




'''simple docstring'''




import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
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 torch import nn

     from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
     from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor


class 						__UpperCamelCase :






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase=3,	lowerCAmelCase=32,	lowerCAmelCase=3,	lowerCAmelCase=10,	lowerCAmelCase=[10, 20, 30, 40],	lowerCAmelCase=[1, 1, 2, 1],	lowerCAmelCase=True,	lowerCAmelCase=True,	lowerCAmelCase="relu",	lowerCAmelCase=3,	lowerCAmelCase=None,	lowerCAmelCase=["stage2", "stage3", "stage4"],	lowerCAmelCase=[2, 3, 4],	):



              """simple docstring"""







              lowerCamelCase_							=parent
              lowerCamelCase_							=batch_size
              lowerCamelCase_							=image_size
              lowerCamelCase_							=num_channels
              lowerCamelCase_							=embeddings_size
              lowerCamelCase_							=hidden_sizes
              lowerCamelCase_							=depths
              lowerCamelCase_							=is_training
              lowerCamelCase_							=use_labels
              lowerCamelCase_							=hidden_act
              lowerCamelCase_							=num_labels
              lowerCamelCase_							=scope
              lowerCamelCase_							=len(lowerCAmelCase						)
              lowerCamelCase_							=out_features
              lowerCamelCase_							=out_indices






       def   lowercase__  (							self						):



              """simple docstring"""







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

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

              lowerCamelCase_							=self.get_config()

              return config, pixel_values, labels






       def   lowercase__  (							self						):



              """simple docstring"""







              return ResNetConfig(
                  num_channels=self.num_channels,	embeddings_size=self.embeddings_size,	hidden_sizes=self.hidden_sizes,	depths=self.depths,	hidden_act=self.hidden_act,	num_labels=self.num_labels,	out_features=self.out_features,	out_indices=self.out_indices,	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=ResNetModel(config=lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase						)
              # expected last hidden states: B, C, H // 32, W // 32
              self.parent.assertEqual(
                  result.last_hidden_state.shape,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.num_labels
              lowerCamelCase_							=ResNetForImageClassification(lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase,	labels=lowerCAmelCase						)
              self.parent.assertEqual(result.logits.shape,	(self.batch_size, self.num_labels)						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=ResNetBackbone(config=lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase						)

              # verify feature maps
              self.parent.assertEqual(len(result.feature_maps						),	len(config.out_features						)						)
              self.parent.assertListEqual(list(result.feature_maps[0].shape						),	[self.batch_size, self.hidden_sizes[1], 4, 4]						)

              # verify channels
              self.parent.assertEqual(len(model.channels						),	len(config.out_features						)						)
              self.parent.assertListEqual(model.channels,	config.hidden_sizes[1:]						)

              # verify backbone works with out_features=None
              lowerCamelCase_							=None
              lowerCamelCase_							=ResNetBackbone(config=lowerCAmelCase						)
              model.to(lowerCAmelCase						)
              model.eval()
              lowerCamelCase_							=model(lowerCAmelCase						)

              # verify feature maps
              self.parent.assertEqual(len(result.feature_maps						),	1						)
              self.parent.assertListEqual(list(result.feature_maps[0].shape						),	[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

              # verify channels
              self.parent.assertEqual(len(model.channels						),	1						)
              self.parent.assertListEqual(model.channels,	[config.hidden_sizes[-1]]						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.prepare_config_and_inputs()
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=config_and_inputs
              lowerCamelCase_							={'''pixel_values''': pixel_values}
              return config, inputs_dict





@require_torch
class 						__UpperCamelCase (				lowerCamelCase__    ,      lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					List[str]   		=(
           (
               ResNetModel,
               ResNetForImageClassification,
               ResNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       lowercase			:					Tuple   		=(
           {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
           if is_torch_available()
           else {}
       )

       lowercase			:					List[Any]   		=True
       lowercase			:					str   		=False
       lowercase			:					Any   		=False
       lowercase			:					Dict   		=False
       lowercase			:					Optional[int]   		=False






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=ResNetModelTester(self						)
              lowerCamelCase_							=ConfigTester(self,	config_class=lowerCAmelCase,	has_text_modality=lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              self.create_and_test_config_common_properties()
              self.config_tester.create_and_test_config_to_json_string()
              self.config_tester.create_and_test_config_to_json_file()
              self.config_tester.create_and_test_config_from_and_save_pretrained()
              self.config_tester.create_and_test_config_with_num_labels()
              self.config_tester.check_config_can_be_init_without_params()
              self.config_tester.check_config_arguments_init()






       def   lowercase__  (							self						):



