


"""simple docstring"""






from typing import Any, Dict, List, Mapping

import numpy as np
import torch

from ...utils import is_cython_available, requires_backends


if is_cython_available():
					import pyximport

					pyximport.install(setup_args={"""include_dirs""": np.get_include()})
					from . import algos_graphormer  # noqa E402



def 			A__		(					_UpperCAmelCase    :      Tuple				,    _UpperCAmelCase    :      List[str] = 5_12	)							->	int:

	'''simple docstring'''


	snake_case__  :						List[Any]		=      x.shape[1] if len(x.shape	) > 1 else 1
	snake_case__  :						int		=      1 + np.arange(0				,    feature_num * offset				,    _UpperCAmelCase				,    dtype=np.intaa	)
	snake_case__  :						Dict		=      x + feature_offset
	return x



def 			A__		(					_UpperCAmelCase    :      Dict				,    _UpperCAmelCase    :      int=True	)							->	str:

	'''simple docstring'''


	requires_backends(_UpperCAmelCase				,    ["cython"]	)

	if keep_features and "edge_attr" in item.keys():  # edge_attr
		snake_case__  :						List[str]		=      np.asarray(item["edge_attr"]				,    dtype=np.intaa	)
	else:
		snake_case__  :						Any		=      np.ones((len(item["edge_index"][0]	), 1)				,    dtype=np.intaa	)  # same embedding for all

	if keep_features and "node_feat" in item.keys():  # input_nodes
		snake_case__  :						Any		=      np.asarray(item["node_feat"]				,    dtype=np.intaa	)
	else:
		snake_case__  :						List[Any]		=      np.ones((item["num_nodes"], 1)				,    dtype=np.intaa	)  # same embedding for all

	snake_case__  :						Dict		=      np.asarray(item["edge_index"]				,    dtype=np.intaa	)

	snake_case__  :						Any		=      convert_to_single_emb(_UpperCAmelCase	) + 1
	snake_case__  :						Dict		=      item['''num_nodes''']

	if len(edge_attr.shape	) == 1:
		snake_case__  :						Any		=      edge_attr[:, None]
	snake_case__  :						List[Any]		=      np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]]				,    dtype=np.intaa	)
	snake_case__  :						str		=      convert_to_single_emb(_UpperCAmelCase	) + 1

	# node adj matrix [num_nodes, num_nodes] bool
	snake_case__  :						Tuple		=      np.zeros([num_nodes, num_nodes]				,    dtype=_UpperCAmelCase	)
	snake_case__  :						List[str]		=      True

	snake_case__  :						Union[str, Any]		=      algos_graphormer.floyd_warshall(_UpperCAmelCase	)
	snake_case__  :						str		=      np.amax(_UpperCAmelCase	)

	snake_case__  :						str		=      algos_graphormer.gen_edge_input(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)
	snake_case__  :						List[str]		=      np.zeros([num_nodes + 1, num_nodes + 1]				,    dtype=np.single	)  # with graph token

	# combine
	snake_case__  :						List[Any]		=      input_nodes + 1  # we shift all indices by one for padding
	snake_case__  :						Any		=      attn_bias
	snake_case__  :						Dict		=      attn_edge_type
	snake_case__  :						List[str]		=      shortest_path_result.astype(np.intaa	) + 1  # we shift all indices by one for padding
	snake_case__  :						Optional[Any]		=      np.sum(_UpperCAmelCase				,    axis=1	).reshape(-1	) + 1  # we shift all indices by one for padding
	snake_case__  :						List[str]		=      item['''in_degree''']  # for undirected graph
	snake_case__  :						Tuple		=      input_edges + 1  # we shift all indices by one for padding
	if "labels" not in item:
		snake_case__  :						Tuple		=      item['''y''']

	return item



class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__=20	,					lowerCamelCase__=False)   ->	Optional[int]:







								'''simple docstring'''




								if not is_cython_available():
									raise ImportError("Graphormer preprocessing needs Cython (pyximport)")

								snake_case__  :						Tuple		=      spatial_pos_max
								snake_case__  :						Tuple		=      on_the_fly_processing







							def __call__(						self	,					lowerCamelCase__)   ->	Any:







								'''simple docstring'''




								if self.on_the_fly_processing:
									snake_case__  :						Dict		=      [preprocess_item(lowerCamelCase__) for i in features]

								if not isinstance(features[0]	,					lowerCamelCase__):
									snake_case__  :						Optional[int]		=      [vars(lowerCamelCase__) for f in features]
								snake_case__  :						Any		=      {}

								snake_case__  :						str		=      max(len(i["input_nodes"]) for i in features)
								snake_case__  :						Optional[int]		=      len(features[0]["input_nodes"][0])
								snake_case__  :						int		=      len(features[0]["attn_edge_type"][0][0])
								snake_case__  :						int		=      max(len(i["input_edges"][0][0]) for i in features)
								snake_case__  :						Optional[int]		=      len(features[0]["input_edges"][0][0][0])
								snake_case__  :						Dict		=      len(lowerCamelCase__)

								snake_case__  :						Any		=      torch.zeros(lowerCamelCase__	,					max_node_num + 1	,					max_node_num + 1	,					dtype=torch.float)
								snake_case__  :						Any		=      torch.zeros(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					dtype=torch.long)
								snake_case__  :						Optional[int]		=      torch.zeros(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					dtype=torch.long)
								snake_case__  :						int		=      torch.zeros(lowerCamelCase__	,					lowerCamelCase__	,					dtype=torch.long)
								snake_case__  :						Optional[int]		=      torch.zeros(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					dtype=torch.long)
								snake_case__  :						Dict		=      torch.zeros(
								    lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					dtype=torch.long)

								for ix, f in enumerate(lowerCamelCase__):
									for k in ["attn_bias", "attn_edge_type", "spatial_pos", "in_degree", "input_nodes", "input_edges"]:
										snake_case__  :						List[str]		=      torch.tensor(f[k])

									if len(f["attn_bias"][1:, 1:][f["spatial_pos"] >= self.spatial_pos_max]) > 0:
										snake_case__  :						Dict		=      float("-inf")

									snake_case__  :						Union[str, Any]		=      f['''attn_bias''']
									snake_case__  :						Dict		=      f[
									    '''attn_edge_type'''
									]
									snake_case__  :						int		=      f['''spatial_pos''']
									snake_case__  :						Tuple		=      f['''in_degree''']
									snake_case__  :						List[str]		=      f['''input_nodes''']
									snake_case__  :						Dict		=      f['''input_edges''']

								snake_case__  :						Any		=      batch['''in_degree''']

								snake_case__  :						Dict		=      features[0]['''labels''']
								if len(lowerCamelCase__) == 1:  # one task
									if isinstance(sample[0]	,					lowerCamelCase__):  # regression
										snake_case__  :						List[str]		=      torch.from_numpy(np.concatenate([i["labels"] for i in features]))
									else:  # binary classification
										snake_case__  :						List[str]		=      torch.from_numpy(np.concatenate([i["labels"] for i in features]))
								else:  # multi task classification, left to float to keep the NaNs
									snake_case__  :						str		=      torch.from_numpy(np.stack([i["labels"] for i in features]	,					axis=0))

								return batch











'''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()




