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  __snake_case  (			_UpperCamelCase	):




					def __init__(   self		)     ->  int:



									'''simple docstring'''







									snake_case__					:	Dict												=      []




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Optional[Any]:



									'''simple docstring'''







									self.events.append('on_init_end'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  List[str]:



									'''simple docstring'''







									self.events.append('on_train_begin'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Optional[int]:



									'''simple docstring'''







									self.events.append('on_train_end'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  List[str]:



									'''simple docstring'''







									self.events.append('on_epoch_begin'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Any:



									'''simple docstring'''







									self.events.append('on_epoch_end'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Dict:



									'''simple docstring'''







									self.events.append('on_step_begin'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Any:



									'''simple docstring'''







									self.events.append('on_step_end'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Dict:



									'''simple docstring'''







									self.events.append('on_evaluate'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Union[str, Any]:



									'''simple docstring'''







									self.events.append('on_predict'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Any:



									'''simple docstring'''







									self.events.append('on_save'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Optional[Any]:



									'''simple docstring'''







									self.events.append('on_log'		)




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       __UpperCamelCase    ,       **__UpperCamelCase		)     ->  Optional[Any]:



									'''simple docstring'''







									self.events.append('on_prediction_step'		)





@require_torch
class  __snake_case  (			unittest.TestCase	):




					def 			__a			(   self		)     ->  str:



									'''simple docstring'''







									snake_case__					:	Optional[int]												=      tempfile.mkdtemp()




					def 			__a			(   self		)     ->  Optional[Any]:



									'''simple docstring'''







									shutil.rmtree(self.output_dir		)




					def 			__a			(   self    ,       __UpperCamelCase=0    ,       __UpperCamelCase=0    ,       __UpperCamelCase=64    ,       __UpperCamelCase=64    ,       __UpperCamelCase=None    ,       __UpperCamelCase=False    ,       **__UpperCamelCase		)     ->  Union[str, Any]:



									'''simple docstring'''







									snake_case__					:	int												=      RegressionDataset(length=_UpperCAmelCase		)
									snake_case__					:	str												=      RegressionDataset(length=_UpperCAmelCase		)
									snake_case__					:	List[Any]												=      RegressionModelConfig(a=_UpperCAmelCase    ,       b=_UpperCAmelCase		)
									snake_case__					:	Optional[int]												=      RegressionPreTrainedModel(_UpperCAmelCase		)

									snake_case__					:	Optional[int]												=      TrainingArguments(self.output_dir    ,       disable_tqdm=_UpperCAmelCase    ,       report_to=[]    ,       **_UpperCAmelCase		)
									return Trainer(
									    _UpperCAmelCase    ,       _UpperCAmelCase    ,       train_dataset=_UpperCAmelCase    ,       eval_dataset=_UpperCAmelCase    ,       callbacks=_UpperCAmelCase    ,       )




					def 			__a			(   self    ,       __UpperCamelCase    ,       __UpperCamelCase		)     ->  str:



									'''simple docstring'''







									self.assertEqual(len(_UpperCAmelCase		)    ,       len(_UpperCAmelCase		)		)

									# Order doesn't matter
									snake_case__					:	List[Any]												=      sorted(_UpperCAmelCase    ,       key=lambda __UpperCamelCase		: cb.__name__ if isinstance(_UpperCAmelCase    ,       _UpperCAmelCase		) else cb.__class__.__name__		)
									snake_case__					:	List[str]												=      sorted(_UpperCAmelCase    ,       key=lambda __UpperCamelCase		: cb.__name__ if isinstance(_UpperCAmelCase    ,       _UpperCAmelCase		) else cb.__class__.__name__		)

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




					def 			__a			(   self    ,       __UpperCamelCase		)     ->  int:



									'''simple docstring'''







									snake_case__					:	str												=      ['on_init_end', 'on_train_begin']
									snake_case__					:	str												=      0
									snake_case__					:	Optional[int]												=      len(trainer.get_eval_dataloader()		)
									snake_case__					:	List[Any]												=      ['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(_UpperCAmelCase		):
																	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		)     ->  List[Any]:



									'''simple docstring'''







									snake_case__					:	str												=      self.get_trainer()
									snake_case__					:	Union[str, Any]												=      DEFAULT_CALLBACKS.copy() + [ProgressCallback]
									self.check_callbacks_equality(trainer.callback_handler.callbacks    ,       _UpperCAmelCase		)

									# Callbacks passed at init are added to the default callbacks
									snake_case__					:	Optional[int]												=      self.get_trainer(callbacks=[MyTestTrainerCallback]		)
									expected_callbacks.append(_UpperCAmelCase		)
									self.check_callbacks_equality(trainer.callback_handler.callbacks    ,       _UpperCAmelCase		)

									# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
									snake_case__					:	Union[str, Any]												=      self.get_trainer(disable_tqdm=_UpperCAmelCase		)
									snake_case__					:	Union[str, Any]												=      DEFAULT_CALLBACKS.copy() + [PrinterCallback]
									self.check_callbacks_equality(trainer.callback_handler.callbacks    ,       _UpperCAmelCase		)




					def 			__a			(   self		)     ->  Optional[Any]:



									'''simple docstring'''







									snake_case__					:	List[str]												=      DEFAULT_CALLBACKS.copy() + [ProgressCallback]
									snake_case__					:	Optional[int]												=      self.get_trainer()

