






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()
lowercase    				=   logging.get_logger(__name__)


lowercase    				=   "https://openaipublic.azureedge.net/jukebox/models/"
lowercase    				=   {
    "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 		__UpperCAmelCase     (	a_):
							if key.endswith('.model.1.bias') and len(key.split('.')) > 10:
														snake_case_           =				key.replace('.model.1.bias'				,	'.conv1d_1.bias')
							elif key.endswith('.model.1.weight') and len(key.split('.')) > 10:
														snake_case_           =				key.replace('.model.1.weight'				,	'.conv1d_1.weight')
							elif key.endswith('.model.3.bias') and len(key.split('.')) > 10:
														snake_case_           =				key.replace('.model.3.bias'				,	'.conv1d_2.bias')
							elif key.endswith('.model.3.weight') and len(key.split('.')) > 10:
														snake_case_           =				key.replace('.model.3.weight'				,	'.conv1d_2.weight')

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

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

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														snake_case_           =				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:
														snake_case_           =				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 		__UpperCAmelCase     (	a_				,	a_				,	a_				,	a_):
							snake_case_           =				{}
							import re

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

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

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

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

														elif re_encoder_block_proj_out.fullmatch(a_):
																					snake_case_           =				re_encoder_block_proj_out.match(a_)
																					snake_case_           =				regex_match.groups()
																					snake_case_           =				f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
																					snake_case_           =				re_encoder_block_proj_out.sub(a_				,	a_)

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

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

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

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

														elif re_prior_cond_proj_in.fullmatch(a_):
																					snake_case_           =				re_prior_cond_proj_in.match(a_)
																					snake_case_           =				regex_match.groups()
																					snake_case_           =				f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
																					snake_case_           =				re_prior_cond_proj_in.sub(a_				,	a_)

														# keep original key
														else:
																					snake_case_           =				original_key

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

														snake_case_           =				original_key
														snake_case_           =				value

							return new_dict





@torch.no_grad()
def 		__UpperCAmelCase     (	a_=None				,	a_=None):
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/")[-1]}'''):
																					snake_case_           =				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)

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

							snake_case_           =				JukeboxConfig.from_pretrained(a_)
							snake_case_           =				JukeboxModel(a_)

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

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

														snake_case_           =				'vqvae' if i == 0 else f'''priors.{3 - i}'''
														snake_case_           =				fix_jukebox_keys(a_				,	model.state_dict()				,	a_				,	a_)
														weight_dict.append(a_)

							snake_case_           =				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__":
	lowercase    				=   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.",
	)
	lowercase    				=   parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)













import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


lowercase    				=   4
lowercase    				=   3




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	pass






def 		__UpperCAmelCase     (	a_):
							for shard in shards:
														for i in range(a_):
																					yield {"i": i, "shard": shard}





def 		__UpperCAmelCase     (	):
							snake_case_           =				int(os.environ['RANK'])
							snake_case_           =				int(os.environ['WORLD_SIZE'])

							snake_case_           =				ArgumentParser()
							parser.add_argument('--streaming'				,	type=a_)
							parser.add_argument('--local_rank'				,	type=a_)
							parser.add_argument('--num_workers'				,	type=a_				,	default=0)
							snake_case_           =				parser.parse_args()
							snake_case_           =				args.streaming
							snake_case_           =				args.num_workers

							snake_case_           =				{'shards': [f'''shard_{shard_idx}''' for shard_idx in range(a_)]}
							snake_case_           =				IterableDataset.from_generator(a_				,	gen_kwargs=a_)
							if not streaming:
														snake_case_           =				Dataset.from_list(list(a_))

							snake_case_           =				split_dataset_by_node(a_				,	rank=a_				,	world_size=a_)
							snake_case_           =				torch.utils.data.DataLoader(a_				,	num_workers=a_)

							snake_case_           =				NUM_SHARDS * NUM_ITEMS_PER_SHARD
							snake_case_           =				full_size // world_size
							expected_local_size += int(rank < (full_size % world_size))

							snake_case_           =				sum(1 for _ in dataloader)
							if local_size != expected_local_size:
														raise FailedTestError(f'''local_size {local_size} != expected_local_size {expected_local_size}''')


if __name__ == "__main__":
	main()





