


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()
_lowercase       :      List[Any]	=	logging.get_logger(__name__)


_lowercase       :      Optional[int]	=	"https://openaipublic.azureedge.net/jukebox/models/"
_lowercase       :      List[Any]	=	{
    "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   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]						) ->   Union[str, Any]:





	"""simple docstring"""







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

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

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

	if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
		A								=				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:
		A								=				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   _lowerCAmelCase				(   UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     List[Any]						) ->   Optional[int]:





	"""simple docstring"""







	A								=				{}
	import re

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

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

	A								=				re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""						)
	A								=				re.compile(
	    r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""						)
	A								=				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(UpperCamelCase__						):
			A								=				re_encoder_block_conv_in.match(UpperCamelCase__						)
			A								=				regex_match.groups()
			A								=				int(groups[2]						) * 2 + int(groups[3]						)
			A								=				f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
			A								=				re_encoder_block_conv_in.sub(UpperCamelCase__							,					UpperCamelCase__						)

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

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

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

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

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

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

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

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

		# keep original key
		else:
			A								=				original_key

		A								=				replace_key(UpperCamelCase__						)

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

		A								=				original_key
		A								=				value

	return new_dict





@torch.no_grad()
def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]=None							,					UpperCamelCase__:     Union[str, Any]=None						) ->   Any:





	"""simple docstring"""







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

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

	A								=				JukeboxConfig.from_pretrained(UpperCamelCase__						)
	A								=				JukeboxModel(UpperCamelCase__						)

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

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

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

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

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

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

	return weight_dict


if __name__ == "__main__":
				_lowercase       :      List[Any]	=	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.",
				)
				_lowercase       :      Optional[Any]	=	parser.parse_args()
				convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







from __future__ import annotations

import requests





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   dict:





	"""simple docstring"""







	A								=				f'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
	return requests.get(UpperCamelCase__						).json()





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   list[dict]:





	"""simple docstring"""







	A								=				"""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
	A								=				requests.get(UpperCamelCase__						).json()[:max_stories]
	return [get_hackernews_story(UpperCamelCase__						) for story_id in story_ids]





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   str:





	"""simple docstring"""







	A								=				hackernews_top_stories(UpperCamelCase__						)
	return "\n".join("""* [{title}]({url})""".format(**UpperCamelCase__						) for story in stories						)


if __name__ == "__main__":
				print(hackernews_top_stories_as_markdown())



