






'''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()
__A    	=   logging.get_logger(__name__)


__A    	=   '''https://openaipublic.azureedge.net/jukebox/models/'''
__A    	=   {
    '''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  (				A  :       Dict  )    -> Optional[Any]:







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

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

   if "prime_prior" in key:
      __snake_case				:      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:
      __snake_case				:      Optional[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				:      int     							= 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  (				A  :       Optional[int]  ,		A  :       Tuple  ,		A  :       int  ,		A  :       Union[str, Any]  )    -> List[Any]:







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

   __snake_case				:      Any     							= re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'  )
   __snake_case				:      Union[str, Any]     							= re.compile(
       R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'  )
   __snake_case				:      Dict     							= re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

   __snake_case				:      Union[str, Any]     							= 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				:      Any     							= re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'  )

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

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

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

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

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

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

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

      elif re_prior_cond_proj_in.fullmatch(A  ):
         __snake_case				:      Optional[Any]     							= re_prior_cond_proj_in.match(A  )
         __snake_case				:      str     							= regex_match.groups()
         __snake_case				:      Optional[Any]     							= F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
         __snake_case				:      Tuple     							= re_prior_cond_proj_in.sub(A  ,		A  )

      # keep original key
      else:
         __snake_case				:      int     							= original_key

      __snake_case				:      Optional[int]     							= 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				:      str     							= model_state_dict[F"""{key_prefix}.{key}"""]
         print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""  )
         __snake_case				:      List[str]     							= original_key

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

   return new_dict




@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (				A  :       str=None  ,		A  :       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]}"""  ):
         __snake_case				:      Union[str, 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				:      Union[str, Any]     							= MODEL_MAPPING[model_name.split('/'  )[-1]]

   __snake_case				:      Any     							= JukeboxConfig.from_pretrained(A  )
   __snake_case				:      Optional[Any]     							= JukeboxModel(A  )

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

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

      __snake_case				:      Union[str, Any]     							= 'vqvae' if i == 0 else F"""priors.{3 - i}"""
      __snake_case				:      Any     							= fix_jukebox_keys(A  ,		model.state_dict()  ,		A  ,		A  )
      weight_dict.append(A  )

   __snake_case				:      Any     							= 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    	=   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    	=   parser.parse_args()
      convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A    	=   {
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BioGptForCausalLM''',
          '''BioGptForTokenClassification''',
          '''BioGptForSequenceClassification''',
          '''BioGptModel''',
          '''BioGptPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)