




'''simple docstring'''




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


a_   :	List[str]							   = """
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
"""

a_   :	int							   = logging.getLogger(__name__)


@dataclass
class 						__UpperCamelCase :
       lowercase			:					Optional[str]   		=field(
           default='roberta-large-mnli'    ,      metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}       )
       lowercase			:					Optional[str]   		=field(
           default='This example is {}.'    ,      metadata={
               'help': (
                   'Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'
                   'where class name is inserted.'
               )
           }    ,      )
       lowercase			:					Optional[int]   		=field(
           default=32    ,      metadata={'help': 'Batch size for generating teacher predictions.'}       )
       lowercase			:					Optional[bool]   		=field(
           default=lowerCamelCase__    ,      metadata={
               'help': (
                   'Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'
                   'multi-class multi-label classification).'
               )
           }    ,      )
       lowercase			:					Optional[float]   		=field(
           default=1.0    ,      metadata={'help': 'Temperature applied to teacher softmax for distillation.'}       )





@dataclass
class 						__UpperCamelCase :
       lowercase			:					Optional[str]   		=field(
           default='distilbert-base-uncased'    ,      metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}       )





@dataclass
class 						__UpperCamelCase :
       lowercase			:					str   		=field(metadata={'help': 'Text file with one unlabeled instance per line.'}       )
       lowercase			:					str   		=field(metadata={'help': 'Text file with one class name per line.'}       )
       lowercase			:					bool   		=field(
           default=lowerCamelCase__    ,      metadata={'help': 'Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'}    ,      )





@dataclass
class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					Optional[str]   		=field(
           default=lowerCamelCase__    ,      metadata={'help': 'The output directory where the model predictions and checkpoints will be written.'}    ,      )
       lowercase			:					int   		=field(
           default=32    ,      metadata={'help': 'Batch size per GPU/TPU core/CPU for training.'}       )
       lowercase			:					int   		=field(
           default=1_28    ,      metadata={'help': 'Batch size per GPU/TPU core/CPU for evaluation.'}       )
       lowercase			:					float   		=field(default=1.0    ,      metadata={'help': 'Total number of training epochs to perform.'}       )
       lowercase			:					bool   		=field(default=lowerCamelCase__    ,      metadata={'help': 'Whether to run training of student model.'}       )
       lowercase			:					bool   		=field(
           default=lowerCamelCase__    ,      metadata={
               'help': (
                   'Whether to evaluate the agreement of the final student predictions and the teacher predictions'
                   'after training.'
               )
           }    ,      )
       lowercase			:					Optional[int]   		=field(
           default=0    ,      metadata={
               'help': (
                   'Limit the total amount of checkpoints.'
                   'Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'
               )
           }    ,      )





class 						__UpperCamelCase (				lowerCamelCase__       ):






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase=False						):



              """simple docstring"""







              lowerCamelCase_							=inputs['''labels''']
              lowerCamelCase_							=model(inputs['''input_ids'''],	attention_mask=inputs['''attention_mask''']						)
              lowerCamelCase_							=outputs[0]

              lowerCamelCase_							=-torch.sum(target_p * logits.log_softmax(dim=-1						),	axis=-1						).mean()

              if return_outputs:
                     return loss, outputs

              return loss





def   a_							(   __snake_case							:    str     )							->		int:



       """simple docstring"""
       lowerCamelCase_							=[]
       with open(__snake_case     , '''r'''     ) as f:
              for line in f:
                     lowerCamelCase_							=line.strip()
                     if len(__snake_case     ) > 0:
                            lines.append(__snake_case     )
       return lines





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    List[str]     , __snake_case							:    str     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=[]
       lowerCamelCase_							=[]
       for example in examples:
              for name in class_names:
                     premises.append(__snake_case     )
                     hypotheses.append(hypothesis_template.format(__snake_case     )     )
       return premises, hypotheses





def   a_							(   __snake_case							:    Tuple     )							->		Tuple:



