


'''simple docstring'''






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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowercase							=logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-classification/requirements.txt')

lowercase							=list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
lowercase							=tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)





def lowerCamelCase__      (						__lowerCamelCase	:							str							):







   '''simple docstring'''




   with open(__lowerCamelCase	,   'rb'							) as f:
      _UpperCAmelCase       :       List[Any]							   =Image.open(__lowerCamelCase							)
      return im.convert('RGB'							)






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={
               "help": "Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."
           } ,)
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}    )
       UpperCAmelCase										=field(default=lowerCAmelCase ,metadata={"help": "A folder containing the training data."}    )
       UpperCAmelCase										=field(default=lowerCAmelCase ,metadata={"help": "A folder containing the validation data."}    )
       UpperCAmelCase										=field(
           default=0.15 ,metadata={"help": "Percent to split off of train for validation."}    )
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of training examples to this "
                   "value if set."
               )
           } ,)
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={
               "help": (
                   "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                   "value if set."
               )
           } ,)






       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
             raise ValueError(
                 'You must specify either a dataset name from the hub or a train and/or validation directory.')






@dataclass
class  __magic_name__							:
       UpperCAmelCase										=field(
           default="google/vit-base-patch16-224-in21k" ,metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} ,)
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(lowerCAmelCase    )} ,)
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={"help": "Pretrained config name or path if not the same as model_name"}    )
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}    )
       UpperCAmelCase										=field(
           default="main" ,metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} ,)
       UpperCAmelCase										=field(default=lowerCAmelCase ,metadata={"help": "Name or path of preprocessor config."}    )
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={
               "help": (
                   "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                   "with private models)."
               )
           } ,)
       UpperCAmelCase										=field(
           default=lowerCAmelCase ,metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."} ,)






def lowerCamelCase__      (						__lowerCamelCase	:							List[Any]							):







   '''simple docstring'''




   _UpperCAmelCase       :       List[str]							   =torch.stack([example['pixel_values'] for example in examples]							)
   _UpperCAmelCase       :       Union[str, Any]							   =torch.tensor([example['labels'] for example in examples]							)
   return {"pixel_values": pixel_values, "labels": labels}





def lowerCamelCase__      (						):







   '''simple docstring'''




   _UpperCAmelCase       :       Dict							   =HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
   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.
      _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       Any							   =parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]							)							)
   else:
      _UpperCAmelCase   ,	_UpperCAmelCase   ,	_UpperCAmelCase       :       List[Any]							   =parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry('run_image_classification'	,   __lowerCamelCase	,   __lowerCamelCase							)

   # 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							)]	,   )

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   _UpperCAmelCase       :       Optional[Any]							   =training_args.get_process_log_level()
   logger.setLevel(__lowerCamelCase							)
   transformers.utils.logging.set_verbosity(__lowerCamelCase							)
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # 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}"							)
   logger.info(f"Training/evaluation parameters {training_args}"							)

   # Detecting last checkpoint.
   _UpperCAmelCase       :       Tuple							   =None
   if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
      _UpperCAmelCase       :       int							   =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 and training_args.resume_from_checkpoint is 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.'							)

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

   # Initialize our dataset and prepare it for the 'image-classification' task.
   if data_args.dataset_name is not None:
      _UpperCAmelCase       :       Any							   =load_dataset(
          data_args.dataset_name	,   data_args.dataset_config_name	,   cache_dir=model_args.cache_dir	,   task='image-classification'	,   use_auth_token=True if model_args.use_auth_token else None	,   )
   else:
      _UpperCAmelCase       :       Tuple							   ={}
      if data_args.train_dir is not None:
         _UpperCAmelCase       :       List[str]							   =os.path.join(data_args.train_dir	,   '**'							)
      if data_args.validation_dir is not None:
         _UpperCAmelCase       :       Tuple							   =os.path.join(data_args.validation_dir	,   '**'							)
      _UpperCAmelCase       :       Any							   =load_dataset(
          'imagefolder'	,   data_files=__lowerCamelCase	,   cache_dir=model_args.cache_dir	,   task='image-classification'	,   )

