




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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


_lowerCamelCase      			=				16
_lowerCamelCase      			=				32




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Accelerator	,					__UpperCamelCase      :						int = 16				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					AutoTokenizer.from_pretrained('''bert-base-cased'''				)
    UpperCAmelCase_										=					load_dataset('''glue'''	,					'''mrpc'''				)

    def tokenize_function(__UpperCamelCase      :						int				):
        # max_length=None => use the model max length (it's actually the default)
        UpperCAmelCase_										=					tokenizer(examples['''sentence1''']	,					examples['''sentence2''']	,					truncation=__UpperCamelCase	,					max_length=__UpperCamelCase				)
        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(
            __UpperCamelCase	,					batched=__UpperCamelCase	,					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(__UpperCamelCase      :						List[Any]				):
        # 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(
            __UpperCamelCase	,					padding='''longest'''	,					max_length=__UpperCamelCase	,					pad_to_multiple_of=__UpperCamelCase	,					return_tensors='''pt'''	,					)

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

    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

 _lowerCamelCase      			=				mocked_dataloaders  # noqa: F811




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]				)  ->							List[Any]:






    if os.environ.get('''TESTING_MOCKED_DATALOADERS'''	,					__UpperCamelCase				) == "1":
        UpperCAmelCase_										=					2
    # Initialize accelerator
    UpperCAmelCase_										=					Accelerator(cpu=args.cpu	,					mixed_precision=args.mixed_precision				)
    # 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'''				)

    # If the batch size is too big we use gradient accumulation
    UpperCAmelCase_										=					1
    if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
        UpperCAmelCase_										=					batch_size // MAX_GPU_BATCH_SIZE
        UpperCAmelCase_										=					MAX_GPU_BATCH_SIZE

    set_seed(__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_dataloaders(__UpperCamelCase	,					__UpperCamelCase				)
    # 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=__UpperCamelCase				)

    # 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=__UpperCamelCase				)

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

    # 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(
        __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

    # Now we train the model
    for epoch in range(__UpperCamelCase				):
        model.train()
        for step, batch in enumerate(__UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.loss
            UpperCAmelCase_										=					loss / gradient_accumulation_steps
            accelerator.backward(__UpperCamelCase				)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

        model.eval()
        UpperCAmelCase_										=					0
        for step, batch in enumerate(__UpperCamelCase				):
            # We could avoid this line since we set the accelerator with `device_placement=True`.
            batch.to(accelerator.device				)
            with torch.no_grad():
                UpperCAmelCase_										=					model(**__UpperCamelCase				)
            UpperCAmelCase_										=					outputs.logits.argmax(dim=-1				)
            UpperCAmelCase_			,							UpperCAmelCase_										=					accelerator.gather((predictions, batch['''labels'''])				)
            # New Code #
            # First we check if it's a distributed system
            if accelerator.use_distributed:
                # Then see if we're on the last batch of our eval dataloader
                if step == len(__UpperCamelCase				) - 1:
                    # Last batch needs to be truncated on distributed systems as it contains additional samples
                    UpperCAmelCase_										=					predictions[: len(eval_dataloader.dataset				) - samples_seen]
                    UpperCAmelCase_										=					references[: len(eval_dataloader.dataset				) - samples_seen]
                else:
                    # Otherwise we add the number of samples seen
                    samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
            metric.add_batch(
                predictions=__UpperCamelCase	,					references=__UpperCamelCase	,					)

        UpperCAmelCase_										=					metric.compute()
        # Use accelerator.print to print only on the main process.
        accelerator.print(f'epoch {epoch}:'	,					__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	)  ->							str:
    UpperCAmelCase_										=					argparse.ArgumentParser(description='''Simple example of training script.'''				)
    parser.add_argument(
        '''--mixed_precision'''	,					type=__UpperCamelCase	,					default=__UpperCamelCase	,					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.'''	,					)
    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(__UpperCamelCase	,					__UpperCamelCase				)


if __name__ == "__main__":
 main()











from __future__ import annotations

import os
from collections.abc import Mapping

_lowerCamelCase      			=						tuple[int, int]



class       a					:

     '''simple docstring'''





     def __init__(     self       :      str ,   __snake_case       :      set[int] ,   __snake_case       :      Mapping[EdgeT, int]    ):
         UpperCAmelCase_										=					vertices
         UpperCAmelCase_										=					{
             (min(__snake_case    ), max(__snake_case    )): weight for edge, weight in edges.items()
         }
     def   lowerCamelCase_       (     self       :      Any ,   __snake_case       :      EdgeT ,   __snake_case       :      int    ):
         self.vertices.add(edge[0]    )
         self.vertices.add(edge[1]    )
         UpperCAmelCase_										=					weight




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					Graph({min(self.vertices    )} ,   {}    )
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42
         UpperCAmelCase_										=					42

         while len(subgraph.vertices    ) < len(self.vertices    ):
             UpperCAmelCase_										=					max(self.edges.values()    ) + 1
             for edge, weight in self.edges.items():
                 if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
                     if weight < min_weight:
                         UpperCAmelCase_										=					edge
                         UpperCAmelCase_										=					weight

             subgraph.add_edge(__snake_case ,   __snake_case    )

         return subgraph




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str = "p107_network.txt"				)  ->							int:
    UpperCAmelCase_										=					os.path.abspath(os.path.dirname(__UpperCamelCase				)				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					{}
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42
    UpperCAmelCase_										=					42

    with open(__UpperCamelCase				) as f:
        UpperCAmelCase_										=					f.read().strip().split('''\n'''				)

    UpperCAmelCase_										=					[line.split(''','''				) for line in data]

    for edgea in range(1	,					len(__UpperCamelCase				)				):
        for edgea in range(__UpperCamelCase				):
            if adjaceny_matrix[edgea][edgea] != "-":
                UpperCAmelCase_										=					int(adjaceny_matrix[edgea][edgea]				)

    UpperCAmelCase_										=					Graph(set(range(len(__UpperCamelCase				)				)				)	,					__UpperCamelCase				)

    UpperCAmelCase_										=					graph.prims_algorithm()

    UpperCAmelCase_										=					sum(graph.edges.values()				)
    UpperCAmelCase_										=					sum(subgraph.edges.values()				)

    return initial_total - optimal_total


if __name__ == "__main__":
 print(F"{solution() = }")





