
from manim import *






class   UpperCAmelCase		(						A_ ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Dict							=      Rectangle(height=0.5    ,						width=0.5 )
          snake_case       :   Optional[Any]							=      Rectangle(height=0.46    ,						width=0.46 ).set_stroke(width=0 )

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

          snake_case       :   Any							=      [mem.copy() for i in range(1 )]
          snake_case       :   str							=      VGroup(*snake_case__ ).arrange(snake_case__    ,						buff=0 )
          snake_case       :   Tuple							=      Text("GPU"    ,						font_size=24 )
          snake_case       :   List[Any]							=      Group(snake_case__    ,						snake_case__ ).arrange(snake_case__    ,						buff=0.5    ,						aligned_edge=snake_case__ )
          gpu.align_to(snake_case__    ,						snake_case__ )
          gpu.set_x(gpu.get_x() - 1 )

          self.add(snake_case__ )

          snake_case       :   Any							=      [mem.copy() for i in range(6 )]
          snake_case       :   Tuple							=      VGroup(*snake_case__ ).arrange(snake_case__    ,						buff=0 )

          snake_case       :   int							=      Text("Model"    ,						font_size=24 )
          snake_case       :   List[str]							=      Group(snake_case__    ,						snake_case__ ).arrange(snake_case__    ,						buff=0.5    ,						aligned_edge=snake_case__ )
          model.move_to([3, -1.0, 0] )

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

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

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

          snake_case       :   Optional[int]							=      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(snake_case__    ,						run_time=2.5 )    ,						Write(snake_case__ )    ,						Write(snake_case__ ) )

          self.add(snake_case__ )

          snake_case       :   str							=      []
          snake_case       :   str							=      []
          snake_case       :   Dict							=      []
          for i, rect in enumerate(snake_case__ ):
                 snake_case       :   int							=      Rectangle(height=0.46    ,						width=0.46 ).set_stroke(width=0.0 ).set_fill(snake_case__    ,						opacity=0.7 )
                 cpu_target.move_to(snake_case__ )
                 cpu_target.generate_target()
                 snake_case       :   Optional[int]							=      0.46 / 4
                 snake_case       :   int							=      0.46 / 3

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

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

          self.play(*snake_case__ )
          self.play(*snake_case__ )

          self.wait()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCamelCase							=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase							=       [
       """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BioGptForCausalLM""",
       """BioGptForTokenClassification""",
       """BioGptForSequenceClassification""",
       """BioGptModel""",
       """BioGptPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
   from .tokenization_biogpt import BioGptTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_biogpt import (
          BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BioGptForCausalLM,
          BioGptForSequenceClassification,
          BioGptForTokenClassification,
          BioGptModel,
          BioGptPreTrainedModel,
      )


else:
   import sys

   __lowerCamelCase							=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