   # If we don't have a validation split, split off a percentage of train as validation.
   _UpperCAmelCase       :       List[Any]							   =None if 'validation' in dataset.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split	,   __lowerCamelCase							) and data_args.train_val_split > 0.0:
      _UpperCAmelCase       :       Tuple							   =dataset['train'].train_test_split(data_args.train_val_split							)
      _UpperCAmelCase       :       Optional[int]							   =split['train']
      _UpperCAmelCase       :       Union[str, Any]							   =split['test']

   # Prepare label mappings.
   # We'll include these in the model's config to get human readable labels in the Inference API.
   _UpperCAmelCase       :       Tuple							   =dataset['train'].features['labels'].names
   _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   ={}, {}
   for i, label in enumerate(__lowerCamelCase							):
      _UpperCAmelCase       :       Any							   =str(__lowerCamelCase							)
      _UpperCAmelCase       :       int							   =label

   # Load the accuracy metric from the datasets package
   _UpperCAmelCase       :       List[Any]							   =evaluate.load('accuracy'							)

   # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
   # predictions and label_ids field) and has to return a dictionary string to float.
   def compute_metrics(__lowerCamelCase	:							Union[str, Any]							):
      return metric.compute(predictions=np.argmax(p.predictions	,   axis=1							)	,   references=p.label_ids							)

   _UpperCAmelCase       :       Dict							   =AutoConfig.from_pretrained(
       model_args.config_name or model_args.model_name_or_path	,   num_labels=len(__lowerCamelCase							)	,   labelaid=__lowerCamelCase	,   idalabel=__lowerCamelCase	,   finetuning_task='image-classification'	,   cache_dir=model_args.cache_dir	,   revision=model_args.model_revision	,   use_auth_token=True if model_args.use_auth_token else None	,   )
   _UpperCAmelCase       :       str							   =AutoModelForImageClassification.from_pretrained(
       model_args.model_name_or_path	,   from_tf=bool('.ckpt' in model_args.model_name_or_path							)	,   config=__lowerCamelCase	,   cache_dir=model_args.cache_dir	,   revision=model_args.model_revision	,   use_auth_token=True if model_args.use_auth_token else None	,   ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,   )
   _UpperCAmelCase       :       Optional[Any]							   =AutoImageProcessor.from_pretrained(
       model_args.image_processor_name or model_args.model_name_or_path	,   cache_dir=model_args.cache_dir	,   revision=model_args.model_revision	,   use_auth_token=True if model_args.use_auth_token else None	,   )

   # Define torchvision transforms to be applied to each image.
   if "shortest_edge" in image_processor.size:
      _UpperCAmelCase       :       List[str]							   =image_processor.size['shortest_edge']
   else:
      _UpperCAmelCase       :       Tuple							   =(image_processor.size['height'], image_processor.size['width'])
   _UpperCAmelCase       :       int							   =Normalize(mean=image_processor.image_mean	,   std=image_processor.image_std							)
   _UpperCAmelCase       :       Tuple							   =Compose(
       [
           RandomResizedCrop(__lowerCamelCase							),
           RandomHorizontalFlip(),
           ToTensor(),
           normalize,
       ]							)
   _UpperCAmelCase       :       str							   =Compose(
       [
           Resize(__lowerCamelCase							),
           CenterCrop(__lowerCamelCase							),
           ToTensor(),
           normalize,
       ]							)

   def train_transforms(__lowerCamelCase	:							List[Any]							):
      _UpperCAmelCase       :       List[str]							   =[
          _train_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch['image']
      ]
      return example_batch

   def val_transforms(__lowerCamelCase	:							List[Any]							):
      _UpperCAmelCase       :       List[str]							   =[_val_transforms(pil_img.convert('RGB'							)							) for pil_img in example_batch['image']]
      return example_batch

