
"""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							)						->					Optional[Any]:
          super().__init__()
          UpperCAmelCase									=					nn.Linear(3   ,       4							)
          UpperCAmelCase									=					nn.BatchNormad(4							)
          UpperCAmelCase									=					nn.Linear(4   ,       5							)




      def     a_      (							self   ,       lowercase_							)						->					Optional[Any]:
          return self.lineara(self.batchnorm(self.lineara(lowercase_							)							)							)

class      _UpperCAmelCase							(		unittest.TestCase							):




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					ModelForTest()
          with TemporaryDirectory() as tmp_dir:
              offload_state_dict(lowercase_   ,       model.state_dict()							)
              UpperCAmelCase									=					os.path.join(lowercase_   ,       'index.json'							)
              self.assertTrue(os.path.isfile(lowercase_							)							)
              # TODO: add tests on what is inside the index

              for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                  UpperCAmelCase									=					os.path.join(lowercase_   ,       F"{key}.dat"							)
                  self.assertTrue(os.path.isfile(lowercase_							)							)
                  # TODO: add tests on the fact weights are properly loaded




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					[torch.floataa, torch.floataa, torch.bfloataa]

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

                  UpperCAmelCase									=					load_offloaded_weight(lowercase_   ,       index['weight']							)
                  self.assertTrue(torch.equal(lowercase_   ,       lowercase_							)							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					ModelForTest()
          UpperCAmelCase									=					model.state_dict()
          UpperCAmelCase									=					{k: v for k, v in state_dict.items() if 'linear2' not in k}
          UpperCAmelCase									=					{k: v for k, v in state_dict.items() if 'linear2' in k}

          with TemporaryDirectory() as tmp_dir:
              offload_state_dict(lowercase_   ,       lowercase_							)
              UpperCAmelCase									=					OffloadedWeightsLoader(state_dict=lowercase_   ,       save_folder=lowercase_							)

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

          UpperCAmelCase									=					{k: v for k, v in state_dict.items() if 'weight' in k}
          UpperCAmelCase									=					{k: v for k, v in state_dict.items() if 'weight' not in k}

          with TemporaryDirectory() as tmp_dir:
              offload_state_dict(lowercase_   ,       lowercase_							)
              UpperCAmelCase									=					OffloadedWeightsLoader(state_dict=lowercase_   ,       save_folder=lowercase_							)

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

          with TemporaryDirectory() as tmp_dir:
              offload_state_dict(lowercase_   ,       lowercase_							)
              # Duplicates are removed
              UpperCAmelCase									=					OffloadedWeightsLoader(state_dict=lowercase_   ,       save_folder=lowercase_							)

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




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					{'a.1': 0, 'a.10': 1, 'a.2': 2}
          UpperCAmelCase									=					extract_submodules_state_dict(lowercase_   ,       ['a.1', 'a.2']							)
          self.assertDictEqual(lowercase_   ,       {'a.1': 0, 'a.2': 2}							)

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





"""simple docstring"""

import sys
import turtle




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ) ->      tuple[float, float]:






    """simple docstring"""




    return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2




def     lowercase__						(					lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					tuple[float, float] ,				lowerCAmelCase				:					int ,				) ->      None:






    """simple docstring"""




    my_pen.up()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.down()
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )
    my_pen.goto(vertexa[0] ,				vertexa[1] )

    if depth == 0:
        return

    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )
    triangle(lowerCAmelCase ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				get_mid(lowerCAmelCase ,				lowerCAmelCase ) ,				depth - 1 )


if __name__ == "__main__":
   if len(sys.argv) != 2:
      raise ValueError(
          '''Correct format for using this script: '''
          '''python fractals.py <int:depth_for_fractal>'''
      )
   SCREAMING_SNAKE_CASE_    					=						turtle.Turtle()
   my_pen.ht()
   my_pen.speed(5)
   my_pen.pencolor('''red''')

   SCREAMING_SNAKE_CASE_    					=						[(-175, -125), (0, 175), (175, -125)]  # vertices of triangle
   triangle(vertices[0], vertices[1], vertices[2], int(sys.argv[1]))



