


"""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			(  A    ,       A			):
   if isinstance(_UpperCAmelCase    ,       (list, tuple)			):
      return honor_type(_UpperCAmelCase    ,       (move_to_device(_UpperCAmelCase    ,       _UpperCAmelCase			) for t in state)			)
   elif isinstance(_UpperCAmelCase    ,       _UpperCAmelCase			):
      return type(_UpperCAmelCase			)({k: move_to_device(_UpperCAmelCase    ,       _UpperCAmelCase			) for k, v in state.items()}			)
   elif isinstance(_UpperCAmelCase    ,       torch.Tensor			):
      return state.to(_UpperCAmelCase			)
   return state
class 						__lowerCAmelCase   (			torch.optim.Optimizer       ):







  '''simple docstring'''


  def __init__(      self:		int				,			UpperCamelCase_:		List[str]				,			UpperCamelCase_:		Tuple=True				,			UpperCamelCase_:		Tuple=None					):
     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(SCREAMING_SNAKE_CASE_				,			self.accelerator_state.device					)
        else:
           UpperCamelCase_     =move_to_device(SCREAMING_SNAKE_CASE_				,			self.accelerator_state.device					)
        self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_					)
  @property
  def 					UpperCamelCase__   (      self:		Union[str, Any]					):
     return self.optimizer.state
  @state.setter
  def 					UpperCamelCase__   (      self:		Optional[Any]				,			UpperCamelCase_:		List[str]					):
     UpperCamelCase_     =state
  @property
  def 					UpperCamelCase__   (      self:		List[str]					):
     return self.optimizer.param_groups
  @param_groups.setter
  def 					UpperCamelCase__   (      self:		int				,			UpperCamelCase_:		List[Any]					):
     UpperCamelCase_     =param_groups
  @property
  def 					UpperCamelCase__   (      self:		Dict					):
     return self.optimizer.defaults
  @defaults.setter
  def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		Tuple					):
     UpperCamelCase_     =defaults
  def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Optional[Any]					):
     self.optimizer.add_param_group(SCREAMING_SNAKE_CASE_					)
  def 					UpperCamelCase__   (      self:		List[Any]				,			UpperCamelCase_:		Dict					):
     if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
        xm.send_cpu_data_to_device(SCREAMING_SNAKE_CASE_				,			self.accelerator_state.device					)
     self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_					)
  def 					UpperCamelCase__   (      self:		List[str]					):
     return self.optimizer.state_dict()
  def 					UpperCamelCase__   (      self:		Optional[int]				,			UpperCamelCase_:		str=None					):
     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=SCREAMING_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:		int				,			UpperCamelCase_:		List[Any]=None					):
     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=SCREAMING_SNAKE_CASE_					)
        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				,			SCREAMING_SNAKE_CASE_					)
           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(SCREAMING_SNAKE_CASE_					)
  def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		Union[str, Any]					):
     for param_group in self.optimizer.param_groups:
        UpperCamelCase_     =[parameters_map.get(SCREAMING_SNAKE_CASE_				,			SCREAMING_SNAKE_CASE_					) for p in param_group["params"]]
  @property
  def 					UpperCamelCase__   (      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."				,			SCREAMING_SNAKE_CASE_				,			)
     return self._is_overflow
  @property
  def 					UpperCamelCase__   (      self:		Tuple					):
     return self._is_overflow
  def __getstate__(      self:		Dict					):
     return self.__dict__.copy()





  def __setstate__(      self:		List[Any]				,			UpperCamelCase_:		Any					):
     self.__dict__.update(SCREAMING_SNAKE_CASE_					)








"""simple docstring"""






from __future__ import annotations

from pathlib import Path




def 						_UpperCamelCase			(  A    ,       A			):
   return pointa[0] * pointa[1] - pointa[1] * pointa[0]




def 						_UpperCamelCase			(  A    ,       A    ,       A    ,       A    ,       A    ,       A			):
   UpperCamelCase_     =(xa, ya)
   UpperCamelCase_     =(xa - xa, ya - ya)
   UpperCamelCase_     =(xa - xa, ya - ya)
   UpperCamelCase_     =-vector_product(A    ,       A			) / vector_product(
       A    ,       A			)
   UpperCamelCase_     =+vector_product(A    ,       A			) / vector_product(
       A    ,       A			)

   return a > 0 and b > 0 and a + b < 1




def 						_UpperCamelCase			(  A = "p102_triangles.txt"			):
   UpperCamelCase_     =Path(A			).parent.joinpath(A			).read_text(encoding="utf-8"			)

   UpperCamelCase_     =[]
   for line in data.strip().split("\n"			):
      triangles.append([int(A			) for number in line.split(","			)]			)

   UpperCamelCase_     =0
   UpperCamelCase_     =42

   for triangle in triangles:
      ret += contains_origin(*A			)

   return ret


if __name__ == "__main__":
    print(f'''{solution() = }''')