   if training_args.do_train:
      if "train" not in dataset:
         raise ValueError('--do_train requires a train dataset'							)
      if data_args.max_train_samples is not None:
         _UpperCAmelCase       :       Dict							   =(
             dataset['train'].shuffle(seed=training_args.seed							).select(range(data_args.max_train_samples							)							)
         )
      # Set the training transforms
      dataset["train"].set_transform(__lowerCamelCase							)

   if training_args.do_eval:
      if "validation" not in dataset:
         raise ValueError('--do_eval requires a validation dataset'							)
      if data_args.max_eval_samples is not None:
         _UpperCAmelCase       :       Tuple							   =(
             dataset['validation'].shuffle(seed=training_args.seed							).select(range(data_args.max_eval_samples							)							)
         )
      # Set the validation transforms
      dataset["validation"].set_transform(__lowerCamelCase							)

   # Initalize our trainer
   _UpperCAmelCase       :       List[str]							   =Trainer(
       model=__lowerCamelCase	,   args=__lowerCamelCase	,   train_dataset=dataset['train'] if training_args.do_train else None	,   eval_dataset=dataset['validation'] if training_args.do_eval else None	,   compute_metrics=__lowerCamelCase	,   tokenizer=__lowerCamelCase	,   data_collator=__lowerCamelCase	,   )

   # Training
   if training_args.do_train:
      _UpperCAmelCase       :       Any							   =None
      if training_args.resume_from_checkpoint is not None:
         _UpperCAmelCase       :       Optional[Any]							   =training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _UpperCAmelCase       :       Dict							   =last_checkpoint
      _UpperCAmelCase       :       Dict							   =trainer.train(resume_from_checkpoint=__lowerCamelCase							)
      trainer.save_model()
      trainer.log_metrics('train'	,   train_result.metrics							)
      trainer.save_metrics('train'	,   train_result.metrics							)
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      _UpperCAmelCase       :       Optional[int]							   =trainer.evaluate()
      trainer.log_metrics('eval'	,   __lowerCamelCase							)
      trainer.save_metrics('eval'	,   __lowerCamelCase							)

   # Write model card and (optionally) push to hub
   _UpperCAmelCase       :       Dict							   ={
       'finetuned_from': model_args.model_name_or_path,
       'tasks': 'image-classification',
       'dataset': data_args.dataset_name,
       'tags': ['image-classification', 'vision'],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__lowerCamelCase							)
   else:
      trainer.create_model_card(**__lowerCamelCase							)


if __name__ == "__main__":
 main()





'''simple docstring'''






from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


lowercase							=logging.get_logger(__name__)  # pylint: disable=invalid-name






class  __magic_name__							(		lowerCAmelCase ,lowerCAmelCase    ):






       @register_to_config
       def __init__(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = None)						->  Union[str, Any]:




          '''simple docstring'''
          super().__init__()

          _UpperCAmelCase       :       List[Any]							   =learnable

          if self.learnable:
             assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
             assert length is not None, "learnable=True requires `length` to be set"

             _UpperCAmelCase       :       str							   =torch.zeros(snake_case  ,							snake_case)
          else:
             _UpperCAmelCase       :       Tuple							   =None

          _UpperCAmelCase       :       int							   =torch.nn.Parameter(snake_case)






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42
       UpperCAmelCase										=42






       def __init__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							snake_case  ,							)						->  Tuple:




          '''simple docstring'''
          super().__init__()

          self.register_modules(
              vqvae=snake_case  ,							transformer=snake_case  ,							text_encoder=snake_case  ,							tokenizer=snake_case  ,							scheduler=snake_case  ,							learned_classifier_free_sampling_embeddings=snake_case  ,							)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =len(snake_case) if isinstance(snake_case  ,							snake_case) else 1

