



"""simple docstring"""






import math

class        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  Optional[int],  lowerCamelCase							:  Any=0     )->					Any:  # a graph with Node 0,1,...,N-1
								lowerCamelCase__		:		Optional[Any]			=n
								lowerCamelCase__		:		Any			=[
								    [math.inf for j in range(0,  SCREAMING_SNAKE_CASE__     )] for i in range(0,  SCREAMING_SNAKE_CASE__     )
								]  # adjacency matrix for weight
								lowerCamelCase__		:		int			=[
								    [math.inf for j in range(0,  SCREAMING_SNAKE_CASE__     )] for i in range(0,  SCREAMING_SNAKE_CASE__     )
								]  # dp[i][j] stores minimum distance from i to j



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  Any,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int]     )->					Optional[Any]:
								lowerCamelCase__		:		List[Any]			=w



					def 		snake_case       (       self							:  Tuple     )->					int:
								for k in range(0,  self.n     ):
											for i in range(0,  self.n     ):
														for j in range(0,  self.n     ):
																	lowerCamelCase__		:		Union[str, Any]			=min(self.dp[i][j],  self.dp[i][k] + self.dp[k][j]     )




					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  int     )->					Dict:
								return self.dp[u][v]


if __name__ == "__main__":
		_lowercase : Any      						=						Graph(5)
		graph.add_edge(0, 2, 9)
		graph.add_edge(0, 4, 1_0)
		graph.add_edge(1, 3, 5)
		graph.add_edge(2, 3, 7)
		graph.add_edge(3, 0, 1_0)
		graph.add_edge(3, 1, 2)
		graph.add_edge(3, 2, 1)
		graph.add_edge(3, 4, 6)
		graph.add_edge(4, 1, 3)
		graph.add_edge(4, 2, 4)
		graph.add_edge(4, 3, 9)
		graph.floyd_warshall()
		graph.show_min(1, 4)
		graph.show_min(0, 3)






import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
					from transformers.trainer import DEFAULT_CALLBACKS

					from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel

class    lowerCAmelCase__     (	__lowercase			):






		def __init__(		self		:							int  )     ->							Optional[int]:
						__lowerCamelCase    					=	[]






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							str							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Tuple:
						self.events.append('''on_init_end'''  )






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Any:
						self.events.append('''on_train_begin'''  )






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Dict							,			**SCREAMING_SNAKE_CASE__		:							int  )     ->							Any:
						self.events.append('''on_train_end'''  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							int:
						self.events.append('''on_epoch_begin'''  )






		def  __A       (		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Any							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			**SCREAMING_SNAKE_CASE__		:							Tuple  )     ->							Optional[Any]:
						self.events.append('''on_epoch_end'''  )






		def  __A       (		self		:							str							,			SCREAMING_SNAKE_CASE__		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			**SCREAMING_SNAKE_CASE__		:							str  )     ->							List[Any]:
						self.events.append('''on_step_begin'''  )






		def  __A       (		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							Any							,			**SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							Tuple:
						self.events.append('''on_step_end'''  )






		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			**SCREAMING_SNAKE_CASE__		:							List[Any]  )     ->							Union[str, Any]:
						self.events.append('''on_evaluate'''  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			**SCREAMING_SNAKE_CASE__		:							str  )     ->							str:
						self.events.append('''on_predict'''  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Tuple:
						self.events.append('''on_save'''  )






		def  __A       (		self		:							Optional[int]							,			SCREAMING_SNAKE_CASE__		:							Dict							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			**SCREAMING_SNAKE_CASE__		:							List[str]  )     ->							List[str]:
						self.events.append('''on_log'''  )







		def  __A       (		self		:							int							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							List[Any]							,			**SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							str:
						self.events.append('''on_prediction_step'''  )





@require_torch
class    lowerCAmelCase__     (	unittest.TestCase			):






		def  __A       (		self		:							Union[str, Any]  )     ->							Optional[Any]:
						__lowerCamelCase    					=	tempfile.mkdtemp()






		def  __A       (		self		:							int  )     ->							List[str]:
						shutil.rmtree(self.output_dir  )






		def  __A       (		self		:							Tuple							,			SCREAMING_SNAKE_CASE__		:							List[Any]=0							,			SCREAMING_SNAKE_CASE__		:							Any=0							,			SCREAMING_SNAKE_CASE__		:							List[str]=64							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=64							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=None							,			SCREAMING_SNAKE_CASE__		:							Optional[int]=False							,			**SCREAMING_SNAKE_CASE__		:							Optional[Any]  )     ->							Union[str, Any]:
						# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
						# its set to False since the tests later on depend on its value.
						__lowerCamelCase    					=	RegressionDataset(length=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	RegressionDataset(length=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	RegressionModelConfig(a=SCREAMING_SNAKE_CASE__							,			b=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	RegressionPreTrainedModel(SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	TrainingArguments(self.output_dir							,			disable_tqdm=SCREAMING_SNAKE_CASE__							,			report_to=[]							,			**SCREAMING_SNAKE_CASE__  )
						return Trainer(
						    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			train_dataset=SCREAMING_SNAKE_CASE__							,			eval_dataset=SCREAMING_SNAKE_CASE__							,			callbacks=SCREAMING_SNAKE_CASE__							,			)






		def  __A       (		self		:							List[Any]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str  )     ->							Optional[int]:
						self.assertEqual(len(SCREAMING_SNAKE_CASE__  )							,			len(SCREAMING_SNAKE_CASE__  )  )

