



"""simple docstring"""




import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory

def 		__snake_case		( )    ->  int:





     '''simple docstring'''

     raise RuntimeError("CUDA out of memory."							)






class       UpperCAmelCase_    (							nn.Module		):



      def __init__(	self	:      int		):
           super().__init__()
           _UpperCAmelCase     :      int														=       nn.Linear(3	,      4		)
           _UpperCAmelCase     :      str														=       nn.BatchNormad(4		)
           _UpperCAmelCase     :      Optional[int]														=       nn.Linear(4	,      5		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      str		):
           return self.lineara(self.batchnorm(self.lineara(A		)		)		)







class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Optional[int]														=       []

           @find_executable_batch_size(starting_batch_size=1_2_8		)
           def mock_training_loop_function(A	:      Any		):
                nonlocal batch_sizes
                batch_sizes.append(A		)
                if batch_size != 8:
                     raise_fake_out_of_memory()

           mock_training_loop_function()
           self.assertListEqual(A	,      [1_2_8, 6_4, 3_2, 1_6, 8]		)



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       []

           @find_executable_batch_size(starting_batch_size=1_2_8		)
           def mock_training_loop_function(A	:      Tuple	,      A	:      str		):
                nonlocal batch_sizes
                batch_sizes.append(A		)
                if batch_size != 8:
                     raise_fake_out_of_memory()
                return batch_size, arga

           _UpperCAmelCase							,			_UpperCAmelCase     :      Tuple														=       mock_training_loop_function("hello"		)
           self.assertListEqual(A	,      [1_2_8, 6_4, 3_2, 1_6, 8]		)
           self.assertListEqual([bs, arga]	,      [8, "hello"]		)



      def 		snake_case_   (	self	:      str		):
           @find_executable_batch_size(starting_batch_size=0		)
           def mock_training_loop_function(A	:      Any		):
                pass

           with self.assertRaises(A		) as cm:
                mock_training_loop_function()
                self.assertIn("No executable batch size found, reached zero."	,      cm.exception.args[0]		)



      def 		snake_case_   (	self	:      str		):
           @find_executable_batch_size(starting_batch_size=1_6		)
           def mock_training_loop_function(A	:      Dict		):
                if batch_size > 0:
                     raise_fake_out_of_memory()
                pass

           with self.assertRaises(A		) as cm:
                mock_training_loop_function()
                self.assertIn("No executable batch size found, reached zero."	,      cm.exception.args[0]		)



      def 		snake_case_   (	self	:      Dict		):
           @find_executable_batch_size(starting_batch_size=1_2_8		)
           def mock_training_loop_function(A	:      Tuple	,      A	:      Optional[Any]	,      A	:      str		):
                if batch_size != 8:
                     raise raise_fake_out_of_memory()

           with self.assertRaises(A		) as cm:
                mock_training_loop_function(1_2_8	,      "hello"	,      "world"		)
                self.assertIn("Batch size was passed into `f`"	,      cm.exception.args[0]		)
                self.assertIn("`f(arg1='hello', arg2='world')"	,      cm.exception.args[0]		)



      def 		snake_case_   (	self	:      Dict		):
           @find_executable_batch_size(starting_batch_size=1_6		)
           def mock_training_loop_function(A	:      Tuple		):
                raise ValueError("Oops, we had an error!"		)

           with self.assertRaises(A		) as cm:
                mock_training_loop_function()
                self.assertIn("Oops, we had an error!"	,      cm.exception.args[0]		)



      @require_cuda
      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      List[Any]														=       torch.cuda.memory_allocated()
           _UpperCAmelCase     :      Optional[Any]														=       ModelForTest()
           model.cuda()
           self.assertGreater(torch.cuda.memory_allocated()	,      A		)
           _UpperCAmelCase     :      Tuple														=       release_memory(A		)
           self.assertEqual(torch.cuda.memory_allocated()	,      A		)





"""simple docstring"""




from manim import *






class       UpperCAmelCase_    (							_UpperCamelCase		):



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      str														=       Rectangle(height=0.5	,      width=0.5		)
           _UpperCAmelCase     :      List[Any]														=       Rectangle(height=0.25	,      width=0.25		)
           _UpperCAmelCase     :      Tuple														=       Rectangle(height=0.46	,      width=0.46		).set_stroke(width=0		)

           _UpperCAmelCase     :      List[Any]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      int														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Dict														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      int														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      str														=       Text("CPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Union[str, Any]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           cpu.move_to([-2.5, -0.5, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       [mem.copy() for i in range(4		)]
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Any														=       Text("GPU"	,      font_size=2_4		)
           _UpperCAmelCase     :      Dict														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           gpu.move_to([-1, -1, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      Tuple														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      str														=       VGroup(*A		).arrange(A	,      buff=0		)

