
"""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   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):

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




class      UpperCamelCase	(       torch.optim.Optimizer      ):

				"""simple docstring"""



				def __init__(				self		,	lowercase__		,	lowercase__=True		,	lowercase__=None			)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	optimizer
									SCREAMING_SNAKE_CASE				=	scaler
									SCREAMING_SNAKE_CASE				=	AcceleratorState()
									SCREAMING_SNAKE_CASE				=	GradientState()
									SCREAMING_SNAKE_CASE				=	device_placement
									SCREAMING_SNAKE_CASE				=	False
									SCREAMING_SNAKE_CASE				=	None

									# Handle device placement
									if device_placement:
														SCREAMING_SNAKE_CASE				=	self.optimizer.state_dict()
														if self.accelerator_state.distributed_type == DistributedType.TPU:
																			xm.send_cpu_data_to_device(_A		,	self.accelerator_state.device			)
														else:
																			SCREAMING_SNAKE_CASE				=	move_to_device(_A		,	self.accelerator_state.device			)
														self.optimizer.load_state_dict(_A			)


				@property
				def 							A					(				self			)    ->    Any:
									"""simple docstring"""







									return self.optimizer.state


				@state.setter
				def 							A					(				self		,	lowercase__			)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	state


				@property
				def 							A					(				self			)    ->    Optional[int]:
									"""simple docstring"""







									return self.optimizer.param_groups


				@param_groups.setter
				def 							A					(				self		,	lowercase__			)    ->    Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	param_groups


				@property
				def 							A					(				self			)    ->    Optional[int]:
									"""simple docstring"""







									return self.optimizer.defaults


				@defaults.setter
				def 							A					(				self		,	lowercase__			)    ->    int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	defaults


				def 							A					(				self		,	lowercase__			)    ->    List[Any]:
									"""simple docstring"""







									self.optimizer.add_param_group(_A			)


				def 							A					(				self		,	lowercase__			)    ->    int:
									"""simple docstring"""







									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 							A					(				self			)    ->    Union[str, Any]:
									"""simple docstring"""







									return self.optimizer.state_dict()


				def 							A					(				self		,	lowercase__=None			)    ->    List[str]:
									"""simple docstring"""







									if self.gradient_state.sync_gradients:
														SCREAMING_SNAKE_CASE				=	'set_to_none' in inspect.signature(self.optimizer.zero_grad			).parameters
														if accept_arg:
																			if set_to_none is None:
																								SCREAMING_SNAKE_CASE				=	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 							A					(				self		,	lowercase__=None			)    ->    List[Any]:
									"""simple docstring"""







									if self.gradient_state.sync_gradients:
														if self.accelerator_state.distributed_type == DistributedType.TPU:
																			SCREAMING_SNAKE_CASE				=	{'closure': closure} if closure is not None else {}
																			xm.optimizer_step(self.optimizer		,	optimizer_args=_A			)
														elif self.scaler is not None:
																			SCREAMING_SNAKE_CASE				=	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
																								SCREAMING_SNAKE_CASE				=	self.scaler.get_scale()
																								SCREAMING_SNAKE_CASE				=	True
																			self.scaler.step(self.optimizer		,	_A			)
																			self.scaler.update()
																			SCREAMING_SNAKE_CASE				=	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.
																								SCREAMING_SNAKE_CASE				=	scale_after < self._last_scale
																			SCREAMING_SNAKE_CASE				=	scale_after

														else:
																			self.optimizer.step(_A			)


				def 							A					(				self		,	lowercase__			)    ->    List[Any]:
									"""simple docstring"""







									for param_group in self.optimizer.param_groups:
														SCREAMING_SNAKE_CASE				=	[parameters_map.get(_A		,	_A			) for p in param_group['params']]


				@property
				def 							A					(				self			)    ->    Dict:
									"""simple docstring"""







									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 							A					(				self			)    ->    List[str]:
									"""simple docstring"""







									return self._is_overflow


				def __getstate__(				self			)    ->    Any:
									"""simple docstring"""







									return self.__dict__.copy()




				def __setstate__(				self		,	lowercase__			)    ->    Any:
									"""simple docstring"""







									self.__dict__.update(_A			)


import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_A  					= 50_00_00

_A					,  _A  					= os.path.split(__file__)
_A  					= os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))



@get_duration
def 						lowercase_			(   A__		)							->							str:
    """simple docstring"""

    snake_case      =					dataset.select(range(0 ,		len(A__		) ,		2		)		)



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.sort("numbers"		)



@get_duration
def 						lowercase_			(   A__		)							->							Optional[int]:
    """simple docstring"""

    snake_case      =					dataset.shuffle()



@get_duration
def 						lowercase_			(   A__		)							->							int:
    """simple docstring"""

    snake_case      =					dataset.train_test_split(0.1		)



@get_duration
def 						lowercase_			(   A__ ,		A__=10		)							->							List[Any]:
    """simple docstring"""

    for shard_id in range(A__		):
        snake_case      =					dataset.shard(A__ ,		A__		)



def 						lowercase_			(   )							->							List[str]:
    """simple docstring"""

    snake_case      =					{"num examples": SPEED_TEST_N_EXAMPLES}
    snake_case      =					(select, sort, shuffle, train_test_split, shard)
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"		)
        snake_case      =					datasets.Features({"text": datasets.Value("string"		), "numbers": datasets.Value("float32"		)}		)
        snake_case      =					generate_example_dataset(
            os.path.join(A__ ,		"dataset.arrow"		) ,		A__ ,		num_examples=A__		)
        print("Functions"		)
        for func in functions:
            print(func.__name__		)
            snake_case      =					func(A__		)

    with open(A__ ,		"wb"		) as f:
        f.write(json.dumps(A__		).encode("utf-8"		)		)


if __name__ == "__main__":  # useful to run the profiler
   benchmark_indices_mapping()

