
import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       int				,       _snake_case		:       Optional[Any]				):
          if isinstance(_snake_case				,       _snake_case				):
              # Don't modify user's data should they want to reuse it (e.g. in tests), because once we
              # modified it, it will not be accepted here again, since `auto` values would have been overridden
              __lowercase		:			Tuple	    =       deepcopy(_snake_case				)
          elif os.path.exists(_snake_case				):
              with io.open(_snake_case				,       '''r'''				,       encoding='''utf-8'''				) as f:
                  __lowercase		:			str	    =       json.load(_snake_case				)
          else:
              try:
                  __lowercase		:			Optional[Any]	    =       baseaa.urlsafe_baadecode(_snake_case				).decode('''utf-8'''				)
                  __lowercase		:			List[Any]	    =       json.loads(_snake_case				)
              except (UnicodeDecodeError, AttributeError, ValueError):
                  raise ValueError(
                      F'Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}'				)

          __lowercase		:			Union[str, Any]	    =       config

          self.set_stage_and_offload()




      def 		snake_case_			(      self		:       Optional[Any]				):
          # zero stage - this is done as early as possible, before model is created, to allow
          # ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
          # during ``zero.Init()`` which needs to know the dtype, and some other hparams.
          __lowercase		:			Optional[Any]	    =       self.get_value('''zero_optimization.stage'''				,       -1				)

          # offload
          __lowercase		:			Optional[int]	    =       False
          if self.is_zeroa() or self.is_zeroa():
              __lowercase		:			Any	    =       set(['''cpu''', '''nvme''']				)
              __lowercase		:			List[str]	    =       set(
                  [
                      self.get_value('''zero_optimization.offload_optimizer.device'''				),
                      self.get_value('''zero_optimization.offload_param.device'''				),
                  ]				)
              if len(offload_devices & offload_devices_valid				) > 0:
                  __lowercase		:			str	    =       True




      def 		snake_case_			(      self		:       List[str]				,       _snake_case		:       str				):
          __lowercase		:			List[str]	    =       self.config

          # find the config node of interest if it exists
          __lowercase		:			Union[str, Any]	    =       ds_key_long.split('''.'''				)
          __lowercase		:			int	    =       nodes.pop()
          for node in nodes:
              __lowercase		:			List[str]	    =       config.get(_snake_case				)
              if config is None:
                  return None, ds_key

          return config, ds_key




      def 		snake_case_			(      self		:       Any				,       _snake_case		:       Any				,       _snake_case		:       Dict=None				):
          __lowercase    ,      __lowercase		:			Tuple	    =       self.find_config_node(_snake_case				)
          if config is None:
              return default
          return config.get(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       Any				,       _snake_case		:       List[str]=False				):
          __lowercase		:			List[Any]	    =       self.config

          # find the config node of interest if it exists
          __lowercase		:			Optional[Any]	    =       ds_key_long.split('''.'''				)
          for node in nodes:
              __lowercase		:			List[str]	    =       config
              __lowercase		:			List[str]	    =       config.get(_snake_case				)
              if config is None:
                  if must_exist:
                      raise ValueError(F'Can\'t find {ds_key_long} entry in the config: {self.config}'				)
                  else:
                      return

        # if found remove it
          if parent_config is not None:
              parent_config.pop(_snake_case				)




      def 		snake_case_			(      self		:       List[Any]				,       _snake_case		:       Dict				):
          __lowercase		:			Dict	    =       self.get_value(_snake_case				)
          return False if value is None else bool(_snake_case				)




      def 		snake_case_			(      self		:       int				,       _snake_case		:       List[str]				):
          __lowercase		:			str	    =       self.get_value(_snake_case				)
          return False if value is None else not bool(_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          return self._stage == 2




      def 		snake_case_			(      self		:       Any				):
          return self._stage == 3




      def 		snake_case_			(      self		:       str				):
          return self._offload






class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       Optional[int]				,       _snake_case		:       Dict				):
          __lowercase		:			str	    =       engine




      def 		snake_case_			(      self		:       Optional[Any]				,       _snake_case		:       Union[str, Any]				,       **_snake_case		:       Optional[int]				):
          # runs backpropagation and handles mixed precision
          self.engine.backward(_snake_case				,       **_snake_case				)