           _UpperCAmelCase     :      Any														=       Text("Model"	,      font_size=2_4		)
           _UpperCAmelCase     :      Any														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           model.move_to([3, -1.0, 0]		)
           self.add(A		)

           _UpperCAmelCase     :      List[str]														=       []
           _UpperCAmelCase     :      Optional[int]														=       []
           _UpperCAmelCase     :      str														=       []

           for i, rect in enumerate(A		):
                rect.set_stroke(A		)

                _UpperCAmelCase     :      Dict														=       Rectangle(height=0.46 / 4	,      width=0.46 / 3		).set_stroke(width=0.0		).set_fill(A	,      opacity=0.7		)

                if i == 0:
                     cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)	,      buff=0.02	,      direction=A		)
                     cpu_target.set_x(cpu_target.get_x() + 0.1		)
                elif i == 3:
                     cpu_target.next_to(model_cpu_arr[0]	,      direction=A	,      buff=0.0		)
                else:
                     cpu_target.next_to(model_cpu_arr[i - 1]	,      direction=A	,      buff=0.0		)
                self.add(A		)
                model_cpu_arr.append(A		)

           self.add(*A	,      *A	,      *A		)

           _UpperCAmelCase     :      Dict														=       [mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       VGroup(*A		).arrange(A	,      buff=0		)

           _UpperCAmelCase     :      List[str]														=       Text("Loaded Checkpoint"	,      font_size=2_4		)
           _UpperCAmelCase     :      Optional[int]														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           checkpoint.move_to([3, 0.5, 0]		)

           self.add(A		)

           _UpperCAmelCase     :      Union[str, Any]														=       []
           _UpperCAmelCase     :      Union[str, Any]														=       []

           for i, rect in enumerate(A		):
                _UpperCAmelCase     :      str														=       fill.copy().set_fill(A	,      opacity=0.7		)
                target.move_to(A		)
                ckpt_arr.append(A		)

                _UpperCAmelCase     :      Dict														=       target.copy()
                if i < 5:
                     cpu_target.move_to(cpu_left_col_base[i + 1]		)
                else:
                     cpu_target.move_to(cpu_right_col_base[i - 5]		)
                ckpt_cpu_arr.append(A		)
           self.add(*A	,      *A		)

           _UpperCAmelCase     :      List[str]														=       Square(side_length=2.2		)
           key.move_to([-5, 2, 0]		)

           _UpperCAmelCase     :      List[Any]														=       MarkupText(
               f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'	,      font_size=1_8	,      )

           key_text.move_to([-5, 2.4, 0]		)

           self.add(A	,      A		)

           _UpperCAmelCase     :      Tuple														=       MarkupText(
               f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'	,      font_size=1_8	,      )

           blue_text.next_to(A	,      DOWN * 2.4	,      aligned_edge=key_text.get_left()		)
           self.add(A		)

           _UpperCAmelCase     :      Any														=       MarkupText(
               f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'	,      font_size=2_4	,      )
           step_a.move_to([2, 2, 0]		)

           _UpperCAmelCase     :      int														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      Optional[int]														=       [meta_mem.copy() for i in range(6		)]
           _UpperCAmelCase     :      List[str]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Optional[int]														=       VGroup(*A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      List[Any]														=       VGroup(A	,      A		).arrange(A	,      buff=0		)
           _UpperCAmelCase     :      Union[str, Any]														=       Text("Disk"	,      font_size=2_4		)
           _UpperCAmelCase     :      int														=       Group(A	,      A		).arrange(A	,      buff=0.5	,      aligned_edge=A		)
           disk.move_to([-4.0, -1.25, 0]		)
           self.play(Write(A	,      run_time=3		)	,      Write(A	,      run_time=1		)	,      Create(A	,      run_time=1		)		)

           _UpperCAmelCase     :      List[Any]														=       []
           for i, rect in enumerate(A		):
                _UpperCAmelCase     :      Any														=       rect.copy()
                target.generate_target()
                target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
                animations.append(MoveToTarget(A	,      run_time=1.5		)		)
           self.play(*A		)

           self.play(FadeOut(A		)		)

           _UpperCAmelCase     :      int														=       MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.'	,      font_size=2_4		)
           step_a.move_to([2, 2, 0]		)

           self.play(Write(A	,      run_time=3		)		)

           self.play(
               FadeOut(A	,      A	,      *A	,      *A		)	,      )

           self.wait()
