"""simple docstring"""




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 						__A							(			snake_case_   ,     snake_case_		):






     @register_to_config
     def __init__(		self    ,					*,
         a__ = 4    ,					a__ = 768    ,					a__    ,					a__    ,					):
       super().__init__()

       _lowerCAmelCase						:	int        =					nn.Parameter(torch.zeros(_A  )  )

       # parameters for additional clip time embeddings
       _lowerCAmelCase						:	Tuple        =					nn.Linear(_A    ,					_A  )
       _lowerCAmelCase						:	List[Any]        =					nn.Linear(_A    ,					_A  )

       # parameters for encoder hidden states
       _lowerCAmelCase						:	Union[str, Any]        =					clip_extra_context_tokens
       _lowerCAmelCase						:	str        =					nn.Linear(
           _A    ,					self.clip_extra_context_tokens * cross_attention_dim  )
       _lowerCAmelCase						:	Any        =					nn.Linear(_A    ,					_A  )
       _lowerCAmelCase						:	Tuple        =					nn.LayerNorm(_A  )






     def    __A  (		self    ,					*, a__    ,					a__    ,					a__    ,					a__  ):


       if do_classifier_free_guidance:
         # Add the classifier free guidance embeddings to the image embeddings
         _lowerCAmelCase						:	Optional[int]        =					image_embeddings.shape[0]
         _lowerCAmelCase						:	Optional[Any]        =					self.learned_classifier_free_guidance_embeddings.unsqueeze(0  )
         _lowerCAmelCase						:	Optional[Any]        =					classifier_free_guidance_embeddings.expand(
             _A    ,					-1  )
         _lowerCAmelCase						:	Any        =					torch.cat([classifier_free_guidance_embeddings, image_embeddings]    ,					dim=0  )

       # The image embeddings batch size and the text embeddings batch size are equal
       assert image_embeddings.shape[0] == prompt_embeds.shape[0]

       _lowerCAmelCase						:	str        =					prompt_embeds.shape[0]

       # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
       # adding CLIP embeddings to the existing timestep embedding, ...
       _lowerCAmelCase						:	str        =					self.embedding_proj(_A  )
       _lowerCAmelCase						:	Dict        =					self.clip_image_embeddings_project_to_time_embeddings(_A  )
       _lowerCAmelCase						:	Tuple        =					time_projected_image_embeddings + time_projected_prompt_embeds

       # ... and by projecting CLIP embeddings into four
       # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
       _lowerCAmelCase						:	List[str]        =					self.clip_extra_context_tokens_proj(_A  )
       _lowerCAmelCase						:	Optional[Any]        =					clip_extra_context_tokens.reshape(_A    ,					-1    ,					self.clip_extra_context_tokens  )
       _lowerCAmelCase						:	int        =					clip_extra_context_tokens.permute(0    ,					2    ,					1  )

       _lowerCAmelCase						:	Optional[int]        =					self.encoder_hidden_states_proj(_A  )
       _lowerCAmelCase						:	Any        =					self.text_encoder_hidden_states_norm(_A  )
       _lowerCAmelCase						:	Dict        =					torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]    ,					dim=1  )

       return text_encoder_hidden_states, additive_clip_time_embeddings





"""simple docstring"""




from manim import *
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					Rectangle(height=0.5    ,					width=0.5  )
       _lowerCAmelCase						:	List[Any]        =					Rectangle(height=0.4_6    ,					width=0.4_6  ).set_stroke(width=0  )

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

       _lowerCAmelCase						:	Dict        =					[mem.copy() for i in range(4  )]
       _lowerCAmelCase						:	Any        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )
       _lowerCAmelCase						:	Tuple        =					Text("""GPU"""    ,					font_size=24  )
       _lowerCAmelCase						:	Optional[int]        =					Group(a__    ,					a__  ).arrange(a__    ,					buff=0.5    ,					aligned_edge=a__  )
       gpu.move_to([-1, -1, 0]  )
       self.add(a__  )

       _lowerCAmelCase						:	Optional[int]        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	Any        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )

       _lowerCAmelCase						:	List[str]        =					Text("""Model"""    ,					font_size=24  )
       _lowerCAmelCase						:	Any        =					Group(a__    ,					a__  ).arrange(a__    ,					buff=0.5    ,					aligned_edge=a__  )
       model.move_to([3, -1.0, 0]  )
       self.add(a__  )

       _lowerCAmelCase						:	Tuple        =					[]
       for i, rect in enumerate(a__  ):
         rect.set_stroke(a__  )
         # target = fill.copy().set_fill(YELLOW, opacity=0.7)
         # target.move_to(rect)
         # self.add(target)

         _lowerCAmelCase						:	List[str]        =					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(cpu_targs[0]    ,					direction=a__    ,					buff=0.0  )
         else:
           cpu_target.next_to(cpu_targs[i - 1]    ,					direction=a__    ,					buff=0.0  )
         self.add(a__  )
         cpu_targs.append(a__  )

       _lowerCAmelCase						:	Any        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	List[str]        =					VGroup(*a__  ).arrange(a__    ,					buff=0  )

       _lowerCAmelCase						:	int        =					Text("""Loaded Checkpoint"""    ,					font_size=24  )
       _lowerCAmelCase						:	Optional[int]        =					Group(a__    ,					a__  ).arrange(a__    ,					aligned_edge=a__    ,					buff=0.4  )
       checkpoint.move_to([3, 0.5, 0]  )

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

       _lowerCAmelCase						:	List[str]        =					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__  )

       _lowerCAmelCase						:	int        =					MarkupText(
           F"<span fgcolor='{BLUE}'>●</span> Checkpoint"    ,					font_size=18    ,					)

       blue_text.next_to(a__    ,					DOWN * 2.4    ,					aligned_edge=key_text.get_left()  )

       _lowerCAmelCase						:	List[Any]        =					MarkupText(
           F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."    ,					font_size=24    ,					)
       step_a.move_to([2, 2, 0]  )
       self.play(Write(a__  )    ,					Write(a__  )  )

       self.play(Write(a__    ,					run_time=1  )    ,					Create(a__    ,					run_time=1  )  )

       _lowerCAmelCase						:	int        =					[]
       _lowerCAmelCase						:	List[Any]        =					[]
       for i, rect in enumerate(a__  ):
         _lowerCAmelCase						:	Tuple        =					fill.copy().set_fill(a__    ,					opacity=0.7  )
         target.move_to(a__  )
         first_animations.append(GrowFromCenter(a__    ,					run_time=1  )  )

         _lowerCAmelCase						:	Optional[Any]        =					target.copy()
         cpu_target.generate_target()
         if i < 5:
           cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
         else:
           cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
         second_animations.append(MoveToTarget(a__    ,					run_time=1.5  )  )

       self.play(*a__  )
       self.play(*a__  )
       self.wait()




