






"""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
_lowerCAmelCase            =	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      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default="""cifar10"""						,						metadata={"""help""": """Name of a dataset from the datasets package"""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """The column name of the images in the files."""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(default=__snake_case						,						metadata={"""help""": """A folder containing the training data."""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(default=__snake_case						,						metadata={"""help""": """A folder containing the validation data."""}     )
						_SCREAMING_SNAKE_CASE  : Optional[float]												=		field(
						    default=0.1_5						,						metadata={"""help""": """Percent to split off of train for validation."""}     )
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		field(
						    default=__snake_case						,						metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of training examples to this """
						            """value if set."""
						        )
						    }						,						)
						_SCREAMING_SNAKE_CASE  : Optional[int]												=		field(
						    default=__snake_case						,						metadata={
						        """help""": (
						            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
						            """value if set."""
						        )
						    }						,						)






						def      __snake_case      (		self  :Optional[Any]				)      ->Optional[int]:
										lowercase    :      List[str]       = {}
										if self.train_dir is not None:
														lowercase    :      Optional[int]       = self.train_dir
										if self.validation_dir is not None:
														lowercase    :      Union[str, Any]       = self.validation_dir
										lowercase    :      str       = data_files if data_files else None


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    default=__snake_case						,						metadata={
						        """help""": (
						            """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
						        )
						    }						,						)
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__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"""
						        )
						    }						,						)
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}     )
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    default="""main"""						,						metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}						,						)
						_SCREAMING_SNAKE_CASE  : str												=		field(default=__snake_case						,						metadata={"""help""": """Name or path of preprocessor config."""}     )
						_SCREAMING_SNAKE_CASE  : bool												=		field(
						    default=__snake_case						,						metadata={
						        """help""": (
						            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
						            """with private models)."""
						        )
						    }						,						)
						_SCREAMING_SNAKE_CASE  : float												=		field(
						    default=0.7_5						,						metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""}     )
						_SCREAMING_SNAKE_CASE  : bool												=		field(
						    default=__snake_case						,						metadata={"""help""": """Whether or not to train with normalized pixel values as target."""}     )


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


def      UpperCamelCase       ( _A )     ->       Optional[int]:
				lowercase    :      int       = torch.stack([example["""pixel_values"""] for example in examples] )
				return {"pixel_values": pixel_values}


def      UpperCamelCase       ( )     ->       List[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    :      Any       = 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.
								lowercase      ,      lowercase      ,      lowercase    :      Union[str, Any]       = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								lowercase      ,      lowercase      ,      lowercase    :      List[Any]       = 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"""  ,				_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    :      Optional[Any]       = 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    :      Optional[Any]       = None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								lowercase    :      List[str]       = 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    :      Optional[int]       = 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    :      Tuple       = 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    :      List[Any]       = ds["""train"""].train_test_split(data_args.train_val_split )
								lowercase    :      Dict       = split["""train"""]
								lowercase    :      str       = 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.
				lowercase    :      int       = {
				    """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:
								lowercase    :      List[Any]       = ViTMAEConfig.from_pretrained(model_args.config_name  ,				**_lowercase )
				elif model_args.model_name_or_path:
								lowercase    :      Optional[int]       = ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,				**_lowercase )
				else:
								lowercase    :      Union[str, Any]       = 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:
								lowercase    :      Dict       = ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,				**_lowercase )
				elif model_args.model_name_or_path:
								lowercase    :      List[Any]       = ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,				**_lowercase )
				else:
								lowercase    :      Tuple       = ViTImageProcessor()

				# create model
				if model_args.model_name_or_path:
								lowercase    :      Optional[int]       = ViTMAEForPreTraining.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    :      Optional[int]       = ViTMAEForPreTraining(_lowercase )

				if training_args.do_train:
								lowercase    :      str       = ds["""train"""].column_names
				else:
								lowercase    :      Optional[int]       = ds["""validation"""].column_names

				if data_args.image_column_name is not None:
								lowercase    :      Union[str, Any]       = data_args.image_column_name
				elif "image" in column_names:
								lowercase    :      List[Any]       = """image"""
				elif "img" in column_names:
								lowercase    :      str       = """img"""
				else:
								lowercase    :      Any       = 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:
								lowercase    :      Tuple       = image_processor.size["""shortest_edge"""]
				else:
								lowercase    :      List[str]       = (image_processor.size["""height"""], image_processor.size["""width"""])
				lowercase    :      Union[str, Any]       = Compose(
				    [
				        Lambda(lambda _A : img.convert("""RGB""" ) if img.mode != "RGB" else img ),
				        RandomResizedCrop(_lowercase  ,				scale=(0.2, 1.0)  ,				interpolation=InterpolationMode.BICUBIC ),
				        RandomHorizontalFlip(),
				        ToTensor(),
				        Normalize(mean=image_processor.image_mean  ,				std=image_processor.image_std ),
				    ] )

				def preprocess_images(_A ):
								lowercase    :      int       = [transforms(_lowercase ) 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:
												lowercase    :      Union[str, Any]       = 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    :      Optional[Any]       = (
												    ds["""validation"""].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
												)
								# Set the validation transforms
								ds["validation"].set_transform(_lowercase )

				# Compute absolute learning rate
				lowercase    :      List[Any]       = (
				    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
				)
				if training_args.base_learning_rate is not None:
								lowercase    :      Dict       = training_args.base_learning_rate * total_train_batch_size / 256

				# Initialize our trainer
				lowercase    :      Any       = 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    :      List[Any]       = None
								if training_args.resume_from_checkpoint is not None:
												lowercase    :      str       = training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												lowercase    :      List[Any]       = last_checkpoint
								lowercase    :      Tuple       = 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    :      Any       = trainer.evaluate()
								trainer.log_metrics("""eval"""  ,				_lowercase )
								trainer.save_metrics("""eval"""  ,				_lowercase )

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


def      UpperCamelCase       ( _A )     ->       int:
				# For xla_spawn (TPUs)
				main()


if __name__ == "__main__":
				main()


from decimal import Decimal, getcontext
from math import ceil, factorial

def  __lowerCamelCase					( _lowercase			)       ->      str:

     if not isinstance(_lowercase						,     _lowercase			):
          raise TypeError('Undefined for non-integers'			)
     elif precision < 1:
          raise ValueError('Undefined for non-natural numbers'			)

     UpperCamelCase						    =					precision
     UpperCamelCase						    =					ceil(precision / 14			)
     UpperCamelCase						    =					426880 * Decimal(10005			).sqrt()
     UpperCamelCase						    =					1
     UpperCamelCase						    =					13591409
     UpperCamelCase						    =					Decimal(_lowercase			)
     for k in range(1						,     _lowercase			):
          UpperCamelCase						    =					factorial(6 * k			) // (factorial(3 * k			) * factorial(_lowercase			) ** 3)
          linear_term += 545140134
          exponential_term *= -262537412640768000
          partial_sum += Decimal(multinomial_term * linear_term			) / exponential_term
     return str(constant_term / partial_sum			)[:-1]


if __name__ == "__main__":
 _snake_case      = 50
 print(F"The first {n} digits of pi is: {pi(n)}")


