
"""simple docstring"""
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_       :						Tuple     					=	'''true'''





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[Any],						lowerCamelCase__							:							Any=8_2,						lowerCamelCase__							:							Dict=1_6				)  ->    Dict:
  set_seed(4_2				)
  _SCREAMING_SNAKE_CASE   :				Any							    = RegressionModel()
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = deepcopy(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = RegressionDataset(length=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				str							    = DataLoader(lowerCamelCase__,						batch_size=lowerCamelCase__				)
  model.to(accelerator.device				)
  _SCREAMING_SNAKE_CASE   :				Any							    = accelerator.prepare(lowerCamelCase__,						lowerCamelCase__				)
  return model, ddp_model, dataloader





def    _lowerCAmelCase    (     lowerCamelCase__							:							Accelerator,						lowerCamelCase__							:							Dict=False				)  ->    Dict:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"				)
  _SCREAMING_SNAKE_CASE   :				Dict							    = load_dataset("glue",						"mrpc",						split="validation"				)

  def tokenize_function(lowerCamelCase__							:							Optional[int]				):
    _SCREAMING_SNAKE_CASE   :				List[str]							    = tokenizer(examples["sentence1"],						examples["sentence2"],						truncation=lowerCamelCase__,						max_length=lowerCamelCase__				)
    return outputs

  with accelerator.main_process_first():
    _SCREAMING_SNAKE_CASE   :				List[str]							    = dataset.map(
        lowerCamelCase__,						batched=lowerCamelCase__,						remove_columns=["idx", "sentence1", "sentence2"],						)

  _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tokenized_datasets.rename_column("label",						"labels"				)

  def collate_fn(lowerCamelCase__							:							int				):
    if use_longest:
      return tokenizer.pad(lowerCamelCase__,						padding="longest",						return_tensors="pt"				)
    return tokenizer.pad(lowerCamelCase__,						padding="max_length",						max_length=1_2_8,						return_tensors="pt"				)

  return DataLoader(lowerCamelCase__,						shuffle=lowerCamelCase__,						collate_fn=lowerCamelCase__,						batch_size=1_6				)





def    _lowerCAmelCase    (     lowerCamelCase__							:							List[str],						lowerCamelCase__							:							List[str]				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = Accelerator(dispatch_batches=lowerCamelCase__,						split_batches=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = get_dataloader(lowerCamelCase__,						not dispatch_batches				)
  _SCREAMING_SNAKE_CASE   :				Optional[int]							    = AutoModelForSequenceClassification.from_pretrained(
      "hf-internal-testing/mrpc-bert-base-cased",						return_dict=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = accelerator.prepare(lowerCamelCase__,						lowerCamelCase__				)
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def    _lowerCAmelCase    (     lowerCamelCase__							:							int,						lowerCamelCase__							:							Any,						lowerCamelCase__							:							Tuple				)  ->    int:
  _SCREAMING_SNAKE_CASE   :				Dict							    = []
  for batch in dataloader:
    _SCREAMING_SNAKE_CASE   :				Tuple							    = batch.values()
    with torch.no_grad():
      _SCREAMING_SNAKE_CASE   :				int							    = model(lowerCamelCase__				)
      _SCREAMING_SNAKE_CASE   :				str							    = accelerator.gather_for_metrics((logit, target)				)
      logits_and_targets.append((logit, target)				)
  _SCREAMING_SNAKE_CASE   :				int							    = [], []
  for logit, targ in logits_and_targets:
    logits.append(lowerCamelCase__				)
    targs.append(lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Any							    = torch.cat(lowerCamelCase__				), torch.cat(lowerCamelCase__				)
  return logits, targs





def    _lowerCAmelCase    (     lowerCamelCase__							:							Accelerator,						lowerCamelCase__							:							Dict=8_2,						lowerCamelCase__							:							List[str]=False,						lowerCamelCase__							:							Optional[Any]=False,						lowerCamelCase__							:							Dict=1_6				)  ->    Optional[int]:
  _SCREAMING_SNAKE_CASE   :				str							    = get_basic_setup(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = generate_predictions(lowerCamelCase__,						lowerCamelCase__,						lowerCamelCase__				)
  assert (
      len(lowerCamelCase__				) == num_samples
  ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__				)}'''





