


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







@dataclass
class        __lowercase       :



		"""simple docstring"""

		UpperCamelCase     :					Optional[str]			       =		field(
		    default="cifar10"			,							metadata={"help": "Name of a dataset from the datasets package"}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default=a_			,							metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default=a_			,							metadata={"help": "The column name of the images in the files. If not set, will try to use 'image' or 'img'."}			,							)
		UpperCamelCase     :					Optional[str]			       =		field(default=a_			,							metadata={"help": "A folder containing the training data."}  )
		UpperCamelCase     :					Optional[str]			       =		field(default=a_			,							metadata={"help": "A folder containing the validation data."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.1_5			,							metadata={"help": "Percent to split off of train for validation."}  )
		UpperCamelCase     :					int			       =		field(default=3_2			,							metadata={"help": "The size of the square patches to use for masking."}  )
		UpperCamelCase     :					float			       =		field(
		    default=0.6			,							metadata={"help": "Percentage of patches to mask."}			,							)
		UpperCamelCase     :					Optional[int]			       =		field(
		    default=a_			,							metadata={
		        "help": (
		            "For debugging purposes or quicker training, truncate the number of training examples to this "
		            "value if set."
		        )
		    }			,							)
		UpperCamelCase     :					Optional[int]			       =		field(
		    default=a_			,							metadata={
		        "help": (
		            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
		            "value if set."
		        )
		    }			,							)







		def 					__A   ( self	)  -> int:







					'''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        __lowercase       :



		"""simple docstring"""

		UpperCamelCase     :					str			       =		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."
		        )
		    }			,							)
		UpperCamelCase     :					Optional[str]			       =		field(
		    default=a_			,							metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(a_  )}			,							)
		UpperCamelCase     :					Optional[str]			       =		field(
		    default=a_			,							metadata={"help": "Pretrained config name or path if not the same as model_name"}  )
		UpperCamelCase     :					Optional[str]			       =		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"
		        )
		    }			,							)
		UpperCamelCase     :					Optional[str]			       =		field(
		    default=a_			,							metadata={"help": "Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"}			,							)
		UpperCamelCase     :					str			       =		field(
		    default="main"			,							metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}			,							)
		UpperCamelCase     :					str			       =		field(default=a_			,							metadata={"help": "Name or path of preprocessor config."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={
		        "help": (
		            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
		            "with private models)."
		        )
		    }			,							)
		UpperCamelCase     :					Optional[int]			       =		field(
		    default=a_			,							metadata={
		        "help": (
		            "The size (resolution) of each image. If not specified, will use `image_size` of the configuration."
		        )
		    }			,							)
		UpperCamelCase     :					Optional[int]			       =		field(
		    default=a_			,							metadata={
		        "help": (
		            "The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."
		        )
		    }			,							)
		UpperCamelCase     :					Optional[int]			       =		field(
		    default=a_			,							metadata={"help": "Stride to use for the encoder."}			,							)







class        __lowercase       :



		"""simple docstring"""

		def __init__( self		,      A=1_92		,      A=32		,      A=4		,      A=0.6	)  -> List[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	)  -> Optional[Any]:







					'''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 	__lowerCamelCase						(							lowerCamelCase__		:						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 	__lowerCamelCase						(							):





			'''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"""  ,							lowerCamelCase__  ,							lowerCamelCase__      )

			# 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(lowerCamelCase__      )
			transformers.utils.logging.set_verbosity(lowerCamelCase__      )
			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  ,							lowerCamelCase__      ) 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  ,							**lowerCamelCase__      )
			elif model_args.model_name_or_path:
						lowerCamelCase    		=  AutoConfig.from_pretrained(model_args.model_name_or_path  ,							**lowerCamelCase__      )
			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(lowerCamelCase__  ,							"""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  ,							**lowerCamelCase__      )
			elif model_args.model_name_or_path:
						lowerCamelCase    		=  AutoImageProcessor.from_pretrained(model_args.model_name_or_path  ,							**lowerCamelCase__      )
			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=lowerCamelCase__  ,							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(lowerCamelCase__      )

			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 lowerCamelCase__      : img.convert("""RGB"""      ) if img.mode != "RGB" else img      ),
			        RandomResizedCrop(model_args.image_size  ,							scale=(0.6_7, 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(lowerCamelCase__		:						Union[str, Any]      ):
						lowerCamelCase    		=  [transforms(lowerCamelCase__      ) 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(lowerCamelCase__      )

			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(lowerCamelCase__      )

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

			# 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=lowerCamelCase__      )
						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"""  ,							lowerCamelCase__      )
						trainer.save_metrics("""eval"""  ,							lowerCamelCase__      )

			# 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(**lowerCamelCase__      )
			else:
						trainer.create_model_card(**lowerCamelCase__      )


if __name__ == "__main__":
		main()





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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

