





'''simple docstring'''

from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any

@dataclass
class       UpperCAmelCase :
		UpperCAmelCase					:	Any 			=		42
		UpperCAmelCase					:	Optional[Any] 			=		None
		UpperCAmelCase					:	Tuple 			=		None







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = Node(1							)
				lowercase         = Node(2							)
				lowercase         = Node(3							)
				lowercase         = Node(4							)
				lowercase         = Node(5							)
				return tree







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return [root.data, *preorder(root.left							), *preorder(root.right							)] if root else []







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return postorder(root.left							) + postorder(root.right							) + [root.data] if root else []







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return [*inorder(root.left							), root.data, *inorder(root.right							)] if root else []







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				return (max(height(root.left							)		,							height(root.right							)							) + 1) if root else 0







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = []

				if root is None:
								return output

				lowercase         = deque([root]							)

				while process_queue:
								lowercase         = process_queue.popleft()
								output.append(node.data							)

								if node.left:
												process_queue.append(node.left							)
								if node.right:
												process_queue.append(node.right							)
				return output







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = []

				def populate_output(lowerCAmelCase_		,							lowerCAmelCase_							) -> None:
								if not root:
												return
								if level == 1:
												output.append(root.data							)
								elif level > 1:
												populate_output(root.left		,							level - 1							)
												populate_output(root.right		,							level - 1							)

				populate_output(_lowerCamelCase		,							_lowerCamelCase							)
				return output







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = []

				def populate_output(lowerCAmelCase_		,							lowerCAmelCase_							) -> None:
								if root is None:
												return
								if level == 1:
												output.append(root.data							)
								elif level > 1:
												populate_output(root.right		,							level - 1							)
												populate_output(root.left		,							level - 1							)

				populate_output(_lowerCamelCase		,							_lowerCamelCase							)
				return output







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if root is None:
								return []

				lowercase         = []

				lowercase         = 0
				lowercase         = height(_lowerCamelCase							)

				for h in range(1		,							height_tree + 1							):
								if not flag:
												output.append(get_nodes_from_left_to_right(_lowerCamelCase		,							_lowerCamelCase							)							)
												lowercase         = 1
								else:
												output.append(get_nodes_from_right_to_left(_lowerCamelCase		,							_lowerCamelCase							)							)
												lowercase         = 0

				return output







def 							UpperCAmelCase_    (	):  # Main function for testing.

				"""simple docstring"""






				lowercase         = make_tree()
				print(f'In-order Traversal: {inorder(_lowerCamelCase							)}'							)
				print(f'Pre-order Traversal: {preorder(_lowerCamelCase							)}'							)
				print(f'Post-order Traversal: {postorder(_lowerCamelCase							)}'		,							"\n"							)

				print(f'Height of Tree: {height(_lowerCamelCase							)}'		,							"\n"							)

				print("Complete Level Order Traversal: "							)
				print(level_order(_lowerCamelCase							)		,							"\n"							)

				print("Level-wise order Traversal: "							)

				for level in range(1		,							height(_lowerCamelCase							) + 1							):
								print(f'Level {level}:'		,							get_nodes_from_left_to_right(_lowerCamelCase		,							level=_lowerCamelCase							)							)

				print("\nZigZag order Traversal: "							)
				print(zigzag(_lowerCamelCase							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()











'''simple docstring'''

import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase : List[str]											=			logging.get_logger(__name__)


__lowerCamelCase : List[Any]											=			"https://openaipublic.azureedge.net/jukebox/models/"
__lowerCamelCase : Tuple											=			{
    "jukebox-1b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "1b_lyrics/prior_level_2.pth.tar",
    ],
    "jukebox-5b-lyrics": [
        "5b/vqvae.pth.tar",
        "5b/prior_level_0.pth.tar",
        "5b/prior_level_1.pth.tar",
        "5b_lyrics/prior_level_2.pth.tar",
    ],
}







