



'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel


def 							__UpperCAmelCase				(				UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:Tuple			)		->						Any:
				snake_case__      :   List[Any]      =    UNetaDModel(**UpperCamelCase__			)
				snake_case__      :   int      =    checkpoint['''all_modules.0.W'''].data
				snake_case__      :   List[Any]      =    checkpoint['''all_modules.0.W'''].data
				snake_case__      :   Dict      =    checkpoint['''all_modules.1.weight'''].data
				snake_case__      :   Dict      =    checkpoint['''all_modules.1.bias'''].data

				snake_case__      :   Tuple      =    checkpoint['''all_modules.2.weight'''].data
				snake_case__      :   Optional[int]      =    checkpoint['''all_modules.2.bias'''].data

				snake_case__      :   Optional[Any]      =    checkpoint['''all_modules.3.weight'''].data
				snake_case__      :   List[str]      =    checkpoint['''all_modules.3.bias'''].data

				snake_case__      :   Any      =    checkpoint[list(checkpoint.keys()			)[-4]].data
				snake_case__      :   List[str]      =    checkpoint[list(checkpoint.keys()			)[-3]].data
				snake_case__      :   Optional[Any]      =    checkpoint[list(checkpoint.keys()			)[-2]].data
				snake_case__      :   Any      =    checkpoint[list(checkpoint.keys()			)[-1]].data

				snake_case__      :   Any      =    4

				def set_attention_weights(UpperCamelCase__				:Dict   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Dict			):
								snake_case__      :   int      =    old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
								snake_case__      :   str      =    old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
								snake_case__      :   List[str]      =    old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

								snake_case__      :   List[str]      =    old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
								snake_case__      :   Tuple      =    old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
								snake_case__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

								snake_case__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
								snake_case__      :   List[Any]      =    old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

				def set_resnet_weights(UpperCamelCase__				:List[Any]   ,				UpperCamelCase__				:int   ,				UpperCamelCase__				:Dict			):
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
								snake_case__      :   str      =    old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
								snake_case__      :   Any      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

								snake_case__      :   int      =    old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
								snake_case__      :   Dict      =    old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
								snake_case__      :   Union[str, Any]      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

								snake_case__      :   int      =    old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
								snake_case__      :   Optional[int]      =    old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

								if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
												snake_case__      :   Any      =    old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
												snake_case__      :   Any      =    old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

				for i, block in enumerate(new_model_architecture.downsample_blocks			):
								snake_case__      :   Optional[int]      =    hasattr(UpperCamelCase__   ,				'''attentions'''			)
								for j in range(len(block.resnets			)			):
												set_resnet_weights(block.resnets[j]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1
												if has_attentions:
																set_attention_weights(block.attentions[j]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
																module_index += 1

								if hasattr(UpperCamelCase__   ,				'''downsamplers'''			) and block.downsamplers is not None:
												set_resnet_weights(block.resnet_down   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1
												snake_case__      :   Any      =    checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
												snake_case__      :   Optional[int]      =    checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
												module_index += 1

				set_resnet_weights(new_model_architecture.mid_block.resnets[0]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
				module_index += 1
				set_attention_weights(new_model_architecture.mid_block.attentions[0]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
				module_index += 1
				set_resnet_weights(new_model_architecture.mid_block.resnets[1]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
				module_index += 1

				for i, block in enumerate(new_model_architecture.up_blocks			):
								snake_case__      :   Optional[Any]      =    hasattr(UpperCamelCase__   ,				'''attentions'''			)
								for j in range(len(block.resnets			)			):
												set_resnet_weights(block.resnets[j]   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1
								if has_attentions:
												set_attention_weights(
												    block.attentions[0]   ,				UpperCamelCase__   ,				UpperCamelCase__			)  # why can there only be a single attention layer for up?
												module_index += 1

								if hasattr(UpperCamelCase__   ,				'''resnet_up'''			) and block.resnet_up is not None:
												snake_case__      :   Union[str, Any]      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
												snake_case__      :   List[str]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
												module_index += 1
												snake_case__      :   str      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
												snake_case__      :   Union[str, Any]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
												module_index += 1
												set_resnet_weights(block.resnet_up   ,				UpperCamelCase__   ,				UpperCamelCase__			)
												module_index += 1

