






import argparse
import json
from pathlib import Path

import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download

from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__		:							Any 				=				logging.get_logger(__name__)





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Optional[Any]:

     '''simple docstring'''
     UpperCAmelCase__      :  Optional[int]				      =			ASTConfig()

     if "10-10" in model_name:
          pass
     elif "speech-commands" in model_name:
          UpperCAmelCase__      :  Any				      =			128
     elif "12-12" in model_name:
          UpperCAmelCase__      :  Optional[Any]				      =			12
          UpperCAmelCase__      :  Optional[Any]				      =			12
     elif "14-14" in model_name:
          UpperCAmelCase__      :  Any				      =			14
          UpperCAmelCase__      :  Union[str, Any]				      =			14
     elif "16-16" in model_name:
          UpperCAmelCase__      :  Optional[int]				      =			16
          UpperCAmelCase__      :  List[Any]				      =			16
     else:
          raise ValueError("""Model not supported"""       )

     UpperCAmelCase__      :  List[str]				      =			"""huggingface/label-files"""
     if "speech-commands" in model_name:
          UpperCAmelCase__      :  Tuple				      =			35
          UpperCAmelCase__      :  Optional[Any]				      =			"""speech-commands-v2-id2label.json"""
     else:
          UpperCAmelCase__      :  Any				      =			527
          UpperCAmelCase__      :  Optional[int]				      =			"""audioset-id2label.json"""

     UpperCAmelCase__      :  int				      =			json.load(open(hf_hub_download(__lowerCamelCase ,							__lowerCamelCase ,							repo_type="""dataset"""       ) ,							"""r"""       )       )
     UpperCAmelCase__      :  Dict				      =			{int(__lowerCamelCase       ): v for k, v in idalabel.items()}
     UpperCAmelCase__      :  Dict				      =			idalabel
     UpperCAmelCase__      :  List[Any]				      =			{v: k for k, v in idalabel.items()}

     return config





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Union[str, Any]:

     '''simple docstring'''
     if "module.v" in name:
          UpperCAmelCase__      :  Any				      =			name.replace("""module.v""" ,							"""audio_spectrogram_transformer"""       )
     if "cls_token" in name:
          UpperCAmelCase__      :  Tuple				      =			name.replace("""cls_token""" ,							"""embeddings.cls_token"""       )
     if "dist_token" in name:
          UpperCAmelCase__      :  List[Any]				      =			name.replace("""dist_token""" ,							"""embeddings.distillation_token"""       )
     if "pos_embed" in name:
          UpperCAmelCase__      :  Tuple				      =			name.replace("""pos_embed""" ,							"""embeddings.position_embeddings"""       )
     if "patch_embed.proj" in name:
          UpperCAmelCase__      :  List[Any]				      =			name.replace("""patch_embed.proj""" ,							"""embeddings.patch_embeddings.projection"""       )
     # transformer blocks
     if "blocks" in name:
          UpperCAmelCase__      :  List[str]				      =			name.replace("""blocks""" ,							"""encoder.layer"""       )
     if "attn.proj" in name:
          UpperCAmelCase__      :  Union[str, Any]				      =			name.replace("""attn.proj""" ,							"""attention.output.dense"""       )
     if "attn" in name:
          UpperCAmelCase__      :  Optional[Any]				      =			name.replace("""attn""" ,							"""attention.self"""       )
     if "norm1" in name:
          UpperCAmelCase__      :  int				      =			name.replace("""norm1""" ,							"""layernorm_before"""       )
     if "norm2" in name:
          UpperCAmelCase__      :  str				      =			name.replace("""norm2""" ,							"""layernorm_after"""       )
     if "mlp.fc1" in name:
          UpperCAmelCase__      :  Tuple				      =			name.replace("""mlp.fc1""" ,							"""intermediate.dense"""       )
     if "mlp.fc2" in name:
          UpperCAmelCase__      :  List[str]				      =			name.replace("""mlp.fc2""" ,							"""output.dense"""       )
     # final layernorm
     if "audio_spectrogram_transformer.norm" in name:
          UpperCAmelCase__      :  List[str]				      =			name.replace("""audio_spectrogram_transformer.norm""" ,							"""audio_spectrogram_transformer.layernorm"""       )
     # classifier head
     if "module.mlp_head.0" in name:
          UpperCAmelCase__      :  Union[str, Any]				      =			name.replace("""module.mlp_head.0""" ,							"""classifier.layernorm"""       )
     if "module.mlp_head.1" in name:
          UpperCAmelCase__      :  List[str]				      =			name.replace("""module.mlp_head.1""" ,							"""classifier.dense"""       )

     return name





def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase       ) ->			Tuple:

