





'''simple docstring'''




import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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,
      SegformerForImageClassification,
      SegformerForSemanticSegmentation,
      SegformerModel,
  )
  from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import SegformerImageProcessor







class   A_	(  __lowerCAmelCase			):




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   self.config_class(**self.inputs_dict    )
      self.parent.assertTrue(hasattr(snake_case_	,			"hidden_sizes"    )    )
      self.parent.assertTrue(hasattr(snake_case_	,			"num_attention_heads"    )    )
      self.parent.assertTrue(hasattr(snake_case_	,			"num_encoder_blocks"    )    )







class   A_	:




 def __init__(					self       :    List[str]	,			snake_case_       :    Optional[Any]	,			snake_case_       :    Tuple=1_3	,			snake_case_       :    Optional[int]=6_4	,			snake_case_       :    Dict=3	,			snake_case_       :    Dict=4	,			snake_case_       :    List[str]=[2, 2, 2, 2]	,			snake_case_       :    int=[8, 4, 2, 1]	,			snake_case_       :    int=[1_6, 3_2, 6_4, 1_2_8]	,			snake_case_       :    Dict=[1, 4, 8, 1_6]	,			snake_case_       :    Optional[Any]=[1, 2, 4, 8]	,			snake_case_       :    Any=True	,			snake_case_       :    Tuple=True	,			snake_case_       :    List[str]="gelu"	,			snake_case_       :    Dict=0.1	,			snake_case_       :    int=0.1	,			snake_case_       :    Optional[Any]=0.0_2	,			snake_case_       :    List[str]=3	,			snake_case_       :    Dict=None	,			):
      _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     							=   num_labels
      _UpperCAmelCase     							=   scope




 def      lowercase	(					self       :    int    ):
      _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       :    int    ):
      return SegformerConfig(
          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	,			)




 def      lowercase	(					self       :    int	,			snake_case_       :    str	,			snake_case_       :    str	,			snake_case_       :    Optional[int]    ):
      _UpperCAmelCase     							=   SegformerModel(config=snake_case_    )
      model.to(snake_case_    )
      model.eval()
      _UpperCAmelCase     							=   model(snake_case_    )
      _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       :    Dict	,			snake_case_       :    List[str]	,			snake_case_       :    Any	,			snake_case_       :    Optional[Any]    ):
      _UpperCAmelCase     							=   self.num_labels
      _UpperCAmelCase     							=   SegformerForSemanticSegmentation(snake_case_    )
      model.to(snake_case_    )
      model.eval()
      _UpperCAmelCase     							=   model(snake_case_    )
      self.parent.assertEqual(
          result.logits.shape	,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)    )
      _UpperCAmelCase     							=   model(snake_case_	,			labels=snake_case_    )
      self.parent.assertEqual(
          result.logits.shape	,			(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)    )
      self.parent.assertGreater(result.loss	,			0.0    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    int	,			snake_case_       :    List[Any]	,			snake_case_       :    List[str]    ):
      _UpperCAmelCase     							=   1
      _UpperCAmelCase     							=   SegformerForSemanticSegmentation(config=snake_case_    )
      model.to(snake_case_    )
      model.eval()
      _UpperCAmelCase     							=   torch.randint(0	,			1	,			(self.batch_size, self.image_size, self.image_size)    ).to(snake_case_    )
      _UpperCAmelCase     							=   model(snake_case_	,			labels=snake_case_    )
      self.parent.assertGreater(result.loss	,			0.0    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.prepare_config_and_inputs()
      _UpperCAmelCase     							=   config_and_inputs
      _UpperCAmelCase     							=   {'pixel_values': pixel_values}
      return config, inputs_dict







@require_torch
class   A_	(  __lowerCAmelCase							,						__lowerCAmelCase							,						unittest.TestCase			):
 _lowerCamelCase  : str           =							(
     (
         SegformerModel,
         SegformerForSemanticSegmentation,
         SegformerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 _lowerCamelCase  : Any           =							(
     {
         """feature-extraction""": SegformerModel,
         """image-classification""": SegformerForImageClassification,
         """image-segmentation""": SegformerForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 _lowerCamelCase  : int           =							True
 _lowerCamelCase  : int           =							False
 _lowerCamelCase  : List[Any]           =							False
 _lowerCamelCase  : List[Any]           =							False




