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       a			(   self		,	a		,	a		,	a		,	a  )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   image.to(device=a  )
  			SCREAMING_SNAKE_CASE    	=   image  # take image as image_embeddings
  			SCREAMING_SNAKE_CASE    	=   image_embeddings.unsqueeze(1  )
  
  			# duplicate image embeddings for each generation per prompt, using mps friendly method
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   image_embeddings.shape
  			SCREAMING_SNAKE_CASE    	=   image_embeddings.repeat(1		,	a		,	1  )
  			SCREAMING_SNAKE_CASE    	=   image_embeddings.view(bs_embed * num_images_per_prompt		,	a		,	-1  )
  
  			if do_classifier_free_guidance:
  						SCREAMING_SNAKE_CASE    	=   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
  						SCREAMING_SNAKE_CASE    	=   torch.cat([uncond_embeddings, image_embeddings]  )
  
  			return image_embeddings
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def __init__(    self			:Optional[int]      ,    lowercase			:PriorTransformer      ,    lowercase			:CLIPTokenizer      ,    lowercase			:CLIPTextModelWithProjection      ,    lowercase			:UnCLIPScheduler      ,    lowercase			:Optional[dict] = None      ,    )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				super().__init__()
  
  				SCREAMING_SNAKE_CASE    	=   {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  				SCREAMING_SNAKE_CASE    	=   decoder_pipe_kwargs.get('''torch_dtype'''      ,    lowercase ) or prior.dtype
  
  				SCREAMING_SNAKE_CASE    	=   StableDiffusionImageVariationPipeline.from_pretrained(
  				    '''lambdalabs/sd-image-variations-diffusers'''      ,    **lowercase )
  
  				# replace `_encode_image` method
  				SCREAMING_SNAKE_CASE    	=   types.MethodType(_encode_image      ,    self.decoder_pipe )
  
  				self.register_modules(
  				    prior=lowercase      ,    tokenizer=lowercase      ,    text_encoder=lowercase      ,    prior_scheduler=lowercase      ,    )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any]      ,    lowercase			:str      ,    lowercase			:int      ,    lowercase			:Any      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,    lowercase			:Optional[torch.Tensor] = None      ,    )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if text_model_output is None:
  							SCREAMING_SNAKE_CASE    	=   len(lowercase ) if isinstance(lowercase      ,    lowercase ) else 1
  							# get prompt text embeddings
  							SCREAMING_SNAKE_CASE    	=   self.tokenizer(
  							    lowercase      ,    padding='''max_length'''      ,    max_length=self.tokenizer.model_max_length      ,    return_tensors='''pt'''      ,    )
  							SCREAMING_SNAKE_CASE    	=   text_inputs.input_ids
  							SCREAMING_SNAKE_CASE    	=   text_inputs.attention_mask.bool().to(lowercase )
  
  							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  										SCREAMING_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}""" )
  										SCREAMING_SNAKE_CASE    	=   text_input_ids[:, : self.tokenizer.model_max_length]
  
  							SCREAMING_SNAKE_CASE    	=   self.text_encoder(text_input_ids.to(lowercase ) )
  
  							SCREAMING_SNAKE_CASE    	=   text_encoder_output.text_embeds
  							SCREAMING_SNAKE_CASE    	=   text_encoder_output.last_hidden_state
  
  				else:
  							SCREAMING_SNAKE_CASE    	=   text_model_output[0].shape[0]
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   text_model_output[0], text_model_output[1]
  							SCREAMING_SNAKE_CASE    	=   text_attention_mask
  
  				SCREAMING_SNAKE_CASE    	=   text_embeddings.repeat_interleave(lowercase      ,    dim=0 )
  				SCREAMING_SNAKE_CASE    	=   text_encoder_hidden_states.repeat_interleave(lowercase      ,    dim=0 )
  				SCREAMING_SNAKE_CASE    	=   text_mask.repeat_interleave(lowercase      ,    dim=0 )
  
  				if do_classifier_free_guidance:
  							SCREAMING_SNAKE_CASE    	=   [''''''] * batch_size
  
  							SCREAMING_SNAKE_CASE    	=   self.tokenizer(
  							    lowercase      ,    padding='''max_length'''      ,    max_length=self.tokenizer.model_max_length      ,    truncation=lowercase      ,    return_tensors='''pt'''      ,    )
  							SCREAMING_SNAKE_CASE    	=   uncond_input.attention_mask.bool().to(lowercase )
  							SCREAMING_SNAKE_CASE    	=   self.text_encoder(uncond_input.input_ids.to(lowercase ) )
  
