"""simple docstring"""





class   UpperCAmelCase_			:


      def __init__(     self	,    a = ""	,    a = False							) ->      None:
             # Mapping from the first character of the prefix of the node
             lowercase__    :	dict[str, RadixNode]     					=			{}

             # A node will be a leaf if the tree contains its word
             lowercase__    :	Tuple     					=			is_leaf

             lowercase__    :	List[Any]     					=			prefix


      def 	_UpperCAmelCase (     self	,    a							) ->      tuple[str, str, str]:
             lowercase__    :	Union[str, Any]     					=			0
             for q, w in zip(self.prefix	,    a							):
                    if q != w:
                           break

                    x += 1

             return self.prefix[:x], self.prefix[x:], word[x:]


      def 	_UpperCAmelCase (     self	,    a							) ->      None:
             for word in words:
                    self.insert(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      None:
             # Case 1: If the word is the prefix of the node
             # Solution: We set the current node as leaf
             if self.prefix == word:
                    lowercase__    :	Any     					=			True

             # Case 2: The node has no edges that have a prefix to the word
             # Solution: We create an edge from the current node to a new one
             # containing the word
             elif word[0] not in self.nodes:
                    lowercase__    :	Tuple     					=			RadixNode(prefix=a	,    is_leaf=a							)

             else:
                    lowercase__    :	List[str]     					=			self.nodes[word[0]]
                    lowercase__					,   lowercase__					,   lowercase__    :	Optional[int]     					=			incoming_node.match(
                        a							)

                    # Case 3: The node prefix is equal to the matching
                    # Solution: We insert remaining word on the next node
                    if remaining_prefix == "":
                           self.nodes[matching_string[0]].insert(a							)

                    # Case 4: The word is greater equal to the matching
                    # Solution: Create a node in between both nodes, change
                    # prefixes and add the new node for the remaining word
                    else:
                           lowercase__    :	Optional[int]     					=			remaining_prefix

                           lowercase__    :	List[Any]     					=			self.nodes[matching_string[0]]
                           lowercase__    :	List[str]     					=			RadixNode(a	,    a							)
                           lowercase__    :	Tuple     					=			aux_node

                           if remaining_word == "":
                                  lowercase__    :	Optional[Any]     					=			True
                           else:
                                  self.nodes[matching_string[0]].insert(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      bool:
             lowercase__    :	Union[str, Any]     					=			self.nodes.get(word[0]	,    a							)
             if not incoming_node:
                    return False
             else:
                    lowercase__					,   lowercase__					,   lowercase__    :	Dict     					=			incoming_node.match(
                        a							)
                    # If there is remaining prefix, the word can't be on the tree
                    if remaining_prefix != "":
                           return False
                    # This applies when the word and the prefix are equal
                    elif remaining_word == "":
                           return incoming_node.is_leaf
                    # We have word remaining so we check the next node
                    else:
                           return incoming_node.find(a							)


      def 	_UpperCAmelCase (     self	,    a							) ->      bool:
             lowercase__    :	str     					=			self.nodes.get(word[0]	,    a							)
             if not incoming_node:
                    return False
             else:
                    lowercase__					,   lowercase__					,   lowercase__    :	Optional[int]     					=			incoming_node.match(
                        a							)
                    # If there is remaining prefix, the word can't be on the tree
                    if remaining_prefix != "":
                           return False
                    # We have word remaining so we check the next node
                    elif remaining_word != "":
                           return incoming_node.delete(a							)
                    else:
                           # If it is not a leaf, we don't have to delete
                           if not incoming_node.is_leaf:
                                  return False
                           else:
                                  # We delete the nodes if no edges go from it
                                  if len(incoming_node.nodes							) == 0:
                                         del self.nodes[word[0]]
                                         # We merge the current node with its only child
                                         if len(self.nodes							) == 1 and not self.is_leaf:
                                                lowercase__    :	Optional[Any]     					=			list(self.nodes.values()							)[0]
                                                lowercase__    :	int     					=			merging_node.is_leaf
                                                self.prefix += merging_node.prefix
                                                lowercase__    :	Optional[int]     					=			merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                                  elif len(incoming_node.nodes							) > 1:
                                         lowercase__    :	Any     					=			False
                                  # If there is 1 edge, we merge it with its child
                                  else:
                                         lowercase__    :	Any     					=			list(incoming_node.nodes.values()							)[0]
                                         lowercase__    :	Tuple     					=			merging_node.is_leaf
                                         incoming_node.prefix += merging_node.prefix
                                         lowercase__    :	Dict     					=			merging_node.nodes

                                  return True


      def 	_UpperCAmelCase (     self	,    a = 0							) ->      None:
             if self.prefix != "":
                    print('-' * height	,    self.prefix	,    '  (leaf)' if self.is_leaf else ''							)

             for value in self.nodes.values():
                    value.print_tree(height + 1							)





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Optional[Any]     					=			'banana bananas bandana band apple all beast'.split()
       lowercase__    :	Optional[int]     					=			RadixNode()
       root.insert_many(_lowerCAmelCase  )

       assert all(root.find(_lowerCAmelCase  ) for word in words  )
       assert not root.find('bandanas'  )
       assert not root.find('apps'  )
       root.delete('all'  )
       assert not root.find('all'  )
       root.delete('banana'  )
       assert not root.find('banana'  )
       assert root.find('bananas'  )

       return True





def        a_  (   ):





       '''simple docstring'''

       assert test_trie()





def        a_  (   ):





       '''simple docstring'''

       lowercase__    :	Any     					=			RadixNode()
       lowercase__    :	Union[str, Any]     					=			'banana bananas bandanas bandana band apple all beast'.split()
       root.insert_many(_lowerCAmelCase  )

       print('Words:'     ,				_lowerCAmelCase  )
       print('Tree:'  )
       root.print_tree()


if __name__ == "__main__":
       main()


"""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]

