


from manim import *






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							str   	=	Rectangle(height=0.5,   width=0.5)
           _lowercase :							List[Any]   	=	Rectangle(height=0.4_6,   width=0.4_6).set_stroke(width=0)

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

           _lowercase :							List[Any]   	=	[mem.copy() for i in range(1)]
           _lowercase :							List[Any]   	=	VGroup(*lowerCamelCase).arrange(lowerCamelCase,   buff=0)
           _lowercase :							List[Any]   	=	Text('GPU',   font_size=24)
           _lowercase :							Optional[Any]   	=	Group(lowerCamelCase,   lowerCamelCase).arrange(lowerCamelCase,   buff=0.5,   aligned_edge=lowerCamelCase)
           gpu.align_to(lowerCamelCase,   lowerCamelCase)
           gpu.set_x(gpu.get_x() - 1)

           self.add(lowerCamelCase)

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

           _lowercase :							List[str]   	=	Text('Model',   font_size=24)
           _lowercase :							Optional[Any]   	=	Group(lowerCamelCase,   lowerCamelCase).arrange(lowerCamelCase,   buff=0.5,   aligned_edge=lowerCamelCase)
           model.move_to([3, -1.0, 0])

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

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

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

           _lowercase :							Any   	=	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(lowerCamelCase,   run_time=2.5),   Write(lowerCamelCase),   Write(lowerCamelCase))

           self.add(lowerCamelCase)

           _lowercase :							str   	=	[]
           _lowercase :							Tuple   	=	[]
           _lowercase :							Optional[Any]   	=	[]
           for i, rect in enumerate(lowerCamelCase):
               _lowercase :							Tuple   	=	Rectangle(height=0.4_6,   width=0.4_6).set_stroke(width=0.0).set_fill(lowerCamelCase,   opacity=0.7)
               cpu_target.move_to(lowerCamelCase)
               cpu_target.generate_target()
               _lowercase :							int   	=	0.4_6 / 4
               _lowercase :							str   	=	0.4_6 / 3

               if i == 0:
                   cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT),   buff=0.0_2,   direction=lowerCamelCase)
                   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=lowerCamelCase,   buff=0.0)
               else:
                   cpu_target.target.next_to(cpu_targs[i - 1].target,   direction=lowerCamelCase,   buff=0.0)
               cpu_targs.append(lowerCamelCase)

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

           self.play(*lowerCamelCase)
           self.play(*lowerCamelCase)

           self.wait()






from typing import Dict

from .base import GenericTensor, Pipeline






class _lowerCamelCase(		_a     ):
       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=None,   **lowerCamelCase) ->     int:




           """simple docstring"""

           if tokenize_kwargs is None:
               _lowercase :							Any   	=	{}

           if truncation is not None:
               if "truncation" in tokenize_kwargs:
                   raise ValueError(
                       'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)')
               _lowercase :							List[Any]   	=	truncation

           _lowercase :							Optional[Any]   	=	tokenize_kwargs

           _lowercase :							Tuple   	=	{}
           if return_tensors is not None:
               _lowercase :							Union[str, Any]   	=	return_tensors

           return preprocess_params, {}, postprocess_params
       def     UpperCamelCase  (      self,   lowerCamelCase,   **lowerCamelCase) ->     Dict[str, GenericTensor]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	self.framework
           _lowercase :							Dict   	=	self.tokenizer(lowerCamelCase,   return_tensors=lowerCamelCase,   **lowerCamelCase)
           return model_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           _lowercase :							str   	=	self.model(**lowerCamelCase)
           return model_outputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase=False) ->     Dict:




           """simple docstring"""

           if return_tensors:
               return model_outputs[0]
           if self.framework == "pt":
               return model_outputs[0].tolist()
           elif self.framework == "tf":
               return model_outputs[0].numpy().tolist()




       def __call__(      self,   *lowerCamelCase,   **lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           return super().__call__(*lowerCamelCase,   **lowerCamelCase)