						# Order doesn't matter
						__lowerCamelCase    					=	sorted(SCREAMING_SNAKE_CASE__							,			key=lambda SCREAMING_SNAKE_CASE__  : cb.__name__ if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else cb.__class__.__name__  )
						__lowerCamelCase    					=	sorted(SCREAMING_SNAKE_CASE__							,			key=lambda SCREAMING_SNAKE_CASE__  : cb.__name__ if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) else cb.__class__.__name__  )

						for cba, cba in zip(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
										if isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) and isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
										elif isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) and not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			cba.__class__  )
										elif not isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ) and isinstance(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  ):
														self.assertEqual(cba.__class__							,			SCREAMING_SNAKE_CASE__  )
										else:
														self.assertEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							Tuple:
						__lowerCamelCase    					=	['''on_init_end''', '''on_train_begin''']
						__lowerCamelCase    					=	0
						__lowerCamelCase    					=	len(trainer.get_eval_dataloader()  )
						__lowerCamelCase    					=	['''on_prediction_step'''] * len(trainer.get_eval_dataloader()  ) + ['''on_log''', '''on_evaluate''']
						for _ in range(trainer.state.num_train_epochs  ):
										expected_events.append('''on_epoch_begin'''  )
										for _ in range(SCREAMING_SNAKE_CASE__  ):
														step += 1
														expected_events += ["on_step_begin", "on_step_end"]
														if step % trainer.args.logging_steps == 0:
																		expected_events.append('''on_log'''  )
														if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
																		expected_events += evaluation_events.copy()
														if step % trainer.args.save_steps == 0:
																		expected_events.append('''on_save'''  )
										expected_events.append('''on_epoch_end'''  )
										if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
														expected_events += evaluation_events.copy()
						expected_events += ["on_log", "on_train_end"]
						return expected_events






		def  __A       (		self		:							Union[str, Any]  )     ->							int:
						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						# Callbacks passed at init are added to the default callbacks
						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]  )
						expected_callbacks.append(SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
						__lowerCamelCase    					=	self.get_trainer(disable_tqdm=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	DEFAULT_CALLBACKS.copy() + [PrinterCallback]
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							List[Any]  )     ->							str:
						__lowerCamelCase    					=	DEFAULT_CALLBACKS.copy() + [ProgressCallback]
						__lowerCamelCase    					=	self.get_trainer()

						# We can add, pop, or remove by class name
						trainer.remove_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.remove(SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	trainer.pop_callback(SCREAMING_SNAKE_CASE__  )
						self.assertEqual(cb.__class__							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						trainer.add_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.insert(0							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						# We can also add, pop, or remove by instance
						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[0]
						trainer.remove_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.remove(SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_trainer()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[0]
						__lowerCamelCase    					=	trainer.pop_callback(SCREAMING_SNAKE_CASE__  )
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )

						trainer.add_callback(SCREAMING_SNAKE_CASE__  )
						expected_callbacks.insert(0							,			SCREAMING_SNAKE_CASE__  )
						self.check_callbacks_equality(trainer.callback_handler.callbacks							,			SCREAMING_SNAKE_CASE__  )







		def  __A       (		self		:							Union[str, Any]  )     ->							Any:
						import warnings

						# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
						warnings.simplefilter(action='''ignore'''							,			category=SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						# Independent log/save/eval
						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			logging_steps=5  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			save_steps=5  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			eval_steps=5							,			evaluation_strategy='''steps'''  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						__lowerCamelCase    					=	self.get_trainer(callbacks=[MyTestTrainerCallback]							,			evaluation_strategy='''epoch'''  )
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						# A bit of everything
						__lowerCamelCase    					=	self.get_trainer(
						    callbacks=[MyTestTrainerCallback]							,			logging_steps=3							,			save_steps=10							,			eval_steps=5							,			evaluation_strategy='''steps'''							,			)
						trainer.train()
						__lowerCamelCase    					=	trainer.callback_handler.callbacks[-2].events
						self.assertEqual(SCREAMING_SNAKE_CASE__							,			self.get_expected_events(SCREAMING_SNAKE_CASE__  )  )

						# warning should be emitted for duplicated callbacks
						with patch('''transformers.trainer_callback.logger.warning'''  ) as warn_mock:
										__lowerCamelCase    					=	self.get_trainer(
										    callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]							,			)
										assert str(SCREAMING_SNAKE_CASE__  ) in warn_mock.call_args[0][0]



