
"""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


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




def 			A_			(					self						, lowercase						, lowercase						, lowercase						, lowercase      )     ->							Union[str, Any]:


     """simple docstring"""







     UpperCAmelCase_						:    Union[str, Any]    					=  image.to(device=lowercase      )
     UpperCAmelCase_						:    List[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_					,UpperCAmelCase_					,UpperCAmelCase_						:    Optional[Any]    					=  image_embeddings.shape
     UpperCAmelCase_						:    int    					=  image_embeddings.repeat(1						, lowercase						, 1      )
     UpperCAmelCase_						:    int    					=  image_embeddings.view(bs_embed * num_images_per_prompt						, lowercase						, -1      )

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

     return image_embeddings



class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : PriorTransformer     ,   a_       : CLIPTokenizer     ,   a_       : CLIPTextModelWithProjection     ,   a_       : UnCLIPScheduler     ,   a_       : Optional[dict] = None     ,   )->					Tuple:



      """simple docstring"""

      super().__init__()

      UpperCAmelCase_						:    Tuple    					=  {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

      UpperCAmelCase_						:    List[str]    					=  decoder_pipe_kwargs.get("""torch_dtype"""     ,   a_ ) or prior.dtype

      UpperCAmelCase_						:    Tuple    					=  StableDiffusionImageVariationPipeline.from_pretrained(
          """lambdalabs/sd-image-variations-diffusers"""     ,   **a_ )

      # replace `_encode_image` method
      UpperCAmelCase_						:    Dict    					=  types.MethodType(_encode_image     ,   self.decoder_pipe )

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




 def   a      ( self       : List[str]     ,   a_       : str     ,   a_       : Optional[Any]     ,   a_       : Any     ,   a_       : Optional[int]     ,   a_       : Optional[Union[CLIPTextModelOutput, Tuple]] = None     ,   a_       : Optional[torch.Tensor] = None     ,   )->					str:



      """simple docstring"""

      if text_model_output is None:
           UpperCAmelCase_						:    Union[str, Any]    					=  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_						:    Dict    					=  text_inputs.input_ids
           UpperCAmelCase_						:    Optional[int]    					=  text_inputs.attention_mask.bool().to(a_ )

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

           UpperCAmelCase_						:    Any    					=  self.text_encoder(text_input_ids.to(a_ ) )

           UpperCAmelCase_						:    Optional[int]    					=  text_encoder_output.text_embeds
           UpperCAmelCase_						:    str    					=  text_encoder_output.last_hidden_state

      else:
           UpperCAmelCase_						:    str    					=  text_model_output[0].shape[0]
           UpperCAmelCase_					,UpperCAmelCase_						:    str    					=  text_model_output[0], text_model_output[1]
           UpperCAmelCase_						:    int    					=  text_attention_mask

      UpperCAmelCase_						:    Tuple    					=  text_embeddings.repeat_interleave(a_     ,   dim=0 )
      UpperCAmelCase_						:    Optional[int]    					=  text_encoder_hidden_states.repeat_interleave(a_     ,   dim=0 )
      UpperCAmelCase_						:    List[str]    					=  text_mask.repeat_interleave(a_     ,   dim=0 )

      if do_classifier_free_guidance:
           UpperCAmelCase_						:    str    					=  [""""""] * batch_size

           UpperCAmelCase_						:    int    					=  self.tokenizer(
               a_     ,   padding="""max_length"""     ,   max_length=self.tokenizer.model_max_length     ,   truncation=a_     ,   return_tensors="""pt"""     ,   )
           UpperCAmelCase_						:    Optional[Any]    					=  uncond_input.attention_mask.bool().to(a_ )
           UpperCAmelCase_						:    str    					=  self.text_encoder(uncond_input.input_ids.to(a_ ) )

           UpperCAmelCase_						:    Tuple    					=  uncond_embeddings_text_encoder_output.text_embeds
           UpperCAmelCase_						:    List[str]    					=  uncond_embeddings_text_encoder_output.last_hidden_state

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

           UpperCAmelCase_						:    str    					=  uncond_embeddings.shape[1]
           UpperCAmelCase_						:    List[Any]    					=  uncond_embeddings.repeat(1     ,   a_ )
           UpperCAmelCase_						:    List[Any]    					=  uncond_embeddings.view(batch_size * num_images_per_prompt     ,   a_ )

           UpperCAmelCase_						:    Any    					=  uncond_text_encoder_hidden_states.shape[1]
           UpperCAmelCase_						:    Optional[Any]    					=  uncond_text_encoder_hidden_states.repeat(1     ,   a_     ,   1 )
           UpperCAmelCase_						:    Any    					=  uncond_text_encoder_hidden_states.view(
               batch_size * num_images_per_prompt     ,   a_     ,   -1 )
           UpperCAmelCase_						:    Tuple    					=  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_						:    Optional[int]    					=  torch.cat([uncond_embeddings, text_embeddings] )
           UpperCAmelCase_						:    int    					=  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

