






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  (  _A		):




      """simple docstring"""



      A          =    0
      A          =    False
      A          =    3.0







class 	_UpperCAmelCase  (  unittest.TestCase		):




      """simple docstring"""



      def 			snake_case_      (   self		):



          '''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=_lowerCAmelCase		).to_kwargs()    ,	{"a": 2, "b": True}		)
          self.assertDictEqual(MockClass(a=2    ,	c=2.25		).to_kwargs()    ,	{"a": 2, "c": 2.25}		)
      @require_cuda
      def 			snake_case_      (   self		):



          '''simple docstring'''
          # If no defaults are changed, `to_kwargs` returns an empty dict.
          lowerCAmelCase__   :int      							=				GradScalerKwargs(init_scale=1_024    ,	growth_factor=2		)
          AcceleratorState._reset_state()
          lowerCAmelCase__   :List[Any]      							=				Accelerator(mixed_precision="fp16"    ,	kwargs_handlers=[scaler_handler]		)
          print(accelerator.use_fpaa		)
          lowerCAmelCase__   :str      							=				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    ,	_lowerCAmelCase		)



      @require_multi_gpu
      def 			snake_case_      (   self		):



          '''simple docstring'''
          lowerCAmelCase__   :List[Any]      							=				["torchrun", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__		)]
          execute_subprocess_async(_lowerCAmelCase    ,	env=os.environ.copy()		)


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

 # Check the values changed in kwargs
 _a    :   Union[str, Any]        =			""""""
 _a    :   Dict        =			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)











from __future__ import annotations

import math
from collections.abc import Callable


def 				snake_case__							(							UpperCAmelCase							:  Callable[[int | float], int | float]    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int | float    ,			UpperCAmelCase							:  int = 1_0_0    ,			):
    lowerCAmelCase__   :int      							=				x_start
    lowerCAmelCase__   :int      							=				fnc(UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				0.0

    for _ in range(UpperCAmelCase       ):
        # Approximates curve as a sequence of linear lines and sums their length
        lowerCAmelCase__   :Dict      							=				(x_end - x_start) / steps + xa
        lowerCAmelCase__   :Optional[Any]      							=				fnc(UpperCAmelCase       )
        length += math.hypot(xa - xa    ,			fxa - fxa       )

        # Increment step
        lowerCAmelCase__   :Union[str, Any]      							=				xa
        lowerCAmelCase__   :str      							=				fxa

    return length


if __name__ == "__main__":


 def 				snake_case__							(							UpperCAmelCase							:  int       ):
     return math.sin(1_0 * x       )

 print("""f(x) = sin(10 * x)""")
 print("""The length of the curve from x = -10 to x = 10 is:""")
 _a    :   Any        =			10
 while i <= 10_0000:
  print(f"""With {i} steps: {line_length(f, -10, 10, i)}""")
  i *= 10



