



"""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


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

def 					_lowerCAmelCase (  self						,      lowercase_						,      lowercase_						,      lowercase_						,      lowercase_		):
						UpperCAmelCase			 =					image.to(device=lowercase_		)
						UpperCAmelCase			 =					image  # take image as image_embeddings
						UpperCAmelCase			 =					image_embeddings.unsqueeze(1		)

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

						if do_classifier_free_guidance:
												UpperCAmelCase			 =					torch.zeros_like(lowercase_		)

												# 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
												UpperCAmelCase			 =					torch.cat([uncond_embeddings, image_embeddings]		)

						return image_embeddings







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:Optional[Any]  ,    lowercase_					:PriorTransformer  ,    lowercase_					:CLIPTokenizer  ,    lowercase_					:CLIPTextModelWithProjection  ,    lowercase_					:UnCLIPScheduler  ,    lowercase_					:Optional[dict] = None  ,    )							->     Union[str, Any]:
												super().__init__()

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

												UpperCAmelCase			 =					decoder_pipe_kwargs.get('torch_dtype'  ,    lowercase_   ) or prior.dtype

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

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

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






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

																		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																								UpperCAmelCase			 =					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}"""   )
																								UpperCAmelCase			 =					text_input_ids[:, : self.tokenizer.model_max_length]

																		UpperCAmelCase			 =					self.text_encoder(text_input_ids.to(lowercase_   )   )

																		UpperCAmelCase			 =					text_encoder_output.text_embeds
																		UpperCAmelCase			 =					text_encoder_output.last_hidden_state

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

												UpperCAmelCase			 =					text_embeddings.repeat_interleave(lowercase_  ,    dim=0   )
												UpperCAmelCase			 =					text_encoder_hidden_states.repeat_interleave(lowercase_  ,    dim=0   )
												UpperCAmelCase			 =					text_mask.repeat_interleave(lowercase_  ,    dim=0   )

												if do_classifier_free_guidance:
																		UpperCAmelCase			 =					[''] * batch_size

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

																		UpperCAmelCase			 =					uncond_embeddings_text_encoder_output.text_embeds
																		UpperCAmelCase			 =					uncond_embeddings_text_encoder_output.last_hidden_state

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

																		UpperCAmelCase			 =					uncond_embeddings.shape[1]
																		UpperCAmelCase			 =					uncond_embeddings.repeat(1  ,    lowercase_   )
																		UpperCAmelCase			 =					uncond_embeddings.view(batch_size * num_images_per_prompt  ,    lowercase_   )

																		UpperCAmelCase			 =					uncond_text_encoder_hidden_states.shape[1]
																		UpperCAmelCase			 =					uncond_text_encoder_hidden_states.repeat(1  ,    lowercase_  ,    1   )
																		UpperCAmelCase			 =					uncond_text_encoder_hidden_states.view(
																		    batch_size * num_images_per_prompt  ,    lowercase_  ,    -1   )
																		UpperCAmelCase			 =					uncond_text_mask.repeat_interleave(lowercase_  ,    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
																		UpperCAmelCase			 =					torch.cat([uncond_embeddings, text_embeddings]   )
																		UpperCAmelCase			 =					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

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

												return text_embeddings, text_encoder_hidden_states, text_mask






						@property
						def       UpperCAmelCase__	(   self					:Tuple   )							->     Optional[Any]:
												if self.device != torch.device('meta'   ) or not hasattr(self.prior  ,    '_hf_hook'   ):
																		return self.device
												for module in self.prior.modules():
																		if (
																		    hasattr(lowercase_  ,    '_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       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:List[str]  ,    lowercase_					:str  ,    lowercase_					:Any  ,    lowercase_					:int  ,    lowercase_					:List[Any]  ,    lowercase_					:Tuple   )							->     Any:
												if latents is None:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=lowercase_  ,    dtype=lowercase_   )
												else:
																		if latents.shape != shape:
																								raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""   )
																		UpperCAmelCase			 =					latents.to(lowercase_   )

												UpperCAmelCase			 =					latents * scheduler.init_noise_sigma
												return latents






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:Optional[Union[str, torch.device]] = None   )							->     str:
												self.decoder_pipe.to(lowercase_   )
												super().to(lowercase_   )






						@torch.no_grad()
						def __call__(   self					:Optional[Any]  ,    lowercase_					:Optional[Union[str, List[str]]] = None  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:Optional[int] = None  ,    lowercase_					:int = 1  ,    lowercase_					:int = 25  ,    lowercase_					:Optional[torch.Generator] = None  ,    lowercase_					:Optional[torch.FloatTensor] = None  ,    lowercase_					:Optional[Union[CLIPTextModelOutput, Tuple]] = None  ,    lowercase_					:Optional[torch.Tensor] = None  ,    lowercase_					:float = 4.0  ,    lowercase_					:float = 8.0  ,    lowercase_					:int = 50  ,    lowercase_					:Optional[int] = 1  ,    lowercase_					:float = 0.0  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     List[Any]:
												if prompt is not None:
																		if isinstance(lowercase_  ,    lowercase_   ):
																								UpperCAmelCase			 =					1
																		elif isinstance(lowercase_  ,    lowercase_   ):
																								UpperCAmelCase			 =					len(lowercase_   )
																		else:
																								raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_   )}"""   )
												else:
																		UpperCAmelCase			 =					text_model_output[0].shape[0]

												UpperCAmelCase			 =					self._execution_device

												UpperCAmelCase			 =					batch_size * num_images_per_prompt

												UpperCAmelCase			 =					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

												UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase			 =					self._encode_prompt(
												    lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_  ,    lowercase_   )

												# prior

												self.prior_scheduler.set_timesteps(lowercase_  ,    device=lowercase_   )
												UpperCAmelCase			 =					self.prior_scheduler.timesteps

												UpperCAmelCase			 =					self.prior.config.embedding_dim

												UpperCAmelCase			 =					self.prepare_latents(
												    (batch_size, embedding_dim)  ,    text_embeddings.dtype  ,    lowercase_  ,    lowercase_  ,    lowercase_  ,    self.prior_scheduler  ,    )

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

																		UpperCAmelCase			 =					self.prior(
																		    lowercase_  ,    timestep=lowercase_  ,    proj_embedding=lowercase_  ,    encoder_hidden_states=lowercase_  ,    attention_mask=lowercase_  ,    ).predicted_image_embedding

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

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

																		UpperCAmelCase			 =					self.prior_scheduler.step(
																		    lowercase_  ,    timestep=lowercase_  ,    sample=lowercase_  ,    generator=lowercase_  ,    prev_timestep=lowercase_  ,    ).prev_sample

												UpperCAmelCase			 =					self.prior.post_process_latents(lowercase_   )

												UpperCAmelCase			 =					prior_latents

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










"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





