



import re






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					re.compile(
							    r"^(?:0|94|\+94|0{2}94)" r"7(0|1|2|4|5|6|7|8)" r"(-| |)" r"\d{7}$"						)

							return bool(re.search(_lowerCamelCase       ,				_lowerCamelCase						)						)


if __name__ == "__main__":
						_snake_case	=						"0094702343221"

						print(is_sri_lankan_phone_number(phone))





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()
_snake_case	=						logging.get_logger(__name__)


_snake_case	=						"https://openaipublic.azureedge.net/jukebox/models/"
_snake_case	=						{
    "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						):





							'''simple docstring'''




							if key.endswith(".model.1.bias"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							int				     =					key.replace(".model.1.bias"       ,				".conv1d_1.bias"						)
							elif key.endswith(".model.1.weight"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							Optional[int]				     =					key.replace(".model.1.weight"       ,				".conv1d_1.weight"						)
							elif key.endswith(".model.3.bias"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							Union[str, Any]				     =					key.replace(".model.3.bias"       ,				".conv1d_2.bias"						)
							elif key.endswith(".model.3.weight"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							int				     =					key.replace(".model.3.weight"       ,				".conv1d_2.weight"						)

							if "conditioner_blocks.0." in key:
														_lowerCAmelCase  :							List[str]				     =					key.replace("conditioner_blocks.0"       ,				"conditioner_blocks"						)

							if "prime_prior" in key:
														_lowerCAmelCase  :							int				     =					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  :							int				     =					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  :							Tuple				     =					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       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					{}
							import re

							_lowerCAmelCase  :							Union[str, Any]				     =					re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
							_lowerCAmelCase  :							List[str]				     =					re.compile(
							    r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
							_lowerCAmelCase  :							List[Any]				     =					re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

							_lowerCAmelCase  :							List[Any]				     =					re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
							_lowerCAmelCase  :							List[str]				     =					re.compile(
							    r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
							_lowerCAmelCase  :							int				     =					re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

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

														elif re_encoder_block_resnet.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							List[Any]				     =					re_encoder_block_resnet.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							str				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[int]				     =					int(groups[2]						) * 2 + int(groups[3]						)
																					_lowerCAmelCase  :							str				     =					{"1": 1, "3": 2}[groups[-2]]
																					_lowerCAmelCase  :							Union[str, Any]				     =					F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
																					_lowerCAmelCase  :							Optional[Any]				     =					F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
																					_lowerCAmelCase  :							int				     =					prefix + resnet_block
																					_lowerCAmelCase  :							int				     =					re_encoder_block_resnet.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							Union[str, Any]				     =					re_encoder_block_proj_out.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[Any]				     =					F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
																					_lowerCAmelCase  :							str				     =					re_encoder_block_proj_out.sub(_lowerCamelCase       ,				_lowerCamelCase						)

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

														elif re_decoder_block_resnet.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							List[str]				     =					re_decoder_block_resnet.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[str]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[Any]				     =					int(groups[2]						) * 2 + int(groups[3]						) - 2
																					_lowerCAmelCase  :							Union[str, Any]				     =					{"1": 1, "3": 2}[groups[-2]]
																					_lowerCAmelCase  :							Optional[Any]				     =					F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
																					_lowerCAmelCase  :							Optional[int]				     =					F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
																					_lowerCAmelCase  :							Dict				     =					prefix + resnet_block
																					_lowerCAmelCase  :							Dict				     =					re_decoder_block_resnet.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							Optional[int]				     =					re_decoder_block_proj_in.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							Union[str, Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[Any]				     =					F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
																					_lowerCAmelCase  :							Any				     =					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						):
																					_lowerCAmelCase  :							Optional[int]				     =					re_prior_cond_conv_out.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[int]				     =					int(groups[1]						) * 2 + int(groups[2]						) - 2
																					_lowerCAmelCase  :							Tuple				     =					F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
																					_lowerCAmelCase  :							Optional[int]				     =					re_prior_cond_conv_out.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														elif re_prior_cond_resnet.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							List[str]				     =					re_prior_cond_resnet.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[str]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Union[str, Any]				     =					int(groups[1]						) * 2 + int(groups[2]						) - 2
																					_lowerCAmelCase  :							List[str]				     =					{"1": 1, "3": 2}[groups[-2]]
																					_lowerCAmelCase  :							Optional[Any]				     =					F"conditioner_blocks.upsampler.upsample_block.{block_index}."
																					_lowerCAmelCase  :							Tuple				     =					F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
																					_lowerCAmelCase  :							List[Any]				     =					prefix + resnet_block
																					_lowerCAmelCase  :							Optional[Any]				     =					re_prior_cond_resnet.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							int				     =					re_prior_cond_proj_in.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							Optional[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[int]				     =					F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
																					_lowerCAmelCase  :							List[str]				     =					re_prior_cond_proj_in.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														# keep original key
														else:
																					_lowerCAmelCase  :							Optional[int]				     =					original_key

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

														_lowerCAmelCase  :							List[Any]				     =					original_key
														_lowerCAmelCase  :							Optional[int]				     =					value

							return new_dict






@torch.no_grad()
def        A       (      _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]}"						):
																					_lowerCAmelCase  :							List[Any]				     =					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						)

							_lowerCAmelCase  :							Optional[Any]				     =					MODEL_MAPPING[model_name.split("/"						)[-1]]

							_lowerCAmelCase  :							Tuple				     =					JukeboxConfig.from_pretrained(_lowerCamelCase						)
							_lowerCAmelCase  :							Optional[int]				     =					JukeboxModel(_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[int]				     =					[]
							_lowerCAmelCase  :							List[Any]				     =					{}
							for i, dict_name in enumerate(_lowerCamelCase						):
														_lowerCAmelCase  :							Any				     =					torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/'						)[-1]}"						)["model"]

														_lowerCAmelCase  :							Union[str, Any]				     =					{}
														for k in old_dic.keys():
																					if k.endswith(".b"						):
																												_lowerCAmelCase  :							Dict				     =					old_dic[k]
																					elif k.endswith(".w"						):
																												_lowerCAmelCase  :							Tuple				     =					old_dic[k]
																					elif "level_2" not in dict_name and "cond.model." in k:
																												_lowerCAmelCase  :							str				     =					old_dic[k]
																					else:
																												_lowerCAmelCase  :							Union[str, Any]				     =					old_dic[k]

														_lowerCAmelCase  :							Union[str, Any]				     =					"vqvae" if i == 0 else F"priors.{3 - i}"
														_lowerCAmelCase  :							Union[str, Any]				     =					fix_jukebox_keys(_lowerCamelCase       ,				model.state_dict()       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														weight_dict.append(_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[Any]				     =					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__":
						_snake_case	=						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.",
						)
						_snake_case	=						parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
