


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			:			Union[str, Any]												=					logging.get_logger(__name__)


a			:			Tuple												=					'''https://openaipublic.azureedge.net/jukebox/models/'''
a			:			Dict												=					{
    '''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 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if key.endswith('''.model.1.bias'''	) and len(key.split('''.'''	)	) > 10:
						__lowercase			       = key.replace('''.model.1.bias'''							,      '''.conv1d_1.bias'''	)
			elif key.endswith('''.model.1.weight'''	) and len(key.split('''.'''	)	) > 10:
						__lowercase			       = key.replace('''.model.1.weight'''							,      '''.conv1d_1.weight'''	)
			elif key.endswith('''.model.3.bias'''	) and len(key.split('''.'''	)	) > 10:
						__lowercase			       = key.replace('''.model.3.bias'''							,      '''.conv1d_2.bias'''	)
			elif key.endswith('''.model.3.weight'''	) and len(key.split('''.'''	)	) > 10:
						__lowercase			       = key.replace('''.model.3.weight'''							,      '''.conv1d_2.weight'''	)

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

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

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						__lowercase			       = 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:
						__lowercase			       = 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 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = {}
			import re

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

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

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

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

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

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

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

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

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

						elif re_prior_cond_proj_in.fullmatch(_UpperCamelCase	):
									__lowercase			       = re_prior_cond_proj_in.match(_UpperCamelCase	)
									__lowercase			       = regex_match.groups()
									__lowercase			       = F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
									__lowercase			       = re_prior_cond_proj_in.sub(_UpperCamelCase							,      _UpperCamelCase	)

						# keep original key
						else:
									__lowercase			       = original_key

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

						__lowercase			       = original_key
						__lowercase			       = value

			return new_dict


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase=None							,      _UpperCamelCase=None	):


			'''simple docstring'''







			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"	)[-1]}'	):
									__lowercase			       = 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	)

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

			__lowercase			       = JukeboxConfig.from_pretrained(_UpperCamelCase	)
			__lowercase			       = JukeboxModel(_UpperCamelCase	)

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

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

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

			__lowercase			       = 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			:			List[str]												=					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			:			List[str]												=					parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							split=snake_case_			,							features=snake_case_			,							cache_dir=snake_case_			,							keep_in_memory=snake_case_			,							streaming=snake_case_			,							num_proc=snake_case_			,							**snake_case_			,							)
								__lowercase			       = path_or_paths if isinstance(snake_case_			,							snake_case_    ) else {self.split: path_or_paths}
								__lowercase			       = Text(
								    cache_dir=snake_case_			,							data_files=snake_case_			,							features=snake_case_			,							**snake_case_			,							)






					def A   (					self    ) ->      int:







								'''simple docstring'''

								if self.streaming:
											__lowercase			       = self.builder.as_streaming_dataset(split=self.split    )
								# Build regular (map-style) dataset
								else:
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None

											self.builder.download_and_prepare(
											    download_config=snake_case_			,							download_mode=snake_case_			,							verification_mode=snake_case_			,							base_path=snake_case_			,							num_proc=self.num_proc			,							)
											__lowercase			       = self.builder.as_dataset(
											    split=self.split			,							verification_mode=snake_case_			,							in_memory=self.keep_in_memory    )
								return dataset