              """simple docstring"""







              return






       @unittest.skip(reason='''ResNet does not use inputs_embeds'''						)
       def   lowercase__  (							self						):



              """simple docstring"""







              pass






       @unittest.skip(reason='''ResNet does not support input and output embeddings'''						)
       def   lowercase__  (							self						):



              """simple docstring"""







              pass






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

                     lowerCamelCase_							=['''pixel_values''']
                     self.assertListEqual(arg_names[:1],	lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_backbone(*lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowerCamelCase_							=model_class(config=lowerCAmelCase						)
                     for name, module in model.named_modules():
                            if isinstance(lowerCAmelCase,	(nn.BatchNormad, nn.GroupNorm)						):
                                   self.assertTrue(
                                       torch.all(module.weight == 1						),	msg=f'''Parameter {name} of model {model_class} seems not properly initialized''',	)
                                   self.assertTrue(
                                       torch.all(module.bias == 0						),	msg=f'''Parameter {name} of model {model_class} seems not properly initialized''',	)






       def   lowercase__  (							self						):



              """simple docstring"""







              def check_hidden_states_output(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):
                     lowerCamelCase_							=model_class(lowerCAmelCase						)
                     model.to(lowerCAmelCase						)
                     model.eval()

                     with torch.no_grad():
                            lowerCamelCase_							=model(**self._prepare_for_class(lowerCAmelCase,	lowerCAmelCase						)						)

                     lowerCamelCase_							=outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                     lowerCamelCase_							=self.model_tester.num_stages
                     self.assertEqual(len(lowerCAmelCase						),	expected_num_stages + 1						)

                     # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
                     self.assertListEqual(
                         list(hidden_states[0].shape[-2:]						),	[self.model_tester.image_size // 4, self.model_tester.image_size // 4],	)

              lowerCamelCase_,   lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()
              lowerCamelCase_							=['''basic''', '''bottleneck''']
              for model_class in self.all_model_classes:
                     for layer_type in layers_type:
                            lowerCamelCase_							=layer_type
                            lowerCamelCase_							=True
                            check_hidden_states_output(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)

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

                            check_hidden_states_output(lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)






       @unittest.skip(reason='''ResNet does not use feedforward chunking'''						)
       def   lowercase__  (							self						):



              """simple docstring"""







              pass






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase						)






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     lowerCamelCase_							=ResNetModel.from_pretrained(lowerCAmelCase						)
                     self.assertIsNotNone(lowerCAmelCase						)





def   a_							(   )							->		Optional[int]:



       """simple docstring"""
       lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
       return image


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






       @cached_property
       def   lowercase__  (							self						):



              """simple docstring"""







              return (
                  AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
                  if is_vision_available()
                  else None
              )






       @slow
       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(lowerCAmelCase						)

              lowerCamelCase_							=self.default_image_processor
              lowerCamelCase_							=prepare_img()
              lowerCamelCase_							=image_processor(images=lowerCAmelCase,	return_tensors='''pt'''						).to(lowerCAmelCase						)

              # forward pass
              with torch.no_grad():
                     lowerCamelCase_							=model(**lowerCAmelCase						)

              # verify the logits
              lowerCamelCase_							=torch.Size((1, 1_000)						)
              self.assertEqual(outputs.logits.shape,	lowerCAmelCase						)

              lowerCamelCase_							=torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]						).to(lowerCAmelCase						)

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





@require_torch
class 						__UpperCamelCase (				lowerCamelCase__    ,      unittest.TestCase       ):
       lowercase			:					Any   		=(ResNetBackbone,) if is_torch_available() else ()
       lowercase			:					str   		=False
       lowercase			:					Optional[int]   		=ResNetConfig






       def   lowercase__  (							self						):



              """simple docstring"""







              lowerCamelCase_							=ResNetModelTester(self						)










'''simple docstring'''




import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


a_   :	Optional[Any]							   = logging.get_logger(__name__)  # pylint: disable=invalid-name





def   a_							(   self							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    List[str]     , __snake_case							:    Dict     , __snake_case							:    str     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=image.to(device=__snake_case     )
       lowerCamelCase_							=image  # take image as image_embeddings
       lowerCamelCase_							=image_embeddings.unsqueeze(1     )

