
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        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : int			  =       0
		__lowercase : bool			  =       False
		__lowercase : float			  =       3.0






class        lowercase      (	unittest.TestCase     ):



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				# 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 )						->      int:
				"""simple docstring"""
				# If no defaults are changed, `to_kwargs` returns an empty dict.
				UpperCamelCase						=  GradScalerKwargs(init_scale=1_024       ,   growth_factor=2 )
				AcceleratorState._reset_state()
				UpperCamelCase						=  Accelerator(mixed_precision='fp16'       ,   kwargs_handlers=[scaler_handler] )
				print(accelerator.use_fpaa )
				UpperCamelCase						=  accelerator.scaler

				# Check the kwargs have been applied
				self.assertEqual(scaler._init_scale       ,   1024.0 )
				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       ,   A_ )



		@require_multi_gpu
		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				UpperCamelCase						=  ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__ )]
				execute_subprocess_async(A_       ,   env=os.environ.copy() )


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

					# Check the values changed in kwargs
					_UpperCAmelCase      :				Dict          = ""
					_UpperCAmelCase      :				Any          = 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)


import re


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		if len(re.findall('[ATCG]'					, lowercase				)				) != len(lowercase				):
				raise ValueError('Invalid Strand'				)

		return dna.translate(dna.maketrans('ATCG'					, 'TAGC'				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