          # Deepspeed's `engine.step` performs the following operations:
          # - gradient accumulation check
          # - gradient clipping
          # - optimizer step
          # - zero grad
          # - checking overflow
          # - lr_scheduler step (only if engine.lr_scheduler is not None)
          self.engine.step()
          # and this plugin overrides the above calls with no-ops when Accelerate runs under
          # Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
          # training loop that works transparently under many training regimes.






class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      def __init__(      self		:       Union[str, Any]				,       _snake_case		:       Dict				):
          super().__init__(_snake_case				,       device_placement=_snake_case				,       scaler=_snake_case				)
          __lowercase		:			Any	    =       hasattr(self.optimizer				,       '''overflow'''				)




      def 		snake_case_			(      self		:       Dict				,       _snake_case		:       Union[str, Any]=None				):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




      def 		snake_case_			(      self		:       Optional[int]				):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




      @property
      def 		snake_case_			(      self		:       List[Any]				):
          if self.__has_overflow__:
              return self.optimizer.overflow
          return False






class 						__lowerCAmelCase	(	lowerCAmelCase_      ):



      """simple docstring"""







      def __init__(      self		:       List[Any]				,       _snake_case		:       Optional[int]				,       _snake_case		:       Dict				):
          super().__init__(_snake_case				,       _snake_case				)




      def 		snake_case_			(      self		:       str				):
          pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       List[Any]				,       _snake_case		:       Union[str, Any]				,       _snake_case		:       Union[str, Any]=0.0_01				,       _snake_case		:       List[Any]=0				,       **_snake_case		:       Tuple				):
          __lowercase		:			List[Any]	    =       params
          __lowercase		:			Dict	    =       lr
          __lowercase		:			int	    =       weight_decay
          __lowercase		:			str	    =       kwargs






class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       int				,       _snake_case		:       int				,       _snake_case		:       List[str]=None				,       _snake_case		:       str=0				,       **_snake_case		:       str				):
          __lowercase		:			str	    =       optimizer
          __lowercase		:			Optional[Any]	    =       total_num_steps
          __lowercase		:			Optional[int]	    =       warmup_num_steps
          __lowercase		:			Tuple	    =       kwargs




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Union[str, Any]	    =       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
          __lowercase		:			Any	    =       get_activation('''gelu'''				)
          self.assertTrue(torch.allclose(gelu_python(_snake_case				)				,       torch_builtin(_snake_case				)				)				)
          self.assertFalse(torch.allclose(gelu_python(_snake_case				)				,       gelu_new(_snake_case				)				)				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Any	    =       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]				)
          __lowercase		:			List[str]	    =       get_activation('''gelu'''				)
          __lowercase		:			Optional[Any]	    =       get_activation('''gelu_10'''				)

          __lowercase		:			Tuple	    =       torch_builtin(_snake_case				)
          __lowercase		:			List[Any]	    =       geluaa(_snake_case				)

          __lowercase		:			Tuple	    =       torch.where(y_gelu_aa < 10.0				,       1				,       0				)

          self.assertTrue(torch.max(_snake_case				).item() == 10.0				)
          self.assertTrue(torch.allclose(y_gelu * clipped_mask				,       y_gelu_aa * clipped_mask				)				)




      def 		snake_case_			(      self		:       Any				):
          get_activation('''gelu'''				)
          get_activation('''gelu_10'''				)
          get_activation('''gelu_fast'''				)
          get_activation('''gelu_new'''				)
          get_activation('''gelu_python'''				)
          get_activation('''gelu_pytorch_tanh'''				)
          get_activation('''linear'''				)
          get_activation('''mish'''				)
          get_activation('''quick_gelu'''				)
          get_activation('''relu'''				)
          get_activation('''sigmoid'''				)
          get_activation('''silu'''				)
          get_activation('''swish'''				)
          get_activation('''tanh'''				)
          with self.assertRaises(_snake_case				):
              get_activation('''bogus'''				)
          with self.assertRaises(_snake_case				):
              get_activation(_snake_case				)




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			Union[str, Any]	    =       get_activation('''gelu'''				)
          __lowercase		:			List[str]	    =       1
          __lowercase		:			Union[str, Any]	    =       get_activation('''gelu'''				)
          self.assertEqual(acta.a				,       1				)
          with self.assertRaises(_snake_case				):
              __lowercase		:			Tuple	    =       acta.a