def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				if key.endswith(".model.1.bias"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.1.bias"		,							".conv1d_1.bias"							)
				elif key.endswith(".model.1.weight"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.1.weight"		,							".conv1d_1.weight"							)
				elif key.endswith(".model.3.bias"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.3.bias"		,							".conv1d_2.bias"							)
				elif key.endswith(".model.3.weight"							) and len(key.split("."							)							) > 10:
								lowercase         = key.replace(".model.3.weight"		,							".conv1d_2.weight"							)

				if "conditioner_blocks.0." in key:
								lowercase         = key.replace("conditioner_blocks.0"		,							"conditioner_blocks"							)

				if "prime_prior" in key:
								lowercase         = key.replace("prime_prior"		,							"encoder"							)

				if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
								lowercase         = key.replace(".emb."		,							"."							)

				if key.endswith("k"							):  # replace vqvae.X.k with vqvae.X.codebook
								return key.replace(".k"		,							".codebook"							)
				if "y_emb." in key:
								return key.replace("y_emb."		,							"metadata_embedding."							)

				if "x_emb.emb." in key:
								lowercase         = key.replace("0.x_emb.emb"		,							"embed_tokens"							)

				if "prime_state_ln" in key:
								return key.replace("prime_state_ln"		,							"encoder.final_layer_norm"							)
				if ".ln" in key:
								return key.replace(".ln"		,							".layer_norm"							)
				if "_ln" in key:
								return key.replace("_ln"		,							"_layer_norm"							)

				if "prime_state_proj" in key:
								return key.replace("prime_state_proj"		,							"encoder.proj_in"							)
				if "prime_x_out" in key:
								return key.replace("prime_x_out"		,							"encoder.lm_head"							)
				if "prior.x_out" in key:
								return key.replace("x_out"		,							"fc_proj_out"							)
				if "x_emb" in key:
								return key.replace("x_emb"		,							"embed_tokens"							)

				return key







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = {}
				import re

				lowercase         = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"							)
				lowercase         = re.compile(
				    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"							)
				lowercase         = re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"							)

				lowercase         = re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"							)
				lowercase         = re.compile(
				    R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"							)
				lowercase         = re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"							)

				lowercase         = re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"							)
				lowercase         = re.compile(
				    R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"							)
				lowercase         = re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)"							)

				for original_key, value in state_dict.items():
								# rename vqvae.encoder keys
								if re_encoder_block_conv_in.fullmatch(lowerCAmelCase_							):
												lowercase         = re_encoder_block_conv_in.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							)
												lowercase         = f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
												lowercase         = re_encoder_block_conv_in.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_encoder_block_resnet.fullmatch(lowerCAmelCase_							):
												lowercase         = re_encoder_block_resnet.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							)
												lowercase         = {"1": 1, "3": 2}[groups[-2]]
												lowercase         = f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
												lowercase         = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
												lowercase         = prefix + resnet_block
												lowercase         = re_encoder_block_resnet.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase_							):
												lowercase         = re_encoder_block_proj_out.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
												lowercase         = re_encoder_block_proj_out.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase_							):
												lowercase         = re_decoder_block_conv_out.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							) - 2
												lowercase         = f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
												lowercase         = re_decoder_block_conv_out.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_decoder_block_resnet.fullmatch(lowerCAmelCase_							):
												lowercase         = re_decoder_block_resnet.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[2]							) * 2 + int(groups[3]							) - 2
												lowercase         = {"1": 1, "3": 2}[groups[-2]]
												lowercase         = f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
												lowercase         = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
												lowercase         = prefix + resnet_block
												lowercase         = re_decoder_block_resnet.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_							):
												lowercase         = re_decoder_block_proj_in.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
												lowercase         = re_decoder_block_proj_in.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								# rename prior cond.model to upsampler.upsample_block and resnet
								elif re_prior_cond_conv_out.fullmatch(lowerCAmelCase_							):
												lowercase         = re_prior_cond_conv_out.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[1]							) * 2 + int(groups[2]							) - 2
												lowercase         = f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
												lowercase         = re_prior_cond_conv_out.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_prior_cond_resnet.fullmatch(lowerCAmelCase_							):
												lowercase         = re_prior_cond_resnet.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = int(groups[1]							) * 2 + int(groups[2]							) - 2
												lowercase         = {"1": 1, "3": 2}[groups[-2]]
												lowercase         = f'conditioner_blocks.upsampler.upsample_block.{block_index}.'
												lowercase         = f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
												lowercase         = prefix + resnet_block
												lowercase         = re_prior_cond_resnet.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_							):
												lowercase         = re_prior_cond_proj_in.match(lowerCAmelCase_							)
												lowercase         = regex_match.groups()
												lowercase         = f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
												lowercase         = re_prior_cond_proj_in.sub(lowerCAmelCase_		,							lowerCAmelCase_							)