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

  # Then do distributed
  _SCREAMING_SNAKE_CASE   :				List[str]							    = setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = model(**lowerCamelCase__				)
    _SCREAMING_SNAKE_CASE   :				Any							    = outputs.logits.argmax(dim=-1				)
    _SCREAMING_SNAKE_CASE   :				Optional[Any]							    = batch["labels"]
    _SCREAMING_SNAKE_CASE   :				Optional[int]							    = accelerator.gather_for_metrics((preds, references)				)
    metric.add_batch(predictions=lowerCamelCase__,						references=lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = 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    _lowerCAmelCase    (     )  ->    int:
  _SCREAMING_SNAKE_CASE   :				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]:
      _SCREAMING_SNAKE_CASE   :				int							    = 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__,						9_9				)
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**"				)
  _SCREAMING_SNAKE_CASE   :				Tuple							    = Accelerator()
  test_torch_metrics(lowerCamelCase__,						5_1_2				)
  accelerator.state._reset_state()





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict				)  ->    int:
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()





"""simple docstring"""
def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str				)  ->    Union[str, Any]:
  print("\nThe shortest path matrix using Floyd Warshall algorithm\n"				)
  for i in range(lowerCamelCase__				):
    for j in range(lowerCamelCase__				):
      if dist[i][j] != float("inf"				):
        print(int(dist[i][j]				),						end="\t"				)
      else:
        print("INF",						end="\t"				)
    print()





def    _lowerCAmelCase    (     lowerCamelCase__							:							Dict,						lowerCamelCase__							:							Any				)  ->    List[str]:
  _SCREAMING_SNAKE_CASE   :				List[Any]							    = [[float("inf"				) for _ in range(lowerCamelCase__				)] for _ in range(lowerCamelCase__				)]

  for i in range(lowerCamelCase__				):
    for j in range(lowerCamelCase__				):
      _SCREAMING_SNAKE_CASE   :				List[Any]							    = graph[i][j]

      # check vertex k against all other vertices (i, j)
  for k in range(lowerCamelCase__				):
    # looping through rows of graph array
    for i in range(lowerCamelCase__				):
      # looping through columns of graph array
      for j in range(lowerCamelCase__				):
        if (
            dist[i][k] != float("inf"				)
            and dist[k][j] != float("inf"				)
            and dist[i][k] + dist[k][j] < dist[i][j]
        ):
          _SCREAMING_SNAKE_CASE   :				List[Any]							    = dist[i][k] + dist[k][j]

  _print_dist(lowerCamelCase__,						lowerCamelCase__				)
  return dist, v


if __name__ == "__main__":
      lowercase_       :						Tuple     					=	int(input('''Enter number of vertices: '''))
      lowercase_       :						List[Any]     					=	int(input('''Enter number of edges: '''))

      lowercase_       :						Optional[Any]     					=	[[float('''inf''') for i in range(v)] for j in range(v)]

      for i in range(v):
            lowercase_       :						Tuple     					=	0.0

            # src and dst are indices that must be within the array size graph[e][v]
            # failure to follow this will result in an error
      for i in range(e):
            print('''\nEdge ''', i + 1)
            lowercase_       :						str     					=	int(input('''Enter source:'''))
            lowercase_       :						Optional[Any]     					=	int(input('''Enter destination:'''))
            lowercase_       :						Union[str, Any]     					=	float(input('''Enter weight:'''))
            lowercase_       :						str     					=	weight

      floyd_warshall(graph, v)

      # Example Input
      # Enter number of vertices: 3
      # Enter number of edges: 2

      # # generated graph from vertex and edge inputs
      # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
      # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

      # specify source, destination and weight for edge #1
      # Edge  1
      # Enter source:1
      # Enter destination:2
      # Enter weight:2

      # specify source, destination and weight for edge #2
      # Edge  2
      # Enter source:2
      # Enter destination:1
      # Enter weight:1

      # # Expected Output from the vertice, edge and src, dst, weight inputs!!
      # 0		INF	INF
      # INF	0	2
      # INF	1	0



