



'''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 							lowercase       (  __a				):


 """simple docstring"""


 def __init__(							self							)		->   List[str]:
        _UpperCAmelCase  :						str			     = []







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   int:
        self.events.append("""on_init_end"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   Optional[Any]:
        self.events.append("""on_train_begin"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   Any:
        self.events.append("""on_train_end"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   Optional[Any]:
        self.events.append("""on_epoch_begin"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   str:
        self.events.append("""on_epoch_end"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   str:
        self.events.append("""on_step_begin"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   Dict:
        self.events.append("""on_step_end"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   Tuple:
        self.events.append("""on_evaluate"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   Optional[int]:
        self.events.append("""on_predict"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   Any:
        self.events.append("""on_save"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   str:
        self.events.append("""on_log"""							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,**a_							)		->   int:
        self.events.append("""on_prediction_step"""							)







@require_torch
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Any			     = tempfile.mkdtemp()







 def _snake_case (							self							)		->   int:
        shutil.rmtree(self.output_dir							)







 def _snake_case (							self  ,a_=0  ,a_=0  ,a_=64  ,a_=64  ,a_=None  ,a_=False  ,**a_							)		->   str:
        _UpperCAmelCase  :						Union[str, Any]			     = RegressionDataset(length=__a							)
        _UpperCAmelCase  :						Any			     = RegressionDataset(length=__a							)
        _UpperCAmelCase  :						Union[str, Any]			     = RegressionModelConfig(a=__a  ,b=__a							)
        _UpperCAmelCase  :						int			     = RegressionPreTrainedModel(__a							)

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







 def _snake_case (							self  ,a_  ,a_							)		->   List[Any]:
        self.assertEqual(len(__a							)  ,len(__a							)							)

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

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







 def _snake_case (							self							)		->   List[str]:
        _UpperCAmelCase  :						int			     = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
        _UpperCAmelCase  :						int			     = 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							)

        _UpperCAmelCase  :						str			     = self.get_trainer()
        _UpperCAmelCase  :						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
        _UpperCAmelCase  :						Dict			     = self.get_trainer()
        _UpperCAmelCase  :						Tuple			     = trainer.callback_handler.callbacks[0]
        trainer.remove_callback(__a							)
        expected_callbacks.remove(__a							)
        self.check_callbacks_equality(trainer.callback_handler.callbacks  ,__a							)

        _UpperCAmelCase  :						Union[str, Any]			     = self.get_trainer()
        _UpperCAmelCase  :						str			     = trainer.callback_handler.callbacks[0]
        _UpperCAmelCase  :						List[str]			     = 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 _snake_case (							self							)		->   List[Any]:
        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							)

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

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

        _UpperCAmelCase  :						Optional[int]			     = self.get_trainer(callbacks=[MyTestTrainerCallback]  ,save_steps=5							)
        trainer.train()
        _UpperCAmelCase  :						Optional[int]			     = trainer.callback_handler.callbacks[-2].events
        self.assertEqual(__a  ,self.get_expected_events(__a							)							)

        _UpperCAmelCase  :						Any			     = self.get_trainer(callbacks=[MyTestTrainerCallback]  ,eval_steps=5  ,evaluation_strategy="""steps"""							)
        trainer.train()
        _UpperCAmelCase  :						Union[str, Any]			     = trainer.callback_handler.callbacks[-2].events
        self.assertEqual(__a  ,self.get_expected_events(__a							)							)

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

        # A bit of everything
        _UpperCAmelCase  :						Optional[int]			     = self.get_trainer(
            callbacks=[MyTestTrainerCallback]  ,logging_steps=3  ,save_steps=10  ,eval_steps=5  ,evaluation_strategy="""steps"""  ,)
        trainer.train()
        _UpperCAmelCase  :						Tuple			     = 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:
               _UpperCAmelCase  :						List[str]			     = self.get_trainer(
                   callbacks=[MyTestTrainerCallback, MyTestTrainerCallback]  ,)
               assert str(__a							) in warn_mock.call_args[0][0]











from __future__ import annotations

import math

lowerCamelCase       :       List[Any]        =				'''2020.9.26'''
lowerCamelCase       :       str        =				'''xcodz-dot, cclaus, dhruvmanila'''
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float					):
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in locals().values()					):
														__lowercase					:							str			=   F"Input values must either be float or int: {list(locals().values()					)}"
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							List[Any]			=   ((x * distance) / (z + distance)) * scale
							__lowercase					:							Tuple			=   ((y * distance) / (z + distance)) * scale
							return projected_x, projected_y
def 					snake_case_						(   lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  float				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  float					):
							if not isinstance(lowerCAmelCase_				,				lowerCAmelCase_					):
														raise TypeError("""Axis must be a str"""					)
							__lowercase					:							Optional[int]			=   locals()
							del input_variables["axis"]
							if not all(isinstance(lowerCAmelCase_				,				(float, int)					) for val in input_variables.values()					):
														__lowercase					:							List[str]			=   (
														    """Input values except axis must either be float or int: """
														    F"{list(input_variables.values()					)}"
														)
														raise TypeError(lowerCAmelCase_					)
							__lowercase					:							Tuple			=   (angle % 360) / 450 * 180 / math.pi
							if axis == "z":
														__lowercase					:							int			=   x * math.cos(lowerCAmelCase_					) - y * math.sin(lowerCAmelCase_					)
														__lowercase					:							Tuple			=   y * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							Union[str, Any]			=   z
							elif axis == "x":
														__lowercase					:							str			=   y * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							Dict			=   z * math.cos(lowerCAmelCase_					) + y * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   x
							elif axis == "y":
														__lowercase					:							List[str]			=   x * math.cos(lowerCAmelCase_					) - z * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[str]			=   z * math.cos(lowerCAmelCase_					) + x * math.sin(lowerCAmelCase_					)
														__lowercase					:							List[Any]			=   y
							else:
														raise ValueError("""not a valid axis, choose one of 'x', 'y', 'z'"""					)

							return new_x, new_y, new_z


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f'''{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }''')
	print(f'''{rotate(1.0, 2.0, 3.0, "y", 90.0) = }''')