													self.assertListEqual(
													    list(self_attentions[0].shape[-3:]				)   ,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]   ,     )







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

										"""simple docstring"""



										def check_hidden_states_output(__UpperCAmelCase   :	Dict   ,     __UpperCAmelCase   :	str   ,     __UpperCAmelCase   :	Optional[Any]				):
													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_encoder_blocks
													self.assertEqual(len(__A				)   ,     __A				)

													# verify the first hidden states (first block)
													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]				)   ,     [
													        self.model_tester.hidden_sizes[0],
													        self.model_tester.image_size // 4,
													        self.model_tester.image_size // 4,
													    ]   ,     )

										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				)







							def        lowercase__							(    self   :	Dict				)		->					Tuple:

										"""simple docstring"""



										if not self.model_tester.is_training:
													return

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

										for model_class in self.all_model_classes:
													if model_class in get_values(__A				):
																continue
													# TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
													# this can then be incorporated into _prepare_for_class in test_modeling_common.py
													if model_class.__name__ == "GLPNForDepthEstimation":
																UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_				,   UpperCamelCase_     				= inputs_dict['pixel_values'].shape
																UpperCamelCase_     				= torch.zeros(
																    [self.model_tester.batch_size, height, width]   ,     device=__A				).long()
													UpperCamelCase_     				= model_class(__A				)
													model.to(__A				)
													model.train()
													UpperCamelCase_     				= self._prepare_for_class(__A   ,     __A   ,     return_labels=__A				)
													UpperCamelCase_     				= model(**__A				).loss
													loss.backward()







							@slow
							def        lowercase__							(    self   :	List[Any]				)		->					Union[str, Any]:

										"""simple docstring"""



										for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCamelCase_     				= GLPNModel.from_pretrained(__A				)
													self.assertIsNotNone(__A				)







def  a_		(							)	->			str:

			'''simple docstring'''


			UpperCamelCase_     				= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
			return image





@require_torch
@require_vision
@slow
class     A					(				unittest.TestCase       ):







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

										"""simple docstring"""



										UpperCamelCase_     				= GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				)
										UpperCamelCase_     				= GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(__A				)

										UpperCamelCase_     				= prepare_img()
										UpperCamelCase_     				= image_processor(images=__A   ,     return_tensors='pt'				).to(__A				)

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

										# verify the predicted depth
										UpperCamelCase_     				= torch.Size([1, 480, 640]				)
										self.assertEqual(outputs.predicted_depth.shape   ,     __A				)

										UpperCamelCase_     				= torch.tensor(
										    [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]				).to(__A				)

										self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]   ,     __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