       # duplicate image embeddings for each generation per prompt, using mps friendly method
       lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=image_embeddings.shape
       lowerCamelCase_							=image_embeddings.repeat(1     , __snake_case     , 1     )
       lowerCamelCase_							=image_embeddings.view(bs_embed * num_images_per_prompt     , __snake_case     , -1     )

       if do_classifier_free_guidance:
              lowerCamelCase_							=torch.zeros_like(__snake_case     )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              lowerCamelCase_							=torch.cat([uncond_embeddings, image_embeddings]     )

       return image_embeddings


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	):



              """simple docstring"""







              super().__init__()

              lowerCamelCase_							={'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

              lowerCamelCase_							=decoder_pipe_kwargs.get('''torch_dtype''',	lowerCAmelCase						) or prior.dtype

              lowerCamelCase_							=StableDiffusionImageVariationPipeline.from_pretrained(
                  '''lambdalabs/sd-image-variations-diffusers''',	**lowerCAmelCase						)

              # replace `_encode_image` method
              lowerCamelCase_							=types.MethodType(_encode_image,	self.decoder_pipe						)

              self.register_modules(
                  prior=lowerCAmelCase,	tokenizer=lowerCAmelCase,	text_encoder=lowerCAmelCase,	prior_scheduler=lowerCAmelCase,	)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	lowerCAmelCase = None,	):



              """simple docstring"""







              if text_model_output is None:
                     lowerCamelCase_							=len(lowerCAmelCase						) if isinstance(lowerCAmelCase,	lowerCAmelCase						) else 1
                     # get prompt text embeddings
                     lowerCamelCase_							=self.tokenizer(
                         lowerCAmelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	return_tensors='''pt''',	)
                     lowerCamelCase_							=text_inputs.input_ids
                     lowerCamelCase_							=text_inputs.attention_mask.bool().to(lowerCAmelCase						)

                     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                            lowerCamelCase_							=self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
                            logger.warning(
                                '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                                f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''						)
                            lowerCamelCase_							=text_input_ids[:, : self.tokenizer.model_max_length]

                     lowerCamelCase_							=self.text_encoder(text_input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=text_encoder_output.text_embeds
                     lowerCamelCase_							=text_encoder_output.last_hidden_state

              else:
                     lowerCamelCase_							=text_model_output[0].shape[0]
                     lowerCamelCase_,   lowerCamelCase_							=text_model_output[0], text_model_output[1]
                     lowerCamelCase_							=text_attention_mask

              lowerCamelCase_							=text_embeddings.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_encoder_hidden_states.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_mask.repeat_interleave(lowerCAmelCase,	dim=0						)

              if do_classifier_free_guidance:
                     lowerCamelCase_							=[''''''] * batch_size

                     lowerCamelCase_							=self.tokenizer(
                         lowerCAmelCase,	padding='''max_length''',	max_length=self.tokenizer.model_max_length,	truncation=lowerCAmelCase,	return_tensors='''pt''',	)
                     lowerCamelCase_							=uncond_input.attention_mask.bool().to(lowerCAmelCase						)
                     lowerCamelCase_							=self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.text_embeds
                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.last_hidden_state

                     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

                     lowerCamelCase_							=uncond_embeddings.shape[1]
                     lowerCamelCase_							=uncond_embeddings.repeat(1,	lowerCAmelCase						)
                     lowerCamelCase_							=uncond_embeddings.view(batch_size * num_images_per_prompt,	lowerCAmelCase						)

                     lowerCamelCase_							=uncond_text_encoder_hidden_states.shape[1]
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.repeat(1,	lowerCAmelCase,	1						)
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.view(
                         batch_size * num_images_per_prompt,	lowerCAmelCase,	-1						)
                     lowerCamelCase_							=uncond_text_mask.repeat_interleave(lowerCAmelCase,	dim=0						)