          # get prompt text embeddings
          _UpperCAmelCase       :       Optional[Any]							   =self.tokenizer(
              snake_case  ,							padding='max_length'  ,							max_length=self.tokenizer.model_max_length  ,							return_tensors='pt'  ,							)
          _UpperCAmelCase       :       Union[str, Any]							   =text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             _UpperCAmelCase       :       str							   =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}")
             _UpperCAmelCase       :       Union[str, Any]							   =text_input_ids[:, : self.tokenizer.model_max_length]
          _UpperCAmelCase       :       Optional[int]							   =self.text_encoder(text_input_ids.to(self.device))[0]

          # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
          # While CLIP does normalize the pooled output of the text transformer when combining
          # the image and text embeddings, CLIP does not directly normalize the last hidden state.
          #
          # CLIP normalizing the pooled output.
          # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
          _UpperCAmelCase       :       List[str]							   =prompt_embeds / prompt_embeds.norm(dim=-1  ,							keepdim=snake_case)

          # duplicate text embeddings for each generation per prompt
          _UpperCAmelCase       :       Optional[Any]							   =prompt_embeds.repeat_interleave(snake_case  ,							dim=0)

          if do_classifier_free_guidance:
             if self.learned_classifier_free_sampling_embeddings.learnable:
                _UpperCAmelCase       :       Dict							   =self.learned_classifier_free_sampling_embeddings.embeddings
                _UpperCAmelCase       :       Any							   =negative_prompt_embeds.unsqueeze(0).repeat(snake_case  ,							1  ,							1)
             else:
                _UpperCAmelCase       :       str							   =[''] * batch_size

                _UpperCAmelCase       :       Dict							   =text_input_ids.shape[-1]
                _UpperCAmelCase       :       str							   =self.tokenizer(
                    snake_case  ,							padding='max_length'  ,							max_length=snake_case  ,							truncation=snake_case  ,							return_tensors='pt'  ,							)
                _UpperCAmelCase       :       str							   =self.text_encoder(uncond_input.input_ids.to(self.device))[0]
                # See comment for normalizing text embeddings
                _UpperCAmelCase       :       Tuple							   =negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1  ,							keepdim=snake_case)

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             _UpperCAmelCase       :       int							   =negative_prompt_embeds.shape[1]
             _UpperCAmelCase       :       List[str]							   =negative_prompt_embeds.repeat(1  ,							snake_case  ,							1)
             _UpperCAmelCase       :       Optional[int]							   =negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,							snake_case  ,							-1)

             # 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
             _UpperCAmelCase       :       str							   =torch.cat([negative_prompt_embeds, prompt_embeds])

          return prompt_embeds






       @torch.no_grad()
       def __call__(      self  ,							snake_case  ,							snake_case = 1_0_0  ,							snake_case = 5.0  ,							snake_case = 1.0  ,							snake_case = 1  ,							snake_case = None  ,							snake_case = None  ,							snake_case = "pil"  ,							snake_case = True  ,							snake_case = None  ,							snake_case = 1  ,							)						->  Union[ImagePipelineOutput, Tuple]:




          '''simple docstring'''
          if isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       Tuple							   =1
          elif isinstance(snake_case  ,							snake_case):
             _UpperCAmelCase       :       int							   =len(snake_case)
          else:
             raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case)}")

          _UpperCAmelCase       :       Optional[Any]							   =batch_size * num_images_per_prompt

          _UpperCAmelCase       :       Union[str, Any]							   =guidance_scale > 1.0

          _UpperCAmelCase       :       Any							   =self._encode_prompt(snake_case  ,							snake_case  ,							snake_case)

          if (callback_steps is None) or (
              callback_steps is not None and (not isinstance(snake_case  ,							snake_case) or callback_steps <= 0)
          ):
             raise ValueError(
                 f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                 f" {type(snake_case)}.")

