

"""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


lowerCamelCase__				:  int													=  logging.get_logger(__name__)  # pylint: disable=invalid-name
def       UpperCamelCase					(			self     :						Optional[int],  _lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						Optional[int],  _lowerCAmelCase     :						int )				->			Optional[int]:
 _UpperCAmelCase    :							Optional[int]									=					image.to(device=_lowerCAmelCase )
 _UpperCAmelCase    :							Union[str, Any]									=					image  # take image as image_embeddings
 _UpperCAmelCase    :							Optional[int]									=					image_embeddings.unsqueeze(1 )

 # duplicate image embeddings for each generation per prompt, using mps friendly method
 _UpperCAmelCase    :							int									=					image_embeddings.shape
 _UpperCAmelCase    :							Dict									=					image_embeddings.repeat(1,  _lowerCAmelCase,  1 )
 _UpperCAmelCase    :							Dict									=					image_embeddings.view(bs_embed * num_images_per_prompt,  _lowerCAmelCase,  -1 )

 if do_classifier_free_guidance:
  _UpperCAmelCase    :							List[Any]									=					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
  _UpperCAmelCase    :							Dict									=					torch.cat([uncond_embeddings, image_embeddings] )

 return image_embeddings






class     _UpperCAmelCase			(		__a):
   def __init__(		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = None      ,		)    ->    Union[str, Any]:






    '''simple docstring'''







    super().__init__()

    _UpperCAmelCase    :							Any									=					{"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

    _UpperCAmelCase    :							Any									=					decoder_pipe_kwargs.get("""torch_dtype"""      ,		_A     ) or prior.dtype

    _UpperCAmelCase    :							str									=					StableDiffusionImageVariationPipeline.from_pretrained(
        """lambdalabs/sd-image-variations-diffusers"""      ,		**_A     )

    # replace `_encode_image` method
    _UpperCAmelCase    :							List[Any]									=					types.MethodType(_encode_image      ,		self.decoder_pipe     )

    self.register_modules(
        prior=_A      ,		tokenizer=_A      ,		text_encoder=_A      ,		prior_scheduler=_A      ,		)
   def 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A = None      ,		_A = None      ,		)    ->    Union[str, Any]:






    '''simple docstring'''







    if text_model_output is None:
     _UpperCAmelCase    :							str									=					len(_A     ) if isinstance(_A      ,		_A     ) else 1
     # get prompt text embeddings
     _UpperCAmelCase    :							Any									=					self.tokenizer(
         _A      ,		padding="""max_length"""      ,		max_length=self.tokenizer.model_max_length      ,		return_tensors="""pt"""      ,		)
     _UpperCAmelCase    :							int									=					text_inputs.input_ids
     _UpperCAmelCase    :							int									=					text_inputs.attention_mask.bool().to(_A     )

     if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
      _UpperCAmelCase    :							Tuple									=					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    :							Tuple									=					text_input_ids[:, : self.tokenizer.model_max_length]

     _UpperCAmelCase    :							List[str]									=					self.text_encoder(text_input_ids.to(_A     )     )

     _UpperCAmelCase    :							List[Any]									=					text_encoder_output.text_embeds
     _UpperCAmelCase    :							Dict									=					text_encoder_output.last_hidden_state

    else:
     _UpperCAmelCase    :							Union[str, Any]									=					text_model_output[0].shape[0]
     _UpperCAmelCase    :							int									=					text_model_output[0], text_model_output[1]
     _UpperCAmelCase    :							Optional[int]									=					text_attention_mask

    _UpperCAmelCase    :							Tuple									=					text_embeddings.repeat_interleave(_A      ,		dim=0     )
    _UpperCAmelCase    :							Tuple									=					text_encoder_hidden_states.repeat_interleave(_A      ,		dim=0     )
    _UpperCAmelCase    :							Tuple									=					text_mask.repeat_interleave(_A      ,		dim=0     )

    if do_classifier_free_guidance:
     _UpperCAmelCase    :							Dict									=					[""""""] * batch_size

     _UpperCAmelCase    :							Dict									=					self.tokenizer(
         _A      ,		padding="""max_length"""      ,		max_length=self.tokenizer.model_max_length      ,		truncation=_A      ,		return_tensors="""pt"""      ,		)
     _UpperCAmelCase    :							List[str]									=					uncond_input.attention_mask.bool().to(_A     )
     _UpperCAmelCase    :							Tuple									=					self.text_encoder(uncond_input.input_ids.to(_A     )     )

     _UpperCAmelCase    :							List[Any]									=					uncond_embeddings_text_encoder_output.text_embeds
     _UpperCAmelCase    :							Optional[Any]									=					uncond_embeddings_text_encoder_output.last_hidden_state