  							SCREAMING_SNAKE_CASE    	=   uncond_embeddings_text_encoder_output.text_embeds
  							SCREAMING_SNAKE_CASE    	=   uncond_embeddings_text_encoder_output.last_hidden_state
  
  							# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  							SCREAMING_SNAKE_CASE    	=   uncond_embeddings.shape[1]
  							SCREAMING_SNAKE_CASE    	=   uncond_embeddings.repeat(1      ,    lowercase )
  							SCREAMING_SNAKE_CASE    	=   uncond_embeddings.view(batch_size * num_images_per_prompt      ,    lowercase )
  
  							SCREAMING_SNAKE_CASE    	=   uncond_text_encoder_hidden_states.shape[1]
  							SCREAMING_SNAKE_CASE    	=   uncond_text_encoder_hidden_states.repeat(1      ,    lowercase      ,    1 )
  							SCREAMING_SNAKE_CASE    	=   uncond_text_encoder_hidden_states.view(
  							    batch_size * num_images_per_prompt      ,    lowercase      ,    -1 )
  							SCREAMING_SNAKE_CASE    	=   uncond_text_mask.repeat_interleave(lowercase      ,    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
  							SCREAMING_SNAKE_CASE    	=   torch.cat([uncond_embeddings, text_embeddings] )
  							SCREAMING_SNAKE_CASE    	=   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )
  
  							SCREAMING_SNAKE_CASE    	=   torch.cat([uncond_text_mask, text_mask] )
  
  				return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
  
  
  
  	@property
  	def    snake_case__    (    self			:Optional[int] )		->			str:
  
  
  
  
  
  
  
  				"""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(lowercase      ,    '''_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			:List[Any]      ,    lowercase			:Optional[Any]      ,    lowercase			:List[str]      ,    lowercase			:Optional[int]      ,    lowercase			:Tuple      ,    lowercase			:Optional[Any]      ,    lowercase			:Union[str, Any] )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if latents is None:
  							SCREAMING_SNAKE_CASE    	=   randn_tensor(lowercase      ,    generator=lowercase      ,    device=lowercase      ,    dtype=lowercase )
  				else:
  							if latents.shape != shape:
  										raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
  							SCREAMING_SNAKE_CASE    	=   latents.to(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   latents * scheduler.init_noise_sigma
  				return latents
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Union[str, torch.device]] = None )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				self.decoder_pipe.to(lowercase )
  				super().to(lowercase )
  
  
  
  
  
  
  
  	@torch.no_grad()
  	def __call__(    self			:Dict      ,    lowercase			:Optional[Union[str, List[str]]] = None      ,    lowercase			:Optional[int] = None      ,    lowercase			:Optional[int] = None      ,    lowercase			:int = 1      ,    lowercase			:int = 2_5      ,    lowercase			:Optional[torch.Generator] = None      ,    lowercase			:Optional[torch.FloatTensor] = None      ,    lowercase			:Optional[Union[CLIPTextModelOutput, Tuple]] = None      ,    lowercase			:Optional[torch.Tensor] = None      ,    lowercase			:float = 4.0      ,    lowercase			:float = 8.0      ,    lowercase			:int = 5_0      ,    lowercase			:Optional[int] = 1      ,    lowercase			:float = 0.0      ,    lowercase			:Optional[str] = "pil"      ,    lowercase			:bool = True      ,    )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if prompt is not None:
  							if isinstance(lowercase      ,    lowercase ):
  										SCREAMING_SNAKE_CASE    	=   1
  							elif isinstance(lowercase      ,    lowercase ):
  										SCREAMING_SNAKE_CASE    	=   len(lowercase )
  							else:
  										raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase )}""" )
  				else:
  							SCREAMING_SNAKE_CASE    	=   text_model_output[0].shape[0]
  
  				SCREAMING_SNAKE_CASE    	=   self._execution_device
  
  				SCREAMING_SNAKE_CASE    	=   batch_size * num_images_per_prompt
  
  				SCREAMING_SNAKE_CASE    	=   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   self._encode_prompt(
  				    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase      ,    lowercase )
  
