




'''simple docstring'''

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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
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  :				List[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')






@dataclass
class 						a				:
				snake_case_      =		field(
				    default="cifar10"   ,     metadata={"help": "Name of a dataset from the datasets package"}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "The column name of the images in the files."}     )
				snake_case_      =		field(default=_lowerCamelCase   ,     metadata={"help": "A folder containing the training data."}     )
				snake_case_      =		field(default=_lowerCamelCase   ,     metadata={"help": "A folder containing the validation data."}     )
				snake_case_      =		field(
				    default=0.15   ,     metadata={"help": "Percent to split off of train for validation."}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of training examples to this "
				            "value if set."
				        )
				    }   ,     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={
				        "help": (
				            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
				            "value if set."
				        )
				    }   ,     )


				def   A_ (		self    :					Any					):
									snake_case_									=				{}
									if self.train_dir is not None:
														snake_case_									=				self.train_dir
									if self.validation_dir is not None:
														snake_case_									=				self.validation_dir
									snake_case_									=				data_files if data_files else None







@dataclass
class 						a				:
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={
				        "help": (
				            "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
				        )
				    }   ,     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     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_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}     )
				snake_case_      =		field(
				    default="main"   ,     metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}   ,     )
				snake_case_      =		field(default=_lowerCamelCase   ,     metadata={"help": "Name or path of preprocessor config."}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={
				        "help": (
				            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
				            "with private models)."
				        )
				    }   ,     )
				snake_case_      =		field(
				    default=0.75   ,     metadata={"help": "The ratio of the number of masked tokens in the input sequence."}     )
				snake_case_      =		field(
				    default=_lowerCamelCase   ,     metadata={"help": "Whether or not to train with normalized pixel values as target."}     )







@dataclass
class 						a				(   _lowerCamelCase     ):
				snake_case_      =		field(
				    default=1e-3   ,     metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}     )







def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				torch.stack([example['''pixel_values'''] for example in examples]	)
					return {"pixel_values": pixel_values}


def 		__magic_name__    (	)		->					Optional[Any]:

					'''simple docstring'''

					snake_case_									=				HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)	)
					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.
										snake_case_			,snake_case_			,snake_case_									=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
					else:
										snake_case_			,snake_case_			,snake_case_									=				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_mae''',      __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()

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

					# Load pretrained model and image processor
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					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,
					}
					if model_args.config_name:
										snake_case_									=				ViTMAEConfig.from_pretrained(model_args.config_name,      **__UpperCAmelCase	)
					elif model_args.model_name_or_path:
										snake_case_									=				ViTMAEConfig.from_pretrained(model_args.model_name_or_path,      **__UpperCAmelCase	)
					else:
										snake_case_									=				ViTMAEConfig()
										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}"	)

    # adapt config
					config.update(
					    {
					        '''mask_ratio''': model_args.mask_ratio,
					        '''norm_pix_loss''': model_args.norm_pix_loss,
					    }	)

					# create image processor
					if model_args.image_processor_name:
										snake_case_									=				ViTImageProcessor.from_pretrained(model_args.image_processor_name,      **__UpperCAmelCase	)
					elif model_args.model_name_or_path:
										snake_case_									=				ViTImageProcessor.from_pretrained(model_args.model_name_or_path,      **__UpperCAmelCase	)
					else:
										snake_case_									=				ViTImageProcessor()

					# create model
					if model_args.model_name_or_path:
										snake_case_									=				ViTMAEForPreTraining.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'''	)
										snake_case_									=				ViTMAEForPreTraining(__UpperCAmelCase	)

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

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

					# transformations as done in original MAE paper
					# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
					if "shortest_edge" in image_processor.size:
										snake_case_									=				image_processor.size['''shortest_edge''']
					else:
										snake_case_									=				(image_processor.size['''height'''], image_processor.size['''width'''])
					snake_case_									=				Compose(
					    [
					        Lambda(lambda __UpperCAmelCase	: img.convert('''RGB'''	) if img.mode != "RGB" else img	),
					        RandomResizedCrop(__UpperCAmelCase,      scale=(0.2, 1.0),      interpolation=InterpolationMode.BICUBIC	),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean,      std=image_processor.image_std	),
					    ]	)

					def preprocess_images(__UpperCAmelCase	):
										snake_case_									=				[transforms(__UpperCAmelCase	) for image in 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:
															snake_case_									=				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:
															snake_case_									=				(
															    ds['''validation'''].shuffle(seed=training_args.seed	).select(range(data_args.max_eval_samples	)	)
															)
										# Set the validation transforms
										ds["validation"].set_transform(__UpperCAmelCase	)

					# Compute absolute learning rate
					snake_case_									=				(
					    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
					)
					if training_args.base_learning_rate is not None:
										snake_case_									=				training_args.base_learning_rate * total_train_batch_size / 256

					# Initialize our trainer
					snake_case_									=				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:
										snake_case_									=				None
										if training_args.resume_from_checkpoint is not None:
															snake_case_									=				training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															snake_case_									=				last_checkpoint
										snake_case_									=				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:
										snake_case_									=				trainer.evaluate()
										trainer.log_metrics('''eval''',      __UpperCAmelCase	)
										trainer.save_metrics('''eval''',      __UpperCAmelCase	)

					# Write model card and (optionally) push to hub
					snake_case_									=				{
					    '''tasks''': '''masked-auto-encoding''',
					    '''dataset''': data_args.dataset_name,
					    '''tags''': ['''masked-auto-encoding'''],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**__UpperCAmelCase	)
					else:
										trainer.create_model_card(**__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					main()


if __name__ == "__main__":
							main()







'''simple docstring'''

from __future__ import annotations

import math


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					if depth < 0:
										raise ValueError('''Depth cannot be less than 0'''	)

					if len(__UpperCAmelCase	) == 0:
										raise ValueError('''Scores cannot be empty'''	)

					if depth == height:
										return scores[node_index]

					if is_max:
										return max(
										    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )

					return min(
					    minimax(depth + 1,      node_index * 2,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      minimax(depth + 1,      node_index * 2 + 1,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	),      )


def 		__magic_name__    (	)		->					None:

					'''simple docstring'''

					snake_case_									=				[90, 23, 6, 33, 21, 65, 123, 3_4423]
					snake_case_									=				math.log(len(__UpperCAmelCase	),      2	)
					print('''Optimal value : ''',      end=''''''	)
					print(minimax(0,      0,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							main()

