
"""simple docstring"""





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
       from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DPTImageProcessor


class 					UpperCAmelCase_     :


      def __init__(       self		,      a		,      a=2		,      a=3_2		,      a=1_6		,      a=3		,      a=True		,      a=True		,      a=3_2		,      a=4		,      a=[0, 1, 2, 3]		,      a=4		,      a=3_7		,      a="gelu"		,      a=0.1		,      a=0.1		,      a=0.02		,      a=3		,      a=False		,      a=None		,      )	-> Tuple:
             lowercase__      :		Any          =     parent
             lowercase__      :		Optional[Any]          =     batch_size
             lowercase__      :		int          =     image_size
             lowercase__      :		Tuple          =     patch_size
             lowercase__      :		List[str]          =     num_channels
             lowercase__      :		Optional[int]          =     is_training
             lowercase__      :		Optional[Any]          =     use_labels
             lowercase__      :		List[str]          =     hidden_size
             lowercase__      :		Union[str, Any]          =     num_hidden_layers
             lowercase__      :		Optional[Any]          =     backbone_out_indices
             lowercase__      :		Dict          =     num_attention_heads
             lowercase__      :		Union[str, Any]          =     intermediate_size
             lowercase__      :		Optional[int]          =     hidden_act
             lowercase__      :		Optional[Any]          =     hidden_dropout_prob
             lowercase__      :		Optional[int]          =     attention_probs_dropout_prob
             lowercase__      :		List[Any]          =     initializer_range
             lowercase__      :		Union[str, Any]          =     num_labels
             lowercase__      :		Any          =     scope
             lowercase__      :		Tuple          =     is_hybrid
             # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
             lowercase__      :		Any          =     (image_size // patch_size) ** 2
             lowercase__      :		List[str]          =     num_patches + 1


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             lowercase__      :		Any          =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             lowercase__      :		List[str]          =     None
             if self.use_labels:
                    lowercase__      :		List[Any]          =     ids_tensor([self.batch_size, self.image_size, self.image_size]		,      self.num_labels							)

             lowercase__      :		int          =     self.get_config()

             return config, pixel_values, labels


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             return DPTConfig(
                 image_size=self.image_size		,      patch_size=self.patch_size		,      num_channels=self.num_channels		,      hidden_size=self.hidden_size		,      num_hidden_layers=self.num_hidden_layers		,      backbone_out_indices=self.backbone_out_indices		,      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		,      is_decoder=__SCREAMING_SNAKE_CASE		,      initializer_range=self.initializer_range		,      is_hybrid=self.is_hybrid		,      )


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> Dict:
             lowercase__      :		Any          =     DPTModel(config=__SCREAMING_SNAKE_CASE							)
             model.to(__SCREAMING_SNAKE_CASE							)
             model.eval()
             lowercase__      :		List[str]          =     model(__SCREAMING_SNAKE_CASE							)
             self.parent.assertEqual(result.last_hidden_state.shape		,      (self.batch_size, self.seq_length, self.hidden_size)							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> str:
             lowercase__      :		Optional[int]          =     self.num_labels
             lowercase__      :		Union[str, Any]          =     DPTForDepthEstimation(__SCREAMING_SNAKE_CASE							)
             model.to(__SCREAMING_SNAKE_CASE							)
             model.eval()
             lowercase__      :		Tuple          =     model(__SCREAMING_SNAKE_CASE							)
             self.parent.assertEqual(result.predicted_depth.shape		,      (self.batch_size, self.image_size, self.image_size)							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a							)	-> str:
             lowercase__      :		Any          =     self.num_labels
             lowercase__      :		Optional[Any]          =     DPTForSemanticSegmentation(__SCREAMING_SNAKE_CASE							)
             model.to(__SCREAMING_SNAKE_CASE							)
             model.eval()
             lowercase__      :		str          =     model(__SCREAMING_SNAKE_CASE		,      labels=__SCREAMING_SNAKE_CASE							)
             self.parent.assertEqual(
                 result.logits.shape		,      (self.batch_size, self.num_labels, self.image_size, self.image_size)							)


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		Dict          =     self.prepare_config_and_inputs()
             lowercase__   ,				lowercase__   ,				lowercase__      :		int          =     config_and_inputs
             lowercase__      :		Union[str, Any]          =     {'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class 					UpperCAmelCase_     (   UpperCamelCase_			,							UpperCamelCase_			,							unittest.TestCase):
      lowerCamelCase__		:   Optional[Any]			  =  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      lowerCamelCase__		:   Dict			  =  (
          {
              """depth-estimation""": DPTForDepthEstimation,
              """feature-extraction""": DPTModel,
              """image-segmentation""": DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      lowerCamelCase__		:   str			  =  False
      lowerCamelCase__		:   List[Any]			  =  False
      lowerCamelCase__		:   Optional[int]			  =  False


      def 		_UpperCAmelCase	(       self							)	-> int:
             lowercase__      :		Any          =     DPTModelTester(self							)
             lowercase__      :		int          =     ConfigTester(self		,      config_class=__SCREAMING_SNAKE_CASE		,      has_text_modality=__SCREAMING_SNAKE_CASE		,      hidden_size=3_7							)