								# keep original key
								else:
												lowercase         = original_key

								lowercase         = replace_key(lowerCAmelCase_							)

								if f'{key_prefix}.{key}' not in model_state_dict or key is None:
												print(f'failed converting {original_key} to {key}, does not match'							)

								# handle missmatched shape
								elif value.shape != model_state_dict[f'{key_prefix}.{key}'].shape:
												lowercase         = model_state_dict[f'{key_prefix}.{key}']
												print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'							)
												lowercase         = original_key

								lowercase         = original_key
								lowercase         = value

				return new_dict







@torch.no_grad()
def 							UpperCAmelCase_    (	lowerCAmelCase_=None		,							lowerCAmelCase_=None							):

				"""simple docstring"""






				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'							):
												lowercase         = requests.get(f'{PREFIX}{file}'		,							allow_redirects=lowerCAmelCase_							)
												os.makedirs(f'{pytorch_dump_folder_path}/'		,							exist_ok=lowerCAmelCase_							)
												open(f'{pytorch_dump_folder_path}/{file.split("/"							)[-1]}'		,							"wb"							).write(r.content							)

				lowercase         = MODEL_MAPPING[model_name.split("/"							)[-1]]

				lowercase         = JukeboxConfig.from_pretrained(lowerCAmelCase_							)
				lowercase         = JukeboxModel(lowerCAmelCase_							)

				lowercase         = []
				lowercase         = {}
				for i, dict_name in enumerate(lowerCAmelCase_							):
								lowercase         = torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"							)[-1]}'							)["model"]

								lowercase         = {}
								for k in old_dic.keys():
												if k.endswith(".b"							):
																lowercase         = old_dic[k]
												elif k.endswith(".w"							):
																lowercase         = old_dic[k]
												elif "level_2" not in dict_name and "cond.model." in k:
																lowercase         = old_dic[k]
												else:
																lowercase         = old_dic[k]

								lowercase         = "vqvae" if i == 0 else f'priors.{3 - i}'
								lowercase         = fix_jukebox_keys(lowerCAmelCase_		,							model.state_dict()		,							lowerCAmelCase_		,							lowerCAmelCase_							)
								weight_dict.append(lowerCAmelCase_							)

				lowercase         = weight_dict.pop(0							)
				model.vqvae.load_state_dict(lowerCAmelCase_							)
				for i in range(len(lowerCAmelCase_							)							):
								model.priors[i].load_state_dict(weight_dict[2 - i]							)

				Path(lowerCAmelCase_							).mkdir(exist_ok=lowerCAmelCase_							)
				with open(f'{pytorch_dump_folder_path}/mapping.json'		,							"w"							) as txtfile:
								json.dump(lowerCAmelCase_		,							lowerCAmelCase_							)

				print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
				model.save_pretrained(lowerCAmelCase_							)

				return weight_dict


if __name__ == "__main__":
			__lowerCamelCase : int											=			argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default="jukebox-5b-lyrics",
			    type=str,
			    help="Name of the model you'd like to convert.",
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default="jukebox-5b-lyrics-converted",
			    type=str,
			    help="Path to the output PyTorch model directory.",
			)
			__lowerCamelCase : List[str]											=			parser.parse_args()
			convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




