




"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import BlipImageProcessor

class  __lowercase    (     unittest.TestCase		):


				'''simple docstring'''


				def __init__(				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Any ,SCREAMING_SNAKE_CASE_				:       str=7 ,SCREAMING_SNAKE_CASE_				:       Dict=3 ,SCREAMING_SNAKE_CASE_				:       Any=1_8 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=3_0 ,SCREAMING_SNAKE_CASE_				:       List[Any]=4_0_0 ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=True ,SCREAMING_SNAKE_CASE_				:       Dict=None ,SCREAMING_SNAKE_CASE_				:       Optional[Any]=True ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=False ,SCREAMING_SNAKE_CASE_				:       Dict=[0.48145466, 0.4578275, 0.40821073] ,SCREAMING_SNAKE_CASE_				:       Any=[0.26862954, 0.26130258, 0.27577711] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]=True ,):
										snake_case__    :   List[Any]							     =   size if size is not None else {'''height''': 2_0, '''width''': 2_0}
										snake_case__    :   List[str]							     =   parent
										snake_case__    :   List[str]							     =   batch_size
										snake_case__    :   List[str]							     =   num_channels
										snake_case__    :   Any							     =   image_size
										snake_case__    :   Dict							     =   min_resolution
										snake_case__    :   Optional[int]							     =   max_resolution
										snake_case__    :   List[Any]							     =   do_resize
										snake_case__    :   Tuple							     =   size
										snake_case__    :   str							     =   do_normalize
										snake_case__    :   Union[str, Any]							     =   image_mean
										snake_case__    :   List[str]							     =   image_std
										snake_case__    :   Tuple							     =   do_pad
										snake_case__    :   List[str]							     =   do_convert_rgb

				def  _lowerCamelCase   (				self				:       List[Any] ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										    "do_pad": self.do_pad,
										}





				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any]=False ,SCREAMING_SNAKE_CASE_				:       Optional[int]=False ,SCREAMING_SNAKE_CASE_				:       Any=False ):
										assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

										if equal_resolution:
																snake_case__    :   Optional[int]							     =   []
																for i in range(self.batch_size ):
																						image_inputs.append(
																						    np.random.randint(
																						        2_5_5 ,size=(self.num_channels, self.max_resolution, self.max_resolution) ,dtype=np.uinta ) )
										else:
																snake_case__    :   Dict							     =   []
																for i in range(self.batch_size ):
																						snake_case__     ,		snake_case__    :   Any							     =   np.random.choice(np.arange(self.min_resolution ,self.max_resolution ) ,2 )
																						image_inputs.append(np.random.randint(2_5_5 ,size=(self.num_channels, width, height) ,dtype=np.uinta ) )

										if not numpify and not torchify:
																# PIL expects the channel dimension as last dimension
																snake_case__    :   List[Any]							     =   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_ ,0 ,-1 ) ) for x in image_inputs]

										if torchify:
																snake_case__    :   List[Any]							     =   [torch.from_numpy(SCREAMING_SNAKE_CASE_ ) for x in image_inputs]

										return image_inputs







@require_torch
@require_vision
class  __lowercase    (     snake_case_  ,				unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase		     =BlipImageProcessor if is_vision_available() else None

				def  _lowerCamelCase   (				self				:       int ):
										snake_case__    :   List[str]							     =   BlipImageProcessingTester(self )

				@property
				def  _lowerCamelCase   (				self				:       Optional[int] ):
										return self.image_processor_tester.prepare_image_processor_dict()

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										snake_case__    :   List[Any]							     =   self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''do_resize''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''size''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''do_normalize''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''image_mean''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''image_std''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''do_convert_rgb''' ) )

				def  _lowerCamelCase   (				self				:       List[Any] ):
										pass

				def  _lowerCamelCase   (				self				:       List[str] ):
										# Initialize image_processor
										snake_case__    :   int							     =   self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										snake_case__    :   Optional[Any]							     =   self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,Image.Image )

										# Test not batched input
										snake_case__    :   str							     =   image_processor(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)

										# Test batched
										snake_case__    :   int							     =   image_processor(SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)

				def  _lowerCamelCase   (				self				:       List[Any] ):
										# Initialize image_processor
										snake_case__    :   Optional[Any]							     =   self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										snake_case__    :   int							     =   self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ ,numpify=SCREAMING_SNAKE_CASE_ )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,np.ndarray )

										# Test not batched input
										snake_case__    :   List[str]							     =   image_processor(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)

										# Test batched
										snake_case__    :   Dict							     =   image_processor(SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)





				def  _lowerCamelCase   (				self				:       Any ):
										# Initialize image_processor
										snake_case__    :   Any							     =   self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										snake_case__    :   List[str]							     =   self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ ,torchify=SCREAMING_SNAKE_CASE_ )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,torch.Tensor )