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

     _UpperCAmelCase    :							Dict									=					uncond_embeddings.shape[1]
     _UpperCAmelCase    :							Tuple									=					uncond_embeddings.repeat(1      ,		_A     )
     _UpperCAmelCase    :							Any									=					uncond_embeddings.view(batch_size * num_images_per_prompt      ,		_A     )

     _UpperCAmelCase    :							List[str]									=					uncond_text_encoder_hidden_states.shape[1]
     _UpperCAmelCase    :							Optional[Any]									=					uncond_text_encoder_hidden_states.repeat(1      ,		_A      ,		1     )
     _UpperCAmelCase    :							Optional[Any]									=					uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt      ,		_A      ,		-1     )
     _UpperCAmelCase    :							List[Any]									=					uncond_text_mask.repeat_interleave(_A      ,		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    :							Any									=					torch.cat([uncond_embeddings, text_embeddings]     )
     _UpperCAmelCase    :							Any									=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]     )

     _UpperCAmelCase    :							List[Any]									=					torch.cat([uncond_text_mask, text_mask]     )

    return text_embeddings, text_encoder_hidden_states, text_mask
   @property
   def 							__snake_case (		self     )    ->    Union[str, Any]:






    '''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(_A      ,		"""_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 							__snake_case (		self      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A     )    ->    Optional[int]:






    '''simple docstring'''







    if latents is None:
     _UpperCAmelCase    :							Optional[int]									=					randn_tensor(_A      ,		generator=_A      ,		device=_A      ,		dtype=_A     )
    else:
     if latents.shape != shape:
      raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
     _UpperCAmelCase    :							Dict									=					latents.to(_A     )

    _UpperCAmelCase    :							List[Any]									=					latents * scheduler.init_noise_sigma
    return latents
   def 							__snake_case (		self      ,		_A = None     )    ->    Tuple:






    '''simple docstring'''







    self.decoder_pipe.to(_A     )
    super().to(_A     )
   @torch.no_grad()
   def __call__(		self      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = 1      ,		_A = 25      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = None      ,		_A = 4.0      ,		_A = 8.0      ,		_A = 50      ,		_A = 1      ,		_A = 0.0      ,		_A = "pil"      ,		_A = True      ,		)    ->    int:






    '''simple docstring'''







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

    _UpperCAmelCase    :							Any									=					self._execution_device

    _UpperCAmelCase    :							int									=					batch_size * num_images_per_prompt

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

    _UpperCAmelCase    :							str									=					self._encode_prompt(
        _A      ,		_A      ,		_A      ,		_A      ,		_A      ,		_A     )

    # prior

    self.prior_scheduler.set_timesteps(_A      ,		device=_A     )
    _UpperCAmelCase    :							Optional[int]									=					self.prior_scheduler.timesteps

    _UpperCAmelCase    :							Tuple									=					self.prior.config.embedding_dim

    _UpperCAmelCase    :							Dict									=					self.prepare_latents(
        (batch_size, embedding_dim)      ,		text_embeddings.dtype      ,		_A      ,		_A      ,		_A      ,		self.prior_scheduler      ,		)

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

     _UpperCAmelCase    :							Any									=					self.prior(
         _A      ,		timestep=_A      ,		proj_embedding=_A      ,		encoder_hidden_states=_A      ,		attention_mask=_A      ,		).predicted_image_embedding

     if do_classifier_free_guidance:
      _UpperCAmelCase    :							Dict									=					predicted_image_embedding.chunk(2     )
      _UpperCAmelCase    :							Optional[int]									=					predicted_image_embedding_uncond + prior_guidance_scale * (
          predicted_image_embedding_text - predicted_image_embedding_uncond
      )

     if i + 1 == prior_timesteps_tensor.shape[0]:
      _UpperCAmelCase    :							Optional[Any]									=					None
     else:
      _UpperCAmelCase    :							Dict									=					prior_timesteps_tensor[i + 1]

     _UpperCAmelCase    :							str									=					self.prior_scheduler.step(
         _A      ,		timestep=_A      ,		sample=_A      ,		generator=_A      ,		prev_timestep=_A      ,		).prev_sample

    _UpperCAmelCase    :							List[Any]									=					self.prior.post_process_latents(_A     )

    _UpperCAmelCase    :							Any									=					prior_latents

    _UpperCAmelCase    :							Any									=					self.decoder_pipe(
        image=_A      ,		height=_A      ,		width=_A      ,		num_inference_steps=_A      ,		guidance_scale=_A      ,		generator=_A      ,		output_type=_A      ,		return_dict=_A      ,		num_images_per_prompt=_A      ,		eta=_A      ,		)
    return output








"""simple docstring"""






import safs






class     _UpperCAmelCase			(		safs.SaFileSystem):
			pass





