




'''simple docstring'''




from ...utils import is_transformers_available
from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline


if is_transformers_available():
     from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline










'''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




