
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        lowercase	(      SCREAMING_SNAKE_CASE__	):




							def __init__(       self):
												lowercase					=		[]




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_init_end''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_train_begin''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_train_end''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_epoch_begin''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_epoch_end''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_step_begin''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_step_end''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_evaluate''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_predict''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_save''')




							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_log''')





							def 				A__   (       self     ,A__     ,A__     ,A__     ,**A__):
												self.events.append('''on_prediction_step''')







@require_torch
class        lowercase	(      unittest.TestCase	):




							def 				A__   (       self):
												lowercase					=		tempfile.mkdtemp()




							def 				A__   (       self):
												shutil.rmtree(self.output_dir)




							def 				A__   (       self     ,A__=0     ,A__=0     ,A__=6_4     ,A__=6_4     ,A__=None     ,A__=False     ,**A__):
												# 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.
												lowercase					=		RegressionDataset(length=A__)
												lowercase					=		RegressionDataset(length=A__)
												lowercase					=		RegressionModelConfig(a=A__     ,b=A__)
												lowercase					=		RegressionPreTrainedModel(A__)

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




							def 				A__   (       self     ,A__     ,A__):
												self.assertEqual(len(A__)     ,len(A__))

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

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




							def 				A__   (       self     ,A__):
												lowercase					=		['''on_init_end''', '''on_train_begin''']
												lowercase					=		0
												lowercase					=		len(trainer.get_eval_dataloader())
												lowercase					=		['''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(A__):
																						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):
												lowercase					=		self.get_trainer()
												lowercase					=		DEFAULT_CALLBACKS.copy() + [ProgressCallback]
												self.check_callbacks_equality(trainer.callback_handler.callbacks     ,A__)

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

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




							def 				A__   (       self):
												lowercase					=		DEFAULT_CALLBACKS.copy() + [ProgressCallback]
												lowercase					=		self.get_trainer()

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

												lowercase					=		self.get_trainer()
												lowercase					=		trainer.pop_callback(A__)
												self.assertEqual(cb.__class__     ,A__)
												self.check_callbacks_equality(trainer.callback_handler.callbacks     ,A__)

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

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

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

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





							def 				A__   (       self):
												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=A__)

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

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

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

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

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

												# A bit of everything
												lowercase					=		self.get_trainer(
												    callbacks=[MyTestTrainerCallback]     ,logging_steps=3     ,save_steps=1_0     ,eval_steps=5     ,evaluation_strategy='''steps'''     ,)
												trainer.train()
												lowercase					=		trainer.callback_handler.callbacks[-2].events
												self.assertEqual(A__     ,self.get_expected_events(A__))

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






from __future__ import annotations



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < 2:
										raise ValueError('''Monogons and Digons are not polygons in the Euclidean space'''	)
					if any(i <= 0 for i in nums	):
										raise ValueError('''All values must be greater than 0'''	)
					lowercase					=		nums.copy()
					copy_nums.sort()
					return copy_nums[-1] < sum(copy_nums[:-1]	)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




