






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
# and perform 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
#
########################################################################


SCREAMING_SNAKE_CASE__				     =			16
SCREAMING_SNAKE_CASE__				     =			32

def        UpperCAmelCase__		(						lowerCamelCase_       :       Accelerator   ,  lowerCamelCase_       :       int = 1_6				):
			__a						:  List[Any]									=     AutoTokenizer.from_pretrained('bert-base-cased'				)
			__a						:  int									=     load_dataset('glue'   ,  'mrpc'				)

			def tokenize_function(lowerCamelCase_       :       int				):
						# max_length=None => use the model max length (it's actually the default)
						__a						:  Dict									=     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():
						__a						:  str									=     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
			__a						:  Union[str, Any]									=     tokenized_datasets.rename_column('label'   ,  'labels'				)

			def collate_fn(lowerCamelCase_       :       Union[str, Any]				):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						__a						:  Tuple									=     1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									__a						:  Any									=     1_6
						elif accelerator.mixed_precision != "no":
									__a						:  Optional[Any]									=     8
						else:
									__a						:  str									=     None

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

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

							SCREAMING_SNAKE_CASE__				     =			mocked_dataloaders  # noqa: F811

def        UpperCAmelCase__		(						lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Dict				):
			# For testing only
			if os.environ.get('TESTING_MOCKED_DATALOADERS'   ,  lowerCamelCase_				) == "1":
						__a						:  List[str]									=     2
			# New Code #
			__a						:  Dict									=     int(args.gradient_accumulation_steps				)
			# Initialize accelerator
			__a						:  Optional[Any]									=     Accelerator(
			    cpu=args.cpu   ,  mixed_precision=args.mixed_precision   ,  gradient_accumulation_steps=lowerCamelCase_				)
			if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
						raise NotImplementedError(
						    'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`'				)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			__a						:  Union[str, Any]									=     config['lr']
			__a						:  List[Any]									=     int(config['num_epochs']				)
			__a						:  Optional[int]									=     int(config['seed']				)
			__a						:  Dict									=     int(config['batch_size']				)

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

			set_seed(lowerCamelCase_				)
			__a						,					__a						:  List[Any]									=     get_dataloaders(lowerCamelCase_   ,  lowerCamelCase_				)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			__a						:  int									=     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).
			__a						:  Any									=     model.to(accelerator.device				)

			# Instantiate optimizer
			__a						:  Dict									=     AdamW(params=model.parameters()   ,  lr=lowerCamelCase_				)

			# Instantiate scheduler
			__a						:  List[str]									=     get_linear_schedule_with_warmup(
			    optimizer=lowerCamelCase_   ,  num_warmup_steps=1_0_0   ,  num_training_steps=(len(lowerCamelCase_				) * 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.
			__a						,					__a						,					__a						,					__a						,					__a						:  Dict									=     accelerator.prepare(
			    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)

			# Now we train the model
			for epoch in range(lowerCamelCase_				):
						model.train()
						for step, batch in enumerate(lowerCamelCase_				):
									# 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(lowerCamelCase_				):
												__a						:  Any									=     model(**lowerCamelCase_				)
												__a						:  Dict									=     output.loss
												accelerator.backward(lowerCamelCase_				)
												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`.
									batch.to(accelerator.device				)
									with torch.no_grad():
												__a						:  Any									=     model(**lowerCamelCase_				)
									__a						:  Optional[Any]									=     outputs.logits.argmax(dim=-1				)
									__a						,					__a						:  Dict									=     accelerator.gather_for_metrics((predictions, batch['labels'])				)
									metric.add_batch(
									    predictions=lowerCamelCase_   ,  references=lowerCamelCase_   ,  )

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

def        UpperCAmelCase__		(						):
			__a						:  Union[str, Any]									=     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.'   ,  )
			# New Code #
			parser.add_argument(
			    '--gradient_accumulation_steps'   ,  type=lowerCamelCase_   ,  default=1   ,  help='The number of minibatches to be ran before gradients are accumulated.'   ,  )
			parser.add_argument('--cpu'   ,  action='store_true'   ,  help='If passed, will train on the CPU.'				)
			__a						:  Any									=     parser.parse_args()
			__a						:  Optional[Any]									=     {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
			training_function(lowerCamelCase_   ,  lowerCamelCase_				)


if __name__ == "__main__":
							main()









"""simple docstring"""

from __future__ import annotations


def lowerCAmelCase_   (					UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			Any			):






 """simple docstring"""







 print(f'''Vertex\tShortest Distance from vertex {src}'''			)
 for i, d in enumerate(UpperCamelCase__			):
  print(f'''{i}\t\t{d}'''			)


def lowerCAmelCase_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			list[float]					, UpperCamelCase__						:			int			):






 """simple docstring"""







 for j in range(UpperCamelCase__			):
  __lowercase				,		__lowercase				,		__lowercase						=						(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_   (					UpperCamelCase__						:			list[dict[str, int]]					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int					, UpperCamelCase__						:			int			):






 """simple docstring"""







 __lowercase						=						[float("""inf"""			)] * vertex_count
 __lowercase						=						0.0

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

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

 __lowercase						=						check_negative_cycle(UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)
 if negative_cycle_exists:
  raise Exception("""Negative cycle found"""			)

 return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       UpperCAmelCase__					=int(input("Enter number of vertices: ").strip())
       UpperCAmelCase__					=int(input("Enter number of edges: ").strip())

       UpperCAmelCase__					=[{} for _ in range(E)]

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

       UpperCAmelCase__					=int(input("\nEnter shortest path source:").strip())
       UpperCAmelCase__					=bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)
