




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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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
lowerCamelCase       :	str									=     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")

lowerCamelCase       :	Union[str, Any]									=     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
lowerCamelCase       :	Any									=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		field(
     default='''cifar10'''							,   metadata={'''help''': '''Name of a dataset from the datasets package'''}     )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}     )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}							,   )
 UpperCamelCase		     =		field(default=UpperCamelCase							,   metadata={'''help''': '''A folder containing the training data.'''}     )
 UpperCamelCase		     =		field(default=UpperCamelCase							,   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=32							,   metadata={'''help''': '''The size of the square patches to use for masking.'''}     )
 UpperCamelCase		     =		field(
     default=0.6							,   metadata={'''help''': '''Percentage of patches to mask.'''}							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of training examples to this '''
             '''value if set.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
             '''value if set.'''
         )
     }							,   )

 def        a__				(       self      :						str						)     ->			List[Any]:

       """simple docstring"""






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







@dataclass
class 						A:



 '''simple docstring'''


 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '''
             '''checkpoint identifier on the hub. '''
             '''Don\'t set if you want to train a model from scratch.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase     )}							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   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=UpperCamelCase							,   metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}							,   )
 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=UpperCamelCase							,   metadata={'''help''': '''Name or path of preprocessor config.'''}     )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
             '''with private models).'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={
         '''help''': (
             '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
         )
     }							,   )
 UpperCamelCase		     =		field(
     default=UpperCamelCase							,   metadata={'''help''': '''Stride to use for the encoder.'''}							,   )







class 						A:



 '''simple docstring'''


 def __init__(       self      :						Any		,							A_      :						List[str]=192		,							A_      :						Optional[Any]=32		,							A_      :						Tuple=4		,							A_      :						str=0.6						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					input_size
       lowerCamelCase_	  =					mask_patch_size
       lowerCamelCase_	  =					model_patch_size
       lowerCamelCase_	  =					mask_ratio

       if self.input_size % self.mask_patch_size != 0:
             raise ValueError('Input size must be divisible by mask patch size'						)
       if self.mask_patch_size % self.model_patch_size != 0:
             raise ValueError('Mask patch size must be divisible by model patch size'						)

       lowerCamelCase_	  =					self.input_size // self.mask_patch_size
       lowerCamelCase_	  =					self.mask_patch_size // self.model_patch_size

       lowerCamelCase_	  =					self.rand_size**2
       lowerCamelCase_	  =					int(np.ceil(self.token_count * self.mask_ratio						)						)





 def __call__(       self      :						Any						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					np.random.permutation(self.token_count						)[: self.mask_count]
       lowerCamelCase_	  =					np.zeros(self.token_count		,							dtype=A_						)
       lowerCamelCase_	  =					1

       lowerCamelCase_	  =					mask.reshape((self.rand_size, self.rand_size)						)
       lowerCamelCase_	  =					mask.repeat(self.scale		,							axis=0						).repeat(self.scale		,							axis=1						)

       return torch.tensor(mask.flatten()						)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: List[str]  ):


      '''simple docstring'''



      lowerCamelCase_	  =					torch.stack([example['pixel_values'] for example in examples]  )
      lowerCamelCase_	  =					torch.stack([example['mask'] for example in examples]  )
      return {"pixel_values": pixel_values, "bool_masked_pos": mask}

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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

      lowerCamelCase_	  =					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.
      lowerCamelCase_	  =					None
      if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
            lowerCamelCase_	  =					get_last_checkpoint(training_args.output_dir  )
            if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                  raise ValueError(
                      f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                      'Use --overwrite_output_dir to overcome.'  )
            elif last_checkpoint is not None 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_	  =					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_	  =					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:
            lowerCamelCase_	  =					ds['train'].train_test_split(data_args.train_val_split  )
            lowerCamelCase_	  =					split['train']
            lowerCamelCase_	  =					split['test']

      # Create config
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      lowerCamelCase_	  =					{
          '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_or_path:
            lowerCamelCase_	  =					AutoConfig.from_pretrained(model_args.config_name_or_path			,							**lowercase  )
      elif model_args.model_name_or_path:
            lowerCamelCase_	  =					AutoConfig.from_pretrained(model_args.model_name_or_path			,							**lowercase  )
      else:
            lowerCamelCase_	  =					CONFIG_MAPPING[model_args.model_type]()
            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}"""  )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
      if hasattr(lowercase			,							'decoder_type'  ):
            lowerCamelCase_	  =					'simmim'