     '''simple docstring'''
     for key in orig_state_dict.copy().keys():
          UpperCAmelCase__      :  List[Any]				      =			orig_state_dict.pop(__lowerCamelCase       )

          if "qkv" in key:
               UpperCAmelCase__      :  Any				      =			key.split("""."""       )
               UpperCAmelCase__      :  Tuple				      =			int(key_split[3]       )
               UpperCAmelCase__      :  Dict				      =			config.hidden_size
               if "weight" in key:
                    UpperCAmelCase__      :  Optional[int]				      =			val[:dim, :]
                    UpperCAmelCase__      :  Optional[Any]				      =			val[dim : dim * 2, :]
                    UpperCAmelCase__      :  Optional[int]				      =			val[-dim:, :]
               else:
                    UpperCAmelCase__      :  Union[str, Any]				      =			val[:dim]
                    UpperCAmelCase__      :  Any				      =			val[dim : dim * 2]
                    UpperCAmelCase__      :  Union[str, Any]				      =			val[-dim:]
          else:
               UpperCAmelCase__      :  Optional[Any]				      =			val

     return orig_state_dict





def  _lowerCamelCase						(						__lowerCamelCase       ) ->			Dict:

     '''simple docstring'''
     UpperCAmelCase__      :  Union[str, Any]				      =			[
         """module.v.head.weight""",
         """module.v.head.bias""",
         """module.v.head_dist.weight""",
         """module.v.head_dist.bias""",
     ]
     for k in ignore_keys:
          state_dict.pop(__lowerCamelCase ,							__lowerCamelCase       )





@torch.no_grad()
def  _lowerCamelCase						(						__lowerCamelCase ,							__lowerCamelCase ,							__lowerCamelCase=False       ) ->			List[str]:

     '''simple docstring'''
     UpperCAmelCase__      :  List[Any]				      =			get_audio_spectrogram_transformer_config(__lowerCamelCase       )

     UpperCAmelCase__      :  Dict				      =			{
         """ast-finetuned-audioset-10-10-0.4593""": (
             """https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"""
         ),
         """ast-finetuned-audioset-10-10-0.450""": (
             """https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"""
         ),
         """ast-finetuned-audioset-10-10-0.448""": (
             """https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"""
         ),
         """ast-finetuned-audioset-10-10-0.448-v2""": (
             """https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"""
         ),
         """ast-finetuned-audioset-12-12-0.447""": (
             """https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"""
         ),
         """ast-finetuned-audioset-14-14-0.443""": (
             """https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"""
         ),
         """ast-finetuned-audioset-16-16-0.442""": (
             """https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"""
         ),
         """ast-finetuned-speech-commands-v2""": (
             """https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"""
         ),
     }

     # load original state_dict
     UpperCAmelCase__      :  str				      =			model_name_to_url[model_name]
     UpperCAmelCase__      :  Dict				      =			torch.hub.load_state_dict_from_url(__lowerCamelCase ,							map_location="""cpu"""       )
     # remove some keys
     remove_keys(__lowerCamelCase       )
     # rename some keys
     UpperCAmelCase__      :  Any				      =			convert_state_dict(__lowerCamelCase ,							__lowerCamelCase       )

     # load 🤗 model
     UpperCAmelCase__      :  Optional[int]				      =			ASTForAudioClassification(__lowerCamelCase       )
     model.eval()

     model.load_state_dict(__lowerCamelCase       )

