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     UpperCAmelCase__       (    self   :				List[str]			,   _A   :				Union[str, Any]			,   _A   :				Any			,   _A   :				Union[str, Any]			,   _A   :				List[str]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=image.to(device=_UpperCAmelCase							)
    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			,   _UpperCAmelCase			,   1							)
    a__						=image_embeddings.view(bs_embed * num_images_per_prompt			,   _UpperCAmelCase			,   -1							)
  
    if do_classifier_free_guidance:
      a__						=torch.zeros_like(_UpperCAmelCase							)
  
      # 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__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = None,					)							->					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''',					SCREAMING_SNAKE_CASE_ ) or prior.dtype
  
       a__						=StableDiffusionImageVariationPipeline.from_pretrained(
           '''lambdalabs/sd-image-variations-diffusers''',					**SCREAMING_SNAKE_CASE_ )
  
       # replace `_encode_image` method
       a__						=types.MethodType(_encode_image,					self.decoder_pipe )
  
       self.register_modules(
           prior=SCREAMING_SNAKE_CASE_,					tokenizer=SCREAMING_SNAKE_CASE_,					text_encoder=SCREAMING_SNAKE_CASE_,					prior_scheduler=SCREAMING_SNAKE_CASE_,					)
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = None,					lowercase_ = None,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if text_model_output is None:
         a__						=len(SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ ) else 1
         # get prompt text embeddings
         a__						=self.tokenizer(
             SCREAMING_SNAKE_CASE_,					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(SCREAMING_SNAKE_CASE_ )
  
         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(SCREAMING_SNAKE_CASE_ ) )
  
         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(SCREAMING_SNAKE_CASE_,					dim=0 )
       a__						=text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_,					dim=0 )
       a__						=text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_,					dim=0 )
  
       if do_classifier_free_guidance:
         a__						=[''''''] * batch_size
  
         a__						=self.tokenizer(
             SCREAMING_SNAKE_CASE_,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=SCREAMING_SNAKE_CASE_,					return_tensors='''pt''',					)
         a__						=uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )
         a__						=self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_ ) )
  
         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,					SCREAMING_SNAKE_CASE_ )
         a__						=uncond_embeddings.view(batch_size * num_images_per_prompt,					SCREAMING_SNAKE_CASE_ )
  
         a__						=uncond_text_encoder_hidden_states.shape[1]
         a__						=uncond_text_encoder_hidden_states.repeat(1,					SCREAMING_SNAKE_CASE_,					1 )
         a__						=uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt,					SCREAMING_SNAKE_CASE_,					-1 )
         a__						=uncond_text_mask.repeat_interleave(SCREAMING_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
         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 )							->					Dict:
  
  
  
  
  
  
       """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(SCREAMING_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,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if latents is None:
         a__						=randn_tensor(SCREAMING_SNAKE_CASE_,					generator=SCREAMING_SNAKE_CASE_,					device=SCREAMING_SNAKE_CASE_,					dtype=SCREAMING_SNAKE_CASE_ )
       else:
         if latents.shape != shape:
           raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
         a__						=latents.to(SCREAMING_SNAKE_CASE_ )
  
       a__						=latents * scheduler.init_noise_sigma
       return latents
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ = None )							->					int:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       self.decoder_pipe.to(SCREAMING_SNAKE_CASE_ )
       super().to(SCREAMING_SNAKE_CASE_ )
  
  
  
     @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,					)							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if prompt is not None:
         if isinstance(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ ):
           a__						=1
         elif isinstance(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ ):
           a__						=len(SCREAMING_SNAKE_CASE_ )
         else:
           raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}""" )
       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(
           SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_ )
  
       # prior
  
       self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_,					device=SCREAMING_SNAKE_CASE_ )
       a__						=self.prior_scheduler.timesteps
  
       a__						=self.prior.config.embedding_dim
  
       a__						=self.prepare_latents(
           (batch_size, embedding_dim),					text_embeddings.dtype,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					self.prior_scheduler,					)
  
       for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
         # 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(
             SCREAMING_SNAKE_CASE_,					timestep=SCREAMING_SNAKE_CASE_,					proj_embedding=SCREAMING_SNAKE_CASE_,					encoder_hidden_states=SCREAMING_SNAKE_CASE_,					attention_mask=SCREAMING_SNAKE_CASE_,					).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(
             SCREAMING_SNAKE_CASE_,					timestep=SCREAMING_SNAKE_CASE_,					sample=SCREAMING_SNAKE_CASE_,					generator=SCREAMING_SNAKE_CASE_,					prev_timestep=SCREAMING_SNAKE_CASE_,					).prev_sample
  