      # adapt config
      lowerCamelCase_	  =					model_args.image_size if model_args.image_size is not None else config.image_size
      lowerCamelCase_	  =					model_args.patch_size if model_args.patch_size is not None else config.patch_size
      lowerCamelCase_	  =					(
          model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
      )

      config.update(
          {
              'image_size': model_args.image_size,
              'patch_size': model_args.patch_size,
              'encoder_stride': model_args.encoder_stride,
          }  )

      # create image processor
      if model_args.image_processor_name:
            lowerCamelCase_	  =					AutoImageProcessor.from_pretrained(model_args.image_processor_name			,							**lowercase  )
      elif model_args.model_name_or_path:
            lowerCamelCase_	  =					AutoImageProcessor.from_pretrained(model_args.model_name_or_path			,							**lowercase  )
      else:
            lowerCamelCase_	  =					{
                conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
            }
            lowerCamelCase_	  =					IMAGE_PROCESSOR_TYPES[model_args.model_type]()

      # create model
      if model_args.model_name_or_path:
            lowerCamelCase_	  =					AutoModelForMaskedImageModeling.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'  )
            lowerCamelCase_	  =					AutoModelForMaskedImageModeling.from_config(lowercase  )

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

      if data_args.image_column_name is not None:
            lowerCamelCase_	  =					data_args.image_column_name
      elif "image" in column_names:
            lowerCamelCase_	  =					'image'
      elif "img" in column_names:
            lowerCamelCase_	  =					'img'
      else:
            lowerCamelCase_	  =					column_names[0]

      # transformations as done in original SimMIM paper
      # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
      lowerCamelCase_	  =					Compose(
          [
              Lambda(lambda lowercase  : img.convert('RGB'  ) if img.mode != "RGB" else img  ),
              RandomResizedCrop(model_args.image_size			,							scale=(0.67, 1.0)			,							ratio=(3.0 / 4.0, 4.0 / 3.0)  ),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean			,							std=image_processor.image_std  ),
          ]  )

      # create mask generator
      lowerCamelCase_	  =					MaskGenerator(
          input_size=model_args.image_size			,							mask_patch_size=data_args.mask_patch_size			,							model_patch_size=model_args.patch_size			,							mask_ratio=data_args.mask_ratio			,							)

      def preprocess_images(lowercase			: Any  ):
            lowerCamelCase_	  =					[transforms(lowercase  ) for image in examples[image_column_name]]
            lowerCamelCase_	  =					[mask_generator() for i in range(len(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_	  =					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:
                  lowerCamelCase_	  =					(
                      ds['validation'].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
                  )
            # Set the validation transforms
            ds["validation"].set_transform(lowercase  )

      # Initialize our trainer
      lowerCamelCase_	  =					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:
            lowerCamelCase_	  =					None
            if training_args.resume_from_checkpoint is not None:
                  lowerCamelCase_	  =					training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowerCamelCase_	  =					last_checkpoint
            lowerCamelCase_	  =					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:
            lowerCamelCase_	  =					trainer.evaluate()
            trainer.log_metrics('eval'			,							lowercase  )
            trainer.save_metrics('eval'			,							lowercase  )

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


if __name__ == "__main__":
 main()









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops

 from transformers import GradientAccumulator, create_optimizer







@require_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






       self.assertEqual(len(A_						)		,							len(A_						)						)
       for a, b in zip(A_		,							A_						):
             self.assertAlmostEqual(A_		,							A_		,							delta=A_						)

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(A_						):
             accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step		,							3						)
       self.assertEqual(len(accumulator.gradients						)		,							1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[-2.0, 5.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[0.0, 0.0]		,							tol=1E-2						)





 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

       def apply_on_replica():
             optimizer.apply_gradients(list(zip(accumulator.gradients		,							[variable]						)						)						)

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
             with strategy.scope():
                   strategy.run(A_						)

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							tol=1E-2						)

       accumulate([1.0, 2.0]		,							[-1.0, 1.0]						)
       accumulate([3.0, -1.0]		,							[-1.0, -1.0]						)
       accumulate([-2.0, 2.0]		,							[3.0, -2.0]						)
       self.assertEqual(accumulator.step		,							3						)
       _check_local_values([2.0, 3.0]		,							[1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()		,							[4.0, 3.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       _check_local_values([0.0, 0.0]		,							[0.0, 0.0]						)