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

									snake_case__					:	Optional[Any]												=      self.get_trainer()
									snake_case__					:	List[Any]												=      trainer.pop_callback(_UpperCAmelCase		)
									self.assertEqual(cb.__class__    ,       _UpperCAmelCase		)
									self.check_callbacks_equality(trainer.callback_handler.callbacks    ,       _UpperCAmelCase		)

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

									# We can also add, pop, or remove by instance
									snake_case__					:	str												=      self.get_trainer()
									snake_case__					:	Optional[Any]												=      trainer.callback_handler.callbacks[0]
									trainer.remove_callback(_UpperCAmelCase		)
									expected_callbacks.remove(_UpperCAmelCase		)
									self.check_callbacks_equality(trainer.callback_handler.callbacks    ,       _UpperCAmelCase		)

									snake_case__					:	Tuple												=      self.get_trainer()
									snake_case__					:	Optional[Any]												=      trainer.callback_handler.callbacks[0]
									snake_case__					:	Optional[Any]												=      trainer.pop_callback(_UpperCAmelCase		)
									self.assertEqual(_UpperCAmelCase    ,       _UpperCAmelCase		)
									self.check_callbacks_equality(trainer.callback_handler.callbacks    ,       _UpperCAmelCase		)

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




					def 			__a			(   self		)     ->  Any:



									'''simple docstring'''







									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=_UpperCAmelCase		)

									snake_case__					:	List[Any]												=      self.get_trainer(callbacks=[MyTestTrainerCallback]		)
									trainer.train()
									snake_case__					:	Optional[int]												=      trainer.callback_handler.callbacks[-2].events
									self.assertEqual(_UpperCAmelCase    ,       self.get_expected_events(_UpperCAmelCase		)		)

									# Independent log/save/eval
									snake_case__					:	Optional[Any]												=      self.get_trainer(callbacks=[MyTestTrainerCallback]    ,       logging_steps=5		)
									trainer.train()
									snake_case__					:	Optional[int]												=      trainer.callback_handler.callbacks[-2].events
									self.assertEqual(_UpperCAmelCase    ,       self.get_expected_events(_UpperCAmelCase		)		)

									snake_case__					:	Dict												=      self.get_trainer(callbacks=[MyTestTrainerCallback]    ,       save_steps=5		)
									trainer.train()
									snake_case__					:	str												=      trainer.callback_handler.callbacks[-2].events
									self.assertEqual(_UpperCAmelCase    ,       self.get_expected_events(_UpperCAmelCase		)		)

									snake_case__					:	Dict												=      self.get_trainer(callbacks=[MyTestTrainerCallback]    ,       eval_steps=5    ,       evaluation_strategy='steps'		)
									trainer.train()
									snake_case__					:	Tuple												=      trainer.callback_handler.callbacks[-2].events
									self.assertEqual(_UpperCAmelCase    ,       self.get_expected_events(_UpperCAmelCase		)		)

									snake_case__					:	Union[str, Any]												=      self.get_trainer(callbacks=[MyTestTrainerCallback]    ,       evaluation_strategy='epoch'		)
									trainer.train()
									snake_case__					:	str												=      trainer.callback_handler.callbacks[-2].events
									self.assertEqual(_UpperCAmelCase    ,       self.get_expected_events(_UpperCAmelCase		)		)

									# A bit of everything
									snake_case__					:	Optional[int]												=      self.get_trainer(
									    callbacks=[MyTestTrainerCallback]    ,       logging_steps=3    ,       save_steps=10    ,       eval_steps=5    ,       evaluation_strategy='steps'    ,       )
									trainer.train()
									snake_case__					:	List[str]												=      trainer.callback_handler.callbacks[-2].events
									self.assertEqual(_UpperCAmelCase    ,       self.get_expected_events(_UpperCAmelCase		)		)

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







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=1024						,			lowerCAmelCase_=False						,			**lowerCAmelCase_  )			->      List[Any]:
		_a     :      str        =							AutoTokenizer.from_pretrained(lowerCAmelCase_  )
		_a     :      List[Any]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='train'						,			**lowerCAmelCase_  )
		_a     :      List[str]        =							tok.pad_token_id

		def get_lens(lowerCAmelCase_  ):
				_a     :      Dict        =							tqdm(
				    DataLoader(lowerCAmelCase_						,			batch_size=512						,			num_workers=8						,			shuffle=lowerCAmelCase_						,			collate_fn=ds.collate_fn  )						,			desc=str(ds.len_file  )						,			)
				_a     :      Union[str, Any]        =							[]
				for batch in dl:
						_a     :      Optional[Any]        =							batch['input_ids'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						_a     :      Optional[Any]        =							batch['labels'].ne(lowerCAmelCase_  ).sum(1  ).tolist()
						if consider_target:
								for src, tgt in zip(lowerCAmelCase_						,			lowerCAmelCase_  ):
										max_lens.append(max(lowerCAmelCase_						,			lowerCAmelCase_  )  )
						else:
								max_lens.extend(lowerCAmelCase_  )
				return max_lens

		_a     :      str        =							get_lens(lowerCAmelCase_  )
		_a     :      Optional[int]        =							SeqaSeqDataset(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			type_path='val'						,			**lowerCAmelCase_  )
		_a     :      Dict        =							get_lens(lowerCAmelCase_  )
		pickle_save(lowerCAmelCase_						,			train_ds.len_file  )
		pickle_save(lowerCAmelCase_						,			val_ds.len_file  )


if __name__ == "__main__":
							fire.Fire(save_len_file)




