




import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__a							     =					16
__a							     =					32







def lowerCamelCase__  (     _lowercase					,		_lowercase = 16					,		_lowercase = "bert-base-cased" ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[Any]  =     AutoTokenizer.from_pretrained(_lowercase )
     UpperCAmelCase_     :				int  =     load_dataset('''glue'''					,		'''mrpc''' )

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

     # Apply the method we just defined to all the examples in all the splits of the dataset
     UpperCAmelCase_     :				Tuple  =     datasets.map(
         _lowercase					,		batched=_lowercase					,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,		load_from_cache_file=_lowercase )

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

     def collate_fn(_lowercase ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_lowercase					,		padding='''max_length'''					,		max_length=128					,		return_tensors='''pt''' )
          return tokenizer.pad(_lowercase					,		padding='''longest'''					,		return_tensors='''pt''' )

     # Instantiate dataloaders.
     UpperCAmelCase_     :				Union[str, Any]  =     DataLoader(
         tokenized_datasets['''train''']					,		shuffle=_lowercase					,		collate_fn=_lowercase					,		batch_size=_lowercase )
     UpperCAmelCase_     :				int  =     DataLoader(
         tokenized_datasets['''validation''']					,		shuffle=_lowercase					,		collate_fn=_lowercase					,		batch_size=_lowercase )

     return train_dataloader, eval_dataloader







def lowerCamelCase__  (     _lowercase					,		_lowercase					,		_lowercase					,		_lowercase ):

     '''simple docstring'''


     model.eval()
     UpperCAmelCase_     :				List[str]  =     0
     for step, batch in enumerate(_lowercase ):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device )
          with torch.no_grad():
               UpperCAmelCase_     :				Any  =     model(**_lowercase )
          UpperCAmelCase_     :				Optional[Any]  =     outputs.logits.argmax(dim=-1 )
          # It is slightly faster to call this once, than multiple times
          UpperCAmelCase_,       UpperCAmelCase_     :				int  =     accelerator.gather(
              (predictions, batch['''labels''']) )  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(_lowercase ) - 1:
                    UpperCAmelCase_     :				Dict  =     predictions[: len(eval_dataloader.dataset ) - samples_seen]
                    UpperCAmelCase_     :				Union[str, Any]  =     references[: len(eval_dataloader.dataset ) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=_lowercase					,		references=_lowercase					,		)

     UpperCAmelCase_     :				Union[str, Any]  =     metric.compute()
     return eval_metric["accuracy"]







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				int  =     Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase_     :				Optional[Any]  =     config['''lr''']
     UpperCAmelCase_     :				Union[str, Any]  =     int(config['''num_epochs'''] )
     UpperCAmelCase_     :				Optional[Any]  =     int(config['''seed'''] )
     UpperCAmelCase_     :				int  =     int(config['''batch_size'''] )
     UpperCAmelCase_     :				List[str]  =     args.model_name_or_path

     set_seed(_lowercase )
     UpperCAmelCase_,       UpperCAmelCase_     :				int  =     get_dataloaders(_lowercase					,		_lowercase					,		_lowercase )

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase_     :				Optional[Any]  =     AutoModelForSequenceClassification.from_pretrained(_lowercase					,		return_dict=_lowercase )

     # Instantiate optimizer
     UpperCAmelCase_     :				List[Any]  =     (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     UpperCAmelCase_     :				str  =     optimizer_cls(params=model.parameters()					,		lr=_lowercase )

     if accelerator.state.deepspeed_plugin is not None:
          UpperCAmelCase_     :				Optional[int]  =     accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          UpperCAmelCase_     :				List[Any]  =     1
     UpperCAmelCase_     :				Optional[Any]  =     (len(_lowercase ) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          UpperCAmelCase_     :				List[str]  =     get_linear_schedule_with_warmup(
              optimizer=_lowercase					,		num_warmup_steps=0					,		num_training_steps=_lowercase					,		)
     else:
          UpperCAmelCase_     :				List[str]  =     DummyScheduler(_lowercase					,		total_num_steps=_lowercase					,		warmup_num_steps=0 )

     # 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.
     UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_,       UpperCAmelCase_     :				Union[str, Any]  =     accelerator.prepare(
         _lowercase					,		_lowercase					,		_lowercase					,		_lowercase					,		_lowercase )

