
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 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							int									=		0
     UpperCAmelCase__    :							bool									=		False
     UpperCAmelCase__    :							float									=		3.0







class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   int       )						->						List[Any]:
         # 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=_A       ).to_kwargs()    ,   {"a": 2, "b": True}       )
         self.assertDictEqual(MockClass(a=2    ,   c=2.25       ).to_kwargs()    ,   {"a": 2, "c": 2.25}       )





     @require_cuda
     def 		UpperCAmelCase(self				:   Any       )						->						List[Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         snake_case								= GradScalerKwargs(init_scale=1_0_2_4    ,   growth_factor=2       )
         AcceleratorState._reset_state()
         snake_case								= Accelerator(mixed_precision="fp16"    ,   kwargs_handlers=[scaler_handler]       )
         print(accelerator.use_fpaa       )
         snake_case								= 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_0_0_0       )
         self.assertEqual(scaler._enabled    ,   _A       )





     @require_multi_gpu
     def 		UpperCAmelCase(self				:   List[Any]       )						->						Any:
         snake_case								= ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__       )]
         execute_subprocess_async(_A    ,   env=os.environ.copy()       )


if __name__ == "__main__":
   _A   =			DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
   _A   =			Accelerator(kwargs_handlers=[ddp_scaler])
   _A   =			torch.nn.Linear(1_00, 2_00)
   _A   =			accelerator.prepare(model)

   # Check the values changed in kwargs
   _A   =			""
   _A   =			model.bucket_bytes_cap // (10_24 * 10_24)
   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)






import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)







class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   Tuple       )						->						List[str]:
         snake_case								= ["a", "b", "c"]

         # Defaults to last layer if both are None
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   _A    ,   _A       )
         self.assertEqual(_A    ,   ["c"]       )
         self.assertEqual(_A    ,   [2]       )

         # Out indices set to match out features
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(["a", "c"]    ,   _A    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [0, 2]       )

         # Out features set to match out indices
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   [0, 2]    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [0, 2]       )

         # Out features selected from negative indices
         snake_case					,							snake_case								= get_aligned_output_features_output_indices(_A    ,   [-3, -1]    ,   _A       )
         self.assertEqual(_A    ,   ["a", "c"]       )
         self.assertEqual(_A    ,   [-3, -1]       )





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         # Stage names must be set
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 1)    ,   _A       )

         # Out features must be a list
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(("a", "b")    ,   (0, 1)    ,   ["a", "b"]       )

         # Out features must be a subset of stage names
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 1)    ,   ["a"]       )

         # Out indices must be a list or tuple
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(_A    ,   0    ,   ["a", "b"]       )

         # Out indices must be a subset of stage names
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(_A    ,   (0, 1)    ,   ["a"]       )

         # Out features and out indices must be the same length
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0,)    ,   ["a", "b", "c"]       )

         # Out features should match out indices
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 2)    ,   ["a", "b", "c"]       )

         # Out features and out indices should be in order
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["b", "a"]    ,   (0, 1)    ,   ["a", "b"]       )

         # Check passes with valid inputs
         verify_out_features_out_indices(["a", "b", "d"]    ,   (0, 1, -1)    ,   ["a", "b", "c", "d"]       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						str:
         snake_case								= BackboneMixin()

         snake_case								= ["a", "b", "c"]
         snake_case								= ["a", "c"]
         snake_case								= [0, 2]

         # Check that the output features and indices are set correctly
         self.assertEqual(backbone.out_features    ,   ["a", "c"]       )
         self.assertEqual(backbone.out_indices    ,   [0, 2]       )

         # Check out features and indices are updated correctly
         snake_case								= ["a", "b"]
         self.assertEqual(backbone.out_features    ,   ["a", "b"]       )
         self.assertEqual(backbone.out_indices    ,   [0, 1]       )

         snake_case								= [-3, -1]
         self.assertEqual(backbone.out_features    ,   ["a", "c"]       )
         self.assertEqual(backbone.out_indices    ,   [-3, -1]       )

