'''simple docstring'''





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 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         self.assertTrue(torch.allclose(gelu_python(lowercase__ )    ,							torch_builtin(lowercase__ ) ) )
         self.assertFalse(torch.allclose(gelu_python(lowercase__ )    ,							gelu_new(lowercase__ ) ) )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
         SCREAMING_SNAKE_CASE					:			str    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			Dict    	=       get_activation('gelu_10' )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch_builtin(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       geluaa(lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.where(y_gelu_aa < 1_0.0    ,							1    ,							0 )

         self.assertTrue(torch.max(lowercase__ ).item() == 1_0.0 )
         self.assertTrue(torch.allclose(y_gelu * clipped_mask    ,							y_gelu_aa * clipped_mask ) )






    def       _UpperCamelCase    (       self )							-> Optional[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(lowercase__ ):
              get_activation('bogus' )
         with self.assertRaises(lowercase__ ):
              get_activation(lowercase__ )






    def       _UpperCamelCase    (       self )							-> int:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       get_activation('gelu' )
         SCREAMING_SNAKE_CASE					:			str    	=       1
         SCREAMING_SNAKE_CASE					:			List[str]    	=       get_activation('gelu' )
         self.assertEqual(acta.a    ,							1 )
         with self.assertRaises(lowercase__ ):
              SCREAMING_SNAKE_CASE					:			Dict    	=       acta.a




'''simple docstring'''





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						0
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						3.0






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()    ,							{} )
         self.assertDictEqual(MockClass(a=2 ).to_kwargs()    ,							{'a': 2} )
         self.assertDictEqual(MockClass(a=2    ,							b=lowercase__ ).to_kwargs()    ,							{'a': 2, 'b': True} )
         self.assertDictEqual(MockClass(a=2    ,							c=2.2_5 ).to_kwargs()    ,							{'a': 2, 'c': 2.2_5} )






    @require_cuda
    def       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,							1_0_2_4.0 )
         self.assertEqual(scaler._growth_factor    ,							2.0 )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor    ,							0.5 )
         self.assertEqual(scaler._growth_interval    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


if __name__ == "__main__":
 _lowerCAmelCase  :int  				=    DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 _lowerCAmelCase  :int  				=    Accelerator(kwargs_handlers=[ddp_scaler])
 _lowerCAmelCase  :str  				=    torch.nn.Linear(100, 200)
 _lowerCAmelCase  :Optional[Any]  				=    accelerator.prepare(model)

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)



