


import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def __UpperCamelCase   (			*__lowerCamelCase		: int				)      ->     Optional[Any]:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase      ,  __lowerCamelCase				):
          _a						=		list(__lowerCamelCase				)
     for i in range(len(__lowerCamelCase				)				):
          _a						=		None
     gc.collect()
     if is_xpu_available():
          torch.xpu.empty_cache()
     elif is_npu_available():
          torch.npu.empty_cache()
     else:
          torch.cuda.empty_cache()
     return objects






def __UpperCamelCase   (			__lowerCamelCase		: Exception				)      ->     bool:







     '''simple docstring'''






     _a						=		[
         "CUDA out of memory.",  # CUDA OOM
         "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.",  # CUDNN SNAFU
         "DefaultCPUAllocator: can't allocate memory",  # CPU OOM
     ]
     if isinstance(__lowerCamelCase      ,  __lowerCamelCase				) and len(exception.args				) == 1:
          return any(err in exception.args[0] for err in _statements				)
     return False






def __UpperCamelCase   (			__lowerCamelCase		: callable = None      ,  __lowerCamelCase		: int = 128				)      ->     Dict:







     '''simple docstring'''






     if function is None:
          return functools.partial(__lowerCamelCase      ,  starting_batch_size=__lowerCamelCase				)

     _a						=		starting_batch_size

     def decorator(*__lowerCamelCase		: Union[str, Any]      ,  **__lowerCamelCase		: Optional[int]				):
          nonlocal batch_size
          gc.collect()
          if is_xpu_available():
               torch.xpu.empty_cache()
          elif is_npu_available():
               torch.npu.empty_cache()
          else:
               torch.cuda.empty_cache()
          _a						=		list(inspect.signature(__lowerCamelCase				).parameters.keys()				)
          # Guard against user error
          if len(__lowerCamelCase				) < (len(__lowerCamelCase				) + 1):
               _a						=		", ".join([F"{arg}={value}" for arg, value in zip(params[1:]      ,  args[1:]				)]				)
               raise TypeError(
                   F"Batch size was passed into `{function.__name__}` as the first argument when called."
                   F"Remove this as the decorator already does so: `{function.__name__}({arg_str})`"				)
          while True:
               if batch_size == 0:
                    raise RuntimeError("No executable batch size found, reached zero."				)
               try:
                    return function(__lowerCamelCase      ,  *__lowerCamelCase      ,  **__lowerCamelCase				)
               except Exception as e:
                    if should_reduce_batch_size(__lowerCamelCase				):
                         gc.collect()
                         if is_xpu_available():
                              torch.xpu.empty_cache()
                         elif is_npu_available():
                              torch.npu.empty_cache()
                         else:
                              torch.cuda.empty_cache()
                         batch_size //= 2
                    else:
                         raise

     return decorator





'''simple docstring'''

from manim import *





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def  a_					(			self		)       ->    str:
          _a						=		Rectangle(height=0.5     ,							width=0.5		)
          _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0		)

          _a						=		[mem.copy() for i in range(6		)]
          _a						=		[mem.copy() for i in range(6		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		VGroup(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("CPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          cpu.move_to([-2.5, -0.5, 0]		)
          self.add(__UpperCamelCase		)

          _a						=		[mem.copy() for i in range(1		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)
          _a						=		Text("GPU"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          gpu.align_to(__UpperCamelCase     ,							__UpperCamelCase		)
          gpu.set_x(gpu.get_x() - 1		)

          self.add(__UpperCamelCase		)

          _a						=		[mem.copy() for i in range(6		)]
          _a						=		VGroup(*__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0		)

          _a						=		Text("Model"     ,							font_size=24		)
          _a						=		Group(__UpperCamelCase     ,							__UpperCamelCase		).arrange(__UpperCamelCase     ,							buff=0.5     ,							aligned_edge=__UpperCamelCase		)
          model.move_to([3, -1.0, 0]		)

          self.play(
              Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							Create(__UpperCamelCase     ,							run_time=1		)     ,							)

          _a						=		MarkupText(
              f"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."     ,							font_size=24     ,							)

          _a						=		Square(side_length=2.2		)
          key.move_to([-5, 2, 0]		)

          _a						=		MarkupText(
              f"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"     ,							font_size=18     ,							)

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

          step_a.move_to([2, 2, 0]		)
          self.play(Write(__UpperCamelCase     ,							run_time=2.5		)     ,							Write(__UpperCamelCase		)     ,							Write(__UpperCamelCase		)		)

          self.add(__UpperCamelCase		)

          _a						=		[]
          _a						=		[]
          _a						=		[]
          for i, rect in enumerate(__UpperCamelCase		):
               _a						=		Rectangle(height=0.46     ,							width=0.46		).set_stroke(width=0.0		).set_fill(__UpperCamelCase     ,							opacity=0.7		)
               cpu_target.move_to(__UpperCamelCase		)
               cpu_target.generate_target()
               _a						=		0.46 / 4
               _a						=		0.46 / 3

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

               first_animations.append(rect.animate(run_time=0.5		).set_stroke(__UpperCamelCase		)		)
               second_animations.append(MoveToTarget(__UpperCamelCase     ,							run_time=1.5		)		)

          self.play(*__UpperCamelCase		)
          self.play(*__UpperCamelCase		)

          self.wait()