                     # done duplicates

                     # For classifier free guidance, we need to do two forward passes.
                     # Here we concatenate the unconditional and text embeddings into a single batch
                     # to avoid doing two forward passes
                     lowerCamelCase_							=torch.cat([uncond_embeddings, text_embeddings]						)
                     lowerCamelCase_							=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

                     lowerCamelCase_							=torch.cat([uncond_text_mask, text_mask]						)

              return text_embeddings, text_encoder_hidden_states, text_mask






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              if self.device != torch.device('''meta'''						) or not hasattr(self.prior,	'''_hf_hook'''						):
                     return self.device
              for module in self.prior.modules():
                     if (
                         hasattr(lowerCAmelCase,	'''_hf_hook'''						)
                         and hasattr(module._hf_hook,	'''execution_device'''						)
                         and module._hf_hook.execution_device is not None
                     ):
                            return torch.device(module._hf_hook.execution_device						)
              return self.device






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              if latents is None:
                     lowerCamelCase_							=randn_tensor(lowerCAmelCase,	generator=lowerCAmelCase,	device=lowerCAmelCase,	dtype=lowerCAmelCase						)
              else:
                     if latents.shape != shape:
                            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
                     lowerCamelCase_							=latents.to(lowerCAmelCase						)

              lowerCamelCase_							=latents * scheduler.init_noise_sigma
              return latents






       def   lowercase__  (							self,	lowerCAmelCase = None						):



              """simple docstring"""







              self.decoder_pipe.to(lowerCAmelCase						)
              super().to(lowerCAmelCase						)






       @torch.no_grad()
       def __call__(							self,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 1,	lowerCAmelCase = 25,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 4.0,	lowerCAmelCase = 8.0,	lowerCAmelCase = 50,	lowerCAmelCase = 1,	lowerCAmelCase = 0.0,	lowerCAmelCase = "pil",	lowerCAmelCase = True,	):



              """simple docstring"""







              if prompt is not None:
                     if isinstance(lowerCAmelCase,	lowerCAmelCase						):
                            lowerCamelCase_							=1
                     elif isinstance(lowerCAmelCase,	lowerCAmelCase						):
                            lowerCamelCase_							=len(lowerCAmelCase						)
                     else:
                            raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase						)}'''						)
              else:
                     lowerCamelCase_							=text_model_output[0].shape[0]

              lowerCamelCase_							=self._execution_device

              lowerCamelCase_							=batch_size * num_images_per_prompt

              lowerCamelCase_							=prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._encode_prompt(
                  lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)

              # prior

              self.prior_scheduler.set_timesteps(lowerCAmelCase,	device=lowerCAmelCase						)
              lowerCamelCase_							=self.prior_scheduler.timesteps

              lowerCamelCase_							=self.prior.config.embedding_dim

              lowerCamelCase_							=self.prepare_latents(
                  (batch_size, embedding_dim),	text_embeddings.dtype,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	self.prior_scheduler,	)

              for i, t in enumerate(self.progress_bar(lowerCAmelCase						)						):
                     # expand the latents if we are doing classifier free guidance
                     lowerCamelCase_							=torch.cat([prior_latents] * 2						) if do_classifier_free_guidance else prior_latents

                     lowerCamelCase_							=self.prior(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	proj_embedding=lowerCAmelCase,	encoder_hidden_states=lowerCAmelCase,	attention_mask=lowerCAmelCase,	).predicted_image_embedding

                     if do_classifier_free_guidance:
                            lowerCamelCase_,   lowerCamelCase_							=predicted_image_embedding.chunk(2						)
                            lowerCamelCase_							=predicted_image_embedding_uncond + prior_guidance_scale * (
                                predicted_image_embedding_text - predicted_image_embedding_uncond
                            )

                     if i + 1 == prior_timesteps_tensor.shape[0]:
                            lowerCamelCase_							=None
                     else:
                            lowerCamelCase_							=prior_timesteps_tensor[i + 1]

                     lowerCamelCase_							=self.prior_scheduler.step(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	sample=lowerCAmelCase,	generator=lowerCAmelCase,	prev_timestep=lowerCAmelCase,	).prev_sample

              lowerCamelCase_							=self.prior.post_process_latents(lowerCAmelCase						)

              lowerCamelCase_							=prior_latents

              lowerCamelCase_							=self.decoder_pipe(
                  image=lowerCAmelCase,	height=lowerCAmelCase,	width=lowerCAmelCase,	num_inference_steps=lowerCAmelCase,	guidance_scale=lowerCAmelCase,	generator=lowerCAmelCase,	output_type=lowerCAmelCase,	return_dict=lowerCAmelCase,	num_images_per_prompt=lowerCAmelCase,	eta=lowerCAmelCase,	)
              return output