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   SegformerModelTester(self    )
      _UpperCAmelCase     							=   SegformerConfigTester(self	,			config_class=snake_case_    )




 def      lowercase	(					self       :    Any    ):
      self.config_tester.run_common_tests()




 def      lowercase	(					self       :    Optional[Any]    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_    )




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_binary_image_segmentation(*snake_case_    )




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_segmentation(*snake_case_    )




 @unittest.skip("SegFormer does not use inputs_embeds"    )
 def      lowercase	(					self       :    Optional[Any]    ):
      pass




 @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"    )
 def      lowercase	(					self       :    int    ):
      pass




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _UpperCAmelCase     							=   model_class(snake_case_    )
           _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]	,			snake_case_    )




 def      lowercase	(					self       :    Tuple    ):
      _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(snake_case_    )
           model.to(snake_case_    )
           model.eval()
           with torch.no_grad():
                _UpperCAmelCase     							=   model(**self._prepare_for_class(snake_case_	,			snake_case_    )    )
           _UpperCAmelCase     							=   outputs.attentions

           _UpperCAmelCase     							=   sum(self.model_tester.depths    )
           self.assertEqual(len(snake_case_    )	,			snake_case_    )

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

           self.assertEqual(len(snake_case_    )	,			snake_case_    )

           # 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 // 3_2) ** 2
           _UpperCAmelCase     							=   (self.model_tester.image_size // (3_2 * 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(snake_case_    )

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

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

           _UpperCAmelCase     							=   outputs.attentions

           self.assertEqual(len(snake_case_    )	,			snake_case_    )
           # 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[Any]    ):





      def check_hidden_states_output(snake_case_       :    Tuple	,			snake_case_       :    Dict	,			snake_case_       :    Any    ):
           _UpperCAmelCase     							=   model_class(snake_case_    )
           model.to(snake_case_    )
           model.eval()

           with torch.no_grad():
                _UpperCAmelCase     							=   model(**self._prepare_for_class(snake_case_	,			snake_case_    )    )

           _UpperCAmelCase     							=   outputs.hidden_states

           _UpperCAmelCase     							=   self.model_tester.num_encoder_blocks
           self.assertEqual(len(snake_case_    )	,			snake_case_    )

           # 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     							=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _UpperCAmelCase     							=   True
           check_hidden_states_output(snake_case_	,			snake_case_	,			snake_case_    )

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

           check_hidden_states_output(snake_case_	,			snake_case_	,			snake_case_    )




 def      lowercase	(					self       :    List[str]    ):





      if not self.model_tester.is_training:
           return

      _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(snake_case_    ):
                continue

           _UpperCAmelCase     							=   model_class(snake_case_    )
           model.to(snake_case_    )
           model.train()
           _UpperCAmelCase     							=   self._prepare_for_class(snake_case_	,			snake_case_	,			return_labels=snake_case_    )
           _UpperCAmelCase     							=   model(**snake_case_    ).loss
           loss.backward()




 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
 def      lowercase	(					self       :    Optional[Any]    ):
      pass




 @slow
 def      lowercase	(					self       :    List[str]    ):





      for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase     							=   SegformerModel.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )





def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
     return image







@require_torch
class   A_	(  unittest.TestCase			):




 @slow
 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase     							=   SegformerImageProcessor(
          image_scale=(5_1_2, 5_1_2)	,			keep_ratio=snake_case_	,			align=snake_case_	,			do_random_crop=snake_case_    )
      _UpperCAmelCase     							=   SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"    ).to(
          snake_case_    )

      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(images=snake_case_	,			return_tensors="pt"    )
      _UpperCAmelCase     							=   encoded_inputs.pixel_values.to(snake_case_    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(snake_case_    )

      _UpperCAmelCase     							=   torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)    )
      self.assertEqual(outputs.logits.shape	,			snake_case_    )

      _UpperCAmelCase     							=   torch.tensor(
          [
              [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
              [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
              [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
          ]    ).to(snake_case_    )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]	,			snake_case_	,			atol=1e-4    )    )




 @slow
 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   SegformerImageProcessor(
          image_scale=(5_1_2, 5_1_2)	,			keep_ratio=snake_case_	,			align=snake_case_	,			do_random_crop=snake_case_    )
      _UpperCAmelCase     							=   SegformerForSemanticSegmentation.from_pretrained(
          "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"    ).to(snake_case_    )

      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(images=snake_case_	,			return_tensors="pt"    )
      _UpperCAmelCase     							=   encoded_inputs.pixel_values.to(snake_case_    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(snake_case_    )