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


      @unittest.skip(reason='DPT does not use inputs_embeds'							)
      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             pass


      def 		_UpperCAmelCase	(       self							)	-> List[Any]:
             lowercase__   ,				lowercase__      :		List[str]          =     self.model_tester.prepare_config_and_inputs_for_common()

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


      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             lowercase__   ,				lowercase__      :		Dict          =     self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__      :		Any          =     ['pixel_values']
                    self.assertListEqual(arg_names[:1]		,      __SCREAMING_SNAKE_CASE							)


      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)


      def 		_UpperCAmelCase	(       self							)	-> List[str]:
             lowercase__      :		Any          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*__SCREAMING_SNAKE_CASE							)


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		List[Any]          =     self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*__SCREAMING_SNAKE_CASE							)


      def 		_UpperCAmelCase	(       self							)	-> Dict:
             for model_class in self.all_model_classes:
                    if model_class.__name__ == "DPTForDepthEstimation":
                           continue

                    lowercase__   ,				lowercase__      :		str          =     self.model_tester.prepare_config_and_inputs_for_common()
                    lowercase__      :		Tuple          =     True

                    if model_class in get_values(__SCREAMING_SNAKE_CASE							):
                           continue

                    lowercase__      :		Optional[int]          =     model_class(__SCREAMING_SNAKE_CASE							)
                    model.to(__SCREAMING_SNAKE_CASE							)
                    model.train()
                    lowercase__      :		Optional[Any]          =     self._prepare_for_class(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      return_labels=__SCREAMING_SNAKE_CASE							)
                    lowercase__      :		Tuple          =     model(**__SCREAMING_SNAKE_CASE							).loss
                    loss.backward()


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             for model_class in self.all_model_classes:
                    if model_class.__name__ == "DPTForDepthEstimation":
                           continue

                    lowercase__   ,				lowercase__      :		str          =     self.model_tester.prepare_config_and_inputs_for_common()
                    lowercase__      :		Optional[int]          =     False
                    lowercase__      :		str          =     True

                    if model_class in get_values(__SCREAMING_SNAKE_CASE							) or not model_class.supports_gradient_checkpointing:
                           continue
                    lowercase__      :		Dict          =     model_class(__SCREAMING_SNAKE_CASE							)
                    model.to(__SCREAMING_SNAKE_CASE							)
                    model.gradient_checkpointing_enable()
                    model.train()
                    lowercase__      :		List[str]          =     self._prepare_for_class(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE		,      return_labels=__SCREAMING_SNAKE_CASE							)
                    lowercase__      :		List[Any]          =     model(**__SCREAMING_SNAKE_CASE							).loss
                    loss.backward()


      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             lowercase__   ,				lowercase__      :		Dict          =     self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__      :		List[str]          =     _config_zero_init(__SCREAMING_SNAKE_CASE							)
             for model_class in self.all_model_classes:
                    lowercase__      :		Optional[int]          =     model_class(config=__SCREAMING_SNAKE_CASE							)
                    # Skip the check for the backbone
                    lowercase__      :		Optional[int]          =     []
                    for name, module in model.named_modules():
                           if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                                  lowercase__      :		Optional[int]          =     [f"""{name}.{key}""" for key in module.state_dict().keys()]
                                  break

                    for name, param in model.named_parameters():
                           if param.requires_grad:
                                  if name in backbone_params:
                                         continue
                                  self.assertIn(
                                      ((param.data.mean() * 1e9).round() / 1e9).item()		,      [0.0, 1.0]		,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,      )


      @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
      def 		_UpperCAmelCase	(       self							)	-> Optional[int]:
             pass


      @slow
      def 		_UpperCAmelCase	(       self							)	-> Optional[Any]:
             for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      :		List[str]          =     DPTModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
                    self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)





def     a_					(				):





       '''simple docstring'''

       lowercase__      :		Optional[int]          =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image






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


      def 		_UpperCAmelCase	(       self							)	-> Union[str, Any]:
             lowercase__      :		Tuple          =     DPTImageProcessor.from_pretrained('Intel/dpt-large'							)
             lowercase__      :		Optional[Any]          =     DPTForDepthEstimation.from_pretrained('Intel/dpt-large'							).to(__SCREAMING_SNAKE_CASE							)

             lowercase__      :		Optional[Any]          =     prepare_img()
             lowercase__      :		Any          =     image_processor(images=__SCREAMING_SNAKE_CASE		,      return_tensors='pt'							).to(__SCREAMING_SNAKE_CASE							)

