


"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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




class 	lowercase__				(					A_				):





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   )  ->  Union[str, Any]:
      super().__init__()

      if safety_checker is None:
         logger.warning(
             F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
             """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
             """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
             """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
             """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
             """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""")

      self.register_modules(
          speech_model=SCREAMING_SNAKE_CASE      ,   speech_processor=SCREAMING_SNAKE_CASE      ,   vae=SCREAMING_SNAKE_CASE      ,   text_encoder=SCREAMING_SNAKE_CASE      ,   tokenizer=SCREAMING_SNAKE_CASE      ,   unet=SCREAMING_SNAKE_CASE      ,   scheduler=SCREAMING_SNAKE_CASE      ,   feature_extractor=SCREAMING_SNAKE_CASE      ,   )





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE = "auto")  ->  Dict:
      if slice_size == "auto":
         _lowerCamelCase	:					int   =		self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Any:
      self.enable_attention_slicing(SCREAMING_SNAKE_CASE)





   @torch.no_grad()
   def __call__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=1_6000      ,   SCREAMING_SNAKE_CASE = 512      ,   SCREAMING_SNAKE_CASE = 512      ,   SCREAMING_SNAKE_CASE = 50      ,   SCREAMING_SNAKE_CASE = 7.5      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 1      ,   SCREAMING_SNAKE_CASE = 0.0      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = "pil"      ,   SCREAMING_SNAKE_CASE = True      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = 1      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  Dict:
      _lowerCamelCase	:					Dict   =		self.speech_processor.feature_extractor(
          SCREAMING_SNAKE_CASE      ,   return_tensors="""pt"""      ,   sampling_rate=SCREAMING_SNAKE_CASE).input_features.to(self.device)
      _lowerCamelCase	:					Union[str, Any]   =		self.speech_model.generate(SCREAMING_SNAKE_CASE      ,   max_length=48_0000)

      _lowerCamelCase	:					Optional[int]   =		self.speech_processor.tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE      ,   normalize=SCREAMING_SNAKE_CASE)[
          0
      ]

      if isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					Tuple   =		1
      elif isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
         _lowerCamelCase	:					Union[str, Any]   =		len(SCREAMING_SNAKE_CASE)
      else:
         raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE)}')

      if height % 8 != 0 or width % 8 != 0:
         raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.')

      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_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(SCREAMING_SNAKE_CASE)}.')

      # get prompt text embeddings
      _lowerCamelCase	:					Tuple   =		self.tokenizer(
          SCREAMING_SNAKE_CASE      ,   padding="""max_length"""      ,   max_length=self.tokenizer.model_max_length      ,   return_tensors="""pt"""      ,   )
      _lowerCamelCase	:					Any   =		text_inputs.input_ids

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

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					int   =		text_embeddings.shape
      _lowerCamelCase	:					Optional[int]   =		text_embeddings.repeat(1      ,   SCREAMING_SNAKE_CASE      ,   1)
      _lowerCamelCase	:					Optional[int]   =		text_embeddings.view(bs_embed * num_images_per_prompt      ,   SCREAMING_SNAKE_CASE      ,   -1)

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      _lowerCamelCase	:					Optional[int]   =		guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
         _lowerCamelCase	:					List[str]
         if negative_prompt is None:
            _lowerCamelCase	:					List[Any]   =		[""""""] * batch_size
         elif type(SCREAMING_SNAKE_CASE) is not type(SCREAMING_SNAKE_CASE):
            raise TypeError(
                F'`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE)} !='
                F' {type(SCREAMING_SNAKE_CASE)}.')
         elif isinstance(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE):
            _lowerCamelCase	:					Optional[Any]   =		[negative_prompt]
         elif batch_size != len(SCREAMING_SNAKE_CASE):
            raise ValueError(
                F'`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE)}, but `prompt`:'
                F' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
                """ the batch size of `prompt`.""")
         else:
            _lowerCamelCase	:					Any   =		negative_prompt

         _lowerCamelCase	:					int   =		text_input_ids.shape[-1]
         _lowerCamelCase	:					str   =		self.tokenizer(
             SCREAMING_SNAKE_CASE      ,   padding="""max_length"""      ,   max_length=SCREAMING_SNAKE_CASE      ,   truncation=SCREAMING_SNAKE_CASE      ,   return_tensors="""pt"""      ,   )
         _lowerCamelCase	:					List[Any]   =		self.text_encoder(uncond_input.input_ids.to(self.device))[0]

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         _lowerCamelCase	:					List[Any]   =		uncond_embeddings.shape[1]
         _lowerCamelCase	:					Optional[int]   =		uncond_embeddings.repeat(1      ,   SCREAMING_SNAKE_CASE      ,   1)
         _lowerCamelCase	:					List[Any]   =		uncond_embeddings.view(batch_size * num_images_per_prompt      ,   SCREAMING_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
         _lowerCamelCase	:					Optional[Any]   =		torch.cat([uncond_embeddings, text_embeddings])

