



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_   :List[Any]        =      logging.get_logger(__name__)


a_   :str        =      "https://openaipublic.azureedge.net/jukebox/models/"
a_   :Optional[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        lowercase_							(A : int      ):
       if key.endswith('.model.1.bias'      ) and len(key.split('.'      )      ) > 1_0:
              snake_case__							:	Tuple   	=	key.replace('.model.1.bias'		,					'.conv1d_1.bias'      )
       elif key.endswith('.model.1.weight'      ) and len(key.split('.'      )      ) > 1_0:
              snake_case__							:	List[Any]   	=	key.replace('.model.1.weight'		,					'.conv1d_1.weight'      )
       elif key.endswith('.model.3.bias'      ) and len(key.split('.'      )      ) > 1_0:
              snake_case__							:	Dict   	=	key.replace('.model.3.bias'		,					'.conv1d_2.bias'      )
       elif key.endswith('.model.3.weight'      ) and len(key.split('.'      )      ) > 1_0:
              snake_case__							:	int   	=	key.replace('.model.3.weight'		,					'.conv1d_2.weight'      )

       if "conditioner_blocks.0." in key:
              snake_case__							:	Optional[Any]   	=	key.replace('conditioner_blocks.0'		,					'conditioner_blocks'      )

       if "prime_prior" in key:
              snake_case__							:	Dict   	=	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__							:	Union[str, Any]   	=	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__							:	List[Any]   	=	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_							(A : str		,					A : Any		,					A : int		,					A : int      ):
       snake_case__							:	List[str]   	=	{}
       import re

       snake_case__							:	Dict   	=	re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
       snake_case__							:	Dict   	=	re.compile(
           r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
       snake_case__							:	Any   	=	re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

       snake_case__							:	str   	=	re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
       snake_case__							:	List[Any]   	=	re.compile(
           r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
       snake_case__							:	Optional[Any]   	=	re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

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

              elif re_encoder_block_resnet.fullmatch(A      ):
                     snake_case__							:	Tuple   	=	re_encoder_block_resnet.match(A      )
                     snake_case__							:	str   	=	regex_match.groups()
                     snake_case__							:	Any   	=	int(groups[2]      ) * 2 + int(groups[3]      )
                     snake_case__							:	int   	=	{'1': 1, '3': 2}[groups[-2]]
                     snake_case__							:	List[Any]   	=	F'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
                     snake_case__							:	str   	=	F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                     snake_case__							:	int   	=	prefix + resnet_block
                     snake_case__							:	Optional[Any]   	=	re_encoder_block_resnet.sub(A		,					A      )

              elif re_encoder_block_proj_out.fullmatch(A      ):
                     snake_case__							:	Dict   	=	re_encoder_block_proj_out.match(A      )
                     snake_case__							:	Any   	=	regex_match.groups()
                     snake_case__							:	Optional[int]   	=	F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
                     snake_case__							:	int   	=	re_encoder_block_proj_out.sub(A		,					A      )

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

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

              elif re_decoder_block_proj_in.fullmatch(A      ):
                     snake_case__							:	List[str]   	=	re_decoder_block_proj_in.match(A      )
                     snake_case__							:	Any   	=	regex_match.groups()
                     snake_case__							:	int   	=	F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
                     snake_case__							:	Tuple   	=	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__							:	int   	=	re_prior_cond_conv_out.match(A      )
                     snake_case__							:	List[str]   	=	regex_match.groups()
                     snake_case__							:	Dict   	=	int(groups[1]      ) * 2 + int(groups[2]      ) - 2
                     snake_case__							:	Optional[int]   	=	F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
                     snake_case__							:	List[str]   	=	re_prior_cond_conv_out.sub(A		,					A      )

              elif re_prior_cond_resnet.fullmatch(A      ):
                     snake_case__							:	Tuple   	=	re_prior_cond_resnet.match(A      )
                     snake_case__							:	int   	=	regex_match.groups()
                     snake_case__							:	int   	=	int(groups[1]      ) * 2 + int(groups[2]      ) - 2
                     snake_case__							:	Optional[int]   	=	{'1': 1, '3': 2}[groups[-2]]
                     snake_case__							:	Tuple   	=	F'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
                     snake_case__							:	str   	=	F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
                     snake_case__							:	Union[str, Any]   	=	prefix + resnet_block
                     snake_case__							:	Union[str, Any]   	=	re_prior_cond_resnet.sub(A		,					A      )

              elif re_prior_cond_proj_in.fullmatch(A      ):
                     snake_case__							:	List[str]   	=	re_prior_cond_proj_in.match(A      )
                     snake_case__							:	Dict   	=	regex_match.groups()
                     snake_case__							:	str   	=	F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
                     snake_case__							:	Optional[int]   	=	re_prior_cond_proj_in.sub(A		,					A      )

              # keep original key
              else:
                     snake_case__							:	Any   	=	original_key

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

              snake_case__							:	List[str]   	=	original_key
              snake_case__							:	Union[str, Any]   	=	value

       return new_dict
@torch.no_grad()
def        lowercase_							(A : List[Any]=None		,					A : Any=None      ):
       for file in MODEL_MAPPING[model_name]:
              if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/"      )[-1]}'''      ):
                     snake_case__							:	Optional[Any]   	=	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__							:	Dict   	=	MODEL_MAPPING[model_name.split('/'      )[-1]]

       snake_case__							:	int   	=	JukeboxConfig.from_pretrained(A      )
       snake_case__							:	int   	=	JukeboxModel(A      )

       snake_case__							:	Union[str, Any]   	=	[]
       snake_case__							:	List[str]   	=	{}
       for i, dict_name in enumerate(A      ):
              snake_case__							:	Union[str, Any]   	=	torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split("/"      )[-1]}'''      )['model']

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

              snake_case__							:	List[str]   	=	'vqvae' if i == 0 else F'''priors.{3 - i}'''
              snake_case__							:	Tuple   	=	fix_jukebox_keys(A		,					model.state_dict()		,					A		,					A      )
              weight_dict.append(A      )

       snake_case__							:	int   	=	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__":
 a_   :List[Any]        =      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_   :str        =      parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)











import argparse

import torch

from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging


logging.set_verbosity_info()
def        lowercase_							(A : str		,					A : List[Any]		,					A : Any      ):
       # Initialise PyTorch model
       snake_case__							:	List[Any]   	=	LxmertConfig.from_json_file(A      )
       print(F'''Building PyTorch model from configuration: {config}'''      )
       snake_case__							:	List[str]   	=	LxmertForPreTraining(A      )

       # Load weights from tf checkpoint
       load_tf_weights_in_lxmert(A		,					A		,					A      )

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


if __name__ == "__main__":
 a_   :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(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_   :Optional[int]        =      parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






