






"""simple docstring"""







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
				import torch

if is_vision_available():
				from PIL import Image

				from transformers import VivitImageProcessor


class      UpperCamelCase		(unittest.TestCase     ):






						def __init__(		self  :List[str]			, __magic_name__  :List[Any]			, __magic_name__  :Optional[Any]=7			, __magic_name__  :Dict=3			, __magic_name__  :Any=10			, __magic_name__  :Optional[int]=18			, __magic_name__  :Optional[int]=30			, __magic_name__  :Optional[int]=400			, __magic_name__  :List[str]=True			, __magic_name__  :Optional[Any]=None			, __magic_name__  :int=True			, __magic_name__  :Optional[int]=[0.5, 0.5, 0.5]			, __magic_name__  :List[str]=[0.5, 0.5, 0.5]			, __magic_name__  :Optional[Any]=None			, )      ->Any:
										lowercase    :      List[str]       = size if size is not None else {"""shortest_edge""": 18}
										lowercase    :      List[str]       = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

										lowercase    :      Union[str, Any]       = parent
										lowercase    :      Any       = batch_size
										lowercase    :      Union[str, Any]       = num_channels
										lowercase    :      Union[str, Any]       = num_frames
										lowercase    :      List[str]       = image_size
										lowercase    :      List[str]       = min_resolution
										lowercase    :      int       = max_resolution
										lowercase    :      Optional[Any]       = do_resize
										lowercase    :      Optional[Any]       = size
										lowercase    :      Optional[int]       = do_normalize
										lowercase    :      str       = image_mean
										lowercase    :      List[str]       = image_std
										lowercase    :      Tuple       = crop_size






						def      __snake_case      (		self  :List[str]				)      ->str:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "crop_size": self.crop_size,
										}


@require_torch
@require_vision
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : List[Any]												=		VivitImageProcessor if is_vision_available() else None






						def      __snake_case      (		self  :str				)      ->Tuple:
										lowercase    :      Dict       = VivitImageProcessingTester(self				)






						@property
						def      __snake_case      (		self  :int				)      ->Optional[int]:
										return self.image_processor_tester.prepare_image_processor_dict()






						def      __snake_case      (		self  :Dict				)      ->int:
										lowercase    :      Dict       = self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, """image_mean"""				)				)
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, """image_std"""				)				)
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, """do_normalize"""				)				)
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, """do_resize"""				)				)
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, """do_center_crop"""				)				)
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__			, """size"""				)				)






						def      __snake_case      (		self  :int				)      ->str:
										lowercase    :      Optional[Any]       = self.image_processing_class.from_dict(self.image_processor_dict				)
										self.assertEqual(image_processor.size			, {"""shortest_edge""": 18}				)
										self.assertEqual(image_processor.crop_size			, {"""height""": 18, """width""": 18}				)

										lowercase    :      Union[str, Any]       = self.image_processing_class.from_dict(self.image_processor_dict			, size=42			, crop_size=84				)
										self.assertEqual(image_processor.size			, {"""shortest_edge""": 42}				)
										self.assertEqual(image_processor.crop_size			, {"""height""": 84, """width""": 84}				)






						def      __snake_case      (		self  :str				)      ->Dict:
										lowercase    :      List[Any]       = self.image_processing_class(**self.image_processor_dict				)
										# create random PIL videos
										lowercase    :      List[str]       = prepare_video_inputs(self.image_processor_tester			, equal_resolution=SCREAMING_SNAKE_CASE__				)
										for video in video_inputs:
														self.assertIsInstance(SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__				)
														self.assertIsInstance(video[0]			, Image.Image				)

										# Test not batched input
										lowercase    :      Optional[Any]       = image_processing(video_inputs[0]			, return_tensors="""pt"""				).pixel_values
										self.assertEqual(
										    encoded_videos.shape			, (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )			, )

										# Test batched
										lowercase    :      Optional[Any]       = image_processing(SCREAMING_SNAKE_CASE__			, return_tensors="""pt"""				).pixel_values
										self.assertEqual(
										    encoded_videos.shape			, (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )			, )






						def      __snake_case      (		self  :Union[str, Any]				)      ->Optional[Any]:
										lowercase    :      str       = self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										lowercase    :      Optional[int]       = prepare_video_inputs(self.image_processor_tester			, equal_resolution=SCREAMING_SNAKE_CASE__			, numpify=SCREAMING_SNAKE_CASE__				)
										for video in video_inputs:
														self.assertIsInstance(SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__				)
														self.assertIsInstance(video[0]			, np.ndarray				)

										# Test not batched input
										lowercase    :      List[str]       = image_processing(video_inputs[0]			, return_tensors="""pt"""				).pixel_values
										self.assertEqual(
										    encoded_videos.shape			, (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )			, )

										# Test batched
										lowercase    :      List[str]       = image_processing(SCREAMING_SNAKE_CASE__			, return_tensors="""pt"""				).pixel_values
										self.assertEqual(
										    encoded_videos.shape			, (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )			, )






						def      __snake_case      (		self  :str				)      ->Optional[Any]:
										lowercase    :      List[str]       = self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										lowercase    :      Optional[int]       = prepare_video_inputs(self.image_processor_tester			, equal_resolution=SCREAMING_SNAKE_CASE__			, torchify=SCREAMING_SNAKE_CASE__				)
										for video in video_inputs:
														self.assertIsInstance(SCREAMING_SNAKE_CASE__			, SCREAMING_SNAKE_CASE__				)
														self.assertIsInstance(video[0]			, torch.Tensor				)

										# Test not batched input
										lowercase    :      List[str]       = image_processing(video_inputs[0]			, return_tensors="""pt"""				).pixel_values
										self.assertEqual(
										    encoded_videos.shape			, (
										        1,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )			, )

										# Test batched
										lowercase    :      Dict       = image_processing(SCREAMING_SNAKE_CASE__			, return_tensors="""pt"""				).pixel_values
										self.assertEqual(
										    encoded_videos.shape			, (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_frames,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )			, )


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
_snake_case      = 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       _lowerCAmelCase  :

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

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



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




@dataclass
class       _lowerCAmelCase  :

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




@dataclass
class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					float       						=field(
     default=1E-3			,	metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}							)




def  __lowerCamelCase					( _lowercase			)       ->      Optional[int]:
     UpperCamelCase						    =					torch.stack([example['pixel_values'] for example in examples]			)
     return {"pixel_values": pixel_values}

def  __lowerCamelCase					( )       ->      List[Any]:
     # See all possible arguments in src/transformers/training_args.py
     # or by passing the --help flag to this script.
     # We now keep distinct sets of args, for a cleaner separation of concerns.

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

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

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

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

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

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

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

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

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

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

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

def  __lowerCamelCase					( _lowercase			)       ->      int:
     # For xla_spawn (TPUs)
     main()


if __name__ == "__main__":
 main()


