

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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_a:    List[Any] 							=			16
_a:    str 							=			32



def       __lowerCAmelCase      (						A       ,     A = 16    ):
	UpperCAmelCase_       				=			AutoTokenizer.from_pretrained("bert-base-cased"    )
	UpperCAmelCase_       				=			load_dataset("glue"       ,     "mrpc"    )

	def tokenize_function(A    ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_       				=			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
	# starting with the main process first:
	with accelerator.main_process_first():
		UpperCAmelCase_       				=			datasets.map(
		    A       ,     batched=A       ,     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
	UpperCAmelCase_       				=			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.
		UpperCAmelCase_       				=			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":
			UpperCAmelCase_       				=			16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_       				=			8
		else:
			UpperCAmelCase_       				=			None

		return tokenizer.pad(
		    A       ,     padding="longest"       ,     max_length=A       ,     pad_to_multiple_of=A       ,     return_tensors="pt"       ,     )

	# Instantiate dataloaders.
	UpperCAmelCase_       				=			DataLoader(
	    tokenized_datasets["train"]       ,     shuffle=A       ,     collate_fn=A       ,     batch_size=A    )
	UpperCAmelCase_       				=			DataLoader(
	    tokenized_datasets["validation"]       ,     shuffle=A       ,     collate_fn=A       ,     batch_size=A    )

	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

				_a:    int 							=			mocked_dataloaders  # noqa: F811



def       __lowerCAmelCase      (						A       ,     A    ):
	# For testing only
	if os.environ.get("TESTING_MOCKED_DATALOADERS"       ,     A    ) == "1":
		UpperCAmelCase_       				=			2
	# New Code #
	UpperCAmelCase_       				=			int(args.gradient_accumulation_steps    )
	UpperCAmelCase_       				=			int(args.local_sgd_steps    )
	# Initialize accelerator
	UpperCAmelCase_       				=			Accelerator(
	    cpu=args.cpu       ,     mixed_precision=args.mixed_precision       ,     gradient_accumulation_steps=A    )
	if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
		raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"    )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_       				=			config["lr"]
	UpperCAmelCase_       				=			int(config["num_epochs"]    )
	UpperCAmelCase_       				=			int(config["seed"]    )
	UpperCAmelCase_       				=			int(config["batch_size"]    )

	UpperCAmelCase_       				=			evaluate.load("glue"       ,     "mrpc"    )

	set_seed(A    )
	UpperCAmelCase_				,   UpperCAmelCase_       				=			get_dataloaders(A       ,     A    )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_       				=			AutoModelForSequenceClassification.from_pretrained("bert-base-cased"       ,     return_dict=A    )

	# 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).
	UpperCAmelCase_       				=			model.to(accelerator.device    )

	# Instantiate optimizer
	UpperCAmelCase_       				=			AdamW(params=model.parameters()       ,     lr=A    )

	# Instantiate scheduler
	UpperCAmelCase_       				=			get_linear_schedule_with_warmup(
	    optimizer=A       ,     num_warmup_steps=100       ,     num_training_steps=(len(A    ) * num_epochs)       ,     )

	# 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_       				=			accelerator.prepare(
	    A       ,     A       ,     A       ,     A       ,     A    )

	# Now we train the model
	for epoch in range(A    ):
		model.train()
		with LocalSGD(
		    accelerator=A       ,     model=A       ,     local_sgd_steps=A       ,     enabled=local_sgd_steps is not None    ) as local_sgd:
			for step, batch in enumerate(A    ):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device    )
				# New code #
				# We use the new `accumulate` context manager to perform gradient accumulation
				# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
				with accelerator.accumulate(A    ):
					UpperCAmelCase_       				=			model(**A    )
					UpperCAmelCase_       				=			output.loss
					accelerator.backward(A    )
					optimizer.step()
					lr_scheduler.step()
					optimizer.zero_grad()
					# LocalSGD-specific line
					local_sgd.step()

		model.eval()
		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_       				=			model(**A    )
			UpperCAmelCase_       				=			outputs.logits.argmax(dim=-1    )
			UpperCAmelCase_				,   UpperCAmelCase_       				=			accelerator.gather_for_metrics((predictions, batch["labels"])    )
			metric.add_batch(
			    predictions=A       ,     references=A       ,     )

		UpperCAmelCase_       				=			metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F"epoch {epoch}:"       ,     A    )



def       __lowerCAmelCase      (						):
	UpperCAmelCase_       				=			argparse.ArgumentParser(description="Simple example of training script."    )
	parser.add_argument(
	    "--mixed_precision"       ,     type=A       ,     default=A       ,     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."       ,     )
	# New Code #
	parser.add_argument(
	    "--gradient_accumulation_steps"       ,     type=A       ,     default=1       ,     help="The number of minibatches to be ran before gradients are accumulated."       ,     )
	parser.add_argument(
	    "--local_sgd_steps"       ,     type=A       ,     default=8       ,     help="Number of local SGD steps or None to disable local SGD"    )
	parser.add_argument("--cpu"       ,     action="store_true"       ,     help="If passed, will train on the CPU."    )
	UpperCAmelCase_       				=			parser.parse_args()
	UpperCAmelCase_       				=			{"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
	training_function(A       ,     A    )


if __name__ == "__main__":
				main()


from __future__ import annotations



def       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)