import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_lowercase							:      Dict      							='true'



def A__  (			lowercase:     str,		lowercase:     int=82,		lowercase:     Union[str, Any]=16					)				->		List[Any]:
       set_seed(42					)
       A						:     List[Any]         =RegressionModel()
       A						:     int         =deepcopy(lowerCAmelCase_					)
       A						:     Optional[int]         =RegressionDataset(length=lowerCAmelCase_					)
       A						:     Optional[Any]         =DataLoader(lowerCAmelCase_,		batch_size=lowerCAmelCase_					)
       model.to(accelerator.device					)
       A						:     str         =accelerator.prepare(lowerCAmelCase_,		lowerCAmelCase_					)
       return model, ddp_model, dataloader



def A__  (			lowercase:     str,		lowercase:     Any=False					)				->		Optional[int]:
       A						:     Any         =AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'					)
       A						:     str         =load_dataset('glue',		'mrpc',		split='validation'					)

       def tokenize_function(lowercase:     Tuple					):
              A						:     str         =tokenizer(examples['sentence1'],		examples['sentence2'],		truncation=lowerCAmelCase_,		max_length=lowerCAmelCase_					)
              return outputs

       with accelerator.main_process_first():
              A						:     int         =dataset.map(
                  lowerCAmelCase_,		batched=lowerCAmelCase_,		remove_columns=['idx', 'sentence1', 'sentence2'],		)

       A						:     Any         =tokenized_datasets.rename_column('label',		'labels'					)

       def collate_fn(lowercase:     List[str]					):
              if use_longest:
                     return tokenizer.pad(lowerCAmelCase_,		padding='longest',		return_tensors='pt'					)
              return tokenizer.pad(lowerCAmelCase_,		padding='max_length',		max_length=128,		return_tensors='pt'					)

       return DataLoader(lowerCAmelCase_,		shuffle=lowerCAmelCase_,		collate_fn=lowerCAmelCase_,		batch_size=16					)



def A__  (			lowercase:     Tuple,		lowercase:     Union[str, Any]					)				->		Optional[Any]:
       A						:     List[Any]         =Accelerator(dispatch_batches=lowerCAmelCase_,		split_batches=lowerCAmelCase_					)
       A						:     Dict         =get_dataloader(lowerCAmelCase_,		not dispatch_batches					)
       A						:     List[Any]         =AutoModelForSequenceClassification.from_pretrained(
           'hf-internal-testing/mrpc-bert-base-cased',		return_dict=lowerCAmelCase_					)
       A						:     Tuple         =accelerator.prepare(lowerCAmelCase_,		lowerCAmelCase_					)
       return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator



def A__  (			lowercase:     int,		lowercase:     Optional[int],		lowercase:     Dict					)				->		Optional[int]:
       A						:     str         =[]
       for batch in dataloader:
              A						:     int         =batch.values()
              with torch.no_grad():
                     A						:     List[str]         =model(lowerCAmelCase_					)
                     A						:     Optional[int]         =accelerator.gather_for_metrics((logit, target)					)
                     logits_and_targets.append((logit, target)					)
       A						:     Union[str, Any]         =[], []
       for logit, targ in logits_and_targets:
              logits.append(lowerCAmelCase_					)
              targs.append(lowerCAmelCase_					)
       A						:     Optional[int]         =torch.cat(lowerCAmelCase_					), torch.cat(lowerCAmelCase_					)
       return logits, targs



def A__  (			lowercase:     Any,		lowercase:     Any=82,		lowercase:     Optional[int]=False,		lowercase:     str=False,		lowercase:     str=16					)				->		str:
       A						:     Optional[Any]         =get_basic_setup(lowerCAmelCase_,		lowerCAmelCase_,		lowerCAmelCase_					)
       A						:     Optional[int]         =generate_predictions(lowerCAmelCase_,		lowerCAmelCase_,		lowerCAmelCase_					)
       assert (
           len(lowerCAmelCase_					) == num_samples
       ), F'Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCAmelCase_					)}'



def A__  (			lowercase:     int = False,		lowercase:     Any = False					)				->		List[Any]:
       A						:     Any         =evaluate.load('glue',		'mrpc'					)
       A						:     List[str]         =get_mrpc_setup(lowerCAmelCase_,		lowerCAmelCase_					)
       # First do baseline
       A						:     List[Any]         =setup['''no''']
       model.to(lowerCAmelCase_					)
       model.eval()
       for batch in dataloader:
              batch.to(lowerCAmelCase_					)
              with torch.inference_mode():
                     A						:     Optional[Any]         =model(**lowerCAmelCase_					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              metric.add_batch(predictions=lowerCAmelCase_,		references=batch['labels']					)
       A						:     Optional[int]         =metric.compute()

