





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
A   : Optional[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''')

A   : Dict			=				list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
A   : List[Any]			=				tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


@dataclass
class   A      :
			'''simple docstring'''




			__lowerCamelCase     :	Optional[str]          =   field(
			    default='''cifar10'''       ,					metadata={'''help''': '''Name of a dataset from the datasets package'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(
			    default=a__       ,					metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}   )
			__lowerCamelCase     :	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\'.'''}       ,					)
			__lowerCamelCase     :	Optional[str]          =   field(default=a__       ,					metadata={'''help''': '''A folder containing the training data.'''}   )
			__lowerCamelCase     :	Optional[str]          =   field(default=a__       ,					metadata={'''help''': '''A folder containing the validation data.'''}   )
			__lowerCamelCase     :	Optional[float]          =   field(
			    default=0.15       ,					metadata={'''help''': '''Percent to split off of train for validation.'''}   )
			__lowerCamelCase     :	int          =   field(default=32       ,					metadata={'''help''': '''The size of the square patches to use for masking.'''}   )
			__lowerCamelCase     :	float          =   field(
			    default=0.6       ,					metadata={'''help''': '''Percentage of patches to mask.'''}       ,					)
			__lowerCamelCase     :	Optional[int]          =   field(
			    default=a__       ,					metadata={
			        '''help''': (
			            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
			            '''value if set.'''
			        )
			    }       ,					)
			__lowerCamelCase     :	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       :   Any       )     ->     Optional[Any]:







					"""simple docstring"""






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


@dataclass
class   A      :
			'''simple docstring'''




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


class   A      :
			'''simple docstring'''




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







					"""simple docstring"""






					A__  				=       input_size
					A__  				=       mask_patch_size
					A__  				=       model_patch_size
					A__  				=       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__  				=       self.input_size // self.mask_patch_size
					A__  				=       self.mask_patch_size // self.model_patch_size

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







			def __call__(      self       :   List[Any]       )     ->     str:







					"""simple docstring"""






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

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

					return torch.tensor(mask.flatten()       )







def        __lowerCamelCase (							__a       :int   )		->    int:






		"""simple docstring"""

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







def        __lowerCamelCase (							)		->    Optional[int]:






		"""simple docstring"""

		A__  				=       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__  				=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
		else:
				A__  				=       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"""   ,					snake_case__   ,					snake_case__   )

		# 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__  				=       training_args.get_process_log_level()
		logger.setLevel(snake_case__   )
		transformers.utils.logging.set_verbosity(snake_case__   )
		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__  				=       None
		if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
				A__  				=       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__  				=       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__  				=       None if 'validation' in ds.keys() else data_args.train_val_split
		if isinstance(data_args.train_val_split   ,					snake_case__   ) and data_args.train_val_split > 0.0:
				A__  				=       ds['train'].train_test_split(data_args.train_val_split   )
				A__  				=       split['train']
				A__  				=       split['test']

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

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

		# create model
		if model_args.model_name_or_path:
				A__  				=       AutoModelForMaskedImageModeling.from_pretrained(
				    model_args.model_name_or_path   ,					from_tf=bool(""".ckpt""" in model_args.model_name_or_path   )   ,					config=snake_case__   ,					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__  				=       AutoModelForMaskedImageModeling.from_config(snake_case__   )

		if training_args.do_train:
				A__  				=       ds['train'].column_names
		else:
				A__  				=       ds['validation'].column_names

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

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

		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__  				=       (
						    ds['validation'].shuffle(seed=training_args.seed   ).select(range(data_args.max_eval_samples   )   )
						)
				# Set the validation transforms
				ds["validation"].set_transform(snake_case__   )

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

		# Training
		if training_args.do_train:
				A__  				=       None
				if training_args.resume_from_checkpoint is not None:
						A__  				=       training_args.resume_from_checkpoint
				elif last_checkpoint is not None:
						A__  				=       last_checkpoint
				A__  				=       trainer.train(resume_from_checkpoint=snake_case__   )
				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__  				=       trainer.evaluate()
				trainer.log_metrics("""eval"""   ,					snake_case__   )
				trainer.save_metrics("""eval"""   ,					snake_case__   )

		# Write model card and (optionally) push to hub
		A__  				=       {
		    '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(**snake_case__   )
		else:
				trainer.create_model_card(**snake_case__   )


if __name__ == "__main__":
			main()










def        __lowerCamelCase (							__a       :float   ,					__a       :list[float]   )		->    float:






		"""simple docstring"""

		if discount_rate < 0:
				raise ValueError("""Discount rate cannot be negative"""   )
		if not cash_flows:
				raise ValueError("""Cash flows list cannot be empty"""   )
		A__  				=       sum(
		    cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(__a   )   )
		return round(__a   ,					ndigits=2   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()



