




'''simple docstring'''






import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
      import transformer_engine.pytorch as te




def 		a_(			lowerCamelCase ,					lowerCamelCase=True ,					lowerCamelCase=True ,					lowerCamelCase=True   )					->Tuple:






       '''simple docstring'''




       if not is_fpa_available():
              raise ImportError('''Using `convert_model` requires transformer_engine to be installed.'''   )
       for name, module in model.named_children():
              if isinstance(lowerCamelCase ,					nn.Linear   ) and to_transformer_engine and _convert_linear:
                     # Return early if the linear layer weights are not multiples of 16
                     if any(p % 16 != 0 for p in module.weight.shape   ):
                            return
                     lowercase	:		int    =   module.bias is not None
                     lowercase	:		Any    =   te.Linear(
                         module.in_features ,					module.out_features ,					bias=lowerCamelCase ,					params_dtype=module.weight.dtype   )
                     lowercase	:		Any    =   module.weight.data.clone()
                     if has_bias:
                            lowercase	:		Tuple    =   module.bias.data.clone()

                     setattr(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
              elif isinstance(lowerCamelCase ,					nn.LayerNorm   ) and to_transformer_engine and _convert_ln:
                     lowercase	:		Optional[Any]    =   te.LayerNorm(module.normalized_shape[0] ,					eps=module.eps ,					params_dtype=module.weight.dtype   )
                     lowercase	:		List[str]    =   module.weight.data.clone()
                     lowercase	:		Dict    =   module.bias.data.clone()

                     setattr(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
              elif isinstance(lowerCamelCase ,					te.Linear   ) and not to_transformer_engine and _convert_linear:
                     lowercase	:		Dict    =   module.bias is not None
                     lowercase	:		List[Any]    =   nn.Linear(
                         module.in_features ,					module.out_features ,					bias=lowerCamelCase ,					params_dtype=module.weight.dtype   )
                     lowercase	:		int    =   module.weight.data.clone()
                     if has_bias:
                            lowercase	:		Optional[int]    =   module.bias.data.clone()

                     setattr(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
              elif isinstance(lowerCamelCase ,					te.LayerNorm   ) and not to_transformer_engine and _convert_ln:
                     lowercase	:		int    =   nn.LayerNorm(module.normalized_shape[0] ,					eps=module.eps ,					params_dtype=module.weight.dtype   )
                     lowercase	:		Optional[Any]    =   module.weight.data.clone()
                     lowercase	:		str    =   module.bias.data.clone()

                     setattr(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
              else:
                     convert_model(
                         lowerCamelCase ,					to_transformer_engine=lowerCamelCase ,					_convert_linear=_convert_linear ,					_convert_ln=_convert_ln ,					)





def 		a_(			lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       if not is_fpa_available():
              raise ImportError('''Using `has_transformer_engine_layers` requires transformer_engine to be installed.'''   )
       for m in model.modules():
              if isinstance(lowerCamelCase ,					(te.LayerNorm, te.Linear)   ):
                     return True
       return False










'''simple docstring'''






from manim import *

class 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		str    =   Rectangle(height=0.5 ,		width=0.5			)
        lowercase	:		Optional[Any]    =   Rectangle(height=0.4_6 ,		width=0.4_6			).set_stroke(width=0			)
        lowercase	:		str    =   Rectangle(height=0.2_5 ,		width=0.2_5			)

        lowercase	:		Any    =   [mem.copy() for i in range(6			)]
        lowercase	:		List[str]    =   [mem.copy() for i in range(6			)]
        lowercase	:		List[Any]    =   VGroup(*A			).arrange(A ,		buff=0			)
        lowercase	:		Any    =   VGroup(*A			).arrange(A ,		buff=0			)
        lowercase	:		str    =   VGroup(A ,		A			).arrange(A ,		buff=0			)
        lowercase	:		int    =   Text('''CPU''' ,		font_size=24			)
        lowercase	:		Dict    =   Group(A ,		A			).arrange(A ,		buff=0.5 ,		aligned_edge=A			)
        cpu.move_to([-2.5, -0.5, 0]			)
        self.add(A			)

        lowercase	:		List[str]    =   [mem.copy() for i in range(4			)]
        lowercase	:		Optional[Any]    =   VGroup(*A			).arrange(A ,		buff=0			)
        lowercase	:		Optional[int]    =   Text('''GPU''' ,		font_size=24			)
        lowercase	:		Optional[int]    =   Group(A ,		A			).arrange(A ,		buff=0.5 ,		aligned_edge=A			)
        gpu.move_to([-1, -1, 0]			)
        self.add(A			)

