




"""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     __snake_case   (							_lowercase ,_lowercase						):


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







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=True					,				lowerCamelCase_=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(lowerCamelCase__					,				self.accelerator_state.device)
           else:
               UpperCamelCase										=  move_to_device(lowerCamelCase__					,				self.accelerator_state.device)
           self.optimizer.load_state_dict(lowerCamelCase__)







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







   @state.setter
   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[int]:
       UpperCamelCase										=  state







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







   @param_groups.setter
   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      str:
       UpperCamelCase										=  param_groups







   @property
   def      UpperCAmelCase__ (		self)							->      Tuple:
       return self.optimizer.defaults







   @defaults.setter
   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Tuple:
       UpperCamelCase										=  defaults







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Union[str, Any]:
       self.optimizer.add_param_group(lowerCamelCase__)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[int]:
       if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
           xm.send_cpu_data_to_device(lowerCamelCase__					,				self.accelerator_state.device)
       self.optimizer.load_state_dict(lowerCamelCase__)







   def      UpperCAmelCase__ (		self)							->      int:
       return self.optimizer.state_dict()







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_=None)							->      Tuple:
       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=lowerCamelCase__)
           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					,				lowerCamelCase_=None)							->      str:
       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=lowerCamelCase__)
           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					,				lowerCamelCase__)
               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(lowerCamelCase__)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Dict:
       for param_group in self.optimizer.param_groups:
           UpperCamelCase										=  [parameters_map.get(lowerCamelCase__					,				lowerCamelCase__) for p in param_group["params"]]







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







   @property
   def      UpperCAmelCase__ (		self)							->      int:
       return self._is_overflow







   def __getstate__(		self)							->      List[str]:
       return self.__dict__.copy()







   def __setstate__(		self					,				lowerCamelCase_)							->      Dict:
       self.__dict__.update(lowerCamelCase__)





"""simple docstring"""







import os
from string import ascii_letters

SCREAMING_SNAKE_CASE_      =					ascii_letters + ' \t\n'





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  os.path.split(os.path.realpath(_lowercase						)						)
    UpperCamelCase										=  {}
    with open(path[0] + '''/dictionary.txt'''						) as dictionary_file:
        for word in dictionary_file.read().split('''\n'''						):
            UpperCamelCase										=  None
    return english_words


SCREAMING_SNAKE_CASE_      =					load_dictionary()





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  message.upper()
    UpperCamelCase										=  remove_non_letters(_lowercase						)
    UpperCamelCase										=  message.split()
    UpperCamelCase										=  len([word for word in possible_words if word in ENGLISH_WORDS]						)
    return float(_lowercase						) / len(_lowercase						)





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE						)





def     __snake_case   (							_lowercase ,_lowercase = 20 ,_lowercase = 85						):


    """simple docstring"""







    UpperCamelCase										=  get_english_count(_lowercase						) * 100 >= word_percentage
    UpperCamelCase										=  len(remove_non_letters(_lowercase						)						)
    UpperCamelCase										=  (float(_lowercase						) / len(_lowercase						)) * 100
    UpperCamelCase										=  message_letters_percentage >= letter_percentage
    return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()