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      _lowerCamelCase	:					Optional[int]   =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      _lowerCamelCase	:					Any   =		text_embeddings.dtype
      if latents is None:
         if self.device.type == "mps":
            # randn does not exist on mps
            _lowerCamelCase	:					str   =		torch.randn(SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE      ,   device="""cpu"""      ,   dtype=SCREAMING_SNAKE_CASE).to(
                self.device)
         else:
            _lowerCamelCase	:					List[str]   =		torch.randn(SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE      ,   device=self.device      ,   dtype=SCREAMING_SNAKE_CASE)
      else:
         if latents.shape != latents_shape:
            raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
         _lowerCamelCase	:					str   =		latents.to(self.device)

      # set timesteps
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE)

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      _lowerCamelCase	:					Union[str, Any]   =		self.scheduler.timesteps.to(self.device)

      # scale the initial noise by the standard deviation required by the scheduler
      _lowerCamelCase	:					Any   =		latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      _lowerCamelCase	:					str   =		"""eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
      _lowerCamelCase	:					Dict   =		{}
      if accepts_eta:
         _lowerCamelCase	:					Any   =		eta

      for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE)):
         # expand the latents if we are doing classifier free guidance
         _lowerCamelCase	:					Optional[int]   =		torch.cat([latents] * 2) if do_classifier_free_guidance else latents
         _lowerCamelCase	:					str   =		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)

         # predict the noise residual
         _lowerCamelCase	:					str   =		self.unet(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   encoder_hidden_states=SCREAMING_SNAKE_CASE).sample

         # perform guidance
         if do_classifier_free_guidance:
            _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		noise_pred.chunk(2)
            _lowerCamelCase	:					int   =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCamelCase	:					Tuple   =		self.scheduler.step(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE).prev_sample

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

      _lowerCamelCase	:					Dict   =		1 / 0.1_82_15 * latents
      _lowerCamelCase	:					Tuple   =		self.vae.decode(SCREAMING_SNAKE_CASE).sample

      _lowerCamelCase	:					int   =		(image / 2 + 0.5).clamp(0      ,   1)

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      _lowerCamelCase	:					Optional[Any]   =		image.cpu().permute(0      ,   2      ,   3      ,   1).float().numpy()

      if output_type == "pil":
         _lowerCamelCase	:					Optional[Any]   =		self.numpy_to_pil(SCREAMING_SNAKE_CASE)

      if not return_dict:
         return image

      return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE      ,   nsfw_content_detected=SCREAMING_SNAKE_CASE)





"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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
UpperCAmelCase				=						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-pretraining/requirements.txt""")




@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					field(
       default='''cifar10''' ,metadata={'''help''': '''Name of a dataset from the datasets package'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''The column name of the images in the files.'''}				)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''A folder containing the training data.'''}				)
   __UpperCAmelCase							       =					field(default=A_ ,metadata={'''help''': '''A folder containing the validation data.'''}				)
   __UpperCAmelCase							       =					field(
       default=0.1_5 ,metadata={'''help''': '''Percent to split off of train for validation.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of training examples to this '''
               '''value if set.'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
               '''value if set.'''
           )
       } ,)





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase	:					Any   =		{}
      if self.train_dir is not None:
         _lowerCamelCase	:					int   =		self.train_dir
      if self.validation_dir is not None:
         _lowerCamelCase	:					Tuple   =		self.validation_dir
      _lowerCamelCase	:					Optional[int]   =		data_files if data_files else None







@dataclass
class 	lowercase__				:
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''Override some existing default config settings when a model is trained from scratch. Example: '''
               '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=A_ ,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=A_ ,metadata={'''help''': '''Name or path of preprocessor config.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={
           '''help''': (
               '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
               '''with private models).'''
           )
       } ,)
   __UpperCAmelCase							       =					field(
       default=0.7_5 ,metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}				)
   __UpperCAmelCase							       =					field(
       default=A_ ,metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}				)







@dataclass
class 	lowercase__				(					A_				):
   __UpperCAmelCase							       =					field(
       default=1e-3 ,metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}				)







def 				_snake_case      (   __snake_case							:      Optional[Any]   ):




   """simple docstring"""







   _lowerCamelCase	:					int   =		torch.stack([example["""pixel_values"""] for example in examples]   )
   return {"pixel_values": pixel_values}

def 				_snake_case      (   ):




   """simple docstring"""







   _lowerCamelCase	:					Tuple   =		HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)   )
   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	:					Union[str, Any]   =		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Dict   =		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_mae"""						, __snake_case						, __snake_case   )

   # 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()

   _lowerCamelCase	:					Union[str, Any]   =		training_args.get_process_log_level()
   logger.setLevel(__snake_case   )
   transformers.utils.logging.set_verbosity(__snake_case   )
   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.
   _lowerCamelCase	:					List[Any]   =		None
   if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
      _lowerCamelCase	:					Optional[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."""   )

    # Initialize our dataset.
   _lowerCamelCase	:					Optional[Any]   =		load_dataset(
       data_args.dataset_name						, data_args.dataset_config_name						, data_files=data_args.data_files						, cache_dir=model_args.cache_dir						, use_auth_token=True if model_args.use_auth_token else None						, )