     # verify outputs on dummy input
     # source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
     UpperCAmelCase__      :  int				      =			-4.2_677_393 if """speech-commands""" not in model_name else -6.845_978
     UpperCAmelCase__      :  Optional[Any]				      =			4.5_689_974 if """speech-commands""" not in model_name else 5.5_654_526
     UpperCAmelCase__      :  List[str]				      =			1024 if """speech-commands""" not in model_name else 128
     UpperCAmelCase__      :  Tuple				      =			ASTFeatureExtractor(mean=__lowerCamelCase ,							std=__lowerCamelCase ,							max_length=__lowerCamelCase       )

     if "speech-commands" in model_name:
          UpperCAmelCase__      :  int				      =			load_dataset("""speech_commands""" ,							"""v0.02""" ,							split="""validation"""       )
          UpperCAmelCase__      :  Optional[int]				      =			dataset[0]["""audio"""]["""array"""]
     else:
          UpperCAmelCase__      :  Union[str, Any]				      =			hf_hub_download(
              repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" ,							filename="""sample_audio.flac""" ,							repo_type="""dataset""" ,							)

          UpperCAmelCase__      ,		UpperCAmelCase__      :  Dict				      =			torchaudio.load(__lowerCamelCase       )
          UpperCAmelCase__      :  Tuple				      =			waveform.squeeze().numpy()

     UpperCAmelCase__      :  str				      =			feature_extractor(__lowerCamelCase ,							sampling_rate=1_6000 ,							return_tensors="""pt"""       )

     # forward pass
     UpperCAmelCase__      :  int				      =			model(**__lowerCamelCase       )
     UpperCAmelCase__      :  Dict				      =			outputs.logits

     if model_name == "ast-finetuned-audioset-10-10-0.4593":
          UpperCAmelCase__      :  Any				      =			torch.tensor([-0.8_760, -7.0_042, -8.6_602]       )
     elif model_name == "ast-finetuned-audioset-10-10-0.450":
          UpperCAmelCase__      :  Tuple				      =			torch.tensor([-1.1_986, -7.0_903, -8.2_718]       )
     elif model_name == "ast-finetuned-audioset-10-10-0.448":
          UpperCAmelCase__      :  int				      =			torch.tensor([-2.6_128, -8.0_080, -9.4_344]       )
     elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
          UpperCAmelCase__      :  Optional[Any]				      =			torch.tensor([-1.5_080, -7.4_534, -8.8_917]       )
     elif model_name == "ast-finetuned-audioset-12-12-0.447":
          UpperCAmelCase__      :  Optional[int]				      =			torch.tensor([-0.5_050, -6.5_833, -8.0_843]       )
     elif model_name == "ast-finetuned-audioset-14-14-0.443":
          UpperCAmelCase__      :  Union[str, Any]				      =			torch.tensor([-0.3_826, -7.0_336, -8.2_413]       )
     elif model_name == "ast-finetuned-audioset-16-16-0.442":
          UpperCAmelCase__      :  int				      =			torch.tensor([-1.2_113, -6.9_101, -8.3_470]       )
     elif model_name == "ast-finetuned-speech-commands-v2":
          UpperCAmelCase__      :  Dict				      =			torch.tensor([6.1_589, -8.0_566, -8.7_984]       )
     else:
          raise ValueError("""Unknown model name"""       )
     if not torch.allclose(logits[0, :3] ,							__lowerCamelCase ,							atol=1E-4       ):
          raise ValueError("""Logits don't match"""       )
     print("""Looks ok!"""       )

     if pytorch_dump_folder_path is not None:
          Path(__lowerCamelCase       ).mkdir(exist_ok=__lowerCamelCase       )
          print(F"Saving model {model_name} to {pytorch_dump_folder_path}"       )
          model.save_pretrained(__lowerCamelCase       )
          print(F"Saving feature extractor to {pytorch_dump_folder_path}"       )
          feature_extractor.save_pretrained(__lowerCamelCase       )

     if push_to_hub:
          print("""Pushing model and feature extractor to the hub..."""       )
          model.push_to_hub(F"MIT/{model_name}"       )
          feature_extractor.push_to_hub(F"MIT/{model_name}"       )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE__		:							Any 				=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""ast-finetuned-audioset-10-10-0.4593""",
          type=str,
          help="""Name of the Audio Spectrogram Transformer model you'd like to convert.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      SCREAMING_SNAKE_CASE__		:							Tuple 				=				parser.parse_args()
      convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)













