"""simple docstring"""







import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class        UpperCamelCase		(			nn.Module  ):
   def __init__(			self			)	->      int:


        '''simple docstring'''


        super().__init__()
        lowercase_	:				Optional[Any]													=	nn.Linear(3     ,4			)
        lowercase_	:				Any													=	nn.BatchNormad(4			)
        lowercase_	:				Tuple													=	nn.Linear(4     ,5			)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase			)			)			)







class        UpperCamelCase		(			unittest.TestCase  ):
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Any													=	ModelForTest()
        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase     ,model.state_dict()			)
             lowercase_	:				Tuple													=	os.path.join(__UpperCamelCase     ,'index.json'			)
             self.assertTrue(os.path.isfile(__UpperCamelCase			)			)
             # TODO: add tests on what is inside the index

             for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                  lowercase_	:				Optional[Any]													=	os.path.join(__UpperCamelCase     ,f'''{key}.dat'''			)
                  self.assertTrue(os.path.isfile(__UpperCamelCase			)			)
                  # TODO: add tests on the fact weights are properly loaded
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	[torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
             lowercase_	:				Union[str, Any]													=	torch.randn(2     ,3     ,dtype=__UpperCamelCase			)
             with TemporaryDirectory() as tmp_dir:
                  lowercase_	:				int													=	offload_weight(__UpperCamelCase     ,'weight'     ,__UpperCamelCase     ,{}			)
                  lowercase_	:				str													=	os.path.join(__UpperCamelCase     ,'weight.dat'			)
                  self.assertTrue(os.path.isfile(__UpperCamelCase			)			)
                  self.assertDictEqual(__UpperCamelCase     ,{'weight': {'shape': [2, 3], 'dtype': str(__UpperCamelCase			).split('.'			)[1]}}			)

                  lowercase_	:				Union[str, Any]													=	load_offloaded_weight(__UpperCamelCase     ,index['weight']			)
                  self.assertTrue(torch.equal(__UpperCamelCase     ,__UpperCamelCase			)			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Tuple													=	ModelForTest()
        lowercase_	:				Tuple													=	model.state_dict()
        lowercase_	:				int													=	{k: v for k, v in state_dict.items() if 'linear2' not in k}
        lowercase_	:				List[str]													=	{k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase     ,__UpperCamelCase			)
             lowercase_	:				Union[str, Any]													=	OffloadedWeightsLoader(state_dict=__UpperCamelCase     ,save_folder=__UpperCamelCase			)

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase			)     ,sorted(state_dict.keys()			)			)
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase     ,weight_map[key]			)			)

        lowercase_	:				Tuple													=	{k: v for k, v in state_dict.items() if 'weight' in k}
        lowercase_	:				Optional[Any]													=	{k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase     ,__UpperCamelCase			)
             lowercase_	:				List[Any]													=	OffloadedWeightsLoader(state_dict=__UpperCamelCase     ,save_folder=__UpperCamelCase			)

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase			)     ,sorted(state_dict.keys()			)			)
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase     ,weight_map[key]			)			)

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase     ,__UpperCamelCase			)
             # Duplicates are removed
             lowercase_	:				Optional[int]													=	OffloadedWeightsLoader(state_dict=__UpperCamelCase     ,save_folder=__UpperCamelCase			)

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase			)     ,sorted(state_dict.keys()			)			)
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase     ,weight_map[key]			)			)







   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	{'a.1': 0, 'a.10': 1, 'a.2': 2}
        lowercase_	:				Optional[int]													=	extract_submodules_state_dict(__UpperCamelCase     ,['a.1', 'a.2']			)
        self.assertDictEqual(__UpperCamelCase     ,{'a.1': 0, 'a.2': 2}			)

        lowercase_	:				Tuple													=	{'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        lowercase_	:				Optional[Any]													=	extract_submodules_state_dict(__UpperCamelCase     ,['a.1', 'a.2']			)
        self.assertDictEqual(__UpperCamelCase     ,{'a.1.a': 0, 'a.2.a': 2}			)


"""simple docstring"""







from __future__ import annotations

import math
from collections.abc import Callable



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Callable[[int | float], int | float]     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int | float     ,			__SCREAMING_SNAKE_CASE							:     int = 1_00     ,			):
     lowercase_	:				Dict													=	x_start
     lowercase_	:				Optional[Any]													=	fnc(__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Optional[Any]													=	0.0

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

          # Increment step
          lowercase_	:				List[Any]													=	xa
          lowercase_	:				Optional[Any]													=	fxa

     return length


if __name__ == "__main__":



       def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Any      ):
            return math.sin(10 * x      )

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