             # forward pass
             with torch.no_grad():
                    lowercase__      :		str          =     model(**__SCREAMING_SNAKE_CASE							)
                    lowercase__      :		int          =     outputs.predicted_depth

             # verify the predicted depth
             lowercase__      :		Tuple          =     torch.Size((1, 3_8_4, 3_8_4)							)
             self.assertEqual(predicted_depth.shape		,      __SCREAMING_SNAKE_CASE							)

             lowercase__      :		str          =     torch.tensor(
                 [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]							).to(__SCREAMING_SNAKE_CASE							)

             self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1e-4							)							)


      def 		_UpperCAmelCase	(       self							)	-> Any:
             lowercase__      :		Tuple          =     DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'							)
             lowercase__      :		int          =     DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'							).to(__SCREAMING_SNAKE_CASE							)

             lowercase__      :		Any          =     prepare_img()
             lowercase__      :		Dict          =     image_processor(images=__SCREAMING_SNAKE_CASE		,      return_tensors='pt'							).to(__SCREAMING_SNAKE_CASE							)

             # forward pass
             with torch.no_grad():
                    lowercase__      :		Dict          =     model(**__SCREAMING_SNAKE_CASE							)

             # verify the logits
             lowercase__      :		Dict          =     torch.Size((1, 1_5_0, 4_8_0, 4_8_0)							)
             self.assertEqual(outputs.logits.shape		,      __SCREAMING_SNAKE_CASE							)

             lowercase__      :		List[str]          =     torch.tensor(
                 [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]							).to(__SCREAMING_SNAKE_CASE							)

             self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		,      __SCREAMING_SNAKE_CASE		,      atol=1e-4							)							)


      def 		_UpperCAmelCase	(       self							)	-> Tuple:
             lowercase__      :		Union[str, Any]          =     DPTImageProcessor.from_pretrained('Intel/dpt-large-ade'							)
             lowercase__      :		Union[str, Any]          =     DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade'							).to(__SCREAMING_SNAKE_CASE							)

             lowercase__      :		Optional[int]          =     prepare_img()
             lowercase__      :		Any          =     image_processor(images=__SCREAMING_SNAKE_CASE		,      return_tensors='pt'							).to(__SCREAMING_SNAKE_CASE							)

             # forward pass
             with torch.no_grad():
                    lowercase__      :		Optional[int]          =     model(**__SCREAMING_SNAKE_CASE							)

             lowercase__      :		Union[str, Any]          =     outputs.logits.detach().cpu()

             lowercase__      :		Any          =     image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE		,      target_sizes=[(5_0_0, 3_0_0)]							)
             lowercase__      :		Dict          =     torch.Size((5_0_0, 3_0_0)							)
             self.assertEqual(segmentation[0].shape		,      __SCREAMING_SNAKE_CASE							)

             lowercase__      :		Dict          =     image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE							)
             lowercase__      :		Any          =     torch.Size((4_8_0, 4_8_0)							)
             self.assertEqual(segmentation[0].shape		,      __SCREAMING_SNAKE_CASE							)


"""simple docstring"""





from typing import Optional

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

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


class 					UpperCAmelCase_     (   _a):


      def __init__(       self		,      a		,      a		,      a		,      a		,      a		,      a = 0.93		,      a = True		,      )	-> Any:
             lowercase__      :		Union[str, Any]          =     mixing_coeff
             lowercase__      :		Any          =     leapfrog_steps

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

             lowercase__      :		List[str]          =     2 ** (len(self.vae.config.block_out_channels							) - 1)
             lowercase__      :		Union[str, Any]          =     VaeImageProcessor(vae_scale_factor=self.vae_scale_factor							)


      def 		_UpperCAmelCase	(       self		,      a		,      a = None		,      a = False							)	-> Optional[int]:
             lowercase__      :		str          =     self.tokenizer(
                 a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

             lowercase__      :		str          =     self.text_encoder(text_inputs.input_ids.to(self.device							)							).last_hidden_state

             lowercase__      :		Any          =     prompt_embeds.to(dtype=self.text_encoder.dtype		,      device=self.device							)

             if do_classifier_free_guidance:
                    lowercase__      :		Tuple          =     '' if negative_prompt is None else negative_prompt

                    lowercase__      :		int          =     self.tokenizer(
                        a		,      padding='max_length'		,      max_length=self.tokenizer.model_max_length		,      truncation=a		,      return_tensors='pt'		,      )

                    lowercase__      :		str          =     self.text_encoder(uncond_input.input_ids.to(self.device							)							).last_hidden_state

                    lowercase__      :		str          =     torch.cat([negative_prompt_embeds, prompt_embeds]							)

             return prompt_embeds


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> Optional[Any]:
             lowercase__      :		int          =     self.mixing_coeff * x + (1 - self.mixing_coeff) * y
             lowercase__      :		Tuple          =     self.mixing_coeff * y + (1 - self.mixing_coeff) * x

