
'''simple docstring'''

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__ :			int											=		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[Any]											=		list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
UpperCAmelCase__ :			str											=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class  A  :
	snake_case__   :Optional[str]   =  field(
	    default='cifar10'	,   metadata={'help': 'Name of a dataset from the datasets package'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   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=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'A folder containing the training data.'}     )
	snake_case__   :Optional[str]   =  field(default=SCREAMING_SNAKE_CASE__	,   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=SCREAMING_SNAKE_CASE__	,   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=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
	            'value if set.'
	        )
	    }	,   )





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Tuple				):


				"""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  :
	snake_case__   :str   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   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=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(SCREAMING_SNAKE_CASE__     )}	,   )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Pretrained config name or path if not the same as model_name'}     )
	snake_case__   :Optional[str]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   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=SCREAMING_SNAKE_CASE__	,   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=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Name or path of preprocessor config.'}     )
	snake_case__   :bool   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   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=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
	        )
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={
	        'help': (
	            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
	        )
	    }	,   )
	snake_case__   :Optional[int]   =  field(
	    default=SCREAMING_SNAKE_CASE__	,   metadata={'help': 'Stride to use for the encoder.'}	,   )






class  A  :





	def __init__(       self      :							Any   ,				__magic_name__      :							List[Any]=192   ,				__magic_name__      :							int=32   ,				__magic_name__      :							Dict=4   ,				__magic_name__      :							List[Any]=0.6				):


				"""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[int]				):


				"""simple docstring"""

				lowerCAmelCase__										=				np.random.permutation(self.token_count				)[: self.mask_count]
				lowerCAmelCase__										=				np.zeros(self.token_count   ,				dtype=__magic_name__				)
				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 	A  (   UpperCamelCase_       :  Union[str, Any]   )       ->							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 	A  (   )       ->							Tuple:







			'''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"    ,						UpperCamelCase_    ,						UpperCamelCase_   )

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

			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 UpperCamelCase_   : 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(UpperCamelCase_       :  List[Any]   ):
						lowerCAmelCase__										=				[transforms(UpperCamelCase_   ) 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(UpperCamelCase_   )

			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(UpperCamelCase_   )

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

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

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


if __name__ == "__main__":
		main()










'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a  			=						{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTBigCodeForSequenceClassification",
           "GPTBigCodeForTokenClassification",
           "GPTBigCodeForCausalLM",
           "GPTBigCodeModel",
           "GPTBigCodePreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_bigcode import (
                  GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTBigCodeForCausalLM,
                  GPTBigCodeForSequenceClassification,
                  GPTBigCodeForTokenClassification,
                  GPTBigCodeModel,
                  GPTBigCodePreTrainedModel,
              )


else:
       import sys

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