



def 	__a					(   lowerCAmelCase_       : int = 2_00_00_00    )				->			int:






						'''simple docstring'''



						UpperCAmelCase_=       [0 for i in range(n + 1    )]
						UpperCAmelCase_=       1
						UpperCAmelCase_=       1

						for i in range(2      ,int(n**0.5    ) + 1    ):
												if primality_list[i] == 0:
																		for j in range(i * i      ,n + 1      ,lowerCAmelCase_    ):
																								UpperCAmelCase_=       1
						UpperCAmelCase_=       0
						for i in range(lowerCAmelCase_    ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


if __name__ == "__main__":
	print(f'{solution() = }')









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()
__A       =						logging.get_logger(__name__)


__A       =						'''https://openaipublic.azureedge.net/jukebox/models/'''
__A       =						{
    '''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 	__a					(   lowerCAmelCase_       : List[Any]    )				->			Tuple:






						'''simple docstring'''



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

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

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

						if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
												UpperCAmelCase_=       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:
												UpperCAmelCase_=       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 	__a					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       {}
						import re

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

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

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

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

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

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

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

												elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       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_    ):
																		UpperCAmelCase_=       re_prior_cond_conv_out.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       int(groups[1]    ) * 2 + int(groups[2]    ) - 2
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_conv_out.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_prior_cond_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_proj_in.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												# keep original key
												else:
																		UpperCAmelCase_=       original_key

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

												UpperCAmelCase_=       original_key
												UpperCAmelCase_=       value

						return new_dict



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : str=None    )				->			List[str]:






						'''simple docstring'''



						for file in MODEL_MAPPING[model_name]:
												if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"    )[-1]}"""    ):
																		UpperCAmelCase_=       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    )

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

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

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

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

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

						UpperCAmelCase_=       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__":
	__A       =						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.''',
	)
	__A       =						parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




