




'''simple docstring'''





# This test is meant to be run in on an instance with TPUs like this:
#
#   python examples/pytorch/xla_spawn.py --num_cores=8 tests/test_trainer_tpu.py
#
# Replace 8 with the number of TPU cores you have.
#

import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				logging.get_logger(__name__)


if is_torch_available():
		import torch
		from torch import nn
		from torch.utils.data import Dataset

		from transformers import Trainer




		class 				_snake_case	(			lowercase_							):




					def __init__(      self       ,							a__ = 101							)       ->       Tuple:

												'''simple docstring'''




												snake_case_			      =   length




					def __len__(      self							)       ->       Tuple:

												'''simple docstring'''




												return self.length




					def __getitem__(      self       ,							a__							)       ->       int:

												'''simple docstring'''




												return i




		class 				_snake_case	:




					def __call__(      self       ,							a__							)       ->       Any:

												'''simple docstring'''




												return {"input_ids": torch.tensor(a__							), "labels": torch.tensor(a__							)}




		class 				_snake_case	(			nn.Module							):




					def __init__(      self							)       ->       List[Any]:

												'''simple docstring'''




												super().__init__()
												# Add some (unused) params otherwise DDP will complain.
												snake_case_			      =   nn.Linear(120       ,							80							)




					def      lowerCAmelCase__       (      self       ,							a__       ,							a__=None							)       ->       str:

												'''simple docstring'''




												if labels is not None:
																			return torch.tensor(0.0       ,							device=input_ids.device							), input_ids
												else:
																			return input_ids


def   UpperCamelCase_(			):

							'''simple docstring'''






							snake_case_			      =   HfArgumentParser((TrainingArguments,)  )
							sys.argv += ["--output_dir", "./examples"]
							snake_case_			      =   parser.parse_args_into_dataclasses()[0]

							logger.warning(
							    f'Process rank: {training_args.local_rank}, device: {training_args.device}, '
							    f'tpu_num_cores: {training_args.tpu_num_cores}'							,       )

							# Essentially, what we want to verify in the distributed case is
							# that we get all samples back, in the right order.
							# (this is crucial for prediction for instance)
							for dataset_length in [1_0_0_1, 2_5_6, 1_5]:
														snake_case_			      =   DummyDataset(snake_case  )

														def compute_metrics(snake_case							: EvalPrediction  ) -> Dict:
																					snake_case_			      =   list(range(len(snake_case  )  )  )
																					snake_case_			      =   p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
																					return {"success": success}

														snake_case_			      =   Trainer(
														    model=DummyModel()							,       args=snake_case							,       data_collator=DummyDataCollator()							,       eval_dataset=snake_case							,       compute_metrics=snake_case							,       )
														snake_case_			      =   trainer.evaluate()
														logger.info(snake_case  )
														if metrics["eval_success"] is not True:
																					logger.error(snake_case  )
																					exit(1  )

														snake_case_			      =   trainer.predict(snake_case  )
														logger.info(p.metrics  )
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics  )
																					exit(1  )

														snake_case_			      =   2

														snake_case_			      =   trainer.evaluate()
														logger.info(snake_case  )
														if metrics["eval_success"] is not True:
																					logger.error(snake_case  )
																					exit(1  )

														snake_case_			      =   trainer.predict(snake_case  )
														logger.info(p.metrics  )
														if p.metrics["test_success"] is not True:
																					logger.error(p.metrics  )
																					exit(1  )

														snake_case_			      =   None

							logger.info("🔥 All distributed tests successful"  )


def   UpperCamelCase_(			snake_case							: List[Any]  ):

							'''simple docstring'''






							main()


if __name__ == "__main__":
		main()










'''simple docstring'''





import heapq


def   UpperCamelCase_(			snake_case							: List[Any]							,       snake_case							: List[str]							,       snake_case							: Dict  ):

							'''simple docstring'''






							snake_case_			      =   [(0, start)]  # cost from start node,end node
							snake_case_			      =   set()
							while heap:
														((snake_case_)      ,   (snake_case_))			      =   heapq.heappop(snake_case  )
														if u in visited:
																					continue
														visited.add(snake_case  )
														if u == end:
																					return cost
														for v, c in graph[u]:
																					if v in visited:
																												continue
																					snake_case_			      =   cost + c
																					heapq.heappush(snake_case							,       (next_item, v)  )
							return -1


_SCREAMING_SNAKE_CASE							:     Dict   	=				{
    "A": [["B", 2], ["C", 5]],
    "B": [["A", 2], ["D", 3], ["E", 1], ["F", 1]],
    "C": [["A", 5], ["F", 3]],
    "D": [["B", 3]],
    "E": [["B", 4], ["F", 3]],
    "F": [["C", 3], ["E", 3]],
}
_SCREAMING_SNAKE_CASE							:     Optional[int]   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["F", 3]],
    "F": [],
}
_SCREAMING_SNAKE_CASE							:     Any   	=				{
    "B": [["C", 1]],
    "C": [["D", 1]],
    "D": [["F", 1]],
    "E": [["B", 1], ["G", 2]],
    "F": [],
    "G": [["F", 1]],
}

_SCREAMING_SNAKE_CASE							:     Tuple   	=				dijkstra(G, "E", "C")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

_SCREAMING_SNAKE_CASE							:     str   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 3 --> F == 3

_SCREAMING_SNAKE_CASE							:     List[Any]   	=				dijkstra(Ga, "E", "F")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
		import doctest

		doctest.testmod()