  				# prior
  
  				self.prior_scheduler.set_timesteps(lowercase      ,    device=lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.prior_scheduler.timesteps
  
  				SCREAMING_SNAKE_CASE    	=   self.prior.config.embedding_dim
  
  				SCREAMING_SNAKE_CASE    	=   self.prepare_latents(
  				    (batch_size, embedding_dim)      ,    text_embeddings.dtype      ,    lowercase      ,    lowercase      ,    lowercase      ,    self.prior_scheduler      ,    )
  
  				for i, t in enumerate(self.progress_bar(lowercase ) ):
  							# expand the latents if we are doing classifier free guidance
  							SCREAMING_SNAKE_CASE    	=   torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents
  
  							SCREAMING_SNAKE_CASE    	=   self.prior(
  							    lowercase      ,    timestep=lowercase      ,    proj_embedding=lowercase      ,    encoder_hidden_states=lowercase      ,    attention_mask=lowercase      ,    ).predicted_image_embedding
  
  							if do_classifier_free_guidance:
  										SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   predicted_image_embedding.chunk(2 )
  										SCREAMING_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]:
  										SCREAMING_SNAKE_CASE    	=   None
  							else:
  										SCREAMING_SNAKE_CASE    	=   prior_timesteps_tensor[i + 1]
  
  							SCREAMING_SNAKE_CASE    	=   self.prior_scheduler.step(
  							    lowercase      ,    timestep=lowercase      ,    sample=lowercase      ,    generator=lowercase      ,    prev_timestep=lowercase      ,    ).prev_sample
  
  				SCREAMING_SNAKE_CASE    	=   self.prior.post_process_latents(lowercase )
  
  				SCREAMING_SNAKE_CASE    	=   prior_latents
  
  				SCREAMING_SNAKE_CASE    	=   self.decoder_pipe(
  				    image=lowercase      ,    height=lowercase      ,    width=lowercase      ,    num_inference_steps=lowercase      ,    guidance_scale=lowercase      ,    generator=lowercase      ,    output_type=lowercase      ,    return_dict=lowercase      ,    num_images_per_prompt=lowercase      ,    eta=lowercase      ,    )
  				return output
style_context: 
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	@staticmethod
  	def    snake_case__    (    lowercase			:ArgumentParser )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''login'''      ,    help='''Log in using the same credentials as on huggingface.co''' )
  				login_parser.set_defaults(func=lambda lowercase : LoginCommand(lowercase ) )
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''whoami'''      ,    help='''Find out which huggingface.co account you are logged in as.''' )
  				whoami_parser.set_defaults(func=lambda lowercase : WhoamiCommand(lowercase ) )
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser('''logout'''      ,    help='''Log out''' )
  				logout_parser.set_defaults(func=lambda lowercase : LogoutCommand(lowercase ) )
  
  				# new system: git-based repo system
  				SCREAMING_SNAKE_CASE    	=   parser.add_parser(
  				    '''repo'''      ,    help='''Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos.'''      ,    )
  				SCREAMING_SNAKE_CASE    	=   repo_parser.add_subparsers(
  				    help='''Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands''' )
  				SCREAMING_SNAKE_CASE    	=   repo_subparsers.add_parser(
  				    '''create'''      ,    help='''Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co''' )
  				repo_create_parser.add_argument(
  				    '''name'''      ,    type=lowercase      ,    help='''Name for your model\'s repo. Will be namespaced under your username to build the model id.'''      ,    )
  				repo_create_parser.add_argument('''--organization'''      ,    type=lowercase      ,    help='''Optional: organization namespace.''' )
  				repo_create_parser.add_argument('''-y'''      ,    '''--yes'''      ,    action='''store_true'''      ,    help='''Optional: answer Yes to the prompt''' )
  				repo_create_parser.set_defaults(func=lambda lowercase : RepoCreateCommand(lowercase ) )
  
  
  
  
  
  class 						lowerCamelCase			:
  	UpperCamelCase_     :				List[str]								=  '\u001b[1m'
  	UpperCamelCase_     :				Union[str, Any]								=  '\u001b[31m'
  	UpperCamelCase_     :				Any								=  '\u001b[90m'
  	UpperCamelCase_     :				List[str]								=  '\u001b[0m'
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:List[Any]      ,    lowercase			:Any )		->			List[str]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._bold}{s}{cls._reset}"""
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:Optional[Any]      ,    lowercase			:str )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._bold}{cls._red}{s}{cls._reset}"""
  
