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


_a      :			Optional[int]					=							"""https://openaipublic.azureedge.net/jukebox/models/"""
_a      :			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 				a__   (   a   :				Tuple		):

  """simple docstring"""







  if key.endswith(".model.1.bias"		) and len(key.split("."		)		) > 10:
    _snake_case     :						List[Any]						=   key.replace(".model.1.bias"       ,     ".conv1d_1.bias"		)
  elif key.endswith(".model.1.weight"		) and len(key.split("."		)		) > 10:
    _snake_case     :						Optional[Any]						=   key.replace(".model.1.weight"       ,     ".conv1d_1.weight"		)
  elif key.endswith(".model.3.bias"		) and len(key.split("."		)		) > 10:
    _snake_case     :						Union[str, Any]						=   key.replace(".model.3.bias"       ,     ".conv1d_2.bias"		)
  elif key.endswith(".model.3.weight"		) and len(key.split("."		)		) > 10:
    _snake_case     :						Any						=   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     :						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:
    _snake_case     :						List[str]						=   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     :						Optional[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 				a__   (   a   :				int       ,     a   :				int       ,     a   :				List[str]       ,     a   :				List[str]		):

  """simple docstring"""







  _snake_case     :						int						=   {}
  import re

  _snake_case     :						Union[str, Any]						=   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"		)
  _snake_case     :						Tuple						=   re.compile(
      R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"		)
  _snake_case     :						str						=   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"		)

  _snake_case     :						Optional[Any]						=   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"		)
  _snake_case     :						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     :						List[Any]						=   re.compile(R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)"		)
  _snake_case     :						Any						=   re.compile(
      R"conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"		)
  _snake_case     :						Union[str, 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     :						List[Any]						=   re_encoder_block_conv_in.match(a		)
      _snake_case     :						Optional[int]						=   regex_match.groups()
      _snake_case     :						Any						=   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     :						Tuple						=   re_encoder_block_conv_in.sub(a       ,     a		)

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

    elif re_encoder_block_proj_out.fullmatch(a		):
      _snake_case     :						List[str]						=   re_encoder_block_proj_out.match(a		)
      _snake_case     :						int						=   regex_match.groups()
      _snake_case     :						Tuple						=   f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
      _snake_case     :						str						=   re_encoder_block_proj_out.sub(a       ,     a		)

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

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

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

    elif re_prior_cond_proj_in.fullmatch(a		):
      _snake_case     :						Tuple						=   re_prior_cond_proj_in.match(a		)
      _snake_case     :						List[Any]						=   regex_match.groups()
      _snake_case     :						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     :						Optional[int]						=   original_key

    _snake_case     :						Union[str, 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     :						int						=   model_state_dict[f'{key_prefix}.{key}']
      print(f'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'		)
      _snake_case     :						int						=   original_key

    _snake_case     :						Union[str, Any]						=   original_key
    _snake_case     :						Union[str, Any]						=   value

  return new_dict
@torch.no_grad()
def 				a__   (   a   :				int=None       ,     a   :				Tuple=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     :						Tuple						=   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     :						Any						=   MODEL_MAPPING[model_name.split("/"		)[-1]]

  _snake_case     :						str						=   JukeboxConfig.from_pretrained(a		)
  _snake_case     :						Any						=   JukeboxModel(a		)

  _snake_case     :						List[Any]						=   []
  _snake_case     :						int						=   {}
  for i, dict_name in enumerate(a		):
    _snake_case     :						Optional[Any]						=   torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/"		)[-1]}'		)["model"]

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

    _snake_case     :						int						=   "vqvae" if i == 0 else f'priors.{3 - i}'
    _snake_case     :						int						=   fix_jukebox_keys(a       ,     model.state_dict()       ,     a       ,     a		)
    weight_dict.append(a		)

  _snake_case     :						List[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      :			Union[str, 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      :			Optional[Any]					=							parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :			Optional[int]					=							logging.get_logger(__name__)

_a      :			str					=							{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Optional[Any]  =					"""openai-gpt"""
    __lowercase							: Dict  =					{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }
    def __init__(    self						,    snake_case_=4_04_78						,    snake_case_=5_12						,    snake_case_=7_68						,    snake_case_=12						,    snake_case_=12						,    snake_case_="gelu"						,    snake_case_=0.1						,    snake_case_=0.1						,    snake_case_=0.1						,    snake_case_=1E-5						,    snake_case_=0.02						,    snake_case_="cls_index"						,    snake_case_=True						,    snake_case_=None						,    snake_case_=True						,    snake_case_=0.1						,    **snake_case_						,    ):
      _snake_case     :						Tuple						=   vocab_size
      _snake_case     :						Dict						=   n_positions
      _snake_case     :						Any						=   n_embd
      _snake_case     :						Any						=   n_layer
      _snake_case     :						Optional[int]						=   n_head
      _snake_case     :						Union[str, Any]						=   afn
      _snake_case     :						Dict						=   resid_pdrop
      _snake_case     :						str						=   embd_pdrop
      _snake_case     :						Union[str, Any]						=   attn_pdrop
      _snake_case     :						str						=   layer_norm_epsilon
      _snake_case     :						Union[str, Any]						=   initializer_range
      _snake_case     :						Any						=   summary_type
      _snake_case     :						List[str]						=   summary_use_proj
      _snake_case     :						Optional[int]						=   summary_activation
      _snake_case     :						Union[str, Any]						=   summary_first_dropout
      _snake_case     :						Optional[int]						=   summary_proj_to_labels
      super().__init__(**snake_case_     )

