






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





class 	lowerCamelCase__				(							torch.optim.Optimizer ):






      def __init__(self					:    Optional[int]		,		_snake_case					:    Optional[Any]		,		_snake_case					:    str=True		,		_snake_case					:    Optional[int]=None		)       ->   Optional[Any]:


       """simple docstring"""






       lowerCamelCase_							:    Union[str, Any]							=     optimizer
       lowerCamelCase_							:    Union[str, Any]							=     scaler
       lowerCamelCase_							:    Optional[int]							=     AcceleratorState()
       lowerCamelCase_							:    int							=     GradientState()
       lowerCamelCase_							:    Dict							=     device_placement
       lowerCamelCase_							:    List[str]							=     False
       lowerCamelCase_							:    Optional[Any]							=     None

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






      @property
      def      UpperCAmelCase_     (self					:    Optional[Any]		)       ->   str:


       """simple docstring"""






       return self.optimizer.state






      @state.setter
      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    Union[str, Any]		)       ->   Any:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     state






      @property
      def      UpperCAmelCase_     (self					:    Tuple		)       ->   Optional[Any]:


       """simple docstring"""






       return self.optimizer.param_groups






      @param_groups.setter
      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    Optional[int]		)       ->   Dict:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     param_groups






      @property
      def      UpperCAmelCase_     (self					:    Dict		)       ->   List[Any]:


       """simple docstring"""






       return self.optimizer.defaults






      @defaults.setter
      def      UpperCAmelCase_     (self					:    Optional[Any]		,		_snake_case					:    Tuple		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    int							=     defaults






      def      UpperCAmelCase_     (self					:    Optional[Any]		,		_snake_case					:    Tuple		)       ->   str:


       """simple docstring"""






       self.optimizer.add_param_group(_snake_case		)






      def      UpperCAmelCase_     (self					:    Dict		,		_snake_case					:    List[Any]		)       ->   Any:


       """simple docstring"""






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






      def      UpperCAmelCase_     (self					:    Union[str, Any]		)       ->   Optional[Any]:


       """simple docstring"""






       return self.optimizer.state_dict()






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    Tuple=None		)       ->   Dict:


       """simple docstring"""






       if self.gradient_state.sync_gradients:
        lowerCamelCase_							:    Any							=     'set_to_none' in inspect.signature(self.optimizer.zero_grad		).parameters
        if accept_arg:
         if set_to_none is None:
          lowerCamelCase_							:    List[str]							=     False
         self.optimizer.zero_grad(set_to_none=_snake_case		)
        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					:    Dict		,		_snake_case					:    Optional[Any]=None		)       ->   Dict:


       """simple docstring"""






       if self.gradient_state.sync_gradients:
        if self.accelerator_state.distributed_type == DistributedType.TPU:
         lowerCamelCase_							:    Optional[Any]							=     {'closure': closure} if closure is not None else {}
         xm.optimizer_step(self.optimizer		,		optimizer_args=_snake_case		)
        elif self.scaler is not None:
         lowerCamelCase_							:    List[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
          lowerCamelCase_							:    Optional[Any]							=     self.scaler.get_scale()
          lowerCamelCase_							:    Optional[Any]							=     True
         self.scaler.step(self.optimizer		,		_snake_case		)
         self.scaler.update()
         lowerCamelCase_							:    Tuple							=     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_							:    Dict							=     scale_after < self._last_scale
         lowerCamelCase_							:    int							=     scale_after

        else:
         self.optimizer.step(_snake_case		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    Optional[Any]		)       ->   Union[str, Any]:


       """simple docstring"""






       for param_group in self.optimizer.param_groups:
        lowerCamelCase_							:    Union[str, Any]							=     [parameters_map.get(_snake_case		,		_snake_case		) for p in param_group['params']]






      @property
      def      UpperCAmelCase_     (self					:    Optional[int]		)       ->   str:


       """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.'		,		_snake_case		,		)
       return self._is_overflow






      @property
      def      UpperCAmelCase_     (self					:    Dict		)       ->   Optional[int]:


       """simple docstring"""






       return self._is_overflow






      def __getstate__(self					:    Any		)       ->   Any:


