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


__lowerCamelCase    :				Dict							     =	'''https://openaipublic.azureedge.net/jukebox/models/'''
__lowerCamelCase    :				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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: List[str]			)							->      List[Any]:







  """simple docstring"""





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

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

  if "prime_prior" in key:
    SCREAMING_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:
    SCREAMING_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:
    SCREAMING_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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: int				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: Optional[Any]			)							->      str:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   {}
  import re

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

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

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

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

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

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

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

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

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

    elif re_prior_cond_proj_in.fullmatch(__UpperCamelCase			):
      SCREAMING_SNAKE_CASE__    							=   re_prior_cond_proj_in.match(__UpperCamelCase			)
      SCREAMING_SNAKE_CASE__    							=   regex_match.groups()
      SCREAMING_SNAKE_CASE__    							=   f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
      SCREAMING_SNAKE_CASE__    							=   re_prior_cond_proj_in.sub(__UpperCamelCase				,  __UpperCamelCase			)

    # keep original key
    else:
      SCREAMING_SNAKE_CASE__    							=   original_key

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

    SCREAMING_SNAKE_CASE__    							=   original_key
    SCREAMING_SNAKE_CASE__    							=   value

  return new_dict



@torch.no_grad()
def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: str=None				,  __UpperCamelCase					: Optional[int]=None			)							->      str:







  """simple docstring"""





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

  SCREAMING_SNAKE_CASE__    							=   MODEL_MAPPING[model_name.split("""/"""			)[-1]]

  SCREAMING_SNAKE_CASE__    							=   JukeboxConfig.from_pretrained(__UpperCamelCase			)
  SCREAMING_SNAKE_CASE__    							=   JukeboxModel(__UpperCamelCase			)

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

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

    SCREAMING_SNAKE_CASE__    							=   """vqvae""" if i == 0 else f"""priors.{3 - i}"""
    SCREAMING_SNAKE_CASE__    							=   fix_jukebox_keys(__UpperCamelCase				,  model.state_dict()				,  __UpperCamelCase				,  __UpperCamelCase			)
    weight_dict.append(__UpperCamelCase			)

  SCREAMING_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__":
   __lowerCamelCase    :				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.''',
   )
   __lowerCamelCase    :				str							     =	parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)

import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


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







@dataclass
class __snake_case       (		datasets.BuilderConfig	):
   lowerCAmelCase_														=							1_00_00
   lowerCAmelCase_														=							None
   lowerCAmelCase_														=							None







class __snake_case       (		datasets.ArrowBasedBuilder	):
   lowerCAmelCase_														=							ParquetConfig




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






     return datasets.DatasetInfo(features=self.config.features   )




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[str]   ):



     """simple docstring"""






     if not self.config.data_files:
       raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""   )
     SCREAMING_SNAKE_CASE__    							=   dl_manager.download_and_extract(self.config.data_files   )
     if isinstance(_lowercase ,			(str, list, tuple)   ):
       SCREAMING_SNAKE_CASE__    							=   data_files
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,			gen_kwargs={"""files""": files}   )]
     SCREAMING_SNAKE_CASE__    							=   []
     for split_name, files in data_files.items():
       if isinstance(_lowercase ,			_lowercase   ):
         SCREAMING_SNAKE_CASE__    							=   [files]
       # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
       SCREAMING_SNAKE_CASE__    							=   [dl_manager.iter_files(_lowercase   ) for file in files]
       # Infer features is they are stoed in the arrow schema
       if self.info.features is None:
         for file in itertools.chain.from_iterable(_lowercase   ):
           with open(_lowercase ,			"""rb"""   ) as f:
             SCREAMING_SNAKE_CASE__    							=   datasets.Features.from_arrow_schema(pq.read_schema(_lowercase   )   )
           break
       splits.append(datasets.SplitGenerator(name=_lowercase ,			gen_kwargs={"""files""": files}   )   )
     return splits




   def 			__a		(					self    :       Optional[Any] ,			_lowercase    :       pa.Table   ):



     """simple docstring"""






     if self.info.features is not None:
       # more expensive cast to support nested features with keys in a different order
       # allows str <-> int/float or str to Audio for example
       SCREAMING_SNAKE_CASE__    							=   table_cast(_lowercase ,			self.info.features.arrow_schema   )
     return pa_table




   def 			__a		(					self    :       Dict ,			_lowercase    :       List[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
       if sorted(field.name for field in schema   ) != sorted(self.config.columns   ):
         raise ValueError(
             f"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"""   )
     for file_idx, file in enumerate(itertools.chain.from_iterable(_lowercase   )   ):
       with open(_lowercase ,			"""rb"""   ) as f:
         SCREAMING_SNAKE_CASE__    							=   pq.ParquetFile(_lowercase   )
         try:
           for batch_idx, record_batch in enumerate(
               parquet_file.iter_batches(batch_size=self.config.batch_size ,			columns=self.config.columns   )   ):
             SCREAMING_SNAKE_CASE__    							=   pa.Table.from_batches([record_batch]   )
             # Uncomment for debugging (will print the Arrow table size and elements)
             # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
             # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
             yield f"""{file_idx}_{batch_idx}""", self._cast_table(_lowercase   )
         except ValueError as e:
           logger.error(f"""Failed to read file '{file}' with error {type(_lowercase   )}: {e}"""   )
           raise
