

'''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


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

def   lowerCamelCase__       (      self			,      a__			,      a__			,      a__			,      a__)		->    Union[str, Any]:



    """simple docstring"""







    _snake_case : Any												=		image.to(device=a__)
    _snake_case : Tuple												=		image  # take image as image_embeddings
    _snake_case : Dict												=		image_embeddings.unsqueeze(1)

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

    if do_classifier_free_guidance:
        _snake_case : List[Any]												=		torch.zeros_like(a__)

        # 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 : Optional[int]												=		torch.cat([uncond_embeddings, image_embeddings])

    return image_embeddings




class 			SCREAMING_SNAKE_CASE					(							lowercase_					):






      '''simple docstring'''






      def __init__(					self :		List[Any]     ,					snake_case :		PriorTransformer     ,					snake_case :		CLIPTokenizer     ,					snake_case :		CLIPTextModelWithProjection     ,					snake_case :		UnCLIPScheduler     ,					snake_case :		Optional[dict] = None     ,					):



          """simple docstring"""





          super().__init__()

          _snake_case : List[str]												=		{'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

          _snake_case : List[str]												=		decoder_pipe_kwargs.get('torch_dtype'     ,					snake_case   ) or prior.dtype

          _snake_case : Tuple												=		StableDiffusionImageVariationPipeline.from_pretrained(
              'lambdalabs/sd-image-variations-diffusers'     ,					**snake_case   )

          # replace `_encode_image` method
          _snake_case : Optional[int]												=		types.MethodType(_encode_image     ,					self.decoder_pipe   )

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




      def 	__UpperCAmelCase			(					self :		Dict     ,					snake_case :		List[str]     ,					snake_case :		List[Any]     ,					snake_case :		Any     ,					snake_case :		int     ,					snake_case :		Optional[Union[CLIPTextModelOutput, Tuple]] = None     ,					snake_case :		Optional[torch.Tensor] = None     ,					):



          """simple docstring"""





          if text_model_output is None:
              _snake_case : List[str]												=		len(snake_case   ) if isinstance(snake_case     ,					snake_case   ) else 1
              # get prompt text embeddings
              _snake_case : Tuple												=		self.tokenizer(
                  snake_case     ,					padding='max_length'     ,					max_length=self.tokenizer.model_max_length     ,					return_tensors='pt'     ,					)
              _snake_case : str												=		text_inputs.input_ids
              _snake_case : Dict												=		text_inputs.attention_mask.bool().to(snake_case   )

              if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                  _snake_case : List[Any]												=		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 : List[str]												=		text_input_ids[:, : self.tokenizer.model_max_length]

              _snake_case : Any												=		self.text_encoder(text_input_ids.to(snake_case   )   )

              _snake_case : Dict												=		text_encoder_output.text_embeds
              _snake_case : Tuple												=		text_encoder_output.last_hidden_state

          else:
              _snake_case : Tuple												=		text_model_output[0].shape[0]
              _snake_case					,							_snake_case : Optional[int]												=		text_model_output[0], text_model_output[1]
              _snake_case : Tuple												=		text_attention_mask

          _snake_case : str												=		text_embeddings.repeat_interleave(snake_case     ,					dim=0   )
          _snake_case : List[Any]												=		text_encoder_hidden_states.repeat_interleave(snake_case     ,					dim=0   )
          _snake_case : List[Any]												=		text_mask.repeat_interleave(snake_case     ,					dim=0   )

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

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

              _snake_case : List[Any]												=		uncond_embeddings_text_encoder_output.text_embeds
              _snake_case : Tuple												=		uncond_embeddings_text_encoder_output.last_hidden_state

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

              _snake_case : Any												=		uncond_embeddings.shape[1]
              _snake_case : Union[str, Any]												=		uncond_embeddings.repeat(1     ,					snake_case   )
              _snake_case : Union[str, Any]												=		uncond_embeddings.view(batch_size * num_images_per_prompt     ,					snake_case   )

              _snake_case : Optional[int]												=		uncond_text_encoder_hidden_states.shape[1]
              _snake_case : Tuple												=		uncond_text_encoder_hidden_states.repeat(1     ,					snake_case     ,					1   )
              _snake_case : Optional[Any]												=		uncond_text_encoder_hidden_states.view(
                  batch_size * num_images_per_prompt     ,					snake_case     ,					-1   )
              _snake_case : Optional[Any]												=		uncond_text_mask.repeat_interleave(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 : Tuple												=		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

              _snake_case : Dict												=		torch.cat([uncond_text_mask, text_mask]   )

          return text_embeddings, text_encoder_hidden_states, text_mask




      @property
      def 	__UpperCAmelCase			(					self :		Optional[int]   ):



          """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(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 	__UpperCAmelCase			(					self :		Union[str, Any]     ,					snake_case :		Optional[Any]     ,					snake_case :		Optional[int]     ,					snake_case :		Union[str, Any]     ,					snake_case :		Union[str, Any]     ,					snake_case :		List[Any]     ,					snake_case :		List[str]   ):