       """simple docstring"""
       for label, ind in config.labelaid.items():
              if label.lower().startswith('''entail'''     ):
                     return ind
       logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.'''     )
       return -1





def   a_							(   __snake_case							:    str     , __snake_case							:    List[str]     , __snake_case							:    List[str]     , __snake_case							:    str     , __snake_case							:    int     , __snake_case							:    float     , __snake_case							:    bool     , __snake_case							:    bool     , __snake_case							:    bool     , __snake_case							:    bool     , )							->		Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=AutoModelForSequenceClassification.from_pretrained(__snake_case     )
       lowerCamelCase_							=model.config
       if not no_cuda and torch.cuda.is_available():
              lowerCamelCase_							=nn.DataParallel(model.cuda()     )
              batch_size *= len(model.device_ids     )
       lowerCamelCase_							=AutoTokenizer.from_pretrained(__snake_case     , use_fast=__snake_case     )

       lowerCamelCase_,   lowerCamelCase_							=get_premise_hypothesis_pairs(__snake_case     , __snake_case     , __snake_case     )
       lowerCamelCase_							=[]

       for i in tqdm(range(0     , len(__snake_case     )     , __snake_case     )     ):
              lowerCamelCase_							=premises[i : i + batch_size]
              lowerCamelCase_							=hypotheses[i : i + batch_size]

              lowerCamelCase_							=tokenizer(
                  __snake_case     , __snake_case     , padding=__snake_case     , truncation='''only_first'''     , return_tensors='''pt'''     , )

              with torch.cuda.amp.autocast(enabled=__snake_case     ):
                     with torch.no_grad():
                            lowerCamelCase_							=model(**__snake_case     )
              logits.append(outputs.logits.detach().cpu().float()     )

       lowerCamelCase_							=get_entailment_id(__snake_case     )
       lowerCamelCase_							=-1 if entail_id == 0 else 0
       lowerCamelCase_							=torch.cat(__snake_case     , dim=0     )  # N*K x 3
       lowerCamelCase_							=logits.reshape(len(__snake_case     )     , len(__snake_case     )     , -1     )[..., [contr_id, entail_id]]  # N x K x 2

       if multi_label:
              # softmax over (contr, entail) logits for each class independently
              lowerCamelCase_							=(nli_logits / temperature).softmax(-1     )
       else:
              # softmax over entail logits across classes s.t. class probabilities sum to 1.
              lowerCamelCase_							=(nli_logits / temperature).softmax(1     )

       return nli_prob[..., 1]  # N x K





def   a_							(   )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							=HfArgumentParser(
           (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)     , description=__snake_case     , )

       if len(sys.argv     ) == 2 and sys.argv[1].endswith('''.json'''     ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=parser.parse_json_file(
                  json_file=os.path.abspath(sys.argv[1]     )     )
       else:
              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=parser.parse_args_into_dataclasses()

       # Detecting last checkpoint.
       lowerCamelCase_							=None
       if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
              lowerCamelCase_							=get_last_checkpoint(training_args.output_dir     )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir     )     ) > 0:
                     raise ValueError(
                         F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
                         '''Use --overwrite_output_dir to overcome.'''     )
              elif last_checkpoint is not None:
                     logger.info(
                         F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
                         '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''     )

    # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     , datefmt='''%m/%d/%Y %H:%M:%S'''     , handlers=[logging.StreamHandler(sys.stdout     )]     , )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank     ) else logging.WARN     )

       # Log on each process the small summary:
       logger.warning(
           F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
           + F'''distributed training: {bool(training_args.local_rank != -1     )}, 16-bits training: {training_args.fpaa}'''     )
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank     ):
              utils.logging.set_verbosity_info()
              utils.logging.enable_default_handler()
              utils.logging.enable_explicit_format()

       if training_args.local_rank != -1:
              raise ValueError('''Distributed training is not currently supported.'''     )
       if training_args.tpu_num_cores is not None:
              raise ValueError('''TPU acceleration is not currently supported.'''     )

       logger.info(F'''Training/evaluation parameters {training_args}'''     )

       # Set seed before initializing model.
       set_seed(training_args.seed     )

       # 1. read in data
       lowerCamelCase_							=read_lines(data_args.data_file     )
       lowerCamelCase_							=read_lines(data_args.class_names_file     )

