
'''simple docstring'''


import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
					import torch_xla.core.xla_model as xm




def lowercase_		(				_lowercase     ,    _lowercase			)       ->							Dict:
					'''simple docstring'''

					if isinstance(_lowercase     ,    (list, tuple)			):
										return honor_type(_lowercase     ,    (move_to_device(_lowercase     ,    _lowercase			) for t in state)			)
					elif isinstance(_lowercase     ,    _lowercase			):
										return type(_lowercase			)({k: move_to_device(_lowercase     ,    _lowercase			) for k, v in state.items()}			)
					elif isinstance(_lowercase     ,    torch.Tensor			):
										return state.to(_lowercase			)
					return state






class 		__lowercase			(					torch.optim.Optimizer      ):

			def __init__(self     ,				A     ,				A=True     ,				A=None ):
								lowerCamelCase_   : str       =						optimizer
								lowerCamelCase_   : Tuple       =						scaler
								lowerCamelCase_   : List[Any]       =						AcceleratorState()
								lowerCamelCase_   : Tuple       =						GradientState()
								lowerCamelCase_   : Dict       =						device_placement
								lowerCamelCase_   : Tuple       =						False
								lowerCamelCase_   : Union[str, Any]       =						None

								# Handle device placement
								if device_placement:
													lowerCamelCase_   : List[str]       =						self.optimizer.state_dict()
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																		xm.send_cpu_data_to_device(A     ,				self.accelerator_state.device )
													else:
																		lowerCamelCase_   : Any       =						move_to_device(A     ,				self.accelerator_state.device )
													self.optimizer.load_state_dict(A )

			@property
			def     UpperCAmelCase__	(self ):
								return self.optimizer.state

			@state.setter
			def     UpperCAmelCase__	(self     ,				A ):
								lowerCamelCase_   : List[Any]       =						state

			@property
			def     UpperCAmelCase__	(self ):
								return self.optimizer.param_groups

			@param_groups.setter
			def     UpperCAmelCase__	(self     ,				A ):
								lowerCamelCase_   : List[str]       =						param_groups

			@property
			def     UpperCAmelCase__	(self ):
								return self.optimizer.defaults

			@defaults.setter
			def     UpperCAmelCase__	(self     ,				A ):
								lowerCamelCase_   : Union[str, Any]       =						defaults

			def     UpperCAmelCase__	(self     ,				A ):
								self.optimizer.add_param_group(A )

			def     UpperCAmelCase__	(self     ,				A ):
								if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
													xm.send_cpu_data_to_device(A     ,				self.accelerator_state.device )
								self.optimizer.load_state_dict(A )

			def     UpperCAmelCase__	(self ):
								return self.optimizer.state_dict()

			def     UpperCAmelCase__	(self     ,				A=None ):
								if self.gradient_state.sync_gradients:
													lowerCamelCase_   : List[str]       =						'''set_to_none''' in inspect.signature(self.optimizer.zero_grad ).parameters
													if accept_arg:
																		if set_to_none is None:
																							lowerCamelCase_   : Tuple       =						False
																		self.optimizer.zero_grad(set_to_none=A )
													else:
																		if set_to_none is not None:
																							raise ValueError('''`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer.''' )
																		self.optimizer.zero_grad()

			def     UpperCAmelCase__	(self     ,				A=None ):
								if self.gradient_state.sync_gradients:
													if self.accelerator_state.distributed_type == DistributedType.TPU:
																		lowerCamelCase_   : str       =						{'''closure''': closure} if closure is not None else {}
																		xm.optimizer_step(self.optimizer     ,				optimizer_args=A )
													elif self.scaler is not None:
																		lowerCamelCase_   : Dict       =						False
																		if self._last_scale is None:
																							# `get_scale` is an async operation requiring full synchronization
																							# on CPU and GPUs before finishing. As a result, we store away
																							# the prior one to reduce the call overhead
																							lowerCamelCase_   : Dict       =						self.scaler.get_scale()
																							lowerCamelCase_   : Any       =						True
																		self.scaler.step(self.optimizer     ,				A )
																		self.scaler.update()
																		lowerCamelCase_   : List[Any]       =						self.scaler.get_scale()
																		if not new_scale:
																							# If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
																							lowerCamelCase_   : int       =						scale_after < self._last_scale
																		lowerCamelCase_   : Tuple       =						scale_after

													else:
																		self.optimizer.step(A )

			def     UpperCAmelCase__	(self     ,				A ):
								for param_group in self.optimizer.param_groups:
													lowerCamelCase_   : Optional[Any]       =						[parameters_map.get(A     ,				A ) for p in param_group['''params''']]

			@property
			def     UpperCAmelCase__	(self ):
								warnings.warn(
								    '''The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use '''
								    '''`optimizer.step_was_skipped` instead.'''     ,				A     ,				)
								return self._is_overflow

			@property
			def     UpperCAmelCase__	(self ):
								return self._is_overflow

			def __getstate__(self ):
								return self.__dict__.copy()






			def __setstate__(self     ,				A ):
								self.__dict__.update(A )




'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowercase							:				Optional[Any]					=       logging.getLogger()




def lowercase_		(				)       ->							Union[str, Any]:
					'''simple docstring'''

					lowerCamelCase_   : Any       =						argparse.ArgumentParser()
					parser.add_argument('''-f'''			)
					lowerCamelCase_   : Optional[int]       =						parser.parse_args()
					return args.f






class 		__lowercase			(					_lowercase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Dict       =						logging.StreamHandler(sys.stdout )
								logger.addHandler(A )

								lowerCamelCase_   : int       =						self.get_auto_remove_tmp_dir()
								lowerCamelCase_   : Dict       =						F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

								with patch.object(A     ,				'''argv'''     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						run_glue_with_pabee.main()
													for value in result.values():
																		self.assertGreaterEqual(A     ,				0.75 )