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
a__      :     Dict         =		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 UpperCAmelCase_				:
  __lowerCAmelCase   :						Optional[str]							=   field(
      default="""cifar10"""     ,		metadata={"""help""": """Name of a dataset from the datasets package"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The column name of the images in the files."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """A folder containing the training data."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """A folder containing the validation data."""} )
  __lowerCAmelCase   :						Optional[float]							=   field(
      default=0.15     ,		metadata={"""help""": """Percent to split off of train for validation."""} )
  __lowerCAmelCase   :						Optional[int]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[int]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }     ,		)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       {}
       if self.train_dir is not None:
            A_          =       self.train_dir
       if self.validation_dir is not None:
            A_          =       self.validation_dir
       A_          =       data_files if data_files else None





@dataclass
class UpperCAmelCase_				:
  __lowerCAmelCase   :						str							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		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"""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
  __lowerCAmelCase   :						str							=   field(
      default="""main"""     ,		metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}     ,		)
  __lowerCAmelCase   :						str							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Name or path of preprocessor config."""} )
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }     ,		)
  __lowerCAmelCase   :						float							=   field(
      default=0.75     ,		metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )





@dataclass
class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						float							=   field(
      default=1E-3     ,		metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )




def      UpperCAmelCase_    (    _UpperCAmelCase							:Any				)	->				List[str]:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       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.
          A_   ,    A_   ,    A_          =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          A_   ,    A_   ,    A_          =       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'''  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # 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()

     A_          =       training_args.get_process_log_level()
     logger.setLevel(_UpperCAmelCase				)
     transformers.utils.logging.set_verbosity(_UpperCAmelCase				)
     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.
     A_          =       None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          A_          =       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.
     A_          =       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.
     A_          =       None if '''validation''' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split  ,						_UpperCAmelCase				) and data_args.train_val_split > 0.0:
          A_          =       ds['''train'''].train_test_split(data_args.train_val_split				)
          A_          =       split['''train''']
          A_          =       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.
     A_          =       {
         '''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:
          A_          =       ViTMAEConfig.from_pretrained(model_args.config_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       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:
          A_          =       ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          A_          =       ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path  ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)  ,						config=_UpperCAmelCase  ,						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'''				)
          A_          =       ViTMAEForPreTraining(_UpperCAmelCase				)

     if training_args.do_train:
          A_          =       ds['''train'''].column_names
     else:
          A_          =       ds['''validation'''].column_names

     if data_args.image_column_name is not None:
          A_          =       data_args.image_column_name
     elif "image" in column_names:
          A_          =       '''image'''
     elif "img" in column_names:
          A_          =       '''img'''
     else:
          A_          =       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:
          A_          =       image_processor.size['''shortest_edge''']
     else:
          A_          =       (image_processor.size['''height'''], image_processor.size['''width'''])
     A_          =       Compose(
         [
             Lambda(lambda _UpperCAmelCase				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
             RandomResizedCrop(_UpperCAmelCase  ,						scale=(0.2, 1.0)  ,						interpolation=InterpolationMode.BICUBIC				),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean  ,						std=image_processor.image_std				),
         ]				)

     def preprocess_images(_UpperCAmelCase							:Optional[Any]				):
          A_          =       [transforms(_UpperCAmelCase				) 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:
               A_          =       ds['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
          # Set the training transforms
          ds["train"].set_transform(_UpperCAmelCase				)

     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:
               A_          =       (
                   ds['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_UpperCAmelCase				)

     # Compute absolute learning rate
     A_          =       (
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          A_          =       training_args.base_learning_rate * total_train_batch_size / 256

     # Initialize our trainer
     A_          =       Trainer(
         model=_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						train_dataset=ds['''train'''] if training_args.do_train else None  ,						eval_dataset=ds['''validation'''] if training_args.do_eval else None  ,						tokenizer=_UpperCAmelCase  ,						data_collator=_UpperCAmelCase  ,						)

     # Training
     if training_args.do_train:
          A_          =       None
          if training_args.resume_from_checkpoint is not None:
               A_          =       training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               A_          =       last_checkpoint
          A_          =       trainer.train(resume_from_checkpoint=_UpperCAmelCase				)
          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:
          A_          =       trainer.evaluate()
          trainer.log_metrics('''eval'''  ,						_UpperCAmelCase				)
          trainer.save_metrics('''eval'''  ,						_UpperCAmelCase				)

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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				Dict:
     '''simple docstring'''


     main()


if __name__ == "__main__":
      main()