				snake_case__      :   List[Any]      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
				snake_case__      :   Dict      =    checkpoint[F'''all_modules.{module_index}.bias'''].data
				module_index += 1
				snake_case__      :   Dict      =    checkpoint[F'''all_modules.{module_index}.weight'''].data
				snake_case__      :   List[str]      =    checkpoint[F'''all_modules.{module_index}.bias'''].data

				return new_model_architecture.state_dict()


if __name__ == "__main__":
				_lowercase       :						Tuple						=argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_path",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin",
				    type=str,
				    required=False,
				    help="Path to the checkpoint to convert.",
				)

				parser.add_argument(
				    "--config_file",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/config.json",
				    type=str,
				    required=False,
				    help="The config json file corresponding to the architecture.",
				)

				parser.add_argument(
				    "--dump_path",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt",
				    type=str,
				    required=False,
				    help="Path to the output model.",
				)

				_lowercase       :						Any						=parser.parse_args()

				_lowercase       :						List[Any]						=torch.load(args.checkpoint_path, map_location="cpu")

				with open(args.config_file) as f:
								_lowercase       :						List[Any]						=json.loads(f.read())

				_lowercase       :						List[str]						=convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

				if "sde" in config:
								del config["sde"]

				_lowercase       :						Tuple						=UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								_lowercase       :						str						=ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

								_lowercase       :						List[Any]						=ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)










'''simple docstring'''





# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint






class     lowercase  :






      def __init__(self			,	a__			,	a__       )							-> Optional[int]:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			key
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			freq






      def __str__(self       )							-> Dict:
         return f"""Node(key={self.key}, freq={self.freq})"""


def 					UpperCamelCase						(	snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case						,  snake_case ):




   '''simple docstring'''

   if i > j or i < 0 or j > len(snake_case ) - 1:
      return

   SCREAMING_SNAKE_CASE__      :					Tuple							    =			root[i][j]
   if parent == -1:  # root does not have a parent
      print(f"""{key[node]} is the root of the binary search tree.""" )
   elif is_left:
      print(f"""{key[node]} is the left child of key {parent}.""" )
   else:
      print(f"""{key[node]} is the right child of key {parent}.""" )

   print_binary_search_tree(snake_case						,  snake_case						,  snake_case						,  node - 1						,  key[node]						,  snake_case )
   print_binary_search_tree(snake_case						,  snake_case						,  node + 1						,  snake_case						,  key[node]						,  snake_case )


def 					UpperCamelCase						(	snake_case ):




   '''simple docstring'''

   nodes.sort(key=lambda snake_case : node.key )

   SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			len(snake_case )

   SCREAMING_SNAKE_CASE__      :					List[str]							    =			[nodes[i].key for i in range(snake_case )]
   SCREAMING_SNAKE_CASE__      :					int							    =			[nodes[i].freq for i in range(snake_case )]

   # This 2D array stores the overall tree cost (which's as minimized as possible);
   # for a single key, cost is equal to frequency of the key.
   SCREAMING_SNAKE_CASE__      :					Dict							    =			[[freqs[i] if i == j else 0 for j in range(snake_case )] for i in range(snake_case )]
   # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
   # array
   SCREAMING_SNAKE_CASE__      :					str							    =			[[freqs[i] if i == j else 0 for j in range(snake_case )] for i in range(snake_case )]
   # stores tree roots that will be used later for constructing binary search tree
   SCREAMING_SNAKE_CASE__      :					int							    =			[[i if i == j else 0 for j in range(snake_case )] for i in range(snake_case )]

   for interval_length in range(2						,  n + 1 ):
      for i in range(n - interval_length + 1 ):
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			i + interval_length - 1

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			sys.maxsize  # set the value to "infinity"
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			total[i][j - 1] + freqs[j]

         # Apply Knuth's optimization
         # Loop without optimization: for r in range(i, j + 1):
         for r in range(root[i][j - 1]						,  root[i + 1][j] + 1 ):  # r is a temporal root
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			left + total[i][j] + right

            if dp[i][j] > cost:
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			cost
               SCREAMING_SNAKE_CASE__      :					str							    =			r

   print('''Binary search tree nodes:''' )
   for node in nodes:
      print(snake_case )

   print(f"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.""" )
   print_binary_search_tree(snake_case						,  snake_case						,  0						,  n - 1						,  -1						,  snake_case )


def 					UpperCamelCase						(	):




   '''simple docstring'''

   SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[Node(snake_case						,  randint(1						,  50 ) ) for i in range(10						,  0						,  -1 )]
   find_optimal_binary_search_tree(snake_case )


if __name__ == "__main__":
       main()

