





"""simple docstring"""




from manim import *




class 			_a						(				_lowerCAmelCase    ):






				def     snake_case     (			self		:   Optional[int]							)     ->				Optional[int]:




										'''simple docstring'''



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

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

										_UpperCamelCase      : int         =   [mem.copy() for i in range(1							)]
										_UpperCamelCase      : Any         =   VGroup(*lowerCAmelCase__							).arrange(lowerCAmelCase__,			buff=0							)
										_UpperCamelCase      : Tuple         =   Text('''GPU''',			font_size=2_4							)
										_UpperCamelCase      : Dict         =   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__							)

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

										_UpperCamelCase      : Any         =   Text('''Model''',			font_size=2_4							)
										_UpperCamelCase      : Union[str, 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							),			)

										_UpperCamelCase      : int         =   MarkupText(
										    f"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM.""",			font_size=2_4,			)

										_UpperCamelCase      : Optional[int]         =   Square(side_length=2.2							)
										key.move_to([-5, 2, 0]							)

										_UpperCamelCase      : List[str]         =   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]							)

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

										self.add(lowerCAmelCase__							)

										_UpperCamelCase      : Optional[int]         =   []
										_UpperCamelCase      : List[str]         =   []
										_UpperCamelCase      : Tuple         =   []
										for i, rect in enumerate(lowerCAmelCase__							):
																_UpperCamelCase      : Any         =   Rectangle(height=0.46,			width=0.46							).set_stroke(width=0.0							).set_fill(lowerCAmelCase__,			opacity=0.7							)
																cpu_target.move_to(lowerCAmelCase__							)
																cpu_target.generate_target()
																_UpperCamelCase      : str         =   0.46 / 4
																_UpperCamelCase      : Optional[Any]         =   0.46 / 3

																if i == 0:
																						cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT							),			buff=0.02,			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()










"""simple docstring"""




from ..utils import is_flax_available, is_torch_available


if is_torch_available():
						from .autoencoder_kl import AutoencoderKL
						from .controlnet import ControlNetModel
						from .dual_transformer_ad import DualTransformeraDModel
						from .modeling_utils import ModelMixin
						from .prior_transformer import PriorTransformer
						from .ta_film_transformer import TaFilmDecoder
						from .transformer_ad import TransformeraDModel
						from .unet_ad import UNetaDModel
						from .unet_ad import UNetaDModel
						from .unet_ad_condition import UNetaDConditionModel
						from .unet_ad_condition import UNetaDConditionModel
						from .vq_model import VQModel

if is_flax_available():
						from .controlnet_flax import FlaxControlNetModel
						from .unet_ad_condition_flax import FlaxUNetaDConditionModel
						from .vae_flax import FlaxAutoencoderKL