      _UpperCAmelCase     							=   torch.Size((1, model.config.num_labels, 1_2_8, 1_2_8)    )
      self.assertEqual(outputs.logits.shape	,			snake_case_    )

      _UpperCAmelCase     							=   torch.tensor(
          [
              [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
              [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
              [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
          ]    ).to(snake_case_    )
      self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]	,			snake_case_	,			atol=1e-1    )    )




 @slow
 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   SegformerImageProcessor(
          image_scale=(5_1_2, 5_1_2)	,			keep_ratio=snake_case_	,			align=snake_case_	,			do_random_crop=snake_case_    )
      _UpperCAmelCase     							=   SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"    ).to(
          snake_case_    )

      _UpperCAmelCase     							=   prepare_img()
      _UpperCAmelCase     							=   image_processor(images=snake_case_	,			return_tensors="pt"    )
      _UpperCAmelCase     							=   encoded_inputs.pixel_values.to(snake_case_    )

      with torch.no_grad():
           _UpperCAmelCase     							=   model(snake_case_    )

      _UpperCAmelCase     							=   outputs.logits.detach().cpu()

      _UpperCAmelCase     							=   image_processor.post_process_semantic_segmentation(outputs=snake_case_	,			target_sizes=[(5_0_0, 3_0_0)]    )
      _UpperCAmelCase     							=   torch.Size((5_0_0, 3_0_0)    )
      self.assertEqual(segmentation[0].shape	,			snake_case_    )

      _UpperCAmelCase     							=   image_processor.post_process_semantic_segmentation(outputs=snake_case_    )
      _UpperCAmelCase     							=   torch.Size((1_2_8, 1_2_8)    )
      self.assertEqual(segmentation[0].shape	,			snake_case_    )





"""simple docstring"""
from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__					       =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  if isinstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor   ):
    return image
  elif isinstance(_SCREAMING_SNAKE_CASE		,     PIL.Image.Image   ):
    a__:     Optional[int]										=	[image]

  a__:     str										=	[trans(img.convert('RGB'   )   ) for img in image]
  a__:     Any										=	torch.stack(_SCREAMING_SNAKE_CASE   )
  return image




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Optional[int]:





        '''simple docstring'''





        super().__init__()

        # make sure scheduler can always be converted to DDIM
        a__:     Dict										=	DDIMScheduler.from_config(scheduler.config)

        self.register_modules(unet=lowercase    ,			scheduler=lowercase)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if strength < 0 or strength > 1:
          raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     int										=	min(int(num_inference_steps * strength)    ,			lowercase)

        a__:     Any										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     Union[str, Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        if not isinstance(lowercase    ,			(torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')

        a__:     Tuple										=	image.to(device=lowercase    ,			dtype=lowercase)

        if isinstance(lowercase    ,			lowercase) and len(lowercase) != batch_size:
          raise ValueError(
              f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
              f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

        a__:     List[str]										=	init_latents.shape
        a__:     List[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)

        # get latents
        print('add noise to latents at timestep'    ,			lowercase)
        a__:     int										=	self.scheduler.add_noise(lowercase    ,			lowercase    ,			lowercase)
        a__:     Dict										=	init_latents

        return latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase = None    ,			lowercase = 0.8    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = 0.0    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        self.check_inputs(lowercase)

        # 2. Preprocess image
        a__:     Tuple										=	preprocess(lowercase)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase    ,			device=self.device)
        a__							,    a__:     Union[str, Any]										=	self.get_timesteps(lowercase    ,			lowercase    ,			self.device)
        a__:     Optional[int]										=	timesteps[:1].repeat(lowercase)

        # 4. Prepare latent variables
        a__:     Union[str, Any]										=	self.prepare_latents(lowercase    ,			lowercase    ,			lowercase    ,			self.unet.dtype    ,			self.device    ,			lowercase)
        a__:     Optional[Any]										=	latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase):
          # 1. predict noise model_output
          a__:     Dict										=	self.unet(lowercase    ,			lowercase).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          a__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			eta=lowercase    ,			use_clipped_model_output=lowercase    ,			generator=lowercase    ,			).prev_sample

        a__:     Union[str, Any]										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Optional[int]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
        if output_type == "pil":
          a__:     Dict										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image, latent_timestep.item())

        return ImagePipelineOutput(images=lowercase)


