code: 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__           =				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  def 							_lowerCamelCase(				self						,						__snake_case						,						__snake_case						,						__snake_case						,						__snake_case	)  ->   Optional[int]:
    __snake_case		       =     image.to(device=__snake_case	)
    __snake_case		       =     image  # take image as image_embeddings
    __snake_case		       =     image_embeddings.unsqueeze(1	)
  
    # duplicate image embeddings for each generation per prompt, using mps friendly method
    __snake_case		       =     image_embeddings.shape
    __snake_case		       =     image_embeddings.repeat(1						,						__snake_case						,						1	)
    __snake_case		       =     image_embeddings.view(bs_embed * num_images_per_prompt						,						__snake_case						,						-1	)
  
    if do_classifier_free_guidance:
      __snake_case		       =     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
      __snake_case		       =     torch.cat([uncond_embeddings, image_embeddings]	)
  
    return image_embeddings
  
  
  
  
  
  class 					UpperCamelCase	(     a__      ):
  
  
  
  
       def __init__( self :					List[str]      ,_lowerCAmelCase :					Dict      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					List[Any] = None      ,):
  
  
         """simple docstring"""
  
  
  
  
         super().__init__()
  
         __snake_case		       =     {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
         __snake_case		       =     decoder_pipe_kwargs.get("torch_dtype"      ,__A						) or prior.dtype
  
         __snake_case		       =     StableDiffusionImageVariationPipeline.from_pretrained(
             "lambdalabs/sd-image-variations-diffusers"      ,**__A						)
  
         # replace `_encode_image` method
         __snake_case		       =     types.MethodType(_encode_image      ,self.decoder_pipe						)
  
         self.register_modules(
             prior=__A      ,tokenizer=__A      ,text_encoder=__A      ,prior_scheduler=__A      ,)
  
  
  
  
       def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Tuple      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					int = None      ,_lowerCAmelCase :					Union[str, Any] = None      ,):
  
  
         """simple docstring"""
  
  
  
  
         if text_model_output is None:
           __snake_case		       =     len(__A						) if isinstance(__A      ,__A						) else 1
           # get prompt text embeddings
           __snake_case		       =     self.tokenizer(
               __A      ,padding="max_length"      ,max_length=self.tokenizer.model_max_length      ,return_tensors="pt"      ,)
           __snake_case		       =     text_inputs.input_ids
           __snake_case		       =     text_inputs.attention_mask.bool().to(__A						)
  
           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             __snake_case		       =     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		       =     text_input_ids[:, : self.tokenizer.model_max_length]
  
           __snake_case		       =     self.text_encoder(text_input_ids.to(__A						)						)
  
           __snake_case		       =     text_encoder_output.text_embeds
           __snake_case		       =     text_encoder_output.last_hidden_state
  
         else:
           __snake_case		       =     text_model_output[0].shape[0]
           __snake_case		       =     text_model_output[0], text_model_output[1]
           __snake_case		       =     text_attention_mask
  
         __snake_case		       =     text_embeddings.repeat_interleave(__A      ,dim=0						)
         __snake_case		       =     text_encoder_hidden_states.repeat_interleave(__A      ,dim=0						)
         __snake_case		       =     text_mask.repeat_interleave(__A      ,dim=0						)
  
         if do_classifier_free_guidance:
           __snake_case		       =     [""] * batch_size
  
           __snake_case		       =     self.tokenizer(
               __A      ,padding="max_length"      ,max_length=self.tokenizer.model_max_length      ,truncation=__A      ,return_tensors="pt"      ,)
           __snake_case		       =     uncond_input.attention_mask.bool().to(__A						)
           __snake_case		       =     self.text_encoder(uncond_input.input_ids.to(__A						)						)
  
           __snake_case		       =     uncond_embeddings_text_encoder_output.text_embeds
           __snake_case		       =     uncond_embeddings_text_encoder_output.last_hidden_state
  
           # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
           __snake_case		       =     uncond_embeddings.shape[1]
           __snake_case		       =     uncond_embeddings.repeat(1      ,__A						)
           __snake_case		       =     uncond_embeddings.view(batch_size * num_images_per_prompt      ,__A						)
  