     # We need to keep track of how many total steps we have iterated over
     UpperCAmelCase_     :				Tuple  =     0
     # We also need to keep track of the stating epoch so files are named properly
     UpperCAmelCase_     :				Optional[Any]  =     0
     UpperCAmelCase_     :				str  =     evaluate.load('''glue'''					,		'''mrpc''' )
     UpperCAmelCase_     :				List[str]  =     num_epochs

     if args.partial_train_epoch is not None:
          UpperCAmelCase_     :				Union[str, Any]  =     args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint )
          UpperCAmelCase_     :				Dict  =     args.resume_from_checkpoint.split('''epoch_''' )[1]
          UpperCAmelCase_     :				int  =     ''''''
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          UpperCAmelCase_     :				Union[str, Any]  =     int(_lowercase ) + 1
          UpperCAmelCase_     :				List[str]  =     evaluation_loop(_lowercase					,		_lowercase					,		_lowercase					,		_lowercase )
          accelerator.print('''resumed checkpoint performance:'''					,		_lowercase )
          accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''					,		lr_scheduler.get_lr()[0] )
          accelerator.print('''resumed optimizers\'s lr:'''					,		optimizer.param_groups[0]['''lr'''] )
          with open(os.path.join(args.output_dir					,		f'''state_{starting_epoch-1}.json''' )					,		'''r''' ) as f:
               UpperCAmelCase_     :				Any  =     json.load(_lowercase )
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

    # Now we train the model
     UpperCAmelCase_     :				Union[str, Any]  =     {}
     for epoch in range(_lowercase					,		_lowercase ):
          model.train()
          for step, batch in enumerate(_lowercase ):
               UpperCAmelCase_     :				List[str]  =     model(**_lowercase )
               UpperCAmelCase_     :				int  =     outputs.loss
               UpperCAmelCase_     :				Optional[int]  =     loss / gradient_accumulation_steps
               accelerator.backward(_lowercase )
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          UpperCAmelCase_     :				str  =     f'''epoch_{epoch}'''
          UpperCAmelCase_     :				Optional[Any]  =     os.path.join(args.output_dir					,		_lowercase )
          accelerator.save_state(_lowercase )
          UpperCAmelCase_     :				Any  =     evaluation_loop(_lowercase					,		_lowercase					,		_lowercase					,		_lowercase )
          UpperCAmelCase_     :				Union[str, Any]  =     accuracy
          UpperCAmelCase_     :				List[str]  =     lr_scheduler.get_lr()[0]
          UpperCAmelCase_     :				List[Any]  =     optimizer.param_groups[0]['''lr''']
          UpperCAmelCase_     :				Any  =     epoch
          UpperCAmelCase_     :				Any  =     overall_step
          accelerator.print(f'''epoch {epoch}:'''					,		_lowercase )

          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               with open(os.path.join(args.output_dir					,		f'''state_{epoch}.json''' )					,		'''w''' ) as f:
                    json.dump(_lowercase					,		_lowercase )







def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Any  =     argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
     parser.add_argument(
         '''--model_name_or_path'''					,		type=_lowercase					,		default='''bert-base-cased'''					,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,		required=_lowercase					,		)
     parser.add_argument(
         '''--output_dir'''					,		type=_lowercase					,		default='''.'''					,		help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''					,		)
     parser.add_argument(
         '''--resume_from_checkpoint'''					,		type=_lowercase					,		default=_lowercase					,		help='''If the training should continue from a checkpoint folder.'''					,		)
     parser.add_argument(
         '''--partial_train_epoch'''					,		type=_lowercase					,		default=_lowercase					,		help='''If passed, the training will stop after this number of epochs.'''					,		)
     parser.add_argument(
         '''--num_epochs'''					,		type=_lowercase					,		default=2					,		help='''Number of train epochs.'''					,		)
     UpperCAmelCase_     :				Union[str, Any]  =     parser.parse_args()
     UpperCAmelCase_     :				Dict  =     {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

     training_function(_lowercase					,		_lowercase )


if __name__ == "__main__":
     main()





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







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


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

     UpperCAmelCase_     :				Tuple  =     precision
     UpperCAmelCase_     :				Optional[Any]  =     ceil(precision / 14 )
     UpperCAmelCase_     :				int  =     426880 * Decimal(10005 ).sqrt()
     UpperCAmelCase_     :				Tuple  =     1
     UpperCAmelCase_     :				List[Any]  =     13591409
     UpperCAmelCase_     :				Optional[Any]  =     Decimal(_lowercase )
     for k in range(1					,		_lowercase ):
          UpperCAmelCase_     :				List[str]  =     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__":
     __a							     =					50
     print(F"""The first {n} digits of pi is: {pi(n)}""")