										# Test not batched input
										snake_case__    :   List[str]							     =   image_processor(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)

										# Test batched
										snake_case__    :   Any							     =   image_processor(SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)







@require_torch
@require_vision
class  __lowercase    (     snake_case_  ,				unittest.TestCase		):


				'''simple docstring'''


				_lowerCAmelCase		     =BlipImageProcessor if is_vision_available() else None

				def  _lowerCamelCase   (				self				:       List[Any] ):
										snake_case__    :   Union[str, Any]							     =   BlipImageProcessingTester(self ,num_channels=4 )
										snake_case__    :   Tuple							     =   3

				@property
				def  _lowerCamelCase   (				self				:       Tuple ):
										return self.image_processor_tester.prepare_image_processor_dict()

				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										snake_case__    :   Optional[int]							     =   self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''do_resize''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''size''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''do_normalize''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''image_mean''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''image_std''' ) )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_ ,'''do_convert_rgb''' ) )

				def  _lowerCamelCase   (				self				:       str ):
										pass





				def  _lowerCamelCase   (				self				:       List[Any] ):
										# Initialize image_processor
										snake_case__    :   str							     =   self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										snake_case__    :   Optional[int]							     =   self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE_ )
										for image in image_inputs:
																self.assertIsInstance(SCREAMING_SNAKE_CASE_ ,Image.Image )

										# Test not batched input
										snake_case__    :   Tuple							     =   image_processor(image_inputs[0] ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        1,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)

										# Test batched
										snake_case__    :   Optional[int]							     =   image_processor(SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape ,(
										        self.image_processor_tester.batch_size,
										        self.expected_encoded_image_num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    ) ,)








"""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


_lowercase		 =      logging.get_logger(__name__)  # pylint: disable=invalid-name

def 						lowerCAmelCase							(	self ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Optional[Any]							     =   image.to(device=lowerCamelCase						)
						snake_case__    :   List[Any]							     =   image  # take image as image_embeddings
						snake_case__    :   Tuple							     =   image_embeddings.unsqueeze(1						)

						# duplicate image embeddings for each generation per prompt, using mps friendly method
						snake_case__     ,		snake_case__     ,		snake_case__    :   int							     =   image_embeddings.shape
						snake_case__    :   List[Any]							     =   image_embeddings.repeat(1 ,     lowerCamelCase ,     1						)
						snake_case__    :   int							     =   image_embeddings.view(bs_embed * num_images_per_prompt ,     lowerCamelCase ,     -1						)

						if do_classifier_free_guidance:
												snake_case__    :   Tuple							     =   torch.zeros_like(lowerCamelCase						)

												# 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
												snake_case__    :   Any							     =   torch.cat([uncond_embeddings, image_embeddings]						)

						return image_embeddings

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def __init__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       PriorTransformer ,SCREAMING_SNAKE_CASE_				:       CLIPTokenizer ,SCREAMING_SNAKE_CASE_				:       CLIPTextModelWithProjection ,SCREAMING_SNAKE_CASE_				:       UnCLIPScheduler ,SCREAMING_SNAKE_CASE_				:       Optional[dict] = None ,):
										super().__init__()

										snake_case__    :   Optional[Any]							     =   {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										snake_case__    :   List[Any]							     =   decoder_pipe_kwargs.get('''torch_dtype''' ,SCREAMING_SNAKE_CASE_ ) or prior.dtype

										snake_case__    :   str							     =   StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers''' ,**SCREAMING_SNAKE_CASE_ )

										# replace `_encode_image` method
										snake_case__    :   Tuple							     =   types.MethodType(_encode_image ,self.decoder_pipe )

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

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,):
										if text_model_output is None:
																snake_case__    :   str							     =   len(SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else 1
																# get prompt text embeddings
																snake_case__    :   Dict							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,return_tensors='''pt''' ,)
																snake_case__    :   List[Any]							     =   text_inputs.input_ids
																snake_case__    :   List[str]							     =   text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						snake_case__    :   Optional[int]							     =   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}''' )
																						snake_case__    :   str							     =   text_input_ids[:, : self.tokenizer.model_max_length]

																snake_case__    :   Tuple							     =   self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Any							     =   text_encoder_output.text_embeds
																snake_case__    :   List[Any]							     =   text_encoder_output.last_hidden_state

										else:
																snake_case__    :   List[Any]							     =   text_model_output[0].shape[0]
																snake_case__     ,		snake_case__    :   List[Any]							     =   text_model_output[0], text_model_output[1]
																snake_case__    :   Tuple							     =   text_attention_mask

										snake_case__    :   Tuple							     =   text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )

										if do_classifier_free_guidance:
																snake_case__    :   Optional[Any]							     =   [''''''] * batch_size

																snake_case__    :   str							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,truncation=SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ,)
																snake_case__    :   Union[str, Any]							     =   uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Tuple							     =   self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Optional[Any]							     =   uncond_embeddings_text_encoder_output.text_embeds
																snake_case__    :   Optional[int]							     =   uncond_embeddings_text_encoder_output.last_hidden_state

