




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    (		UpperCamelCase_						):






			'''simple docstring'''





			UpperCamelCase__								=0
			UpperCamelCase__								=False
			UpperCamelCase__								=3.0






class    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								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=2.2_5  ).to_kwargs()       ,						{'''a''': 2, '''c''': 2.2_5}  )

			@require_cuda
			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   int:
								__magic_name__							:	Optional[Any]    							=							GradScalerKwargs(init_scale=1024       ,						growth_factor=2  )
								AcceleratorState._reset_state()
								__magic_name__							:	Any    							=							Accelerator(mixed_precision='''fp16'''       ,						kwargs_handlers=[scaler_handler]  )
								print(accelerator.use_fpaa  )
								__magic_name__							:	List[Any]    							=							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       ,						2000  )
								self.assertEqual(scaler._enabled       ,						__a  )


			@require_multi_gpu
			def 							UpperCAmelCase__       ( self			:   int  )						->   Union[str, Any]:
								__magic_name__							:	str    							=							['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__  )]
								execute_subprocess_async(__a       ,						env=os.environ.copy()  )


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

					# Check the values changed in kwargs
					__lowerCamelCase   :						int				=			''''''
					__lowerCamelCase   :						Optional[Any]				=			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 requests







def 		lowercase__   (		__A:	str							,__A:	str				):





					'''simple docstring'''






					__magic_name__							:	Tuple    							=							{'''Content-Type''': '''application/json'''}
					__magic_name__							:	Union[str, Any]    							=							requests.post(__A							,json={'''text''': message_body}							,headers=__A				)
					if response.status_code != 2_0_0:
										__magic_name__							:	int    							=							(
										    '''Request to slack returned an error '''
										    F'''{response.status_code}, the response is:\n{response.text}'''
										)
										raise ValueError(__A				)


if __name__ == "__main__":
	# Set the slack url to the one provided by Slack when you create the webhook at
	# https://my.slack.com/services/new/incoming-webhook/
	send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')