       # 2. get teacher predictions and load into dataset
       logger.info('''Generating predictions from zero-shot teacher model'''     )
       lowerCamelCase_							=get_teacher_predictions(
           teacher_args.teacher_name_or_path     , __snake_case     , __snake_case     , teacher_args.hypothesis_template     , teacher_args.teacher_batch_size     , teacher_args.temperature     , teacher_args.multi_label     , data_args.use_fast_tokenizer     , training_args.no_cuda     , training_args.fpaa     , )
       lowerCamelCase_							=Dataset.from_dict(
           {
               '''text''': examples,
               '''labels''': teacher_soft_preds,
           }     )

       # 3. create student
       logger.info('''Initializing student model'''     )
       lowerCamelCase_							=AutoModelForSequenceClassification.from_pretrained(
           student_args.student_name_or_path     , num_labels=len(__snake_case     )     )
       lowerCamelCase_							=AutoTokenizer.from_pretrained(student_args.student_name_or_path     , use_fast=data_args.use_fast_tokenizer     )
       lowerCamelCase_							=dict(enumerate(__snake_case     )     )
       lowerCamelCase_							={label: i for i, label in enumerate(__snake_case     )}

       # 4. train student on teacher predictions
       lowerCamelCase_							=dataset.map(__snake_case     , input_columns='''text'''     )
       dataset.set_format('''torch'''     )

       def compute_metrics(__snake_case							:    str     , __snake_case							:    Dict=False     ):
              lowerCamelCase_							=p.predictions.argmax(-1     )
              lowerCamelCase_							=p.label_ids.argmax(-1     )  # "label_ids" are actually distributions
              return {"agreement": (preds == proxy_labels).mean().item()}

       lowerCamelCase_							=DistillationTrainer(
           model=__snake_case     , tokenizer=__snake_case     , args=__snake_case     , train_dataset=__snake_case     , compute_metrics=__snake_case     , )

       if training_args.do_train:
              logger.info('''Training student model on teacher predictions'''     )
              trainer.train()

       if training_args.do_eval:
              lowerCamelCase_							=trainer.evaluate(eval_dataset=__snake_case     )['''eval_agreement''']
              logger.info(F'''Agreement of student and teacher predictions: {agreement * 100:0.2f}%'''     )

       trainer.save_model()


if __name__ == "__main__":
 main()










'''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


a_   :	Optional[Any]							   = logging.get_logger(__name__)  # pylint: disable=invalid-name





def   a_							(   self							:    Union[str, Any]     , __snake_case							:    List[str]     , __snake_case							:    List[str]     , __snake_case							:    Dict     , __snake_case							:    str     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=image.to(device=__snake_case     )
       lowerCamelCase_							=image  # take image as image_embeddings
       lowerCamelCase_							=image_embeddings.unsqueeze(1     )

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

       if do_classifier_free_guidance:
              lowerCamelCase_							=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
              lowerCamelCase_							=torch.cat([uncond_embeddings, image_embeddings]     )

       return image_embeddings


class 						__UpperCamelCase (				lowerCamelCase__       ):






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	):



              """simple docstring"""







              super().__init__()

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

              lowerCamelCase_							=decoder_pipe_kwargs.get('''torch_dtype''',	lowerCAmelCase						) or prior.dtype

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

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

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






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase = None,	lowerCAmelCase = None,	):



              """simple docstring"""







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

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

                     lowerCamelCase_							=self.text_encoder(text_input_ids.to(lowerCAmelCase						)						)

                     lowerCamelCase_							=text_encoder_output.text_embeds
                     lowerCamelCase_							=text_encoder_output.last_hidden_state

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

              lowerCamelCase_							=text_embeddings.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_encoder_hidden_states.repeat_interleave(lowerCAmelCase,	dim=0						)
              lowerCamelCase_							=text_mask.repeat_interleave(lowerCAmelCase,	dim=0						)

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

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

                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.text_embeds
                     lowerCamelCase_							=uncond_embeddings_text_encoder_output.last_hidden_state

