

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        _a		(__magic_name__							):


					'''simple docstring'''






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



class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						# 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=2.2_5     ).to_kwargs()       ,			{"""a""": 2, """c""": 2.2_5}     )






					@require_cuda
					def     __A     (					self     ):
						# If no defaults are changed, `to_kwargs` returns an empty dict.
						A__				:       List[Any]	  =						GradScalerKwargs(init_scale=1024       ,			growth_factor=2     )
						AcceleratorState._reset_state()
						A__				:       List[Any]	  =						Accelerator(mixed_precision="""fp16"""       ,			kwargs_handlers=[scaler_handler]     )
						print(accelerator.use_fpaa     )
						A__				:       Optional[int]	  =						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       ,			2000     )
						self.assertEqual(scaler._enabled       ,			A__     )






					@require_multi_gpu
					def     __A     (					self     ):
						A__				:       List[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__":
	A_			:					str    			=     DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	A_			:					Union[str, Any]    			=     Accelerator(kwargs_handlers=[ddp_scaler])
	A_			:					Any    			=     torch.nn.Linear(100, 200)
	A_			:					List[Any]    			=     accelerator.prepare(model)

	# Check the values changed in kwargs
	A_			:					str    			=     ''
	A_			:					List[str]    			=     model.bucket_bytes_cap // (1024 * 1024)
	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 import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
	import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin



@require_torch
class        _a		(unittest.TestCase						,  __magic_name__							):


					'''simple docstring'''






					def     __A     (					self     ):
						A__				:       List[str]	  =						load_tool("""text-to-speech"""     )
						self.tool.setup()






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Tuple	  =						self.tool("""hey"""     )
						A__				:       Tuple	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )






					def     __A     (					self     ):
						# SpeechT5 isn't deterministic
						torch.manual_seed(0     )
						A__				:       Any	  =						self.tool("""hey"""     )
						A__				:       Optional[Any]	  =						result.to_raw()
						self.assertTrue(
						    torch.allclose(
						        resulting_tensor[:3]       ,			torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]     )       ,			)     )



