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 				=     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 				=     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
_lowerCAmelCase 				=     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class    _UpperCAmelCase							:
      a						       =							field(
          default='''cifar10'''			,       metadata={'''help''': '''Name of a dataset from the datasets package'''}     )
      a						       =							field(
          default=__A			,       metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}     )
      a						       =							field(
          default=__A			,       metadata={'''help''': '''The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'''}			,       )
      a						       =							field(default=__A			,       metadata={'''help''': '''A folder containing the training data.'''}     )
      a						       =							field(default=__A			,       metadata={'''help''': '''A folder containing the validation data.'''}     )
      a						       =							field(
          default=0.15			,       metadata={'''help''': '''Percent to split off of train for validation.'''}     )
      a						       =							field(default=32			,       metadata={'''help''': '''The size of the square patches to use for masking.'''}     )
      a						       =							field(
          default=0.6			,       metadata={'''help''': '''Percentage of patches to mask.'''}			,       )
      a						       =							field(
          default=__A			,       metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of training examples to this '''
                  '''value if set.'''
              )
          }			,       )
      a						       =							field(
          default=__A			,       metadata={
              '''help''': (
                  '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
                  '''value if set.'''
              )
          }			,       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				List[Any]			      =	{}
         if self.train_dir is not None:
            A_     :				Any			      =	self.train_dir
         if self.validation_dir is not None:
            A_     :				int			      =	self.validation_dir
         A_     :				str			      =	data_files if data_files else None






@dataclass
class    _UpperCAmelCase							:
      a						       =							field(
          default=__A			,       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.'''
              )
          }			,       )
      a						       =							field(
          default=__A			,       metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(__A     )}			,       )
      a						       =							field(
          default=__A			,       metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}     )
      a						       =							field(
          default=__A			,       metadata={
              '''help''': (
                  '''Override some existing default config settings when a model is trained from scratch. Example: '''
                  '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
              )
          }			,       )
      a						       =							field(
          default=__A			,       metadata={'''help''': '''Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'''}			,       )
      a						       =							field(
          default='''main'''			,       metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}			,       )
      a						       =							field(default=__A			,       metadata={'''help''': '''Name or path of preprocessor config.'''}     )
      a						       =							field(
          default=__A			,       metadata={
              '''help''': (
                  '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
                  '''with private models).'''
              )
          }			,       )
      a						       =							field(
          default=__A			,       metadata={
              '''help''': (
                  '''The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'''
              )
          }			,       )
      a						       =							field(
          default=__A			,       metadata={
              '''help''': (
                  '''The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'''
              )
          }			,       )
      a						       =							field(
          default=__A			,       metadata={'''help''': '''Stride to use for the encoder.'''}			,       )






class    _UpperCAmelCase							:
      def __init__(		self      ,       a__=192      ,       a__=32      ,       a__=4      ,       a__=0.6       ):
         A_     :				List[str]			      =	input_size
         A_     :				Dict			      =	mask_patch_size
         A_     :				Tuple			      =	model_patch_size
         A_     :				Optional[Any]			      =	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_     :				int			      =	self.input_size // self.mask_patch_size
         A_     :				Tuple			      =	self.mask_patch_size // self.model_patch_size

         A_     :				Union[str, Any]			      =	self.rand_size**2
         A_     :				Union[str, Any]			      =	int(np.ceil(self.token_count * self.mask_ratio       )       )






      def __call__(		self       ):
         A_     :				Optional[Any]			      =	np.random.permutation(self.token_count       )[: self.mask_count]
         A_     :				Tuple			      =	np.zeros(self.token_count      ,       dtype=a__       )
         A_     :				Optional[Any]			      =	1

         A_     :				Any			      =	mask.reshape((self.rand_size, self.rand_size)       )
         A_     :				Any			      =	mask.repeat(self.scale      ,       axis=0       ).repeat(self.scale      ,       axis=1       )

         return torch.tensor(mask.flatten()       )






def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


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



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Tuple			      =	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_     :				Tuple			      =	parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]     )     )
   else:
      A_     :				Tuple			      =	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()

   A_     :				Tuple			      =	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.
   A_     :				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_     :				Tuple			      =	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_     :				List[str]			      =	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:
      A_     :				Dict			      =	ds['train'].train_test_split(data_args.train_val_split     )
      A_     :				Optional[Any]			      =	split['train']
      A_     :				List[str]			      =	split['test']

   # Create config
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   A_     :				int			      =	{
       '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_     :				List[str]			      =	AutoConfig.from_pretrained(model_args.config_name_or_path  ,**__lowercase     )
   elif model_args.model_name_or_path:
      A_     :				List[str]			      =	AutoConfig.from_pretrained(model_args.model_name_or_path  ,**__lowercase     )
   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(__lowercase  ,"""decoder_type"""     ):
      A_     :				List[str]			      =	'simmim'

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

   # create model
   if model_args.model_name_or_path:
      A_     :				Dict			      =	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"""     )
      A_     :				Tuple			      =	AutoModelForMaskedImageModeling.from_config(__lowercase     )

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

   if data_args.image_column_name is not None:
      A_     :				int			      =	data_args.image_column_name
   elif "image" in column_names:
      A_     :				Any			      =	'image'
   elif "img" in column_names:
      A_     :				str			      =	'img'
   else:
      A_     :				Optional[int]			      =	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 _lowerCAmelCase     : 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_     :				Union[str, Any]			      =	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(_lowerCAmelCase     ):
      A_     :				Union[str, Any]			      =	[transforms(__lowercase     ) for image in examples[image_column_name]]
      A_     :				Tuple			      =	[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_     :				List[Any]			      =	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:
         A_     :				Optional[int]			      =	(
             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
   A_     :				List[str]			      =	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:
      A_     :				Tuple			      =	None
      if training_args.resume_from_checkpoint is not None:
         A_     :				Optional[int]			      =	training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         A_     :				Dict			      =	last_checkpoint
      A_     :				Optional[Any]			      =	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:
      A_     :				Dict			      =	trainer.evaluate()
      trainer.log_metrics("""eval"""  ,__lowercase     )
      trainer.save_metrics("""eval"""  ,__lowercase     )

   # Write model card and (optionally) push to hub
   A_     :				int			      =	{
       '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()













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase 				=     {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCAmelCase 				=     [
          """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """NllbMoeForConditionalGeneration""",
          """NllbMoeModel""",
          """NllbMoePreTrainedModel""",
          """NllbMoeTop2Router""",
          """NllbMoeSparseMLP""",
      ]


if TYPE_CHECKING:
      from .configuration_nllb_moe import (
          NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
          NllbMoeConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nllb_moe import (
                NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
                NllbMoeForConditionalGeneration,
                NllbMoeModel,
                NllbMoePreTrainedModel,
                NllbMoeSparseMLP,
                NllbMoeTopaRouter,
            )


else:
      import sys

      _lowerCAmelCase 				=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






