




'''simple docstring'''






import inspect
import unittest




class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														try:
																					import diffusers  # noqa: F401
														except ImportError:
																					assert False







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							List[str]:
														import diffusers
														from diffusers.dependency_versions_table import deps

														lowerCAmelCase__	       =     inspect.getmembers(lowerCamelCase_ ,   inspect.isclass       )

														for cls_name, cls_module in all_classes:
																					if "dummy_" in cls_module.__module__:
																												for backend in cls_module._backends:
																																			if backend == "k_diffusion":
																																										lowerCAmelCase__	       =     '''k-diffusion'''
																																			elif backend == "invisible_watermark":
																																										lowerCAmelCase__	       =     '''invisible-watermark'''
																																			assert backend in deps, F"""{backend} is not in the deps table!"""



'''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)