"""simple docstring"""





from __future__ import annotations





def        a_  (   _lowerCAmelCase						:				list  ):





       '''simple docstring'''

       if len(_lowerCAmelCase  ) == 0:
              return []
       lowercase__					,   lowercase__    :	List[str]     					=			min(_lowerCAmelCase  ), max(_lowerCAmelCase  )
       lowercase__    :	Any     					=			int(max_value - min_value  ) + 1
       lowercase__    :	list[list]     					=			[[] for _ in range(_lowerCAmelCase  )]

       for i in my_list:
              buckets[int(i - min_value  )].append(_lowerCAmelCase  )

       return [v for bucket in buckets for v in sorted(_lowerCAmelCase  )]


if __name__ == "__main__":
       from doctest import testmod

       testmod()
       assert bucket_sort([4, 5, 3, 2, 1]) == [1, 2, 3, 4, 5]
       assert bucket_sort([0, 1, -10, 15, 2, -2]) == [-10, -2, 0, 1, 2, 15]


"""simple docstring"""





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   UpperCAmelCase_			(					_a):


      def __init__(     self							) ->      Any:
             lowercase__    :	Tuple     					=			[]


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_init_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[int]:
             self.events.append('on_train_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_train_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_epoch_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Optional[Any]:
             self.events.append('on_epoch_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_step_begin'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      str:
             self.events.append('on_step_end'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      int:
             self.events.append('on_evaluate'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Tuple:
             self.events.append('on_predict'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Union[str, Any]:
             self.events.append('on_save'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      List[str]:
             self.events.append('on_log'							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    **a							) ->      Any:
             self.events.append('on_prediction_step'							)





@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):


      def 	_UpperCAmelCase (     self							) ->      str:
             lowercase__    :	str     					=			tempfile.mkdtemp()


      def 	_UpperCAmelCase (     self							) ->      Dict:
             shutil.rmtree(self.output_dir							)


      def 	_UpperCAmelCase (     self	,    a=0	,    a=0	,    a=6_4	,    a=6_4	,    a=None	,    a=False	,    **a							) ->      int:
             # 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__    :	str     					=			RegressionDataset(length=a							)
             lowercase__    :	Any     					=			RegressionDataset(length=a							)
             lowercase__    :	Optional[Any]     					=			RegressionModelConfig(a=a	,    b=a							)
             lowercase__    :	Union[str, Any]     					=			RegressionPreTrainedModel(a							)

             lowercase__    :	Tuple     					=			TrainingArguments(self.output_dir	,    disable_tqdm=a	,    report_to=[]	,    **a							)
             return Trainer(
                 a	,    a	,    train_dataset=a	,    eval_dataset=a	,    callbacks=a	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Union[str, Any]:
             self.assertEqual(len(a							)	,    len(a							)							)

             # Order doesn't matter
             lowercase__    :	Optional[int]     					=			sorted(a	,    key=lambda a							: cb.__name__ if isinstance(a	,    a							) else cb.__class__.__name__							)
             lowercase__    :	Tuple     					=			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 	_UpperCAmelCase (     self	,    a							) ->      Optional[Any]:
             lowercase__    :	Dict     					=			['on_init_end', 'on_train_begin']
             lowercase__    :	List[Any]     					=			0
             lowercase__    :	Optional[int]     					=			len(trainer.get_eval_dataloader()							)
             lowercase__    :	Tuple     					=			['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 	_UpperCAmelCase (     self							) ->      Union[str, Any]:
             lowercase__    :	int     					=			self.get_trainer()
             lowercase__    :	str     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             # Callbacks passed at init are added to the default callbacks
             lowercase__    :	str     					=			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__    :	List[Any]     					=			self.get_trainer(disable_tqdm=a							)
             lowercase__    :	Optional[Any]     					=			DEFAULT_CALLBACKS.copy() + [PrinterCallback]
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)


      def 	_UpperCAmelCase (     self							) ->      Any:
             lowercase__    :	int     					=			DEFAULT_CALLBACKS.copy() + [ProgressCallback]
             lowercase__    :	List[str]     					=			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__    :	Optional[Any]     					=			self.get_trainer()
             lowercase__    :	List[Any]     					=			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__    :	int     					=			self.get_trainer()
             lowercase__    :	List[str]     					=			trainer.callback_handler.callbacks[0]
             trainer.remove_callback(a							)
             expected_callbacks.remove(a							)
             self.check_callbacks_equality(trainer.callback_handler.callbacks	,    a							)

             lowercase__    :	Tuple     					=			self.get_trainer()
             lowercase__    :	Dict     					=			trainer.callback_handler.callbacks[0]
             lowercase__    :	Union[str, Any]     					=			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 	_UpperCAmelCase (     self							) ->      Tuple:
             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__    :	Optional[Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]							)
             trainer.train()
             lowercase__    :	Any     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # Independent log/save/eval
             lowercase__    :	List[str]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    logging_steps=5							)
             trainer.train()
             lowercase__    :	int     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	Union[str, Any]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    save_steps=5							)
             trainer.train()
             lowercase__    :	Union[str, Any]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	List[str]     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    eval_steps=5	,    evaluation_strategy='steps'							)
             trainer.train()
             lowercase__    :	Optional[int]     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             lowercase__    :	int     					=			self.get_trainer(callbacks=[MyTestTrainerCallback]	,    evaluation_strategy='epoch'							)
             trainer.train()
             lowercase__    :	str     					=			trainer.callback_handler.callbacks[-2].events
             self.assertEqual(a	,    self.get_expected_events(a							)							)

             # A bit of everything
             lowercase__    :	Any     					=			self.get_trainer(
                 callbacks=[MyTestTrainerCallback]	,    logging_steps=3	,    save_steps=1_0	,    eval_steps=5	,    evaluation_strategy='steps'	,    )
             trainer.train()
             lowercase__    :	Any     					=			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__    :	str     					=			self.get_trainer(
                        callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]	,    )
                    assert str(a							) in warn_mock.call_args[0][0]

