


'''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
__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''')




@dataclass
class 						a__							:





							'''simple docstring'''






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





							'''simple docstring'''






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




@dataclass
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  float      		= field(
							    default=1e-3							,							metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}    )


def    _snake_case  (	A			)		->       Optional[Any]:
							lowerCAmelCase__			  =      torch.stack([example['''pixel_values'''] for example in examples]			)
							return {"pixel_values": pixel_values}


def    _snake_case  (	)		->       Union[str, 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.

							lowerCAmelCase__			  =      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.
														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_mae'''					,       A					,       A			)

							# 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(A			)
							transformers.utils.logging.set_verbosity(A			)
							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					,       A			) 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''']

							# Load pretrained model and image processor
							#
							# 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:
														lowerCAmelCase__			  =      ViTMAEConfig.from_pretrained(model_args.config_name					,       **A			)
							elif model_args.model_name_or_path:
														lowerCAmelCase__			  =      ViTMAEConfig.from_pretrained(model_args.model_name_or_path					,       **A			)
							else:
														lowerCAmelCase__			  =      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:
														lowerCAmelCase__			  =      ViTImageProcessor.from_pretrained(model_args.image_processor_name					,       **A			)
							elif model_args.model_name_or_path:
														lowerCAmelCase__			  =      ViTImageProcessor.from_pretrained(model_args.model_name_or_path					,       **A			)
							else:
														lowerCAmelCase__			  =      ViTImageProcessor()

							# create model
							if model_args.model_name_or_path:
														lowerCAmelCase__			  =      ViTMAEForPreTraining.from_pretrained(
														    model_args.model_name_or_path					,       from_tf=bool('''.ckpt''' in model_args.model_name_or_path			)					,       config=A					,       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__			  =      ViTMAEForPreTraining(A			)

							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 MAE paper
							# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
							if "shortest_edge" in image_processor.size:
														lowerCAmelCase__			  =      image_processor.size['''shortest_edge''']
							else:
														lowerCAmelCase__			  =      (image_processor.size['''height'''], image_processor.size['''width'''])
							lowerCAmelCase__			  =      Compose(
							    [
							        Lambda(lambda A			: img.convert('''RGB'''			) if img.mode != "RGB" else img			),
							        RandomResizedCrop(A					,       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			):
														lowerCAmelCase__			  =      [transforms(A			) 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:
																					lowerCAmelCase__			  =      ds['''train'''].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
														# Set the training transforms
														ds["train"].set_transform(A			)

							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(A			)

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

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

							# 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=A			)
														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'''					,       A			)
														trainer.save_metrics('''eval'''					,       A			)

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


def    _snake_case  (	A			)		->       Tuple:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()



'''simple docstring'''






import re


def    _snake_case  (	A			)		->       bool:
							lowerCAmelCase__			  =      re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$'''			)
							if match := re.search(A					,       A			):
														return match.string == phone
							return False


if __name__ == "__main__":
		print(indian_phone_validator('''+918827897895'''))