

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      UpperCAmelCase__							(     self :					Union[str, Any]      , _A :					Optional[Any]      , _A :					List[Any]      , _A :					List[Any]      , _A :					Tuple				):





  '''simple docstring'''







  a__    =image.to(device=lowerCAmelCase__				)
  a__    =image  # take image as image_embeddings
  a__    =image_embeddings.unsqueeze(1				)

  # duplicate image embeddings for each generation per prompt, using mps friendly method
  a__, a__, a__    =image_embeddings.shape
  a__    =image_embeddings.repeat(1      , lowerCAmelCase__      , 1				)
  a__    =image_embeddings.view(bs_embed * num_images_per_prompt      , lowerCAmelCase__      , -1				)

  if do_classifier_free_guidance:
    a__    =torch.zeros_like(lowerCAmelCase__				)

    # 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
    a__    =torch.cat([uncond_embeddings, image_embeddings]				)

  return image_embeddings







class 		__magic_name__     (				SCREAMING_SNAKE_CASE__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_ = None,       )				->       Optional[Any]:






     """simple docstring"""






     super().__init__()

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

     a__    =decoder_pipe_kwargs.get('''torch_dtype''',       A__			) or prior.dtype

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

     # replace `_encode_image` method
     a__    =types.MethodType(_encode_image,       self.decoder_pipe			)

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



   def 					_UpperCAmelCase					(							self,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_ = None,       lowercase_ = None,       )				->       List[Any]:






     """simple docstring"""






     if text_model_output is None:
       a__    =len(A__			) if isinstance(A__,       A__			) else 1
       # get prompt text embeddings
       a__    =self.tokenizer(
           A__,       padding='''max_length''',       max_length=self.tokenizer.model_max_length,       return_tensors='''pt''',       )
       a__    =text_inputs.input_ids
       a__    =text_inputs.attention_mask.bool().to(A__			)

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         a__    =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}"""			)
         a__    =text_input_ids[:, : self.tokenizer.model_max_length]

       a__    =self.text_encoder(text_input_ids.to(A__			)			)

       a__    =text_encoder_output.text_embeds
       a__    =text_encoder_output.last_hidden_state

     else:
       a__    =text_model_output[0].shape[0]
       a__, a__    =text_model_output[0], text_model_output[1]
       a__    =text_attention_mask

     a__    =text_embeddings.repeat_interleave(A__,       dim=0			)
     a__    =text_encoder_hidden_states.repeat_interleave(A__,       dim=0			)
     a__    =text_mask.repeat_interleave(A__,       dim=0			)

     if do_classifier_free_guidance:
       a__    =[''''''] * batch_size

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

       a__    =uncond_embeddings_text_encoder_output.text_embeds
       a__    =uncond_embeddings_text_encoder_output.last_hidden_state

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

       a__    =uncond_embeddings.shape[1]
       a__    =uncond_embeddings.repeat(1,       A__			)
       a__    =uncond_embeddings.view(batch_size * num_images_per_prompt,       A__			)

       a__    =uncond_text_encoder_hidden_states.shape[1]
       a__    =uncond_text_encoder_hidden_states.repeat(1,       A__,       1			)
       a__    =uncond_text_encoder_hidden_states.view(
           batch_size * num_images_per_prompt,       A__,       -1			)
       a__    =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
       a__    =torch.cat([uncond_embeddings, text_embeddings]			)
       a__    =torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

       a__    =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,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_,       lowercase_			)				->       List[Any]:






     """simple docstring"""






     if latents is None:
       a__    =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}"""			)
       a__    =latents.to(A__			)

     a__    =latents * scheduler.init_noise_sigma
     return latents



   def 					_UpperCAmelCase					(							self,       lowercase_ = None			)				->       Tuple:






     """simple docstring"""






     self.decoder_pipe.to(A__			)
     super().to(A__			)