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

                     lowerCamelCase_							=uncond_embeddings.shape[1]
                     lowerCamelCase_							=uncond_embeddings.repeat(1,	lowerCAmelCase						)
                     lowerCamelCase_							=uncond_embeddings.view(batch_size * num_images_per_prompt,	lowerCAmelCase						)

                     lowerCamelCase_							=uncond_text_encoder_hidden_states.shape[1]
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.repeat(1,	lowerCAmelCase,	1						)
                     lowerCamelCase_							=uncond_text_encoder_hidden_states.view(
                         batch_size * num_images_per_prompt,	lowerCAmelCase,	-1						)
                     lowerCamelCase_							=uncond_text_mask.repeat_interleave(lowerCAmelCase,	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
                     lowerCamelCase_							=torch.cat([uncond_embeddings, text_embeddings]						)
                     lowerCamelCase_							=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]						)

                     lowerCamelCase_							=torch.cat([uncond_text_mask, text_mask]						)

              return text_embeddings, text_encoder_hidden_states, text_mask






       @property
       def   lowercase__  (							self						):



              """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(lowerCAmelCase,	'''_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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              if latents is None:
                     lowerCamelCase_							=randn_tensor(lowerCAmelCase,	generator=lowerCAmelCase,	device=lowerCAmelCase,	dtype=lowerCAmelCase						)
              else:
                     if latents.shape != shape:
                            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
                     lowerCamelCase_							=latents.to(lowerCAmelCase						)

              lowerCamelCase_							=latents * scheduler.init_noise_sigma
              return latents






       def   lowercase__  (							self,	lowerCAmelCase = None						):



              """simple docstring"""







              self.decoder_pipe.to(lowerCAmelCase						)
              super().to(lowerCAmelCase						)






       @torch.no_grad()
       def __call__(							self,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 1,	lowerCAmelCase = 25,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = None,	lowerCAmelCase = 4.0,	lowerCAmelCase = 8.0,	lowerCAmelCase = 50,	lowerCAmelCase = 1,	lowerCAmelCase = 0.0,	lowerCAmelCase = "pil",	lowerCAmelCase = True,	):



              """simple docstring"""







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

              lowerCamelCase_							=self._execution_device

              lowerCamelCase_							=batch_size * num_images_per_prompt

              lowerCamelCase_							=prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

              lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=self._encode_prompt(
                  lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase						)

              # prior

              self.prior_scheduler.set_timesteps(lowerCAmelCase,	device=lowerCAmelCase						)
              lowerCamelCase_							=self.prior_scheduler.timesteps

              lowerCamelCase_							=self.prior.config.embedding_dim

              lowerCamelCase_							=self.prepare_latents(
                  (batch_size, embedding_dim),	text_embeddings.dtype,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	self.prior_scheduler,	)

              for i, t in enumerate(self.progress_bar(lowerCAmelCase						)						):
                     # expand the latents if we are doing classifier free guidance
                     lowerCamelCase_							=torch.cat([prior_latents] * 2						) if do_classifier_free_guidance else prior_latents

                     lowerCamelCase_							=self.prior(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	proj_embedding=lowerCAmelCase,	encoder_hidden_states=lowerCAmelCase,	attention_mask=lowerCAmelCase,	).predicted_image_embedding

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

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

                     lowerCamelCase_							=self.prior_scheduler.step(
                         lowerCAmelCase,	timestep=lowerCAmelCase,	sample=lowerCAmelCase,	generator=lowerCAmelCase,	prev_timestep=lowerCAmelCase,	).prev_sample

              lowerCamelCase_							=self.prior.post_process_latents(lowerCAmelCase						)

              lowerCamelCase_							=prior_latents

              lowerCamelCase_							=self.decoder_pipe(
                  image=lowerCAmelCase,	height=lowerCAmelCase,	width=lowerCAmelCase,	num_inference_steps=lowerCAmelCase,	guidance_scale=lowerCAmelCase,	generator=lowerCAmelCase,	output_type=lowerCAmelCase,	return_dict=lowerCAmelCase,	num_images_per_prompt=lowerCAmelCase,	eta=lowerCAmelCase,	)
              return output




