
"""simple docstring"""





def 				A_  (__a					, __a					):

					'''simple docstring'''





					if number < 0 or shift_amount < 0:
										raise ValueError("both inputs must be positive integers"					)

					A_								=    str(bin(__a					)					)
					binary_number += "0" * shift_amount
					return binary_number
def 				A_  (__a					, __a					):

					'''simple docstring'''





					if number < 0 or shift_amount < 0:
										raise ValueError("both inputs must be positive integers"					)

					A_								=    str(bin(__a					)					)[2:]
					if shift_amount >= len(__a					):
										return "0b0"
					A_								=    binary_number[: len(__a					) - shift_amount]
					return "0b" + shifted_binary_number
def 				A_  (__a					, __a					):

					'''simple docstring'''





					if number >= 0:  # Get binary representation of positive number
										A_								=    "0" + str(bin(__a					)					).strip("-"					)[2:]
					else:  # Get binary (2's complement) representation of negative number
										A_								=    len(bin(__a					)[3:]					)  # Find 2's complement of number
										A_								=    bin(abs(__a					) - (1 << binary_number_length)					)[3:]
										A_								=    (
										    "1" + "0" * (binary_number_length - len(__a					)) + binary_number
										)

					if shift_amount >= len(__a					):
										return "0b" + binary_number[0] * len(__a					)
					return (
					    "0b"
					    + binary_number[0] * shift_amount
					    + binary_number[: len(__a					) - shift_amount]
					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






"""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 __lowerCAmelCase					(       _lowercase   ):




			"""simple docstring"""







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







								"""simple docstring"""
								A_								=    []




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       List[str]							, _snake_case    :       Optional[int]							, _snake_case    :       Optional[int]							, **_snake_case    :       str						)   ->			int:







								"""simple docstring"""
								self.events.append("on_init_end"						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Union[str, Any]							, _snake_case    :       List[Any]							, _snake_case    :       Tuple							, **_snake_case    :       Tuple						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_train_begin"						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Any							, _snake_case    :       Optional[Any]							, _snake_case    :       Tuple							, **_snake_case    :       Optional[int]						)   ->			str:







								"""simple docstring"""
								self.events.append("on_train_end"						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       str							, _snake_case    :       List[Any]							, _snake_case    :       str							, **_snake_case    :       Optional[int]						)   ->			Dict:







								"""simple docstring"""
								self.events.append("on_epoch_begin"						)




			def   lowerCamelCase__  (					self    :       Optional[int]							, _snake_case    :       Optional[int]							, _snake_case    :       Any							, _snake_case    :       Optional[Any]							, **_snake_case    :       Optional[Any]						)   ->			str:







								"""simple docstring"""
								self.events.append("on_epoch_end"						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       List[str]							, _snake_case    :       str							, _snake_case    :       Optional[Any]							, **_snake_case    :       Optional[int]						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_step_begin"						)




			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Union[str, Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       Any							, **_snake_case    :       Union[str, Any]						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_step_end"						)




			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       Dict							, _snake_case    :       Dict							, _snake_case    :       Union[str, Any]							, **_snake_case    :       List[str]						)   ->			Optional[int]:







								"""simple docstring"""
								self.events.append("on_evaluate"						)




			def   lowerCamelCase__  (					self    :       Optional[int]							, _snake_case    :       Optional[Any]							, _snake_case    :       int							, _snake_case    :       int							, **_snake_case    :       List[Any]						)   ->			List[str]:







								"""simple docstring"""
								self.events.append("on_predict"						)




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Union[str, Any]							, _snake_case    :       List[str]							, _snake_case    :       Optional[Any]							, **_snake_case    :       List[Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								self.events.append("on_save"						)




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Dict							, _snake_case    :       List[Any]							, _snake_case    :       Optional[int]							, **_snake_case    :       Dict						)   ->			str:







								"""simple docstring"""
								self.events.append("on_log"						)






			def   lowerCamelCase__  (					self    :       Tuple							, _snake_case    :       Optional[Any]							, _snake_case    :       str							, _snake_case    :       int							, **_snake_case    :       List[Any]						)   ->			int:







								"""simple docstring"""
								self.events.append("on_prediction_step"						)





@require_torch
class __lowerCAmelCase					(       unittest.TestCase   ):




			"""simple docstring"""







			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Dict:







								"""simple docstring"""
								A_								=    tempfile.mkdtemp()




			def   lowerCamelCase__  (					self    :       str						)   ->			Tuple:







								"""simple docstring"""
								shutil.rmtree(self.output_dir						)




			def   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Any=0							, _snake_case    :       Optional[int]=0							, _snake_case    :       int=64							, _snake_case    :       Optional[int]=64							, _snake_case    :       List[Any]=None							, _snake_case    :       List[Any]=False							, **_snake_case    :       Optional[int]						)   ->			List[Any]:







								"""simple docstring"""
								# 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.
								A_								=    RegressionDataset(length=_snake_case						)
								A_								=    RegressionDataset(length=_snake_case						)
								A_								=    RegressionModelConfig(a=_snake_case							, b=_snake_case						)
								A_								=    RegressionPreTrainedModel(_snake_case						)

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




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Optional[Any]							, _snake_case    :       List[Any]						)   ->			Union[str, Any]:







								"""simple docstring"""
								self.assertEqual(len(_snake_case						)							, len(_snake_case						)						)

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

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




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Union[str, Any]						)   ->			Optional[Any]:







								"""simple docstring"""
								A_								=    ["on_init_end", "on_train_begin"]
								A_								=    0
								A_								=    len(trainer.get_eval_dataloader()						)
								A_								=    ["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(_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   lowerCamelCase__  (					self    :       List[Any]						)   ->			int:







								"""simple docstring"""
								A_								=    self.get_trainer()
								A_								=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

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

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




			def   lowerCamelCase__  (					self    :       Optional[Any]						)   ->			List[Any]:







								"""simple docstring"""
								A_								=    DEFAULT_CALLBACKS.copy() + [ProgressCallback]
								A_								=    self.get_trainer()

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

								A_								=    self.get_trainer()
								A_								=    trainer.pop_callback(_snake_case						)
								self.assertEqual(cb.__class__							, _snake_case						)
								self.check_callbacks_equality(trainer.callback_handler.callbacks							, _snake_case						)

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

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

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

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






			def   lowerCamelCase__  (					self    :       Union[str, Any]						)   ->			Union[str, 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=_snake_case						)

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

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

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

								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]							, eval_steps=5							, evaluation_strategy="steps"						)
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

								A_								=    self.get_trainer(callbacks=[MyTestTrainerCallback]							, evaluation_strategy="epoch"						)
								trainer.train()
								A_								=    trainer.callback_handler.callbacks[-2].events
								self.assertEqual(_snake_case							, self.get_expected_events(_snake_case						)						)

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

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




