


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
UpperCAmelCase_      : 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')

UpperCAmelCase_      : str  					=       list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase_      : str  					=       tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class  SCREAMING_SNAKE_CASE__  :
      snake_case__				:     Optional[str]        =						field(
          default='''cifar10'''						,    metadata={'''help''': '''Name of a dataset from the datasets package'''}			)
      snake_case__				:     Optional[str]        =						field(
          default=lowercase__						,    metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}			)
      snake_case__				:     Optional[str]        =						field(
          default=lowercase__						,    metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}						,    )
      snake_case__				:     Optional[str]        =						field(default=lowercase__						,    metadata={'''help''': '''A folder containing the training data.'''}			)
      snake_case__				:     Optional[str]        =						field(default=lowercase__						,    metadata={'''help''': '''A folder containing the validation data.'''}			)
      snake_case__				:     Optional[float]        =						field(
          default=0.15						,    metadata={'''help''': '''Percent to split off of train for validation.'''}			)
      snake_case__				:     int        =						field(default=32						,    metadata={'''help''': '''The size of the square patches to use for masking.'''}			)
      snake_case__				:     float        =						field(
          default=0.6						,    metadata={'''help''': '''Percentage of patches to mask.'''}						,    )
      snake_case__				:     Optional[int]        =						field(
          default=lowercase__						,    metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                  '''value if set.'''
              )
          }						,    )
      snake_case__				:     Optional[int]        =						field(
          default=lowercase__						,    metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                  '''value if set.'''
              )
          }						,    )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     )		->			Any:
             a_					:		List[Any]							=  {}
             if self.train_dir is not None:
                    a_					:		List[str]							=  self.train_dir
             if self.validation_dir is not None:
                    a_					:		List[Any]							=  self.validation_dir
             a_					:		Any							=  data_files if data_files else None
@dataclass
class  SCREAMING_SNAKE_CASE__  :
      snake_case__				:     str        =						field(
          default=lowercase__						,    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.'''
              )
          }						,    )
      snake_case__				:     Optional[str]        =						field(
          default=lowercase__						,    metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(lowercase__			)}						,    )
      snake_case__				:     Optional[str]        =						field(
          default=lowercase__						,    metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}			)
      snake_case__				:     Optional[str]        =						field(
          default=lowercase__						,    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'''
              )
          }						,    )
      snake_case__				:     Optional[str]        =						field(
          default=lowercase__						,    metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}						,    )
      snake_case__				:     str        =						field(
          default='''main'''						,    metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}						,    )
      snake_case__				:     str        =						field(default=lowercase__						,    metadata={'''help''': '''Name or path of preprocessor config.'''}			)
      snake_case__				:     bool        =						field(
          default=lowercase__						,    metadata={
              '''help''': (
                  '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                  '''with private models).'''
              )
          }						,    )
      snake_case__				:     Optional[int]        =						field(
          default=lowercase__						,    metadata={
              '''help''': (
                  '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
              )
          }						,    )
      snake_case__				:     Optional[int]        =						field(
          default=lowercase__						,    metadata={
              '''help''': (
                  '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
              )
          }						,    )
      snake_case__				:     Optional[int]        =						field(
          default=lowercase__						,    metadata={'''help''': '''Stride to use for the encoder.'''}						,    )
class  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       Tuple=1_9_2     ,							SCREAMING_SNAKE_CASE__				:       Dict=3_2     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]=4     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]=0.6     )		->			List[str]:
             a_					:		int							=  input_size
             a_					:		int							=  mask_patch_size
             a_					:		str							=  model_patch_size
             a_					:		Optional[int]							=  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'     )

             a_					:		Optional[Any]							=  self.input_size // self.mask_patch_size
             a_					:		int							=  self.mask_patch_size // self.model_patch_size

             a_					:		Dict							=  self.rand_size**2
             a_					:		int							=  int(np.ceil(self.token_count * self.mask_ratio     )     )

      def __call__(       self				:       Optional[int]     )		->			Optional[int]:
             a_					:		Any							=  np.random.permutation(self.token_count     )[: self.mask_count]
             a_					:		str							=  np.zeros(self.token_count     ,							dtype=SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  1

             a_					:		Union[str, Any]							=  mask.reshape((self.rand_size, self.rand_size)     )
             a_					:		List[Any]							=  mask.repeat(self.scale     ,							axis=0     ).repeat(self.scale     ,							axis=1     )

             return torch.tensor(mask.flatten()     )







def        SCREAMING_SNAKE_CASE_						(			__A :			str				)	->		List[Any]:

       """simple docstring"""

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







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

       """simple docstring"""

       a_					:		List[str]							=  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.
              a_							,		a_							,		a_					:		Optional[Any]							=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
       else:
              a_							,		a_							,		a_					:		Optional[Any]							=  parser.parse_args_into_dataclasses()

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

       # 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_					:		int							=  training_args.get_process_log_level()
       logger.setLevel(__A				)
       transformers.utils.logging.set_verbosity(__A				)
       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_					:		Optional[int]							=  None
       if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
              a_					:		List[Any]							=  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_					:		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.
       a_					:		Union[str, Any]							=  None if 'validation' in ds.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split  ,    __A				) and data_args.train_val_split > 0.0:
              a_					:		str							=  ds['train'].train_test_split(data_args.train_val_split				)
              a_					:		Optional[Any]							=  split['train']
              a_					:		Union[str, Any]							=  split['test']

       # Create config
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.
       a_					:		Union[str, Any]							=  {
           '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:
              a_					:		Tuple							=  AutoConfig.from_pretrained(model_args.config_name_or_path  ,    **__A				)
       elif model_args.model_name_or_path:
              a_					:		Dict							=  AutoConfig.from_pretrained(model_args.model_name_or_path  ,    **__A				)
       else:
              a_					:		Union[str, Any]							=  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(__A  ,    'decoder_type'				):
              a_					:		Tuple							=  'simmim'

       # adapt config
       a_					:		List[str]							=  model_args.image_size if model_args.image_size is not None else config.image_size
       a_					:		int							=  model_args.patch_size if model_args.patch_size is not None else config.patch_size
       a_					:		Dict							=  (
           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:
              a_					:		List[str]							=  AutoImageProcessor.from_pretrained(model_args.image_processor_name  ,    **__A				)
       elif model_args.model_name_or_path:
              a_					:		Any							=  AutoImageProcessor.from_pretrained(model_args.model_name_or_path  ,    **__A				)
       else:
              a_					:		str							=  {
                  conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
              }
              a_					:		List[str]							=  IMAGE_PROCESSOR_TYPES[model_args.model_type]()

       # create model
       if model_args.model_name_or_path:
              a_					:		Any							=  AutoModelForMaskedImageModeling.from_pretrained(
                  model_args.model_name_or_path  ,    from_tf=bool('.ckpt' in model_args.model_name_or_path				)  ,    config=__A  ,    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_					:		List[str]							=  AutoModelForMaskedImageModeling.from_config(__A				)

       if training_args.do_train:
              a_					:		List[Any]							=  ds['train'].column_names
       else:
              a_					:		List[str]							=  ds['validation'].column_names

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

       # transformations as done in original SimMIM paper
       # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
       a_					:		Optional[int]							=  Compose(
           [
               Lambda(lambda __A				: 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
       a_					:		int							=  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(__A :			Optional[Any]				):
              a_					:		Optional[Any]							=  [transforms(__A				) for image in examples[image_column_name]]
              a_					:		List[str]							=  [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:
                     a_					:		Any							=  ds['train'].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
              # Set the training transforms
              ds["train"].set_transform(__A				)

       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_					:		Optional[Any]							=  (
                         ds['validation'].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
                     )
              # Set the validation transforms
              ds["validation"].set_transform(__A				)

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

       # Training
       if training_args.do_train:
              a_					:		Any							=  None
              if training_args.resume_from_checkpoint is not None:
                     a_					:		List[str]							=  training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     a_					:		Tuple							=  last_checkpoint
              a_					:		Any							=  trainer.train(resume_from_checkpoint=__A				)
              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_					:		List[str]							=  trainer.evaluate()
              trainer.log_metrics('eval'  ,    __A				)
              trainer.save_metrics('eval'  ,    __A				)

       # Write model card and (optionally) push to hub
       a_					:		Optional[Any]							=  {
           '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(**__A				)
       else:
              trainer.create_model_card(**__A				)


if __name__ == "__main__":
   main()






import math

import flax.linen as nn
import jax.numpy as jnp







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


