



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__ =						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__ =						list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCamelCase__ =						tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





			lowercase__      :  Optional[str]      =			field(
			    default="""cifar10"""    ,					metadata={"""help""": """Name of a dataset from the datasets package"""}			)
			lowercase__      :  Optional[str]      =			field(
			    default=_UpperCAmelCase    ,					metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}			)
			lowercase__      :  Optional[str]      =			field(
			    default=_UpperCAmelCase    ,					metadata={"""help""": """The column name of the images in the files. If not set, will try to use 'image' or 'img'."""}    ,					)
			lowercase__      :  Optional[str]      =			field(default=_UpperCAmelCase    ,					metadata={"""help""": """A folder containing the training data."""}			)
			lowercase__      :  Optional[str]      =			field(default=_UpperCAmelCase    ,					metadata={"""help""": """A folder containing the validation data."""}			)
			lowercase__      :  Optional[float]      =			field(
			    default=0.15    ,					metadata={"""help""": """Percent to split off of train for validation."""}			)
			lowercase__      :  int      =			field(default=32    ,					metadata={"""help""": """The size of the square patches to use for masking."""}			)
			lowercase__      :  float      =			field(
			    default=0.6    ,					metadata={"""help""": """Percentage of patches to mask."""}    ,					)
			lowercase__      :  Optional[int]      =			field(
			    default=_UpperCAmelCase    ,					metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of training examples to this """
			            """value if set."""
			        )
			    }    ,					)
			lowercase__      :  Optional[int]      =			field(
			    default=_UpperCAmelCase    ,					metadata={
			        """help""": (
			            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
			            """value if set."""
			        )
			    }    ,					)





			def 		snake_case__			(				self	:       Any   )						->					int:
									"""simple docstring"""







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





@dataclass
class 			_lowerCAmelCase   :






			"""simple docstring"""





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





class 			_lowerCAmelCase   :






			"""simple docstring"""





			def __init__(				self	:       List[str]			,     lowercase	:       Optional[Any]=192			,     lowercase	:       int=32			,     lowercase	:       Union[str, Any]=4			,     lowercase	:       List[str]=0.6   )						->					Optional[int]:
									"""simple docstring"""







									__lowercase					=				input_size
									__lowercase					=				mask_patch_size
									__lowercase					=				model_patch_size
									__lowercase					=				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"""   )

									__lowercase					=				self.input_size // self.mask_patch_size
									__lowercase					=				self.mask_patch_size // self.model_patch_size

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







			def __call__(				self	:       Union[str, Any]   )						->					List[str]:
									"""simple docstring"""







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

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

									return torch.tensor(mask.flatten()   )






def  UpperCAmelCase__    (    lowercase__   ) ->      Optional[int]:
						__lowercase					=				torch.stack([example["""pixel_values"""] for example in examples]   )
						__lowercase					=				torch.stack([example["""mask"""] for example in examples]   )
						return {"pixel_values": pixel_values, "bool_masked_pos": mask}






def  UpperCAmelCase__    (    ) ->      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.

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

						__lowercase					=				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.
						__lowercase					=				None
						if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
												__lowercase					=				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.
						__lowercase					=				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.
						__lowercase					=				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:
												__lowercase					=				ds["""train"""].train_test_split(data_args.train_val_split   )
												__lowercase					=				split["""train"""]
												__lowercase					=				split["""test"""]

						# Create config
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.
						__lowercase					=				{
						    """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:
												__lowercase					=				AutoConfig.from_pretrained(model_args.config_name_or_path						,       **lowercase__   )
						elif model_args.model_name_or_path:
												__lowercase					=				AutoConfig.from_pretrained(model_args.model_name_or_path						,       **lowercase__   )
						else:
												__lowercase					=				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"""   ):
												__lowercase					=				"""simmim"""

						# adapt config
						__lowercase					=				model_args.image_size if model_args.image_size is not None else config.image_size
						__lowercase					=				model_args.patch_size if model_args.patch_size is not None else config.patch_size
						__lowercase					=				(
						    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:
												__lowercase					=				AutoImageProcessor.from_pretrained(model_args.image_processor_name						,       **lowercase__   )
						elif model_args.model_name_or_path:
												__lowercase					=				AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,       **lowercase__   )
						else:
												__lowercase					=				{
												    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
												}
												__lowercase					=				IMAGE_PROCESSOR_TYPES[model_args.model_type]()

						# create model
						if model_args.model_name_or_path:
												__lowercase					=				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"""   )
												__lowercase					=				AutoModelForMaskedImageModeling.from_config(lowercase__   )

						if training_args.do_train:
												__lowercase					=				ds["""train"""].column_names
						else:
												__lowercase					=				ds["""validation"""].column_names

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

						# transformations as done in original SimMIM paper
						# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
						__lowercase					=				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
						__lowercase					=				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__   ):
												__lowercase					=				[transforms(lowercase__   ) for image in examples[image_column_name]]
												__lowercase					=				[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:
																		__lowercase					=				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:
																		__lowercase					=				(
																		    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
						__lowercase					=				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:
												__lowercase					=				None
												if training_args.resume_from_checkpoint is not None:
																		__lowercase					=				training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		__lowercase					=				last_checkpoint
												__lowercase					=				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:
												__lowercase					=				trainer.evaluate()
												trainer.log_metrics("""eval"""						,       lowercase__   )
												trainer.save_metrics("""eval"""						,       lowercase__   )

						# Write model card and (optionally) push to hub
						__lowercase					=				{
						    """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_tf_available, is_torch_available


UpperCamelCase__ =						{
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "CTRLForSequenceClassification",
			    "CTRLLMHeadModel",
			    "CTRLModel",
			    "CTRLPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFCTRLForSequenceClassification",
			    "TFCTRLLMHeadModel",
			    "TFCTRLModel",
			    "TFCTRLPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
			from .tokenization_ctrl import CTRLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ctrl import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						    CTRLPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_ctrl import (
						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCTRLForSequenceClassification,
						    TFCTRLLMHeadModel,
						    TFCTRLModel,
						    TFCTRLPreTrainedModel,
						)

else:
			import sys

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





