


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

_UpperCAmelCase			:  Any     =16
_UpperCAmelCase			:  Optional[Any]     =32



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ = 16 )-> str:
				lowerCAmelCase_ :      Dict    				=				AutoTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase_ :      List[Any]    				=				load_dataset('''glue''' ,   '''mrpc''' )

				def tokenize_function(lowerCAmelCase_ ):
								# max_length=None => use the model max length (it's actually the default)
								lowerCAmelCase_ :      List[str]    				=				tokenizer(examples['''sentence1'''] ,   examples['''sentence2'''] ,   truncation=lowerCAmelCase_ ,   max_length=lowerCAmelCase_ )
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								lowerCAmelCase_ :      Optional[int]    				=				datasets.map(
								    lowerCAmelCase_ ,   batched=lowerCAmelCase_ ,   remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,   )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				lowerCAmelCase_ :      Any    				=				tokenized_datasets.rename_column('''label''' ,   '''labels''' )

				def collate_fn(lowerCAmelCase_ ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								lowerCAmelCase_ :      Any    				=				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												lowerCAmelCase_ :      Dict    				=				16
								elif accelerator.mixed_precision != "no":
												lowerCAmelCase_ :      Any    				=				8
								else:
												lowerCAmelCase_ :      List[str]    				=				None

								return tokenizer.pad(
								    lowerCAmelCase_ ,   padding='''longest''' ,   max_length=lowerCAmelCase_ ,   pad_to_multiple_of=lowerCAmelCase_ ,   return_tensors='''pt''' ,   )

				# Instantiate dataloaders.
				lowerCAmelCase_ :      int    				=				DataLoader(
				    tokenized_datasets['''train'''] ,   shuffle=lowerCAmelCase_ ,   collate_fn=lowerCAmelCase_ ,   batch_size=lowerCAmelCase_ )
				lowerCAmelCase_ :      int    				=				DataLoader(
				    tokenized_datasets['''validation'''] ,   shuffle=lowerCAmelCase_ ,   collate_fn=lowerCAmelCase_ ,   batch_size=lowerCAmelCase_ )

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
						from accelerate.test_utils.training import mocked_dataloaders

						_UpperCAmelCase			:  List[str]     =mocked_dataloaders  # noqa: F811



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> Any:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,   lowerCAmelCase_ ) == "1":
								lowerCAmelCase_ :      Any    				=				2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								lowerCAmelCase_ :      List[Any]    				=				Accelerator(
								    cpu=args.cpu ,   mixed_precision=args.mixed_precision ,   log_with='''all''' ,   project_dir=args.project_dir )
				else:
								lowerCAmelCase_ :      Optional[int]    				=				Accelerator(cpu=args.cpu ,   mixed_precision=args.mixed_precision )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				lowerCAmelCase_ :      Tuple    				=				config['''lr''']
				lowerCAmelCase_ :      List[Any]    				=				int(config['''num_epochs'''] )
				lowerCAmelCase_ :      List[str]    				=				int(config['''seed'''] )
				lowerCAmelCase_ :      List[Any]    				=				int(config['''batch_size'''] )
				set_seed(lowerCAmelCase_ )

				lowerCAmelCase_ :      Optional[int]    				=				get_dataloaders(lowerCAmelCase_ ,   lowerCAmelCase_ )
				lowerCAmelCase_ :      Optional[int]    				=				evaluate.load('''glue''' ,   '''mrpc''' )

				# If the batch size is too big we use gradient accumulation
				lowerCAmelCase_ :      Any    				=				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								lowerCAmelCase_ :      Dict    				=				batch_size // MAX_GPU_BATCH_SIZE
								lowerCAmelCase_ :      str    				=				MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				lowerCAmelCase_ :      Union[str, Any]    				=				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,   return_dict=lowerCAmelCase_ )

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				lowerCAmelCase_ :      Dict    				=				model.to(accelerator.device )

				# Instantiate optimizer
				lowerCAmelCase_ :      List[str]    				=				AdamW(params=model.parameters() ,   lr=lowerCAmelCase_ )

				# Instantiate scheduler
				lowerCAmelCase_ :      Tuple    				=				get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase_ ,   num_warmup_steps=100 ,   num_training_steps=(len(lowerCAmelCase_ ) * num_epochs) // gradient_accumulation_steps ,   )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				lowerCAmelCase_ :      str    				=				accelerator.prepare(
				    lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								lowerCAmelCase_ :      List[str]    				=				os.path.split(lowerCAmelCase_ )[-1].split('''.''' )[0]
								accelerator.init_trackers(lowerCAmelCase_ ,   lowerCAmelCase_ )

				# Now we train the model
				for epoch in range(lowerCAmelCase_ ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												lowerCAmelCase_ :      str    				=				0
								for step, batch in enumerate(lowerCAmelCase_ ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device )
												lowerCAmelCase_ :      List[Any]    				=				model(**lowerCAmelCase_ )
												lowerCAmelCase_ :      Union[str, Any]    				=				outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												lowerCAmelCase_ :      Optional[int]    				=				loss / gradient_accumulation_steps
												accelerator.backward(lowerCAmelCase_ )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(lowerCAmelCase_ ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device )
												with torch.no_grad():
																lowerCAmelCase_ :      List[str]    				=				model(**lowerCAmelCase_ )
												lowerCAmelCase_ :      Union[str, Any]    				=				outputs.logits.argmax(dim=-1 )
												lowerCAmelCase_ :      Optional[Any]    				=				accelerator.gather_for_metrics((predictions, batch['''labels''']) )
												metric.add_batch(
												    predictions=lowerCAmelCase_ ,   references=lowerCAmelCase_ ,   )

								lowerCAmelCase_ :      int    				=				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"""epoch {epoch}:""" ,   lowerCAmelCase_ )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        '''accuracy''': eval_metric['''accuracy'''],
												        '''f1''': eval_metric['''f1'''],
												        '''train_loss''': total_loss.item() / len(lowerCAmelCase_ ),
												        '''epoch''': epoch,
												    } ,   step=lowerCAmelCase_ ,   )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()



def 			lowerCAmelCase (		)-> Dict:
				lowerCAmelCase_ :      List[str]    				=				argparse.ArgumentParser(description='''Simple example of training script.''' )
				parser.add_argument(
				    '''--mixed_precision''' ,   type=lowerCAmelCase_ ,   default=lowerCAmelCase_ ,   choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,   help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.''' ,   )
				parser.add_argument('''--cpu''' ,   action='''store_true''' ,   help='''If passed, will train on the CPU.''' )
				parser.add_argument(
				    '''--with_tracking''' ,   action='''store_true''' ,   help='''Whether to load in all available experiment trackers from the environment and use them for logging.''' ,   )
				parser.add_argument(
				    '''--project_dir''' ,   type=lowerCAmelCase_ ,   default='''logs''' ,   help='''Location on where to store experiment tracking logs` and relevent project information''' ,   )
				lowerCAmelCase_ :      Dict    				=				parser.parse_args()
				lowerCAmelCase_ :      List[str]    				=				{'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowerCAmelCase_ ,   lowerCAmelCase_ )


if __name__ == "__main__":
						main()



import re



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


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