




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      lowerCamelCase_						(			UpperCamelCase__							: Any )	->			Dict:





				"""simple docstring"""


				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      lowerCamelCase_						(			UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: str					,	UpperCamelCase__							: str )	->			List[Any]:





				"""simple docstring"""


				__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(UpperCamelCase__ ):
												__lowerCamelCase									=       re_encoder_block_conv_in.match(UpperCamelCase__ )
												__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(UpperCamelCase__					,	UpperCamelCase__ )

								elif re_encoder_block_resnet.fullmatch(UpperCamelCase__ ):
												__lowerCamelCase									=       re_encoder_block_resnet.match(UpperCamelCase__ )
												__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(UpperCamelCase__					,	UpperCamelCase__ )

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

								# rename vqvae.decoder keys
								elif re_decoder_block_conv_out.fullmatch(UpperCamelCase__ ):
												__lowerCamelCase									=       re_decoder_block_conv_out.match(UpperCamelCase__ )
												__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(UpperCamelCase__					,	UpperCamelCase__ )

								elif re_decoder_block_resnet.fullmatch(UpperCamelCase__ ):
												__lowerCamelCase									=       re_decoder_block_resnet.match(UpperCamelCase__ )
												__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(UpperCamelCase__					,	UpperCamelCase__ )

								elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__ ):
												__lowerCamelCase									=       re_decoder_block_proj_in.match(UpperCamelCase__ )
												__lowerCamelCase									=       regex_match.groups()
												__lowerCamelCase									=       F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
												__lowerCamelCase									=       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__ ):
												__lowerCamelCase									=       re_prior_cond_conv_out.match(UpperCamelCase__ )
												__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(UpperCamelCase__					,	UpperCamelCase__ )

								elif re_prior_cond_resnet.fullmatch(UpperCamelCase__ ):
												__lowerCamelCase									=       re_prior_cond_resnet.match(UpperCamelCase__ )
												__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(UpperCamelCase__					,	UpperCamelCase__ )

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

								# keep original key
								else:
												__lowerCamelCase									=       original_key

								__lowerCamelCase									=       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:
												__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      lowerCamelCase_						(			UpperCamelCase__							: Optional[int]=None					,	UpperCamelCase__							: Optional[int]=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]}""" ):
												__lowerCamelCase									=       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 )

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

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

				__lowerCamelCase									=       []
				__lowerCamelCase									=       {}
				for i, dict_name in enumerate(UpperCamelCase__ ):
								__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(UpperCamelCase__					,	model.state_dict()					,	UpperCamelCase__					,	UpperCamelCase__ )
								weight_dict.append(UpperCamelCase__ )

				__lowerCamelCase									=       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__":
					__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)








from datetime import datetime as dt
import os

from github import Github


__A								=       [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def      lowerCamelCase_						(			)	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       Github(os.environ['GITHUB_TOKEN'] )
				__lowerCamelCase									=       g.get_repo('huggingface/transformers' )
				__lowerCamelCase									=       repo.get_issues(state='open' )

				for issue in open_issues:
								__lowerCamelCase									=       sorted([comment for comment in issue.get_comments()]					,	key=lambda UpperCamelCase__ : i.created_at					,	reverse=UpperCamelCase__ )
								__lowerCamelCase									=       comments[0] if len(UpperCamelCase__ ) > 0 else None
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and (dt.utcnow() - issue.updated_at).days > 7
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
												issue.edit(state='closed' )
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# print(f"Would add stale comment to {issue.number}")
												issue.create_comment(
												    'This issue has been automatically marked as stale because it has not had '
												    'recent activity. If you think this still needs to be addressed '
												    'please comment on this thread.\n\nPlease note that issues that do not follow the '
												    '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
												    'are likely to be ignored.' )


if __name__ == "__main__":
					main()


