





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


UpperCAmelCase__						:List[str]          =			"""https://openaipublic.azureedge.net/jukebox/models/"""
UpperCAmelCase__						:Optional[int]          =			{
    """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  (_lowercase     )   ->     int:


     """simple docstring"""
     if key.endswith(""".model.1.bias"""     ) and len(key.split("""."""     )     ) > 10:
          __lowerCamelCase						:       Union[str, Any]				   =				key.replace(""".model.1.bias""",						""".conv1d_1.bias"""     )
     elif key.endswith(""".model.1.weight"""     ) and len(key.split("""."""     )     ) > 10:
          __lowerCamelCase						:       Dict				   =				key.replace(""".model.1.weight""",						""".conv1d_1.weight"""     )
     elif key.endswith(""".model.3.bias"""     ) and len(key.split("""."""     )     ) > 10:
          __lowerCamelCase						:       Optional[Any]				   =				key.replace(""".model.3.bias""",						""".conv1d_2.bias"""     )
     elif key.endswith(""".model.3.weight"""     ) and len(key.split("""."""     )     ) > 10:
          __lowerCamelCase						:       Tuple				   =				key.replace(""".model.3.weight""",						""".conv1d_2.weight"""     )

     if "conditioner_blocks.0." in key:
          __lowerCamelCase						:       Optional[int]				   =				key.replace("""conditioner_blocks.0""",						"""conditioner_blocks"""     )

     if "prime_prior" in key:
          __lowerCamelCase						:       Optional[int]				   =				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						:       Optional[int]				   =				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						:       Tuple				   =				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  (_lowercase,						_lowercase,						_lowercase,						_lowercase     )   ->     int:


     """simple docstring"""
     __lowerCamelCase						:       str				   =				{}
     import re

     __lowerCamelCase						:       Optional[int]				   =				re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""     )
     __lowerCamelCase						:       Tuple				   =				re.compile(
         R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
     __lowerCamelCase						:       Optional[Any]				   =				re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""     )

     __lowerCamelCase						:       Any				   =				re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""     )
     __lowerCamelCase						:       Tuple				   =				re.compile(
         R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
     __lowerCamelCase						:       int				   =				re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""     )

     __lowerCamelCase						:       List[Any]				   =				re.compile(R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"""     )
     __lowerCamelCase						:       Tuple				   =				re.compile(
         R"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""     )
     __lowerCamelCase						:       int				   =				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(_lowercase     ):
               __lowerCamelCase						:       Union[str, Any]				   =				re_encoder_block_conv_in.match(_lowercase     )
               __lowerCamelCase						:       List[str]				   =				regex_match.groups()
               __lowerCamelCase						:       Union[str, Any]				   =				int(groups[2]     ) * 2 + int(groups[3]     )
               __lowerCamelCase						:       Tuple				   =				f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
               __lowerCamelCase						:       List[Any]				   =				re_encoder_block_conv_in.sub(_lowercase,						_lowercase     )

          elif re_encoder_block_resnet.fullmatch(_lowercase     ):
               __lowerCamelCase						:       Tuple				   =				re_encoder_block_resnet.match(_lowercase     )
               __lowerCamelCase						:       Dict				   =				regex_match.groups()
               __lowerCamelCase						:       Dict				   =				int(groups[2]     ) * 2 + int(groups[3]     )
               __lowerCamelCase						:       Any				   =				{"""1""": 1, """3""": 2}[groups[-2]]
               __lowerCamelCase						:       Dict				   =				f"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."
               __lowerCamelCase						:       str				   =				f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               __lowerCamelCase						:       Union[str, Any]				   =				prefix + resnet_block
               __lowerCamelCase						:       str				   =				re_encoder_block_resnet.sub(_lowercase,						_lowercase     )

          elif re_encoder_block_proj_out.fullmatch(_lowercase     ):
               __lowerCamelCase						:       str				   =				re_encoder_block_proj_out.match(_lowercase     )
               __lowerCamelCase						:       Optional[Any]				   =				regex_match.groups()
               __lowerCamelCase						:       int				   =				f"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
               __lowerCamelCase						:       Tuple				   =				re_encoder_block_proj_out.sub(_lowercase,						_lowercase     )

