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    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Dict:



      '''simple docstring'''

      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 			UpperCamelCase					(			torch.optim.Optimizer			):

     '''simple docstring'''

     def __init__(       self					,					A_					,					A_=True					,					A_=None    )			->					Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase           =					optimizer
           _lowerCamelCase           =					scaler
           _lowerCamelCase           =					AcceleratorState()
           _lowerCamelCase           =					GradientState()
           _lowerCamelCase           =					device_placement
           _lowerCamelCase           =					False
           _lowerCamelCase           =					None

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



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






           """simple docstring"""
           return self.optimizer.state



     @state.setter
     def 		UpperCamelCase_    (       self					,					A_    )			->					Tuple:






           """simple docstring"""
           _lowerCamelCase           =					state



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






           """simple docstring"""
           return self.optimizer.param_groups



     @param_groups.setter
     def 		UpperCamelCase_    (       self					,					A_    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					param_groups



     @property
     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           return self.optimizer.defaults



     @defaults.setter
     def 		UpperCamelCase_    (       self					,					A_    )			->					int:






           """simple docstring"""
           _lowerCamelCase           =					defaults



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[Any]:






           """simple docstring"""
           self.optimizer.add_param_group(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					Optional[Any]:






           """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 		UpperCamelCase_    (       self    )			->					str:






           """simple docstring"""
           return self.optimizer.state_dict()



     def 		UpperCamelCase_    (       self					,					A_=None    )			->					Tuple:






           """simple docstring"""
           if self.gradient_state.sync_gradients:
                 _lowerCamelCase           =					'''set_to_none''' in inspect.signature(self.optimizer.zero_grad    ).parameters
                 if accept_arg:
                       if set_to_none is None:
                             _lowerCamelCase           =					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 		UpperCamelCase_    (       self					,					A_=None    )			->					Optional[Any]:






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

                 else:
                       self.optimizer.step(A_    )



     def 		UpperCamelCase_    (       self					,					A_    )			->					List[Any]:






           """simple docstring"""
           for param_group in self.optimizer.param_groups:
                 _lowerCamelCase           =					[parameters_map.get(A_					,					A_    ) for p in param_group['''params''']]



     @property
     def 		UpperCamelCase_    (       self    )			->					int:






           """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 		UpperCamelCase_    (       self    )			->					int:






           """simple docstring"""
           return self._is_overflow



     def __getstate__(       self    )			->					List[Any]:






           """simple docstring"""
           return self.__dict__.copy()





     def __setstate__(       self					,					A_    )			->					Tuple:






           """simple docstring"""
           self.__dict__.update(A_    )
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case__						      =			abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(__UpperCAmelCase		)




def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      from transformers.testing_utils import pytest_terminal_summary_main

      _lowerCamelCase           =					terminalreporter.config.getoption('''--make-reports'''		)
      if make_reports:
            pytest_terminal_summary_main(__UpperCAmelCase			,    id=__UpperCAmelCase		)