       # Then do distributed
       A						:     Any         =setup['''ddp''']
       model.eval()
       for batch in dataloader:
              with torch.inference_mode():
                     A						:     Dict         =model(**lowerCAmelCase_					)
              A						:     Tuple         =outputs.logits.argmax(dim=-1					)
              A						:     Union[str, Any]         =batch['''labels''']
              A						:     Optional[Any]         =accelerator.gather_for_metrics((preds, references)					)
              metric.add_batch(predictions=lowerCAmelCase_,		references=lowerCAmelCase_					)
       A						:     Optional[int]         =metric.compute()

       for key in "accuracy f1".split():
              assert math.isclose(
                  baseline[key],		distributed[key]					), F'Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'



def A__  (			)				->		Any:
       A						:     List[str]         =Accelerator(split_batches=lowerCAmelCase_,		dispatch_batches=lowerCAmelCase_					)
       if accelerator.is_local_main_process:
              datasets.utils.logging.set_verbosity_warning()
              transformers.utils.logging.set_verbosity_warning()
       else:
              datasets.utils.logging.set_verbosity_error()
              transformers.utils.logging.set_verbosity_error()
       # These are a bit slower so they should only be ran on the GPU or TPU
       if torch.cuda.is_available() or is_tpu_available():
              if accelerator.is_local_main_process:
                     print('**Testing gather_for_metrics**'					)
              for split_batches in [True, False]:
                     for dispatch_batches in [True, False]:
                            if accelerator.is_local_main_process:
                                   print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'					)
                            test_mrpc(lowerCAmelCase_,		lowerCAmelCase_					)
                            accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('**Test torch metrics**'					)
       for split_batches in [True, False]:
              for dispatch_batches in [True, False]:
                     A						:     List[Any]         =Accelerator(split_batches=lowerCAmelCase_,		dispatch_batches=lowerCAmelCase_					)
                     if accelerator.is_local_main_process:
                            print(F'With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'					)
                     test_torch_metrics(lowerCAmelCase_,		99					)
                     accelerator.state._reset_state()
       if accelerator.is_local_main_process:
              print('**Test last batch is not dropped when perfectly divisible**'					)
       A						:     Optional[int]         =Accelerator()
       test_torch_metrics(lowerCAmelCase_,		512					)
       accelerator.state._reset_state()



def A__  (			lowercase:     List[Any]					)				->		Optional[int]:
       main()


if __name__ == "__main__":
    main()







import heapq



def A__  (			lowercase:     dict					)				->		set[int]:
       A						:     list[list]         =[]

       # for each node and his adjacency list add them and the rank of the node to queue
       # using heapq module the queue will be filled like a Priority Queue
       # heapq works with a min priority queue, so I used -1*len(v) to build it
       for key, value in graph.items():
              # O(log(n))
              heapq.heappush(lowercase,		[-1 * len(lowercase					), (key, value)]					)

       # chosen_vertices = set of chosen vertices
       A						:     Dict         =set()

       # while queue isn't empty and there are still edges
       #   (queue[0][0] is the rank of the node with max rank)
       while queue and queue[0][0] != 0:
              # extract vertex with max rank from queue and add it to chosen_vertices
              A						:     List[str]         =heapq.heappop(lowercase					)[1][0]
              chosen_vertices.add(lowercase					)

              # Remove all arcs adjacent to argmax
              for elem in queue:
                     # if v haven't adjacent node, skip
                     if elem[0] == 0:
                            continue
                     # if argmax is reachable from elem
                     # remove argmax from elem's adjacent list and update his rank
                     if argmax in elem[1][1]:
                            A						:     str         =elem[1][1].index(lowercase					)
                            del elem[1][1][index]
                            elem[0] += 1
        # re-order the queue
              heapq.heapify(lowercase					)
       return chosen_vertices


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    _lowercase							:      List[Any]      							={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
    print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')






