


'''simple docstring'''
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


__snake_case				 =	16
__snake_case				 =	32


def      a (     __a					,   __a = 16					,   __a = "bert-base-cased"  )    ->      Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					AutoTokenizer.from_pretrained(__a  )
					UpperCamelCase__  :List[str]				   =					load_dataset('''glue'''					,   '''mrpc'''  )

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

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

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

					def collate_fn(__a  ):
										# 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(__a					,   padding='''max_length'''					,   max_length=128					,   return_tensors='''pt'''  )
										return tokenizer.pad(__a					,   padding='''longest'''					,   return_tensors='''pt'''  )

					# Instantiate dataloaders.
					UpperCamelCase__  :Union[str, Any]				   =					DataLoader(
					    tokenized_datasets['''train''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a  )
					UpperCamelCase__  :List[Any]				   =					DataLoader(
					    tokenized_datasets['''validation''']					,   shuffle=__a					,   collate_fn=__a					,   batch_size=__a  )

					return train_dataloader, eval_dataloader


def      a (     __a					,   __a					,   __a					,   __a  )    ->      List[str]:



					'''simple docstring'''






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

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


def      a (     __a					,   __a  )    ->      List[str]:



					'''simple docstring'''






					UpperCamelCase__  :Optional[int]				   =					Accelerator()

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

					set_seed(__a  )
					UpperCamelCase__   ,							UpperCamelCase__  :int				   =					get_dataloaders(__a					,   __a					,   __a  )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					UpperCamelCase__  :Any				   =					AutoModelForSequenceClassification.from_pretrained(__a					,   return_dict=__a  )

					# Instantiate optimizer
					UpperCamelCase__  :Dict				   =					(
					    AdamW
					    if accelerator.state.deepspeed_plugin is None
					    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
					    else DummyOptim
					)
					UpperCamelCase__  :Any				   =					optimizer_cls(params=model.parameters()					,   lr=__a  )

					if accelerator.state.deepspeed_plugin is not None:
										UpperCamelCase__  :Dict				   =					accelerator.state.deepspeed_plugin.deepspeed_config[
										    '''gradient_accumulation_steps'''
										]
					else:
										UpperCamelCase__  :Optional[int]				   =					1
					UpperCamelCase__  :Any				   =					(len(__a  ) * 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=__a					,   num_warmup_steps=0					,   num_training_steps=__a					,   )
					else:
										UpperCamelCase__  :Any				   =					DummyScheduler(__a					,   total_num_steps=__a					,   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__  :List[str]				   =					accelerator.prepare(
					    __a					,   __a					,   __a					,   __a					,   __a  )

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

					if args.partial_train_epoch is not None:
										UpperCamelCase__  :Any				   =					args.partial_train_epoch

					if args.resume_from_checkpoint:
										accelerator.load_state(args.resume_from_checkpoint  )
										UpperCamelCase__  :Union[str, Any]				   =					args.resume_from_checkpoint.split('''epoch_'''  )[1]
										UpperCamelCase__  :List[Any]				   =					''''''
										for char in epoch_string:
															if char.isdigit():
																				state_epoch_num += char
															else:
																				break
										UpperCamelCase__  :int				   =					int(__a  ) + 1
										UpperCamelCase__  :Union[str, Any]				   =					evaluation_loop(__a					,   __a					,   __a					,   __a  )
										accelerator.print('''resumed checkpoint performance:'''					,   __a  )
										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(__a  )
															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__  :Optional[int]				   =					{}
					for epoch in range(__a					,   __a  ):
										model.train()
										for step, batch in enumerate(__a  ):
															UpperCamelCase__  :Dict				   =					model(**__a  )
															UpperCamelCase__  :Optional[int]				   =					outputs.loss
															UpperCamelCase__  :Optional[Any]				   =					loss / gradient_accumulation_steps
															accelerator.backward(__a  )
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

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

										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(__a					,   __a  )


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



					'''simple docstring'''






					UpperCamelCase__  :List[str]				   =					argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''  )
					parser.add_argument(
					    '''--model_name_or_path'''					,   type=__a					,   default='''bert-base-cased'''					,   help='''Path to pretrained model or model identifier from huggingface.co/models.'''					,   required=__a					,   )
					parser.add_argument(
					    '''--output_dir'''					,   type=__a					,   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=__a					,   default=__a					,   help='''If the training should continue from a checkpoint folder.'''					,   )
					parser.add_argument(
					    '''--partial_train_epoch'''					,   type=__a					,   default=__a					,   help='''If passed, the training will stop after this number of epochs.'''					,   )
					parser.add_argument(
					    '''--num_epochs'''					,   type=__a					,   default=2					,   help='''Number of train epochs.'''					,   )
					UpperCamelCase__  :Dict				   =					parser.parse_args()
					UpperCamelCase__  :Union[str, Any]				   =					{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

					training_function(__a					,   __a  )


if __name__ == "__main__":
		main()



'''simple docstring'''
from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass
@dataclass
class   lowercase      :

		"""simple docstring"""

		_a       					=							42
		_a       					=							None
		_a       					=							None



__snake_case				 =	namedtuple('''CoinsDistribResult''', '''moves excess''')


def      a (     __a  )    ->      int:



					'''simple docstring'''






					if root is None:
										return 0

					# Validation
					def count_nodes(__a  ) -> int:
										if node is None:
															return 0

										return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

					def count_coins(__a  ) -> int:
										if node is None:
															return 0

										return count_coins(node.left  ) + count_coins(node.right  ) + node.data

					if count_nodes(__a  ) != count_coins(__a  ):
										raise ValueError('''The nodes number should be same as the number of coins'''  )

					# Main calculation
					def get_distrib(__a  ) -> CoinsDistribResult:

										if node is None:
															return CoinsDistribResult(0					,   1  )

										UpperCamelCase__   ,							UpperCamelCase__  :Optional[Any]				   =					get_distrib(node.left  )
										UpperCamelCase__   ,							UpperCamelCase__  :Union[str, Any]				   =					get_distrib(node.right  )

										UpperCamelCase__  :Tuple				   =					1 - left_distrib_excess
										UpperCamelCase__  :Optional[int]				   =					1 - right_distrib_excess

										UpperCamelCase__  :int				   =					(
										    left_distrib_moves
										    + right_distrib_moves
										    + abs(__a  )
										    + abs(__a  )
										)
										UpperCamelCase__  :Optional[Any]				   =					node.data - coins_to_left - coins_to_right

										return CoinsDistribResult(__a					,   __a  )

					return get_distrib(__a  )[0]


if __name__ == "__main__":
		import doctest

		doctest.testmod()