

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  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Optional[Any]:






    '''simple docstring'''
    if isinstance(SCREAMING_SNAKE_CASE    ,				(list, tuple) ):
        return honor_type(SCREAMING_SNAKE_CASE    ,				(move_to_device(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) for t in state) )
    elif isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ):
        return type(SCREAMING_SNAKE_CASE )({k: move_to_device(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) for k, v in state.items()} )
    elif isinstance(SCREAMING_SNAKE_CASE    ,				torch.Tensor ):
        return state.to(SCREAMING_SNAKE_CASE )
    return state
class    A_				( torch.optim.Optimizer  ):


    '''simple docstring'''






    def __init__(self       , lowercase__       , lowercase__=True       , lowercase__=None					)       -> str:
        __UpperCAmelCase			   =     optimizer
        __UpperCAmelCase			   =     scaler
        __UpperCAmelCase			   =     AcceleratorState()
        __UpperCAmelCase			   =     GradientState()
        __UpperCAmelCase			   =     device_placement
        __UpperCAmelCase			   =     False
        __UpperCAmelCase			   =     None

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


    @property
    def        lowerCAmelCase_  (self					)       -> Dict:
        return self.optimizer.state


    @state.setter
    def        lowerCAmelCase_  (self       , lowercase__					)       -> Optional[Any]:
        __UpperCAmelCase			   =     state


    @property
    def        lowerCAmelCase_  (self					)       -> List[Any]:
        return self.optimizer.param_groups


    @param_groups.setter
    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        __UpperCAmelCase			   =     param_groups


    @property
    def        lowerCAmelCase_  (self					)       -> str:
        return self.optimizer.defaults


    @defaults.setter
    def        lowerCAmelCase_  (self       , lowercase__					)       -> Dict:
        __UpperCAmelCase			   =     defaults


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[Any]:
        self.optimizer.add_param_group(lowercase__					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
            xm.send_cpu_data_to_device(lowercase__       , self.accelerator_state.device					)
        self.optimizer.load_state_dict(lowercase__					)


    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        return self.optimizer.state_dict()


    def        lowerCAmelCase_  (self       , lowercase__=None					)       -> Optional[Any]:
        if self.gradient_state.sync_gradients:
            __UpperCAmelCase			   =     '''set_to_none''' in inspect.signature(self.optimizer.zero_grad					).parameters
            if accept_arg:
                if set_to_none is None:
                    __UpperCAmelCase			   =     False
                self.optimizer.zero_grad(set_to_none=lowercase__					)
            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        lowerCAmelCase_  (self       , lowercase__=None					)       -> List[Any]:
        if self.gradient_state.sync_gradients:
            if self.accelerator_state.distributed_type == DistributedType.TPU:
                __UpperCAmelCase			   =     {'''closure''': closure} if closure is not None else {}
                xm.optimizer_step(self.optimizer       , optimizer_args=lowercase__					)
            elif self.scaler is not None:
                __UpperCAmelCase			   =     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
                    __UpperCAmelCase			   =     self.scaler.get_scale()
                    __UpperCAmelCase			   =     True
                self.scaler.step(self.optimizer       , lowercase__					)
                self.scaler.update()
                __UpperCAmelCase			   =     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.
                    __UpperCAmelCase			   =     scale_after < self._last_scale
                __UpperCAmelCase			   =     scale_after

            else:
                self.optimizer.step(lowercase__					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        for param_group in self.optimizer.param_groups:
            __UpperCAmelCase			   =     [parameters_map.get(lowercase__       , lowercase__					) for p in param_group['''params''']]


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


    @property
    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        return self._is_overflow


    def __getstate__(self					)       -> Any:
        return self.__dict__.copy()






    def __setstate__(self       , lowercase__					)       -> List[Any]:
        self.__dict__.update(lowercase__					)







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