           __snake_case		       =     uncond_text_encoder_hidden_states.shape[1]
           __snake_case		       =     uncond_text_encoder_hidden_states.repeat(1      ,__A      ,1						)
           __snake_case		       =     uncond_text_encoder_hidden_states.view(
               batch_size * num_images_per_prompt      ,__A      ,-1						)
           __snake_case		       =     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
           __snake_case		       =     torch.cat([uncond_embeddings, text_embeddings]						)
           __snake_case		       =     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)
  
           __snake_case		       =     torch.cat([uncond_text_mask, text_mask]						)
  
         return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
       @property
       def 							UpperCamelCase_						( self :					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(__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 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Any      ,_lowerCAmelCase :					List[str]      ,_lowerCAmelCase :					int						):
  
  
         """simple docstring"""
  
  
  
  
         if latents is None:
           __snake_case		       =     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}"""						)
           __snake_case		       =     latents.to(__A						)
  
         __snake_case		       =     latents * scheduler.init_noise_sigma
         return latents
  
  
  
  
       def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					Optional[Any] = None						):
  
  
         """simple docstring"""
  
  
  
  
         self.decoder_pipe.to(__A						)
         super().to(__A						)
  
  
  
  
  
  
  
       @torch.no_grad()
       def __call__( self :					Dict      ,_lowerCAmelCase :					Union[str, Any] = None      ,_lowerCAmelCase :					List[str] = None      ,_lowerCAmelCase :					List[str] = None      ,_lowerCAmelCase :					Dict = 1      ,_lowerCAmelCase :					Optional[int] = 25      ,_lowerCAmelCase :					Optional[int] = None      ,_lowerCAmelCase :					List[Any] = None      ,_lowerCAmelCase :					Union[str, Any] = None      ,_lowerCAmelCase :					Tuple = None      ,_lowerCAmelCase :					Optional[Any] = 4.0      ,_lowerCAmelCase :					List[Any] = 8.0      ,_lowerCAmelCase :					Optional[Any] = 50      ,_lowerCAmelCase :					int = 1      ,_lowerCAmelCase :					Union[str, Any] = 0.0      ,_lowerCAmelCase :					str = "pil"      ,_lowerCAmelCase :					Union[str, Any] = True      ,):
  
  
         """simple docstring"""
  
  
  
  
         if prompt is not None:
           if isinstance(__A      ,__A						):
             __snake_case		       =     1
           elif isinstance(__A      ,__A						):
             __snake_case		       =     len(__A						)
           else:
             raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__A						)}"""						)
         else:
           __snake_case		       =     text_model_output[0].shape[0]
  
         __snake_case		       =     self._execution_device
  
         __snake_case		       =     batch_size * num_images_per_prompt
  
         __snake_case		       =     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
         __snake_case		       =     self._encode_prompt(
             __A      ,__A      ,__A      ,__A      ,__A      ,__A						)
  
         # prior
  
         self.prior_scheduler.set_timesteps(__A      ,device=__A						)
         __snake_case		       =     self.prior_scheduler.timesteps
  
         __snake_case		       =     self.prior.config.embedding_dim
  
         __snake_case		       =     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
           __snake_case		       =     torch.cat([prior_latents] * 2						) if do_classifier_free_guidance else prior_latents
  
           __snake_case		       =     self.prior(
               __A      ,timestep=__A      ,proj_embedding=__A      ,encoder_hidden_states=__A      ,attention_mask=__A      ,).predicted_image_embedding
  
           if do_classifier_free_guidance:
             __snake_case		       =     predicted_image_embedding.chunk(2						)
             __snake_case		       =     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		       =     None
           else:
             __snake_case		       =     prior_timesteps_tensor[i + 1]
  
           __snake_case		       =     self.prior_scheduler.step(
               __A      ,timestep=__A      ,sample=__A      ,generator=__A      ,prev_timestep=__A      ,).prev_sample
  
         __snake_case		       =     self.prior.post_process_latents(__A						)
  
         __snake_case		       =     prior_latents
  
         __snake_case		       =     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
  
  
  
  
  
  
style_context: import posixpath
  from pathlib import Path
  from unittest.mock import patch
  
  import pytest
  from fsspec.implementations.local import AbstractFileSystem, LocalFileSystem, stringify_path
  from fsspec.registry import _registry as _fsspec_registry
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   """mock"""
  