          # rename vqvae.decoder keys
          elif re_decoder_block_conv_out.fullmatch(_lowercase     ):
               __lowerCamelCase						:       str				   =				re_decoder_block_conv_out.match(_lowercase     )
               __lowerCamelCase						:       List[str]				   =				regex_match.groups()
               __lowerCamelCase						:       List[Any]				   =				int(groups[2]     ) * 2 + int(groups[3]     ) - 2
               __lowerCamelCase						:       List[Any]				   =				f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
               __lowerCamelCase						:       Dict				   =				re_decoder_block_conv_out.sub(_lowercase,						_lowercase     )

          elif re_decoder_block_resnet.fullmatch(_lowercase     ):
               __lowerCamelCase						:       Union[str, Any]				   =				re_decoder_block_resnet.match(_lowercase     )
               __lowerCamelCase						:       List[Any]				   =				regex_match.groups()
               __lowerCamelCase						:       int				   =				int(groups[2]     ) * 2 + int(groups[3]     ) - 2
               __lowerCamelCase						:       List[Any]				   =				{"""1""": 1, """3""": 2}[groups[-2]]
               __lowerCamelCase						:       Dict				   =				f"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."
               __lowerCamelCase						:       str				   =				f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               __lowerCamelCase						:       List[Any]				   =				prefix + resnet_block
               __lowerCamelCase						:       Tuple				   =				re_decoder_block_resnet.sub(_lowercase,						_lowercase     )

          elif re_decoder_block_proj_in.fullmatch(_lowercase     ):
               __lowerCamelCase						:       Dict				   =				re_decoder_block_proj_in.match(_lowercase     )
               __lowerCamelCase						:       Any				   =				regex_match.groups()
               __lowerCamelCase						:       Optional[Any]				   =				f"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
               __lowerCamelCase						:       List[Any]				   =				re_decoder_block_proj_in.sub(_lowercase,						_lowercase     )

          # rename prior cond.model to upsampler.upsample_block and resnet
          elif re_prior_cond_conv_out.fullmatch(_lowercase     ):
               __lowerCamelCase						:       Tuple				   =				re_prior_cond_conv_out.match(_lowercase     )
               __lowerCamelCase						:       List[Any]				   =				regex_match.groups()
               __lowerCamelCase						:       List[str]				   =				int(groups[1]     ) * 2 + int(groups[2]     ) - 2
               __lowerCamelCase						:       Union[str, Any]				   =				f"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
               __lowerCamelCase						:       Dict				   =				re_prior_cond_conv_out.sub(_lowercase,						_lowercase     )

          elif re_prior_cond_resnet.fullmatch(_lowercase     ):
               __lowerCamelCase						:       Union[str, Any]				   =				re_prior_cond_resnet.match(_lowercase     )
               __lowerCamelCase						:       int				   =				regex_match.groups()
               __lowerCamelCase						:       Tuple				   =				int(groups[1]     ) * 2 + int(groups[2]     ) - 2
               __lowerCamelCase						:       List[str]				   =				{"""1""": 1, """3""": 2}[groups[-2]]
               __lowerCamelCase						:       Any				   =				f"conditioner_blocks.upsampler.upsample_block.{block_index}."
               __lowerCamelCase						:       Dict				   =				f"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
               __lowerCamelCase						:       int				   =				prefix + resnet_block
               __lowerCamelCase						:       List[str]				   =				re_prior_cond_resnet.sub(_lowercase,						_lowercase     )

          elif re_prior_cond_proj_in.fullmatch(_lowercase     ):
               __lowerCamelCase						:       List[str]				   =				re_prior_cond_proj_in.match(_lowercase     )
               __lowerCamelCase						:       Optional[int]				   =				regex_match.groups()
               __lowerCamelCase						:       Tuple				   =				f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
               __lowerCamelCase						:       Optional[int]				   =				re_prior_cond_proj_in.sub(_lowercase,						_lowercase     )

