


'''simple docstring'''






from math import sqrt


def  _snake_case       (					A = 1000000	)						->   int:
							lowerCAmelCase__	       =     0
							lowerCAmelCase__	       =     0
							lowerCAmelCase__	       =     42

							while num_cuboids <= limit:
														max_cuboid_size += 1
														for sum_shortest_sides in range(2			,   2 * max_cuboid_size + 1	):
																					if sqrt(sum_shortest_sides**2 + max_cuboid_size**2	).is_integer():
																												num_cuboids += (
																												    min(A			,   sum_shortest_sides // 2	)
																												    - max(1			,   sum_shortest_sides - max_cuboid_size	)
																												    + 1
																												)

							return max_cuboid_size


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



'''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()
__UpperCAmelCase       =							logging.get_logger(__name__)


__UpperCAmelCase       =							'''https://openaipublic.azureedge.net/jukebox/models/'''
__UpperCAmelCase       =							{
    '''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  _snake_case       (					A	)						->   Union[str, Any]:
							if key.endswith('''.model.1.bias'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.1.bias'''			,   '''.conv1d_1.bias'''	)
							elif key.endswith('''.model.1.weight'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.1.weight'''			,   '''.conv1d_1.weight'''	)
							elif key.endswith('''.model.3.bias'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.3.bias'''			,   '''.conv1d_2.bias'''	)
							elif key.endswith('''.model.3.weight'''	) and len(key.split('''.'''	)	) > 10:
														lowerCAmelCase__	       =     key.replace('''.model.3.weight'''			,   '''.conv1d_2.weight'''	)

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

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

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														lowerCAmelCase__	       =     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:
														lowerCAmelCase__	       =     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  _snake_case       (					A			,   A			,   A			,   A	)						->   Optional[int]:
							lowerCAmelCase__	       =     {}
							import re

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

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

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

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

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

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

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

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

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

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

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

														# keep original key
														else:
																					lowerCAmelCase__	       =     original_key

														lowerCAmelCase__	       =     replace_key(A	)

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

														lowerCAmelCase__	       =     original_key
														lowerCAmelCase__	       =     value

							return new_dict


@torch.no_grad()
def  _snake_case       (					A=None			,   A=None	)						->   str:
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"	)[-1]}"""	):
																					lowerCAmelCase__	       =     requests.get(F"""{PREFIX}{file}"""			,   allow_redirects=A	)
																					os.makedirs(F"""{pytorch_dump_folder_path}/"""			,   exist_ok=A	)
																					open(F"""{pytorch_dump_folder_path}/{file.split("/"	)[-1]}"""			,   '''wb'''	).write(r.content	)

							lowerCAmelCase__	       =     MODEL_MAPPING[model_name.split('''/'''	)[-1]]

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

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

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

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

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

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

							print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
							model.save_pretrained(A	)

							return weight_dict


if __name__ == "__main__":
		__UpperCAmelCase       =							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.''',
		)
		__UpperCAmelCase       =							parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)