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

																snake_case__    :   Tuple							     =   uncond_embeddings.shape[1]
																snake_case__    :   int							     =   uncond_embeddings.repeat(1 ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[int]							     =   uncond_embeddings.view(batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ )

																snake_case__    :   Tuple							     =   uncond_text_encoder_hidden_states.shape[1]
																snake_case__    :   int							     =   uncond_text_encoder_hidden_states.repeat(1 ,SCREAMING_SNAKE_CASE_ ,1 )
																snake_case__    :   Optional[Any]							     =   uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ ,-1 )
																snake_case__    :   int							     =   uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,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
																snake_case__    :   Dict							     =   torch.cat([uncond_embeddings, text_embeddings] )
																snake_case__    :   Dict							     =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

																snake_case__    :   Optional[int]							     =   torch.cat([uncond_text_mask, text_mask] )

										return text_embeddings, text_encoder_hidden_states, text_mask

				@property
				def  _lowerCamelCase   (				self				:       Dict ):
										if self.device != torch.device('''meta''' ) or not hasattr(self.prior ,'''_hf_hook''' ):
																return self.device
										for module in self.prior.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_ ,'''_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  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										if latents is None:
																snake_case__    :   str							     =   randn_tensor(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )
										else:
																if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
																snake_case__    :   Any							     =   latents.to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Optional[Any]							     =   latents * scheduler.init_noise_sigma
										return latents

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, torch.device]] = None ):
										self.decoder_pipe.to(SCREAMING_SNAKE_CASE_ )
										super().to(SCREAMING_SNAKE_CASE_ )





				@torch.no_grad()
				def __call__(				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, List[str]]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       int = 2_5 ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.FloatTensor] = None ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,SCREAMING_SNAKE_CASE_				:       float = 4.0 ,SCREAMING_SNAKE_CASE_				:       float = 8.0 ,SCREAMING_SNAKE_CASE_				:       int = 5_0 ,SCREAMING_SNAKE_CASE_				:       Optional[int] = 1 ,SCREAMING_SNAKE_CASE_				:       float = 0.0 ,SCREAMING_SNAKE_CASE_				:       Optional[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										if prompt is not None:
																if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   Dict							     =   1
																elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   List[Any]							     =   len(SCREAMING_SNAKE_CASE_ )
																else:
																						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
										else:
																snake_case__    :   Tuple							     =   text_model_output[0].shape[0]

										snake_case__    :   List[str]							     =   self._execution_device

										snake_case__    :   str							     =   batch_size * num_images_per_prompt

										snake_case__    :   List[str]							     =   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										snake_case__     ,		snake_case__     ,		snake_case__    :   Tuple							     =   self._encode_prompt(
										    SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

										# prior

										self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ )
										snake_case__    :   str							     =   self.prior_scheduler.timesteps

										snake_case__    :   str							     =   self.prior.config.embedding_dim

										snake_case__    :   Optional[int]							     =   self.prepare_latents(
										    (batch_size, embedding_dim) ,text_embeddings.dtype ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,self.prior_scheduler ,)

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

																snake_case__    :   Optional[int]							     =   self.prior(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,proj_embedding=SCREAMING_SNAKE_CASE_ ,encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,).predicted_image_embedding

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

																if i + 1 == prior_timesteps_tensor.shape[0]:
																						snake_case__    :   str							     =   None
																else:
																						snake_case__    :   Tuple							     =   prior_timesteps_tensor[i + 1]

																snake_case__    :   List[Any]							     =   self.prior_scheduler.step(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,sample=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,prev_timestep=SCREAMING_SNAKE_CASE_ ,).prev_sample

										snake_case__    :   Union[str, Any]							     =   self.prior.post_process_latents(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Any							     =   prior_latents

										snake_case__    :   Dict							     =   self.decoder_pipe(
										    image=SCREAMING_SNAKE_CASE_ ,height=SCREAMING_SNAKE_CASE_ ,width=SCREAMING_SNAKE_CASE_ ,num_inference_steps=SCREAMING_SNAKE_CASE_ ,guidance_scale=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,output_type=SCREAMING_SNAKE_CASE_ ,return_dict=SCREAMING_SNAKE_CASE_ ,num_images_per_prompt=SCREAMING_SNAKE_CASE_ ,eta=SCREAMING_SNAKE_CASE_ ,)
										return output