          # get the initial completely masked latents unless the user supplied it

          _UpperCAmelCase       :       List[Any]							   =(batch_size, self.transformer.num_latent_pixels)
          if latents is None:
             _UpperCAmelCase       :       Optional[Any]							   =self.transformer.num_vector_embeds - 1
             _UpperCAmelCase       :       Optional[int]							   =torch.full(snake_case  ,							snake_case).to(self.device)
          else:
             if latents.shape != latents_shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
             if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                raise ValueError(
                    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
                    f" {self.transformer.num_vector_embeds - 1} (inclusive).")
             _UpperCAmelCase       :       Optional[Any]							   =latents.to(self.device)

          # set timesteps
          self.scheduler.set_timesteps(snake_case  ,							device=self.device)

          _UpperCAmelCase       :       int							   =self.scheduler.timesteps.to(self.device)

          _UpperCAmelCase       :       Dict							   =latents

          for i, t in enumerate(self.progress_bar(snake_case)):
             # expand the sample if we are doing classifier free guidance
             _UpperCAmelCase       :       Union[str, Any]							   =torch.cat([sample] * 2) if do_classifier_free_guidance else sample

             # predict the un-noised image
             # model_output == `log_p_x_0`
             _UpperCAmelCase       :       Optional[Any]							   =self.transformer(snake_case  ,							encoder_hidden_states=snake_case  ,							timestep=snake_case).sample

             if do_classifier_free_guidance:
                _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =model_output.chunk(2)
                _UpperCAmelCase       :       Dict							   =model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                model_output -= torch.logsumexp(snake_case  ,							dim=1  ,							keepdim=snake_case)

             _UpperCAmelCase       :       Any							   =self.truncate(snake_case  ,							snake_case)

             # remove `log(0)`'s (`-inf`s)
             _UpperCAmelCase       :       int							   =model_output.clamp(-7_0)

             # compute the previous noisy sample x_t -> x_t-1
             _UpperCAmelCase       :       List[Any]							   =self.scheduler.step(snake_case  ,							timestep=snake_case  ,							sample=snake_case  ,							generator=snake_case).prev_sample

             # call the callback, if provided
             if callback is not None and i % callback_steps == 0:
                callback(snake_case  ,							snake_case  ,							snake_case)

          _UpperCAmelCase       :       List[str]							   =self.vqvae.config.vq_embed_dim
          _UpperCAmelCase       :       Optional[int]							   =(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          _UpperCAmelCase       :       int							   =self.vqvae.quantize.get_codebook_entry(snake_case  ,							shape=snake_case)
          _UpperCAmelCase       :       str							   =self.vqvae.decode(snake_case  ,							force_not_quantize=snake_case).sample

          _UpperCAmelCase       :       str							   =(image / 2 + 0.5).clamp(0  ,							1)
          _UpperCAmelCase       :       Tuple							   =image.cpu().permute(0  ,							2  ,							3  ,							1).numpy()

          if output_type == "pil":
             _UpperCAmelCase       :       Optional[int]							   =self.numpy_to_pil(snake_case)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case)						->  torch.FloatTensor:




          '''simple docstring'''
          _UpperCAmelCase   ,	_UpperCAmelCase       :       Dict							   =torch.sort(snake_case  ,							1  ,							descending=snake_case)
          _UpperCAmelCase       :       Dict							   =torch.exp(snake_case)
          _UpperCAmelCase       :       str							   =sorted_p_x_0.cumsum(dim=1) < truncation_rate

          # Ensure that at least the largest probability is not zeroed out
          _UpperCAmelCase       :       Optional[int]							   =torch.full_like(keep_mask[:, 0:1, :]  ,							snake_case)
          _UpperCAmelCase       :       Any							   =torch.cat((all_true, keep_mask)  ,							dim=1)
          _UpperCAmelCase       :       Dict							   =keep_mask[:, :-1, :]

          _UpperCAmelCase       :       Any							   =keep_mask.gather(1  ,							indices.argsort(1))

          _UpperCAmelCase       :       str							   =log_p_x_0.clone()

          _UpperCAmelCase       :       Any							   =-torch.inf  # -inf = log(0)

          return rv