          # keep original key
          else:
               __lowerCamelCase						:       Any				   =				original_key

          __lowerCamelCase						:       Dict				   =				replace_key(_lowercase     )

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

          __lowerCamelCase						:       Optional[int]				   =				original_key
          __lowerCamelCase						:       Tuple				   =				value

     return new_dict
@torch.no_grad()
def     __lowercase  (_lowercase=None,						_lowercase=None     )   ->     int:


     """simple docstring"""
     for file in MODEL_MAPPING[model_name]:
          if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/'     )[-1]}"     ):
               __lowerCamelCase						:       str				   =				requests.get(f"{PREFIX}{file}",						allow_redirects=_lowercase     )
               os.makedirs(f"{pytorch_dump_folder_path}/",						exist_ok=_lowercase     )
               open(f"{pytorch_dump_folder_path}/{file.split('/'     )[-1]}",						"""wb"""     ).write(r.content     )

     __lowerCamelCase						:       Optional[int]				   =				MODEL_MAPPING[model_name.split("""/"""     )[-1]]

     __lowerCamelCase						:       List[str]				   =				JukeboxConfig.from_pretrained(_lowercase     )
     __lowerCamelCase						:       Dict				   =				JukeboxModel(_lowercase     )

     __lowerCamelCase						:       Optional[int]				   =				[]
     __lowerCamelCase						:       Union[str, Any]				   =				{}
     for i, dict_name in enumerate(_lowercase     ):
          __lowerCamelCase						:       Tuple				   =				torch.load(f"{pytorch_dump_folder_path}/{dict_name.split('/'     )[-1]}"     )["""model"""]

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

          __lowerCamelCase						:       List[Any]				   =				"""vqvae""" if i == 0 else f"priors.{3 - i}"
          __lowerCamelCase						:       str				   =				fix_jukebox_keys(_lowercase,						model.state_dict(),						_lowercase,						_lowercase     )
          weight_dict.append(_lowercase     )

     __lowerCamelCase						:       Any				   =				weight_dict.pop(0     )
     model.vqvae.load_state_dict(_lowercase     )
     for i in range(len(_lowercase     )     ):
          model.priors[i].load_state_dict(weight_dict[2 - i]     )

     Path(_lowercase     ).mkdir(exist_ok=_lowercase     )
     with open(f"{pytorch_dump_folder_path}/mapping.json",						"""w"""     ) as txtfile:
          json.dump(_lowercase,						_lowercase     )

     print(f"Saving model {model_name} to {pytorch_dump_folder_path}"     )
     model.save_pretrained(_lowercase     )

     return weight_dict


if __name__ == "__main__":
   UpperCAmelCase__						:Tuple          =			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__						:Any          =			parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)











'''simple docstring'''

import argparse
import json

from tqdm import tqdm
def     __lowercase  ()   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path""",						type=_lowercase,						default="""biencoder-nq-dev.json""",						help="""Path to raw DPR training data""",						)
     parser.add_argument(
         """--evaluation_set""",						type=_lowercase,						help="""where to store parsed evaluation_set file""",						)
     parser.add_argument(
         """--gold_data_path""",						type=_lowercase,						help="""where to store parsed gold_data_path file""",						)
     __lowerCamelCase						:       Tuple				   =				parser.parse_args()

     with open(args.src_path,						"""r"""     ) as src_file, open(args.evaluation_set,						"""w"""     ) as eval_file, open(
         args.gold_data_path,						"""w"""     ) as gold_file:
          __lowerCamelCase						:       int				   =				json.load(_lowercase     )
          for dpr_record in tqdm(_lowercase     ):
               __lowerCamelCase						:       List[Any]				   =				dpr_record["""question"""]
               __lowerCamelCase						:       List[str]				   =				[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""     )
               gold_file.write("""\t""".join(_lowercase     ) + """\n"""     )


if __name__ == "__main__":
   main()