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

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   _lowerCamelCase	:					str   =		{
       """cache_dir""": model_args.cache_dir,
       """revision""": model_args.model_revision,
       """use_auth_token""": True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      _lowerCamelCase	:					Dict   =		ViTMAEConfig.from_pretrained(model_args.config_name						, **__snake_case   )
   elif model_args.model_name_or_path:
      _lowerCamelCase	:					Union[str, Any]   =		ViTMAEConfig.from_pretrained(model_args.model_name_or_path						, **__snake_case   )
   else:
      _lowerCamelCase	:					Optional[Any]   =		ViTMAEConfig()
      logger.warning("""You are instantiating a new config instance from scratch."""   )
      if model_args.config_overrides is not None:
         logger.info(F'Overriding config: {model_args.config_overrides}'   )
         config.update_from_string(model_args.config_overrides   )
         logger.info(F'New config: {config}'   )

    # adapt config
   config.update(
       {
           """mask_ratio""": model_args.mask_ratio,
           """norm_pix_loss""": model_args.norm_pix_loss,
       }   )

   # create image processor
   if model_args.image_processor_name:
      _lowerCamelCase	:					str   =		ViTImageProcessor.from_pretrained(model_args.image_processor_name						, **__snake_case   )
   elif model_args.model_name_or_path:
      _lowerCamelCase	:					Dict   =		ViTImageProcessor.from_pretrained(model_args.model_name_or_path						, **__snake_case   )
   else:
      _lowerCamelCase	:					Union[str, Any]   =		ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      _lowerCamelCase	:					List[Any]   =		ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path						, from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )						, config=__snake_case						, cache_dir=model_args.cache_dir						, revision=model_args.model_revision						, use_auth_token=True if model_args.use_auth_token else None						, )
   else:
      logger.info("""Training new model from scratch"""   )
      _lowerCamelCase	:					Union[str, Any]   =		ViTMAEForPreTraining(__snake_case   )

   if training_args.do_train:
      _lowerCamelCase	:					List[Any]   =		ds["""train"""].column_names
   else:
      _lowerCamelCase	:					Union[str, Any]   =		ds["""validation"""].column_names

   if data_args.image_column_name is not None:
      _lowerCamelCase	:					str   =		data_args.image_column_name
   elif "image" in column_names:
      _lowerCamelCase	:					Optional[Any]   =		"""image"""
   elif "img" in column_names:
      _lowerCamelCase	:					List[Any]   =		"""img"""
   else:
      _lowerCamelCase	:					str   =		column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      _lowerCamelCase	:					Dict   =		image_processor.size["""shortest_edge"""]
   else:
      _lowerCamelCase	:					List[Any]   =		(image_processor.size["""height"""], image_processor.size["""width"""])
   _lowerCamelCase	:					Tuple   =		Compose(
       [
           Lambda(lambda __snake_case   : img.convert("""RGB"""   ) if img.mode != "RGB" else img   ),
           RandomResizedCrop(__snake_case						, scale=(0.2, 1.0)						, interpolation=InterpolationMode.BICUBIC   ),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean						, std=image_processor.image_std   ),
       ]   )

   def preprocess_images(__snake_case							:      Optional[Any]   ):
      _lowerCamelCase	:					Dict   =		[transforms(__snake_case   ) for image in examples[image_column_name]]
      return examples

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

   if training_args.do_eval:
      if "validation" not in ds:
         raise ValueError("""--do_eval requires a validation dataset"""   )
      if data_args.max_eval_samples is not None:
         _lowerCamelCase	:					Union[str, Any]   =		(
             ds["""validation"""].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
         )
      # Set the validation transforms
      ds["validation"].set_transform(__snake_case   )

   # Compute absolute learning rate
   _lowerCamelCase	:					Optional[Any]   =		(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      _lowerCamelCase	:					Tuple   =		training_args.base_learning_rate * total_train_batch_size / 256

   # Initialize our trainer
   _lowerCamelCase	:					Optional[Any]   =		Trainer(
       model=__snake_case						, args=__snake_case						, train_dataset=ds["""train"""] if training_args.do_train else None						, eval_dataset=ds["""validation"""] if training_args.do_eval else None						, tokenizer=__snake_case						, data_collator=__snake_case						, )

   # Training
   if training_args.do_train:
      _lowerCamelCase	:					Any   =		None
      if training_args.resume_from_checkpoint is not None:
         _lowerCamelCase	:					List[Any]   =		training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         _lowerCamelCase	:					Union[str, Any]   =		last_checkpoint
      _lowerCamelCase	:					Optional[Any]   =		trainer.train(resume_from_checkpoint=__snake_case   )
      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:
      _lowerCamelCase	:					int   =		trainer.evaluate()
      trainer.log_metrics("""eval"""						, __snake_case   )
      trainer.save_metrics("""eval"""						, __snake_case   )

   # Write model card and (optionally) push to hub
   _lowerCamelCase	:					Optional[Any]   =		{
       """tasks""": """masked-auto-encoding""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""masked-auto-encoding"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**__snake_case   )
   else:
      trainer.create_model_card(**__snake_case   )

def 				_snake_case      (   __snake_case							:      Dict   ):




   """simple docstring"""







   main()


if __name__ == "__main__":
 main()