          """simple docstring"""





          if latents is None:
              _snake_case : Dict												=		randn_tensor(snake_case     ,					generator=snake_case     ,					device=snake_case     ,					dtype=snake_case   )
          else:
              if latents.shape != shape:
                  raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""   )
              _snake_case : Union[str, Any]												=		latents.to(snake_case   )

          _snake_case : Tuple												=		latents * scheduler.init_noise_sigma
          return latents




      def 	__UpperCAmelCase			(					self :		Union[str, Any]     ,					snake_case :		Optional[Union[str, torch.device]] = None   ):



          """simple docstring"""





          self.decoder_pipe.to(snake_case   )
          super().to(snake_case   )




      @torch.no_grad()
      def __call__(					self :		int     ,					snake_case :		Optional[Union[str, List[str]]] = None     ,					snake_case :		Optional[int] = None     ,					snake_case :		Optional[int] = None     ,					snake_case :		int = 1     ,					snake_case :		int = 25     ,					snake_case :		Optional[torch.Generator] = None     ,					snake_case :		Optional[torch.FloatTensor] = None     ,					snake_case :		Optional[Union[CLIPTextModelOutput, Tuple]] = None     ,					snake_case :		Optional[torch.Tensor] = None     ,					snake_case :		float = 4.0     ,					snake_case :		float = 8.0     ,					snake_case :		int = 50     ,					snake_case :		Optional[int] = 1     ,					snake_case :		float = 0.0     ,					snake_case :		Optional[str] = "pil"     ,					snake_case :		bool = True     ,					):



          """simple docstring"""





          if prompt is not None:
              if isinstance(snake_case     ,					snake_case   ):
                  _snake_case : Any												=		1
              elif isinstance(snake_case     ,					snake_case   ):
                  _snake_case : str												=		len(snake_case   )
              else:
                  raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(snake_case   )}"""   )
          else:
              _snake_case : str												=		text_model_output[0].shape[0]

          _snake_case : List[str]												=		self._execution_device

          _snake_case : Union[str, Any]												=		batch_size * num_images_per_prompt

          _snake_case : Optional[Any]												=		prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

          _snake_case					,							_snake_case					,							_snake_case : Dict												=		self._encode_prompt(
              snake_case     ,					snake_case     ,					snake_case     ,					snake_case     ,					snake_case     ,					snake_case   )

          # prior

          self.prior_scheduler.set_timesteps(snake_case     ,					device=snake_case   )
          _snake_case : Optional[Any]												=		self.prior_scheduler.timesteps

          _snake_case : Optional[int]												=		self.prior.config.embedding_dim

          _snake_case : Optional[Any]												=		self.prepare_latents(
              (batch_size, embedding_dim)     ,					text_embeddings.dtype     ,					snake_case     ,					snake_case     ,					snake_case     ,					self.prior_scheduler     ,					)

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

              _snake_case : List[str]												=		self.prior(
                  snake_case     ,					timestep=snake_case     ,					proj_embedding=snake_case     ,					encoder_hidden_states=snake_case     ,					attention_mask=snake_case     ,					).predicted_image_embedding

              if do_classifier_free_guidance:
                  _snake_case					,							_snake_case : Optional[Any]												=		predicted_image_embedding.chunk(2   )
                  _snake_case : Dict												=		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 : List[str]												=		None
              else:
                  _snake_case : Dict												=		prior_timesteps_tensor[i + 1]

              _snake_case : str												=		self.prior_scheduler.step(
                  snake_case     ,					timestep=snake_case     ,					sample=snake_case     ,					generator=snake_case     ,					prev_timestep=snake_case     ,					).prev_sample

          _snake_case : Dict												=		self.prior.post_process_latents(snake_case   )

          _snake_case : Any												=		prior_latents

          _snake_case : str												=		self.decoder_pipe(
              image=snake_case     ,					height=snake_case     ,					width=snake_case     ,					num_inference_steps=snake_case     ,					guidance_scale=snake_case     ,					generator=snake_case     ,					output_type=snake_case     ,					return_dict=snake_case     ,					num_images_per_prompt=snake_case     ,					eta=snake_case     ,					)
          return output






'''simple docstring'''



from __future__ import annotations

def   lowerCamelCase__       (      a__)		->    int | float:



    """simple docstring"""







    _snake_case : int												=		sorted(a__)
    _snake_case : List[str]												=		len(a__)
    _snake_case : Union[str, Any]												=		length >> 1
    return (
        (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
        if length % 2 == 0
        else sorted_list[mid_index]
    )

def   lowerCamelCase__       (      )		->    Union[str, Any]:



    """simple docstring"""







    import doctest

    doctest.testmod()


if __name__ == "__main__":
   main()



