'''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[int]								=  logging.get_logger(__name__)  # pylint: disable=invalid-name


def  lowerCamelCase__						(  self :				List[Any]    ,				SCREAMING_SNAKE_CASE :				List[str]    ,				SCREAMING_SNAKE_CASE :				Optional[int]    ,				SCREAMING_SNAKE_CASE :				Optional[Any]    ,				SCREAMING_SNAKE_CASE :				Any  ):
  UpperCAmelCase  							=		image.to(device=SCREAMING_SNAKE_CASE_  )
  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    ,				SCREAMING_SNAKE_CASE_    ,				1  )
  UpperCAmelCase  							=		image_embeddings.view(bs_embed * num_images_per_prompt    ,				SCREAMING_SNAKE_CASE_    ,				-1  )

  if do_classifier_free_guidance:
    UpperCAmelCase  							=		torch.zeros_like(SCREAMING_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
    UpperCAmelCase  							=		torch.cat([uncond_embeddings, image_embeddings]  )

  return image_embeddings



class     lowercase_		(						a	):

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_				,	a_				,	a_				,	a_ = None				,	)							->			List[str]:


    """simple docstring"""
    super().__init__()

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

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

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

    # replace `_encode_image` method
    UpperCAmelCase  							=		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				,	)							->			Any:


    """simple docstring"""






    if text_model_output is None:
      UpperCAmelCase  							=		len(a_       ) if isinstance(a_				,	a_       ) else 1
      # get prompt text embeddings
      UpperCAmelCase  							=		self.tokenizer(
          a_				,	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(a_       )

      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(a_       )       )

      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(a_				,	dim=0       )
    UpperCAmelCase  							=		text_encoder_hidden_states.repeat_interleave(a_				,	dim=0       )
    UpperCAmelCase  							=		text_mask.repeat_interleave(a_				,	dim=0       )

    if do_classifier_free_guidance:
      UpperCAmelCase  							=		[''] * batch_size

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

      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				,	a_       )
      UpperCAmelCase  							=		uncond_embeddings.view(batch_size * num_images_per_prompt				,	a_       )

      UpperCAmelCase  							=		uncond_text_encoder_hidden_states.shape[1]
      UpperCAmelCase  							=		uncond_text_encoder_hidden_states.repeat(1				,	a_				,	1       )
      UpperCAmelCase  							=		uncond_text_encoder_hidden_states.view(
          batch_size * num_images_per_prompt				,	a_				,	-1       )
      UpperCAmelCase  							=		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  							=		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   snake_case_     (       self       )							->			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_       )							->			Union[str, Any]:


    """simple docstring"""






    if latents is None:
      UpperCAmelCase  							=		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  							=		latents.to(a_       )

    UpperCAmelCase  							=		latents * scheduler.init_noise_sigma
    return latents
  def   snake_case_     (       self				,	a_ = None       )							->			str:


    """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_ = 2_5				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = None				,	a_ = 4.0				,	a_ = 8.0				,	a_ = 5_0				,	a_ = 1				,	a_ = 0.0				,	a_ = "pil"				,	a_ = True				,	)							->			Optional[Any]:


    """simple docstring"""






    if prompt is not None:
      if isinstance(a_				,	a_       ):
        UpperCAmelCase  							=		1
      elif isinstance(a_				,	a_       ):
        UpperCAmelCase  							=		len(a_       )
      else:
        raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(a_       )}'''       )
    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(
        a_				,	a_				,	a_				,	a_				,	a_				,	a_       )

    # prior

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

    UpperCAmelCase  							=		self.prior.config.embedding_dim

    UpperCAmelCase  							=		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  							=		torch.cat([prior_latents] * 2       ) if do_classifier_free_guidance else prior_latents

      UpperCAmelCase  							=		self.prior(
          a_				,	timestep=a_				,	proj_embedding=a_				,	encoder_hidden_states=a_				,	attention_mask=a_				,	).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(
          a_				,	timestep=a_				,	sample=a_				,	generator=a_				,	prev_timestep=a_				,	).prev_sample

    UpperCAmelCase  							=		self.prior.post_process_latents(a_       )

    UpperCAmelCase  							=		prior_latents

    UpperCAmelCase  							=		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'''




def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				str  ):
  UpperCAmelCase  							=		str(SCREAMING_SNAKE_CASE  ).strip()
  if not bin_string:
    raise ValueError('Empty string was passed to the function'  )
  UpperCAmelCase  							=		bin_string[0] == '-'
  if is_negative:
    UpperCAmelCase  							=		bin_string[1:]
  if not all(char in '01' for char in bin_string  ):
    raise ValueError('Non-binary value was passed to the function'  )
  UpperCAmelCase  							=		0
  for char in bin_string:
    UpperCAmelCase  							=		2 * decimal_number + int(SCREAMING_SNAKE_CASE  )
  return -decimal_number if is_negative else decimal_number


if __name__ == "__main__":
  from doctest import testmod

  testmod()