       """simple docstring"""






       return self.__dict__.copy()






      def __setstate__(self					:    List[str]		,		_snake_case					:    str		)       ->   List[Any]:


       """simple docstring"""






       self.__dict__.update(_snake_case		)








import math




class 	lowerCamelCase__				:






      def __init__(self					:    int		,		_snake_case					:    Dict		)       ->   Union[str, Any]:


       """simple docstring"""






       lowerCamelCase_							:    Optional[Any]							=     len(_snake_case		)
       lowerCamelCase_							:    Optional[Any]							=     [0] * (
           4 * self.N
       )  # approximate the overall size of segment tree with array N
       self.build(1		,		0		,		self.N - 1		)






      def      UpperCAmelCase_     (self					:    List[str]		,		_snake_case					:    List[str]		)       ->   int:


       """simple docstring"""






       return idx * 2






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    Dict		)       ->   Optional[Any]:


       """simple docstring"""






       return idx * 2 + 1






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    str		,		_snake_case					:    List[Any]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if l == r:
        lowerCamelCase_							:    List[Any]							=     A[l]
       else:
        lowerCamelCase_							:    int							=     (l + r) // 2
        self.build(self.left(_snake_case		)		,		_snake_case		,		_snake_case		)
        self.build(self.right(_snake_case		)		,		mid + 1		,		_snake_case		)
        lowerCamelCase_							:    Tuple							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)






      def      UpperCAmelCase_     (self					:    str		,		_snake_case					:    Tuple		,		_snake_case					:    Any		,		_snake_case					:    str		)       ->   Tuple:


       """simple docstring"""






       return self.update_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    Tuple		,		_snake_case					:    List[str]		,		_snake_case					:    Tuple		,		_snake_case					:    Optional[int]		,		_snake_case					:    int		,		_snake_case					:    Tuple		,		_snake_case					:    List[str]		)       ->   Optional[Any]:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return True
       if l == r:
        lowerCamelCase_							:    Tuple							=     val
        return True
       lowerCamelCase_							:    Optional[int]							=     (l + r) // 2
       self.update_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       self.update_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     max(self.st[self.left(_snake_case		)]		,		self.st[self.right(_snake_case		)]		)
       return True






      def      UpperCAmelCase_     (self					:    List[Any]		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[int]		)       ->   str:


       """simple docstring"""






       return self.query_recursive(1		,		0		,		self.N - 1		,		a - 1		,		b - 1		)






      def      UpperCAmelCase_     (self					:    Any		,		_snake_case					:    List[Any]		,		_snake_case					:    Optional[Any]		,		_snake_case					:    int		,		_snake_case					:    Optional[int]		,		_snake_case					:    Optional[Any]		)       ->   str:  # noqa: E741


       """simple docstring"""






       if r < a or l > b:
        return -math.inf
       if l >= a and r <= b:
        return self.st[idx]
       lowerCamelCase_							:    int							=     (l + r) // 2
       lowerCamelCase_							:    Optional[Any]							=     self.query_recursive(self.left(_snake_case		)		,		_snake_case		,		_snake_case		,		_snake_case		,		_snake_case		)
       lowerCamelCase_							:    Union[str, Any]							=     self.query_recursive(self.right(_snake_case		)		,		mid + 1		,		_snake_case		,		_snake_case		,		_snake_case		)
       return max(_snake_case		,		_snake_case		)






      def      UpperCAmelCase_     (self					:    str		)       ->   Tuple:


       """simple docstring"""






       lowerCamelCase_							:    Dict							=     []
       for i in range(1		,		N + 1		):
        show_list += [self.query(_snake_case		,		_snake_case		)]
       print(_snake_case		)


if __name__ == "__main__":
       UpperCamelCase											=      [1, 2, -4, 7, 3, -5, 6, 1_1, -2_0, 9, 1_4, 1_5, 5, 2, -8]
       UpperCamelCase											=      1_5
       UpperCamelCase											=      SegmentTree(A)
       print(segt.query(4, 6))
       print(segt.query(7, 1_1))
       print(segt.query(7, 1_2))
       segt.update(1, 3, 1_1_1)
       print(segt.query(1, 1_5))
       segt.update(7, 8, 2_3_5)
       segt.show_data()
