

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						:  Optional[Any]									=			logging.get_logger(__name__)


_UpperCAmelCase						:  str									=			"""https://openaipublic.azureedge.net/jukebox/models/"""
_UpperCAmelCase						:  Union[str, Any]									=			{
    """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__ ):







							'''simple docstring'''






							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    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							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(UpperCamelCase__ ):
																					snake_case_     							=       re_encoder_block_conv_in.match(UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__ )

														elif re_encoder_block_resnet.fullmatch(UpperCamelCase__ ):
																					snake_case_     							=       re_encoder_block_resnet.match(UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__ )

														elif re_encoder_block_proj_out.fullmatch(UpperCamelCase__ ):
																					snake_case_     							=       re_encoder_block_proj_out.match(UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__ )

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

														elif re_decoder_block_resnet.fullmatch(UpperCamelCase__ ):
																					snake_case_     							=       re_decoder_block_resnet.match(UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__ )

														elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__ ):
																					snake_case_     							=       re_decoder_block_proj_in.match(UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__ )

														# rename prior cond.model to upsampler.upsample_block and resnet
														elif re_prior_cond_conv_out.fullmatch(UpperCamelCase__ ):
																					snake_case_     							=       re_prior_cond_conv_out.match(UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__ )

														elif re_prior_cond_resnet.fullmatch(UpperCamelCase__ ):
																					snake_case_     							=       re_prior_cond_resnet.match(UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__ )

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

														# keep original key
														else:
																					snake_case_     							=       original_key

														snake_case_     							=       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:
																					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    __lowerCamelCase      (					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]}''' ):
																					snake_case_     							=       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 )

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

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

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

							snake_case_     							=       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__":
					_UpperCAmelCase						:  Optional[int]									=			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						:  Optional[Any]									=			parser.parse_args()
					convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       BertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							snake_case_     							=       BertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()  ,				UpperCamelCase__ )


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, Any]									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					_UpperCAmelCase						:  Any									=			parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