  
  
  
  
  
  
  	@classmethod
  	def    snake_case__    (    cls			:List[str]      ,    lowercase			:Any )		->			List[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				return f"""{cls._gray}{s}{cls._reset}"""
  
  
  
  def       a			(   a		,	a  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   [max(len(str(a  )  ) for x in col  ) for col in zip(*a		,	a  )]
  			SCREAMING_SNAKE_CASE    	=   ('''{{:{}}} ''' * len(a  )).format(*a  )
  			SCREAMING_SNAKE_CASE    	=   []
  			lines.append(row_format.format(*a  )  )
  			lines.append(row_format.format(*['''-''' * w for w in col_widths]  )  )
  			for row in rows:
  						lines.append(row_format.format(*a  )  )
  			return "\n".join(a  )
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:List[str]      ,    lowercase			:Optional[Any] )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   args
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[str] )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''ERROR! `huggingface-cli login` uses an outdated login mechanism '''
  				        '''that is not compatible with the Hugging Face Hub backend anymore. '''
  				        '''Please use `huggingface-cli login instead.''' ) )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:List[Any] )		->			Any:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use '''
  				        '''`huggingface-cli whoami` instead.''' ) )
  				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
  				if token is None:
  							print('''Not logged in''' )
  							exit()
  				try:
  							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
  							print(lowercase )
  							if orgs:
  										print(ANSI.bold('''orgs: ''' )      ,    ''','''.join(lowercase ) )
  				except HTTPError as e:
  							print(lowercase )
  							print(ANSI.red(e.response.text ) )
  							exit(1 )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str )		->			Union[str, Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''ERROR! `transformers-cli logout` uses an outdated logout mechanism '''
  				        '''that is not compatible with the Hugging Face Hub backend anymore. '''
  				        '''Please use `huggingface-cli logout instead.''' ) )
  
  
  
  
  
  class 						lowerCamelCase			(					__lowerCamelCase				):
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:int )		->			Optional[Any]:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				print(
  				    ANSI.red(
  				        '''WARNING! Managing repositories through transformers-cli is deprecated. '''
  				        '''Please use `huggingface-cli` instead.''' ) )
  				SCREAMING_SNAKE_CASE    	=   HfFolder.get_token()
  				if token is None:
  							print('''Not logged in''' )
  							exit(1 )
  				try:
  							SCREAMING_SNAKE_CASE    	=   subprocess.check_output(['''git''', '''--version'''] ).decode('''utf-8''' )
  							print(ANSI.gray(stdout.strip() ) )
  				except FileNotFoundError:
  							print('''Looks like you do not have git installed, please install.''' )
  
  				try:
  							SCREAMING_SNAKE_CASE    	=   subprocess.check_output(['''git-lfs''', '''--version'''] ).decode('''utf-8''' )
  							print(ANSI.gray(stdout.strip() ) )
  				except FileNotFoundError:
  							print(
  							    ANSI.red(
  							        '''Looks like you do not have git-lfs installed, please install.'''
  							        ''' You can install from https://git-lfs.github.com/.'''
  							        ''' Then run `git lfs install` (you only have to do this once).''' ) )
  				print('''''' )
  
  				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   whoami(lowercase )
  				SCREAMING_SNAKE_CASE    	=   self.args.organization if self.args.organization is not None else user
  				SCREAMING_SNAKE_CASE    	=   f"""{namespace}/{self.args.name}"""
  				print(f"""You are about to create {ANSI.bold(lowercase )}""" )
  
  				if not self.args.yes:
  							SCREAMING_SNAKE_CASE    	=   input('''Proceed? [Y/n] ''' ).lower()
  							if not (choice == "" or choice == "y" or choice == "yes"):
  										print('''Abort''' )
  										exit()
  				try:
  							SCREAMING_SNAKE_CASE    	=   create_repo(lowercase      ,    name=self.args.name      ,    organization=self.args.organization )
  				except HTTPError as e:
  							print(lowercase )
  							print(ANSI.red(e.response.text ) )
  							exit(1 )
  				print('''\nYour repo now lives at:''' )
  				print(f"""  {ANSI.bold(lowercase )}""" )
  				print('''\nYou can clone it locally with the command below, and commit/push as usual.''' )
  				print(f"""\n  git clone {url}""" )
  				print('''''' )