






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    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> Optional[int]:

     '''simple docstring'''
     if isinstance(snake_case__   ,       (list, tuple)					):
          return honor_type(snake_case__   ,       (move_to_device(snake_case__   ,       snake_case__					) for t in state)					)
     elif isinstance(snake_case__   ,       snake_case__					):
          return type(snake_case__					)({k: move_to_device(snake_case__   ,       snake_case__					) for k, v in state.items()}					)
     elif isinstance(snake_case__   ,       torch.Tensor					):
          return state.to(snake_case__					)
     return state







class 							UpperCAmelCase_     (		torch.optim.Optimizer		):




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase=True  ,	_lowerCAmelCase=None		):
          UpperCAmelCase__			:     Optional[Any]										= optimizer
          UpperCAmelCase__			:     int										= scaler
          UpperCAmelCase__			:     Union[str, Any]										= AcceleratorState()
          UpperCAmelCase__			:     List[Any]										= GradientState()
          UpperCAmelCase__			:     Optional[Any]										= device_placement
          UpperCAmelCase__			:     Union[str, Any]										= False
          UpperCAmelCase__			:     Union[str, Any]										= None

          # Handle device placement
          if device_placement:
               UpperCAmelCase__			:     Optional[Any]										= 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__			:     Dict										= move_to_device(SCREAMING_SNAKE_CASE_  ,	self.accelerator_state.device		)
               self.optimizer.load_state_dict(SCREAMING_SNAKE_CASE_		)




     @property
     def      __UpperCAmelCase     (							self		):
          return self.optimizer.state




     @state.setter
     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     Optional[int]										= state




     @property
     def      __UpperCAmelCase     (							self		):
          return self.optimizer.param_groups




     @param_groups.setter
     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     Any										= param_groups




     @property
     def      __UpperCAmelCase     (							self		):
          return self.optimizer.defaults




     @defaults.setter
     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          UpperCAmelCase__			:     str										= defaults




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          self.optimizer.add_param_group(SCREAMING_SNAKE_CASE_		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          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		):
          return self.optimizer.state_dict()




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase=None		):
          if self.gradient_state.sync_gradients:
               UpperCAmelCase__			:     str										= 'set_to_none' in inspect.signature(self.optimizer.zero_grad		).parameters
               if accept_arg:
                    if set_to_none is None:
                         UpperCAmelCase__			:     List[Any]										= 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  ,	_lowerCAmelCase=None		):
          if self.gradient_state.sync_gradients:
               if self.accelerator_state.distributed_type == DistributedType.TPU:
                    UpperCAmelCase__			:     Tuple										= {'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__			:     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__			:     List[str]										= self.scaler.get_scale()
                         UpperCAmelCase__			:     Optional[Any]										= True
                    self.scaler.step(self.optimizer  ,	SCREAMING_SNAKE_CASE_		)
                    self.scaler.update()
                    UpperCAmelCase__			:     List[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__			:     List[str]										= scale_after < self._last_scale
                    UpperCAmelCase__			:     Optional[int]										= scale_after

               else:
                    self.optimizer.step(SCREAMING_SNAKE_CASE_		)




     def      __UpperCAmelCase     (							self  ,	_lowerCAmelCase		):
          for param_group in self.optimizer.param_groups:
               UpperCAmelCase__			:     Any										= [parameters_map.get(SCREAMING_SNAKE_CASE_  ,	SCREAMING_SNAKE_CASE_		) for p in param_group['params']]




     @property
     def      __UpperCAmelCase     (							self		):
          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		):
          return self._is_overflow




     def __getstate__(							self		):
          return self.__dict__.copy()




     def __setstate__(							self  ,	_lowerCAmelCase		):
          self.__dict__.update(SCREAMING_SNAKE_CASE_		)














import math
import os

import cva
import numpy as np

SCREAMING_SNAKE_CASE__		:       Optional[Any]   							=							255.0





def    _lowerCamelCase	(	__lowerCamelCase   ,       __lowerCamelCase					)   -> float:

     '''simple docstring'''
     UpperCAmelCase__			:     List[Any]										= np.mean((original - contrast) ** 2					)
     if mse == 0:
          return 100

     return 20 * math.logaa(PIXEL_MAX / math.sqrt(__lowerCamelCase					)					)





def    _lowerCamelCase	(	)   -> None:

     '''simple docstring'''
     UpperCAmelCase__			:     Union[str, Any]										= os.path.dirname(os.path.realpath(__lowerCamelCase					)					)
     # Loading images (original image and compressed image)
     UpperCAmelCase__			:     List[Any]										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/original_image.png"""					)					)
     UpperCAmelCase__			:     Dict										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/compressed_image.png"""					)   ,       1					)

     UpperCAmelCase__			:     Tuple										= cva.imread(os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-base.png"""					)					)
     UpperCAmelCase__			:     int										= cva.imread(
         os.path.join(__lowerCamelCase   ,       """image_data/PSNR-example-comp-10.jpg"""					)   ,       1					)

     # Value expected: 29.73dB
     print("""-- First Test --"""					)
     print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)

     # # Value expected: 31.53dB (Wikipedia Example)
     print("""\n-- Second Test --"""					)
     print(F"PSNR value is {peak_signal_to_noise_ratio(__lowerCamelCase   ,       __lowerCamelCase					)} dB"					)


if __name__ == "__main__":
      main()






