






'''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 							_SCREAMING_SNAKE_CASE	(					A	:							List[Any]	,		A	:							List[Any]	)     ->			Optional[Any]:







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





class a_     (     torch.optim.Optimizer				):







   def __init__(self       , __a       , __a=True       , __a=None)      ->   List[str]:
      """simple docstring"""






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

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







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      return self.optimizer.state







   @state.setter
   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Any:
      """simple docstring"""






      __snake_case					:   int  				=			state







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      return self.optimizer.param_groups







   @param_groups.setter
   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			param_groups







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      return self.optimizer.defaults







   @defaults.setter
   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   int  				=			defaults







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   Tuple:
      """simple docstring"""






      self.optimizer.add_param_group(_lowerCAmelCase)







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   str:
      """simple docstring"""






      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      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      return self.optimizer.state_dict()







   def      SCREAMING_SNAKE_CASE__       (self       , __a=None)      ->   Union[str, Any]:
      """simple docstring"""






      if self.gradient_state.sync_gradients:
         __snake_case					:   List[str]  				=			"set_to_none" in inspect.signature(self.optimizer.zero_grad).parameters
         if accept_arg:
            if set_to_none is None:
               __snake_case					:   Optional[Any]  				=			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      SCREAMING_SNAKE_CASE__       (self       , __a=None)      ->   Any:
      """simple docstring"""






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

         else:
            self.optimizer.step(_lowerCAmelCase)







   def      SCREAMING_SNAKE_CASE__       (self       , __a)      ->   List[Any]:
      """simple docstring"""






      for param_group in self.optimizer.param_groups:
         __snake_case					:   str  				=			[parameters_map.get(_lowerCAmelCase       , _lowerCAmelCase) for p in param_group["params"]]







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """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.'       , _lowerCAmelCase       , )
      return self._is_overflow







   @property
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      return self._is_overflow







   def __getstate__(self)      ->   int:
      """simple docstring"""






      return self.__dict__.copy()







   def __setstate__(self       , __a)      ->   Any:
      """simple docstring"""






      self.__dict__.update(_lowerCAmelCase)





import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class  __lowerCAmelCase							(				unittest.TestCase   ,			lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase     :Any   						=    load_tool("image-segmentation"					)
         self.tool.setup()
         lowercase     :int   						=    load_tool("image-segmentation"							,       remote=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :Any   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         lowercase     :Dict   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[Any]   						=    self.remote_tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase     :Optional[Any]   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple					):
         lowercase     :int   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[str]   						=    self.remote_tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)