   @torch.no_grad()
   def __call__(							self,       lowercase_ = None,       lowercase_ = None,       lowercase_ = None,       lowercase_ = 1,       lowercase_ = 25,       lowercase_ = None,       lowercase_ = None,       lowercase_ = None,       lowercase_ = None,       lowercase_ = 4.0,       lowercase_ = 8.0,       lowercase_ = 50,       lowercase_ = 1,       lowercase_ = 0.0,       lowercase_ = "pil",       lowercase_ = True,       )				->       List[str]:






     """simple docstring"""






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

     a__    =self._execution_device

     a__    =batch_size * num_images_per_prompt

     a__    =prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

     a__, a__, a__    =self._encode_prompt(
         A__,       A__,       A__,       A__,       A__,       A__			)

     # prior

     self.prior_scheduler.set_timesteps(A__,       device=A__			)
     a__    =self.prior_scheduler.timesteps

     a__    =self.prior.config.embedding_dim

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

       a__    =self.prior(
           A__,       timestep=A__,       proj_embedding=A__,       encoder_hidden_states=A__,       attention_mask=A__,       ).predicted_image_embedding

       if do_classifier_free_guidance:
         a__, a__    =predicted_image_embedding.chunk(2			)
         a__    =predicted_image_embedding_uncond + prior_guidance_scale * (
             predicted_image_embedding_text - predicted_image_embedding_uncond
         )

       if i + 1 == prior_timesteps_tensor.shape[0]:
         a__    =None
       else:
         a__    =prior_timesteps_tensor[i + 1]

       a__    =self.prior_scheduler.step(
           A__,       timestep=A__,       sample=A__,       generator=A__,       prev_timestep=A__,       ).prev_sample

     a__    =self.prior.post_process_latents(A__			)

     a__    =prior_latents

     a__    =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


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 							lowercase						(   SCREAMING_SNAKE_CASE__						):
							lowercase_			:			Dict			   =['''image_processor''', '''tokenizer''']
							lowercase_			:			List[str]			   ='''AutoImageProcessor'''
							lowercase_			:			Optional[Any]			   ='''AutoTokenizer'''




							def __init__(							self  ,A__=None  ,A__=None  ,**A__):
												lowercase		      =		None
												if "feature_extractor" in kwargs:
																	warnings.warn(
																	    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																	    ''' instead.'''  ,A__  ,)
																	lowercase		      =		kwargs.pop('''feature_extractor''')

												lowercase		      =		image_processor if image_processor is not None else feature_extractor
												if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.''')
												if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.''')

												super().__init__(A__  ,A__)
												lowercase		      =		self.image_processor
												lowercase		      =		False




							def __call__(							self  ,*A__  ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor(*A__  ,**A__)

												lowercase		      =		kwargs.pop('''images'''  ,A__)
												lowercase		      =		kwargs.pop('''text'''  ,A__)
												if len(A__) > 0:
																	lowercase		      =		args[0]
																	lowercase		      =		args[1:]

												if images is None and text is None:
																	raise ValueError('''You need to specify either an `images` or `text` input to process.''')

												if images is not None:
																	lowercase		      =		self.image_processor(A__  ,*A__  ,**A__)
												if text is not None:
																	lowercase		      =		self.tokenizer(A__  ,**A__)

												if text is None:
																	return inputs
												elif images is None:
																	return encodings
												else:
																	lowercase		      =		encodings['''input_ids''']
																	return inputs




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.batch_decode(*A__  ,**A__)




							def 					A__ (							self  ,*A__  ,**A__):
												return self.tokenizer.decode(*A__  ,**A__)




							@contextmanager
							def 					A__ (							self):
												warnings.warn(
												    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
												    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
												    '''your images inputs, or in a separate call.''')
												lowercase		      =		True
												lowercase		      =		self.tokenizer
												yield
												lowercase		      =		self.image_processor
												lowercase		      =		False




							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''  ,A__  ,)
												return self.image_processor_class





							@property
							def 					A__ (							self):
												warnings.warn(
												    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''  ,A__  ,)
												return self.image_processor




