





"""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_	)    ->						Optional[int]:




      """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    __SCREAMING_SNAKE_CASE			(  torch.optim.Optimizer      ):







  '''simple docstring'''


  def __init__(						self      :				str    ,      __a      :				Dict    ,      __a      :				Any=True    ,      __a      :				Dict=None		)						-> Dict:
        _UpperCamelCase					:		Optional[Any]  =				optimizer
        _UpperCamelCase					:		Any  =				scaler
        _UpperCamelCase					:		Tuple  =				AcceleratorState()
        _UpperCamelCase					:		List[str]  =				GradientState()
        _UpperCamelCase					:		Optional[Any]  =				device_placement
        _UpperCamelCase					:		Union[str, Any]  =				False
        _UpperCamelCase					:		Optional[int]  =				None

        # Handle device placement
        if device_placement:
              _UpperCamelCase					:		Any  =				self.optimizer.state_dict()
              if self.accelerator_state.distributed_type == DistributedType.TPU:
                    xm.send_cpu_data_to_device(__a    ,      self.accelerator_state.device		)
              else:
                    _UpperCamelCase					:		Optional[int]  =				move_to_device(__a    ,      self.accelerator_state.device		)
              self.optimizer.load_state_dict(__a		)

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

  @state.setter
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]    ,      __a      :				List[Any]		)						-> Tuple:
        _UpperCamelCase					:		int  =				state

  @property
  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[int]		)						-> Any:
        return self.optimizer.param_groups

  @param_groups.setter
  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				int		)						-> Optional[int]:
        _UpperCamelCase					:		Any  =				param_groups

  @property
  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]		)						-> Optional[int]:
        return self.optimizer.defaults

  @defaults.setter
  def       __SCREAMING_SNAKE_CASE (						self      :				str    ,      __a      :				int		)						-> List[Any]:
        _UpperCamelCase					:		str  =				defaults

  def       __SCREAMING_SNAKE_CASE (						self      :				List[Any]    ,      __a      :				Optional[Any]		)						-> int:
        self.optimizer.add_param_group(__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Optional[Any]    ,      __a      :				str		)						-> List[Any]:
        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       __SCREAMING_SNAKE_CASE (						self      :				List[str]		)						-> List[Any]:
        return self.optimizer.state_dict()

  def       __SCREAMING_SNAKE_CASE (						self      :				List[str]    ,      __a      :				int=None		)						-> Optional[Any]:
        if self.gradient_state.sync_gradients:
              _UpperCamelCase					:		List[Any]  =				"set_to_none" in inspect.signature(self.optimizer.zero_grad		).parameters
              if accept_arg:
                    if set_to_none is None:
                          _UpperCamelCase					:		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       __SCREAMING_SNAKE_CASE (						self      :				Union[str, Any]    ,      __a      :				Tuple=None		)						-> int:
        if self.gradient_state.sync_gradients:
              if self.accelerator_state.distributed_type == DistributedType.TPU:
                    _UpperCamelCase					:		Dict  =				{"closure": closure} if closure is not None else {}
                    xm.optimizer_step(self.optimizer    ,      optimizer_args=__a		)
              elif self.scaler is not None:
                    _UpperCamelCase					:		Union[str, Any]  =				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					:		Any  =				self.scaler.get_scale()
                          _UpperCamelCase					:		List[str]  =				True
                    self.scaler.step(self.optimizer    ,      __a		)
                    self.scaler.update()
                    _UpperCamelCase					:		Union[str, 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.
                          _UpperCamelCase					:		Optional[int]  =				scale_after < self._last_scale
                    _UpperCamelCase					:		Union[str, Any]  =				scale_after

              else:
                    self.optimizer.step(__a		)

  def       __SCREAMING_SNAKE_CASE (						self      :				Tuple    ,      __a      :				Tuple		)						-> Any:
        for param_group in self.optimizer.param_groups:
              _UpperCamelCase					:		Union[str, Any]  =				[parameters_map.get(__a    ,      __a		) for p in param_group["params"]]

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

  def __getstate__(						self      :				Any		)						-> Union[str, Any]:
        return self.__dict__.copy()


  def __setstate__(						self      :				Union[str, Any]    ,      __a      :				Dict		)						-> Tuple:
        self.__dict__.update(__a		)







"""simple docstring"""


from __future__ import annotations




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      assert n > 1 and a > 0 and greatest_common_divisor(lowercase_						,lowercase_	) == 1
      ((_UpperCamelCase),     (_UpperCamelCase),     (_UpperCamelCase))					:		int  =				extended_gcd(lowercase_						,lowercase_	)  # Implemented below
      _UpperCamelCase					:		Any  =				(b * s) % n
      return x




def     lowercase__			(    lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      ((_UpperCamelCase),     (_UpperCamelCase))					:		List[Any]  =				extended_euclid(lowercase_						,lowercase_	)  # Implemented below
      if b < 0:
            _UpperCamelCase					:		Any  =				(b % n + n) % n
      return b




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      _UpperCamelCase					:		Union[str, Any]  =				invert_modulo(lowercase_						,lowercase_	)
      _UpperCamelCase					:		Any  =				(b * s) % n
      return x




def     lowercase__			(    lowercase_						,lowercase_	)    ->						tuple[int, int, int]:




      """simple docstring"""

      assert a >= 0 and b >= 0

      if b == 0:
            _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		str  =				a, 1, 0
      else:
            ((_UpperCamelCase),     (_UpperCamelCase),     (_UpperCamelCase))					:		str  =				extended_gcd(lowercase_						,a % b	)
            _UpperCamelCase					:		Dict  =				q
            _UpperCamelCase					:		Tuple  =				p - q * (a // b)

      assert a % d == 0 and b % d == 0
      assert d == a * x + b * y

      return (d, x, y)




def     lowercase__			(    lowercase_						,lowercase_	)    ->						tuple[int, int]:




      """simple docstring"""

      if b == 0:
            return (1, 0)
      ((_UpperCamelCase),     (_UpperCamelCase))					:		Optional[Any]  =				extended_euclid(lowercase_						,a % b	)
      _UpperCamelCase					:		Dict  =				a // b
      return (y, x - k * y)




def     lowercase__			(    lowercase_						,lowercase_	)    ->						int:




      """simple docstring"""

      if a < b:
            _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				b, a

      while a % b != 0:
            _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				b, a % b

      return b


if __name__ == "__main__":
  from doctest import testmod

  testmod(name="modular_division", verbose=True)
  testmod(name="modular_division2", verbose=True)
  testmod(name="invert_modulo", verbose=True)
  testmod(name="extended_gcd", verbose=True)
  testmod(name="extended_euclid", verbose=True)
  testmod(name="greatest_common_divisor", verbose=True)