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a		,      a							)	-> List[str]:
             lowercase__      :		str          =     (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
             lowercase__      :		Union[str, Any]          =     (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

             return [x, y]


      def 		_UpperCAmelCase	(       self		,      a							)	-> List[str]:
             # as self.alphas_cumprod is always in cpu
             lowercase__      :		Any          =     int(a							)

             lowercase__      :		Any          =     self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

             return alpha_prod, 1 - alpha_prod


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> int:
             lowercase__      :		List[Any]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[Any]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		str          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Tuple          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[int]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

             lowercase__      :		int          =     (base - b_t * model_output) / a_t

             return model_input, next_model_input.to(base.dtype							)


      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      )	-> Tuple:
             lowercase__      :		Optional[int]          =     timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

             lowercase__   ,				lowercase__      :		Optional[int]          =     self._get_alpha_and_beta(a							)
             lowercase__   ,				lowercase__      :		List[Any]          =     self._get_alpha_and_beta(a							)

             lowercase__      :		Union[str, Any]          =     (alpha_prod_t_prev / alpha_prod_t) ** 0.5
             lowercase__      :		Optional[Any]          =     -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
             lowercase__      :		List[str]          =     a_t * base + b_t * model_output

             return model_input, next_model_input.to(base.dtype							)


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a							)	-> Optional[Any]:
             lowercase__      :		Union[str, Any]          =     1 / self.vae.config.scaling_factor * latents
             lowercase__      :		Union[str, Any]          =     self.vae.decode(a							).sample
             lowercase__      :		int          =     (image / 2 + 0.5).clamp(0		,      1							)
             return image


      @torch.no_grad()
      def 		_UpperCAmelCase	(       self		,      a		,      a		,      a		,      a		,      a = None		,      )	-> Dict:
             lowercase__      :		List[str]          =     guidance_scale > 1.0

             lowercase__      :		Union[str, Any]          =     image.to(device=self.device		,      dtype=text_embeds.dtype							)
             lowercase__      :		Tuple          =     self.vae.encode(a							).latent_dist.sample(a							)

             lowercase__      :		Optional[Any]          =     self.vae.config.scaling_factor * latent

             lowercase__      :		Tuple          =     [latent.clone(), latent.clone()]

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

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

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

                           lowercase__      :		Union[str, Any]          =     coupled_latents[j]
                           lowercase__      :		Tuple          =     coupled_latents[k]

                           lowercase__      :		Dict          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		Union[str, Any]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		Optional[Any]          =     self.noise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		str          =     model_input

             return coupled_latents


      @torch.no_grad()
      def __call__(       self		,      a		,      a		,      a		,      a = 3.0		,      a = 5_0		,      a = 0.8		,      a = None		,      a = None		,      a = "pil"		,      )	-> Dict:
             lowercase__      :		List[Any]          =     guidance_scale > 1.0

             lowercase__      :		List[str]          =     self.image_processor.preprocess(a							)

             lowercase__      :		Union[str, Any]          =     self._encode_prompt(a		,      a		,      a							)
             lowercase__      :		int          =     self._encode_prompt(a		,      a		,      a							)

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

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

             lowercase__      :		int          =     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							):
                           lowercase__      :		Optional[Any]          =     k ^ 1

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

                           lowercase__      :		Optional[int]          =     coupled_latents[j]
                           lowercase__      :		int          =     coupled_latents[k]

                           lowercase__      :		List[Any]          =     torch.cat([model_input] * 2							) if do_classifier_free_guidance else model_input

                           lowercase__      :		List[str]          =     self.unet(a		,      a		,      encoder_hidden_states=a							).sample

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

                           lowercase__   ,				lowercase__      :		List[str]          =     self.denoise_step(
                               base=a		,      model_input=a		,      model_output=a		,      timestep=a		,      )

                           lowercase__      :		List[str]          =     model_input

                    lowercase__      :		Union[str, Any]          =     self.denoise_mixing_layer(x=coupled_latents[0]		,      y=coupled_latents[1]							)

             # either one is fine
             lowercase__      :		Optional[Any]          =     coupled_latents[0]

             if output_type not in ["latent", "pt", "np", "pil"]:
                    lowercase__      :		Any          =     (
                        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							)
                    lowercase__      :		str          =     'np'

             if output_type == "latent":
                    lowercase__      :		List[str]          =     final_latent
             else:
                    lowercase__      :		Optional[int]          =     self.decode_latents(a							)
                    lowercase__      :		Optional[int]          =     self.image_processor.postprocess(a		,      output_type=a							)

             return image

