


"""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


lowercase_			     =  1_6
lowercase_			     =  3_2

def        lowercase							(		lowerCAmelCase__ :     Accelerator    ,					lowerCAmelCase__ :     int = 16    ,					lowerCAmelCase__ :     str = "bert-base-cased"			) ->      List[str]:
		__a           =			AutoTokenizer.from_pretrained(lowerCAmelCase__			)
		__a           =			load_dataset('''glue'''    ,					'''mrpc'''			)

		def tokenize_function(lowerCAmelCase__ :     Any			):
				# max_length=None => use the model max length (it's actually the default)
				__a           =			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
		__a           =			datasets.map(
		    lowerCAmelCase__    ,					batched=lowerCAmelCase__    ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']    ,					load_from_cache_file=lowerCAmelCase__			)

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

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

		# Instantiate dataloaders.
		__a           =			DataLoader(
		    tokenized_datasets['''train''']    ,					shuffle=lowerCAmelCase__    ,					collate_fn=lowerCAmelCase__    ,					batch_size=lowerCAmelCase__			)
		__a           =			DataLoader(
		    tokenized_datasets['''validation''']    ,					shuffle=lowerCAmelCase__    ,					collate_fn=lowerCAmelCase__    ,					batch_size=lowerCAmelCase__			)

		return train_dataloader, eval_dataloader

def        lowercase							(		lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     Any			) ->      List[str]:
		# Initialize accelerator
		__a           =			Accelerator()

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

		set_seed(lowerCAmelCase__			)
		__a      ,     __a           =			get_dataloaders(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

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

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

		if accelerator.state.deepspeed_plugin is not None:
				__a           =			accelerator.state.deepspeed_plugin.deepspeed_config[
				    '''gradient_accumulation_steps'''
				]
		else:
				__a           =			1
		__a           =			(len(lowerCAmelCase__			) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				__a           =			get_linear_schedule_with_warmup(
				    optimizer=lowerCAmelCase__    ,					num_warmup_steps=0    ,					num_training_steps=lowerCAmelCase__    ,					)
		else:
				__a           =			DummyScheduler(lowerCAmelCase__    ,					total_num_steps=lowerCAmelCase__    ,					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.
		__a      ,     __a      ,     __a      ,     __a      ,     __a           =			accelerator.prepare(
		    lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)

		# We need to keep track of how many total steps we have iterated over
		__a           =			0
		# We also need to keep track of the stating epoch so files are named properly
		__a           =			0

		# Now we train the model
		__a           =			evaluate.load('''glue'''    ,					'''mrpc'''			)
		__a           =			0
		__a           =			{}
		for epoch in range(lowerCAmelCase__    ,					lowerCAmelCase__			):
				model.train()
				for step, batch in enumerate(lowerCAmelCase__			):
						__a           =			model(**lowerCAmelCase__			)
						__a           =			outputs.loss
						__a           =			loss / gradient_accumulation_steps
						accelerator.backward(lowerCAmelCase__			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

				model.eval()
				__a           =			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			)
						with torch.no_grad():
								__a           =			model(**lowerCAmelCase__			)
						__a           =			outputs.logits.argmax(dim=-1			)
						# It is slightly faster to call this once, than multiple times
						__a      ,     __a           =			accelerator.gather(
						    (predictions, batch['''labels'''])			)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(lowerCAmelCase__			) - 1:
										__a           =			predictions[: len(eval_dataloader.dataset			) - samples_seen]
										__a           =			references[: len(eval_dataloader.dataset			) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=lowerCAmelCase__    ,					references=lowerCAmelCase__    ,					)

				__a           =			metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''    ,					lowerCAmelCase__			)
				__a           =			eval_metric['''accuracy''']

				if best_performance < eval_metric["accuracy"]:
						__a           =			eval_metric['''accuracy''']

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir    ,					'''all_results.json'''			)    ,					'''w'''			) as f:
						json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)

def        lowercase							(		) ->      int:
		__a           =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''			)
		parser.add_argument(
		    '''--model_name_or_path'''    ,					type=lowerCAmelCase__    ,					default='''bert-base-cased'''    ,					help='''Path to pretrained model or model identifier from huggingface.co/models.'''    ,					required=lowerCAmelCase__    ,					)
		parser.add_argument(
		    '''--output_dir'''    ,					type=lowerCAmelCase__    ,					default='''.'''    ,					help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''    ,					)
		parser.add_argument(
		    '''--performance_lower_bound'''    ,					type=lowerCAmelCase__    ,					default=lowerCAmelCase__    ,					help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'''    ,					)
		parser.add_argument(
		    '''--num_epochs'''    ,					type=lowerCAmelCase__    ,					default=3    ,					help='''Number of train epochs.'''    ,					)
		__a           =			parser.parse_args()
		__a           =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
		training_function(lowerCAmelCase__    ,					lowerCAmelCase__			)


if __name__ == "__main__":
				main()








from queue import PriorityQueue
from typing import Any

import numpy as np




def  __magic_name__ (   __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						set		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						PriorityQueue		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						float | int		,  )			->   float | int:
				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								__lowerCamelCase    					=	cst_fwd.get(__lowerCAmelCase		,  np.inf							)
								__lowerCamelCase    					=	cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt)							)
												__lowerCamelCase    					=	new_cost_f
												__lowerCamelCase    					=	v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																__lowerCamelCase    					=	cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance




def  __magic_name__ (   __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						str		,  __lowerCAmelCase	:						dict		,  __lowerCAmelCase	:						dict							)			->   int:
				__lowerCamelCase    					=	-1

				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	set()
				__lowerCamelCase    					=	{source: 0}
				__lowerCamelCase    					=	{destination: 0}
				__lowerCamelCase    					=	{source: None}
				__lowerCamelCase    					=	{destination: None}
				__lowerCamelCase    					=	PriorityQueue()
				__lowerCamelCase    					=	PriorityQueue()

				__lowerCamelCase    					=	np.inf

				queue_forward.put((0, source)							)
				queue_backward.put((0, destination)							)

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								__lowerCamelCase					,    __lowerCamelCase    					=	queue_forward.get()
								visited_forward.add(__lowerCAmelCase							)

								__lowerCamelCase					,    __lowerCamelCase    					=	queue_backward.get()
								visited_backward.add(__lowerCAmelCase							)

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								__lowerCamelCase    					=	pass_and_relaxation(
								    __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  __lowerCAmelCase		,  )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								__lowerCamelCase    					=	shortest_distance
				return shortest_path_distance


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}
SCREAMING_SNAKE_CASE__   :			Optional[int]												=    {
    "B": [["E", 1]],
    "C": [["B", 1]],
    "D": [["C", 1]],
    "F": [["D", 1], ["G", 1]],
    "E": [[None, np.inf]],
    "G": [["E", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()