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

        lowercase	:		int    =   Text('''Model''' ,		font_size=24			)
        lowercase	:		Tuple    =   Group(A ,		A			).arrange(A ,		buff=0.5 ,		aligned_edge=A			)
        model.move_to([3, -1.0, 0]			)
        self.add(A			)

        lowercase	:		str    =   []
        lowercase	:		int    =   []

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

               lowercase	:		List[Any]    =   Rectangle(height=0.4_6 / 4 ,		width=0.4_6 / 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=0.0_2 ,		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			)

        lowercase	:		Optional[int]    =   [meta_mem.copy() for i in range(6			)]
        lowercase	:		List[str]    =   [meta_mem.copy() for i in range(6			)]
        lowercase	:		int    =   VGroup(*A			).arrange(A ,		buff=0			)
        lowercase	:		int    =   VGroup(*A			).arrange(A ,		buff=0			)
        lowercase	:		Any    =   VGroup(A ,		A			).arrange(A ,		buff=0			)
        lowercase	:		Union[str, Any]    =   Text('''Disk''' ,		font_size=24			)
        lowercase	:		Dict    =   Group(A ,		A			).arrange(A ,		buff=0.5 ,		aligned_edge=A			)
        disk.move_to([-4.0, -1.2_5, 0]			)
        self.add(A ,		A			)

        lowercase	:		List[str]    =   []

        for i in range(6			):
               lowercase	:		Tuple    =   fill.copy().set_fill(A ,		opacity=0.8			)
               target.move_to(disk_left_col_base[i]			).scale(0.5			)
               cpu_disk_arr.append(A			)

        self.add(*A			)

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

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

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

        self.add(A ,		A			)

        lowercase	:		List[Any]    =   MarkupText(
            f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,		font_size=18 ,		)

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

        lowercase	:		Optional[Any]    =   MarkupText(f"""The offloaded weights are all sent to the CPU.""" ,		font_size=24			)
        step_a.move_to([2, 2, 0]			)

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

        for i in range(6			):
               lowercase	:		List[str]    =   cpu_disk_arr[i]
               lowercase	:		Optional[Any]    =   rect.copy().set_fill(A ,		opacity=0.8			).scale(2.0			)
               cpa.generate_target()
               cpa.target.move_to(model_base[i]			)

               if i == 0:
                      rect.set_fill(A ,		opacity=0.8			)
                      rect.generate_target()
                      rect.target.move_to(cpu_left_col_base[0]			).scale(2.0			)

                      self.remove(
                          *A ,		*A ,		)

               else:
                      rect.generate_target()
                      rect.target.move_to(cpu_left_col_base[i]			).scale(2.0			)
               self.play(MoveToTarget(A			) ,		MoveToTarget(A			) ,		model_base[i].animate.set_stroke(A			)			)
        self.play(FadeOut(A			)			)

        lowercase	:		Optional[Any]    =   MarkupText(
            f"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed.""" ,		font_size=24 ,		)
        step_a.move_to([2, 2, 0]			)

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

        lowercase	:		Tuple    =   []
        lowercase	:		List[Any]    =   []
        for i in range(6			):
               lowercase	:		List[str]    =   Arrow(start=A ,		end=A ,		color=A ,		buff=0.5			)
               a.next_to(model_base[i].get_left() ,		A ,		buff=0.2			)
               arrows.append(A			)
               animations.append(Write(A			)			)
        self.play(*A			)
        self.wait()