  
  
  
  					def __init__( self :					List[Any]      ,*_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Union[str, Any]      ,**_lowerCAmelCase :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().__init__()
  							__snake_case		       =     LocalFileSystem(*_lowerCAmelCase      ,**_lowerCAmelCase						)
  							__snake_case		       =     Path(_lowerCAmelCase						).resolve().as_posix() + "/"
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]      ,_lowerCAmelCase :					Optional[Any]      ,*_lowerCAmelCase :					Union[str, Any]      ,**_lowerCAmelCase :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.mkdir(_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Tuple      ,*_lowerCAmelCase :					Optional[Any]      ,**_lowerCAmelCase :					Optional[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.makedirs(_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.rmdir(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					str=True      ,*_lowerCAmelCase :					int      ,**_lowerCAmelCase :					List[str]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							__snake_case		       =     self._fs.ls(_lowerCAmelCase      ,detail=_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)
  							if detail:
  									return [{**info, "name": info["name"][len(self.local_root_dir						) :]} for info in out]
  							else:
  									return [name[len(self.local_root_dir						) :] for name in out]
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[str]      ,_lowerCAmelCase :					Any      ,*_lowerCAmelCase :					Any      ,**_lowerCAmelCase :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							__snake_case		       =     dict(self._fs.info(_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)						)
  							__snake_case		       =     out["name"][len(self.local_root_dir						) :]
  							return out
  
  
  
  
  					def 							UpperCamelCase_						( self :					Tuple      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Optional[Any]      ,*_lowerCAmelCase :					int      ,**_lowerCAmelCase :					Optional[int]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.cp_file(_lowerCAmelCase      ,_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					int      ,*_lowerCAmelCase :					List[Any]      ,**_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.rm_file(_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					str      ,*_lowerCAmelCase :					Optional[int]      ,**_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.rm(_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					int      ,_lowerCAmelCase :					Optional[int]      ,*_lowerCAmelCase :					str      ,**_lowerCAmelCase :					Optional[Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs._open(_lowerCAmelCase      ,*_lowerCAmelCase      ,**_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					Tuple						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.created(_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Union[str, Any]      ,_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     posixpath.join(self.local_root_dir      ,self._strip_protocol(_lowerCAmelCase						)						)
  							return self._fs.modified(_lowerCAmelCase						)
  
  
  
  
  
  
  
  					@classmethod
  					def 							UpperCamelCase_						( cls :					Dict      ,_lowerCAmelCase :					Dict						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     stringify_path(_lowerCAmelCase						)
  							if path.startswith("mock://"						):
  									__snake_case		       =     path[7:]
  							return path
  
  
  
  
  class 					UpperCamelCase	(     snake_case__      ):
  					__UpperCamelCase						=   """tmp"""
  					__UpperCamelCase						=   None
  
  
  
  
  					def __init__( self :					int      ,*_lowerCAmelCase :					Tuple      ,**_lowerCAmelCase :					Union[str, Any]						):
  
  
  							"""simple docstring"""
  
  
  
  
  							assert self.tmp_dir is not None, "TmpDirFileSystem.tmp_dir is not set"
  							super().__init__(*_lowerCAmelCase      ,**_lowerCAmelCase      ,local_root_dir=self.tmp_dir      ,auto_mkdir=_lowerCAmelCase						)
  
  
  
  
  
  
  
  					@classmethod
  					def 							UpperCamelCase_						( cls :					int      ,_lowerCAmelCase :					str						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     stringify_path(_lowerCAmelCase						)
  							if path.startswith("tmp://"						):
  									__snake_case		       =     path[6:]
  							return path
  
  
  
  
  @pytest.fixture
  def 							_lowerCamelCase(				)  ->   str:
  		__snake_case		       =     MockFileSystem
  		__snake_case		       =     TmpDirFileSystem
  		yield
  		del _fsspec_registry["mock"]
  		del _fsspec_registry["tmp"]
  
  
  
  @pytest.fixture
  def 							_lowerCamelCase(				__snake_case						,						__snake_case	)  ->   Dict:
  		__snake_case		       =     tmp_path_factory.mktemp("mockfs"	)
  		return MockFileSystem(local_root_dir=__snake_case						,						auto_mkdir=__snake_case	)
  
  
  
  
  
  @pytest.fixture
  def 							_lowerCamelCase(				__snake_case						,						__snake_case	)  ->   List[Any]:
  		__snake_case		       =     tmp_path_factory.mktemp("tmpfs"	)
  		with patch.object(__snake_case						,						"tmp_dir"						,						__snake_case	):
  				yield TmpDirFileSystem()
  
  
  
  
  
  