       a__						=self.prior.post_process_latents(SCREAMING_SNAKE_CASE_ )
  
       a__						=prior_latents
  
       a__						=self.decoder_pipe(
           image=SCREAMING_SNAKE_CASE_,					height=SCREAMING_SNAKE_CASE_,					width=SCREAMING_SNAKE_CASE_,					num_inference_steps=SCREAMING_SNAKE_CASE_,					guidance_scale=SCREAMING_SNAKE_CASE_,					generator=SCREAMING_SNAKE_CASE_,					output_type=SCREAMING_SNAKE_CASE_,					return_dict=SCREAMING_SNAKE_CASE_,					num_images_per_prompt=SCREAMING_SNAKE_CASE_,					eta=SCREAMING_SNAKE_CASE_,					)
       return output
  
style_context: 
  
  import torch
  from transformers import AutoModelForSequenceClassification, AutoTokenizer, Trainer, TrainingArguments
  
  import datasets
  
  
  lowerCamelCase     =      '''\
  @article{eddine2021frugalscore,
    title={FrugalScore: Learning Cheaper, Lighter and Faster Evaluation Metrics for Automatic Text Generation},
    author={Eddine, Moussa Kamal and Shang, Guokan and Tixier, Antoine J-P and Vazirgiannis, Michalis},
    journal={arXiv preprint arXiv:2110.08559},
    year={2021}
  }
  '''
  
  lowerCamelCase     =      '''\
  FrugalScore is a reference-based metric for NLG models evaluation. It is based on a distillation approach that allows to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance.
  '''
  
  
  lowerCamelCase     =      '''
  Calculates how good are predictions given some references, using certain scores.
  Args:
      predictions (list of str): list of predictions to score. Each predictions
          should be a string.
      references (list of str): list of reference for each prediction. Each
          reference should be a string.
      batch_size (int): the batch size for predictions.
      max_length (int): maximum sequence length.
      device (str): either gpu or cpu
  Returns:
      scores (list of int): list of scores.
  Examples:
      >>> frugalscore = datasets.load_metric("frugalscore")
      >>> results = frugalscore.compute(predictions=[\'hello there\', \'huggingface\'], references=[\'hello world\', \'hugging face\'])
      >>> print([round(s, 3) for s in results["scores"]])
      [0.631, 0.645]
  '''
  
  
  
  
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	, _KWARGS_DESCRIPTION							)
  class 		__magic_name__    (							datasets.Metric							):
  
     '''simple docstring'''
  
  
  
  
     def 				_UpperCAmelCase				(       self )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return datasets.MetricInfo(
           description=_DESCRIPTION,					citation=_CITATION,					inputs_description=_KWARGS_DESCRIPTION,					features=datasets.Features(
               {
                   '''predictions''': datasets.Value('''string''' ),
                   '''references''': datasets.Value('''string''' ),
               } ),					homepage='''https://github.com/moussaKam/FrugalScore''',					)
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self.config_name == "default":
         a__						='''moussaKam/frugalscore_tiny_bert-base_bert-score'''
       else:
         a__						=self.config_name
       a__						=AutoModelForSequenceClassification.from_pretrained(lowercase_ )
       a__						=AutoTokenizer.from_pretrained(lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_=32,					lowercase_=128,					lowercase_=None,					)							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       assert len(lowercase_ ) == len(
           lowercase_ ), "predictions and references should have the same number of sentences."
       if device is not None:
         assert device in ["gpu", "cpu"], "device should be either gpu or cpu."
       else:
         a__						='''gpu''' if torch.cuda.is_available() else '''cpu'''
       a__						=TrainingArguments(
           '''trainer''',					fpaa=(device == '''gpu'''),					per_device_eval_batch_size=lowercase_,					report_to='''all''',					no_cuda=(device == '''cpu'''),					log_level='''warning''',					)
       a__						={'''sentence1''': predictions, '''sentence2''': references}
       a__						=datasets.Dataset.from_dict(lowercase_ )
  
       def tokenize_function(lowercase_ ):
         return self.tokenizer(
             data['''sentence1'''],					data['''sentence2'''],					max_length=lowercase_,					truncation=lowercase_,					padding=lowercase_ )
  
       a__						=raw_datasets.map(lowercase_,					batched=lowercase_ )
       tokenized_datasets.remove_columns(['''sentence1''', '''sentence2'''] )
       a__						=Trainer(self.model,					lowercase_,					tokenizer=self.tokenizer )
       a__						=trainer.predict(lowercase_ )
       return {"scores": list(predictions.predictions.squeeze(-1 ) )}
  