           UpperCAmelCase_						:    Tuple    					=  torch.cat([uncond_text_mask, text_mask] )

      return text_embeddings, text_encoder_hidden_states, text_mask




 @property
 def   a      ( self       : Union[str, Any] )->					Tuple:



      """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   a      ( self       : Any     ,   a_       : Union[str, Any]     ,   a_       : Union[str, Any]     ,   a_       : str     ,   a_       : Dict     ,   a_       : Tuple     ,   a_       : Dict )->					List[Any]:



      """simple docstring"""

      if latents is None:
           UpperCAmelCase_						:    str    					=  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_						:    Optional[Any]    					=  latents.to(a_ )

      UpperCAmelCase_						:    List[str]    					=  latents * scheduler.init_noise_sigma
      return latents




 def   a      ( self       : str     ,   a_       : Optional[Union[str, torch.device]] = None )->					Any:



      """simple docstring"""

      self.decoder_pipe.to(a_ )
      super().to(a_ )






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



      """simple docstring"""

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

      UpperCAmelCase_						:    Union[str, Any]    					=  self._execution_device

      UpperCAmelCase_						:    int    					=  batch_size * num_images_per_prompt

      UpperCAmelCase_						:    Union[str, Any]    					=  prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

      UpperCAmelCase_					,UpperCAmelCase_					,UpperCAmelCase_						:    Optional[int]    					=  self._encode_prompt(
          a_     ,   a_     ,   a_     ,   a_     ,   a_     ,   a_ )

      # prior

      self.prior_scheduler.set_timesteps(a_     ,   device=a_ )
      UpperCAmelCase_						:    int    					=  self.prior_scheduler.timesteps

      UpperCAmelCase_						:    Tuple    					=  self.prior.config.embedding_dim

      UpperCAmelCase_						:    Optional[int]    					=  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_						:    Optional[Any]    					=  torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

           UpperCAmelCase_						:    Dict    					=  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_						:    Optional[int]    					=  predicted_image_embedding.chunk(2 )
                UpperCAmelCase_						:    Dict    					=  predicted_image_embedding_uncond + prior_guidance_scale * (
                    predicted_image_embedding_text - predicted_image_embedding_uncond
                )

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

           UpperCAmelCase_						:    int    					=  self.prior_scheduler.step(
               a_     ,   timestep=a_     ,   sample=a_     ,   generator=a_     ,   prev_timestep=a_     ,   ).prev_sample

      UpperCAmelCase_						:    Optional[int]    					=  self.prior.post_process_latents(a_ )

      UpperCAmelCase_						:    Dict    					=  prior_latents

      UpperCAmelCase_						:    Tuple    					=  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 unittest

from transformers.image_processing_utils import get_size_dict



class 		UpperCAmelCase_	(unittest.TestCase						):
 """simple docstring"""




 def   a      ( self       : Union[str, Any] )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  {"""wrong_key""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""height""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  {"""width""": 2_24, """shortest_edge""": 2_24}
      with self.assertRaises(a_ ):
           get_size_dict(a_ )

      # Test a dict with the correct keys is returned as is
      UpperCAmelCase_						:    int    					=  {"""height""": 2_24, """width""": 2_24}
      UpperCAmelCase_						:    Dict    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   a_ )

      UpperCAmelCase_						:    Optional[int]    					=  {"""shortest_edge""": 2_24}
      UpperCAmelCase_						:    Tuple    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      UpperCAmelCase_						:    str    					=  {"""longest_edge""": 2_24, """shortest_edge""": 2_24}
      UpperCAmelCase_						:    Any    					=  get_size_dict(a_ )
      self.assertEqual(a_     ,   {"""longest_edge""": 2_24, """shortest_edge""": 2_24} )

      # Test a single int value which  represents (size, size)
      UpperCAmelCase_						:    Optional[Any]    					=  get_size_dict(2_24 )
      self.assertEqual(a_     ,   {"""height""": 2_24, """width""": 2_24} )

      # Test a single int value which represents the shortest edge
      UpperCAmelCase_						:    List[str]    					=  get_size_dict(2_24     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24} )

      # Test a tuple of ints which represents (height, width)
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict((1_50, 2_00) )
      self.assertEqual(a_     ,   {"""height""": 1_50, """width""": 2_00} )

      # Test a tuple of ints which represents (width, height)
      UpperCAmelCase_						:    List[str]    					=  get_size_dict((1_50, 2_00)     ,   height_width_order=a_ )
      self.assertEqual(a_     ,   {"""height""": 2_00, """width""": 1_50} )

      # Test an int representing the shortest edge and max_size which represents the longest edge
      UpperCAmelCase_						:    List[Any]    					=  get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )
      self.assertEqual(a_     ,   {"""shortest_edge""": 2_24, """longest_edge""": 2_56} )

      # Test int with default_to_square=True and max_size fails
      with self.assertRaises(a_ ):
           get_size_dict(2_24     ,   max_size=2_56     ,   default_to_square=a_ )

