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

import numpy as np
import PIL

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
    fastaa_timesteps,
    smartaa_timesteps,
    smartaa_timesteps,
    smartaaa_timesteps,
    smartaaa_timesteps,
    superaa_timesteps,
    superaa_timesteps,
    superaaa_timesteps,
)


@dataclass
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42
     a_    	=					42


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
    from .pipeline_if import IFPipeline
    from .pipeline_if_imgaimg import IFImgaImgPipeline
    from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
    from .pipeline_if_inpainting import IFInpaintingPipeline
    from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
    from .pipeline_if_superresolution import IFSuperResolutionPipeline
    from .safety_checker import IFSafetyChecker
    from .watermark import IFWatermarker




'''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()
__snake_case:  List[str]								=			logging.get_logger(__name__)


__snake_case:  Dict								=			"https://openaipublic.azureedge.net/jukebox/models/"
__snake_case:  List[str]								=			{
    "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 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







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

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

  if "prime_prior" in key:
    a_      :	Any      =							key.replace("""prime_prior"""     ,		"""encoder"""   )

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    a_      :	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:
    a_      :	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 	_snake_case						(      A_       :    str     ,		A_       :    int     ,		A_       :    str     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	List[str]      =							{}
  import re

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

  a_      :	int      =							re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""   )
  a_      :	List[str]      =							re.compile(
      R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""   )
  a_      :	List[Any]      =							re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""   )

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

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

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

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

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

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

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

    elif re_prior_cond_proj_in.fullmatch(A_   ):
      a_      :	List[Any]      =							re_prior_cond_proj_in.match(A_   )
      a_      :	int      =							regex_match.groups()
      a_      :	Any      =							f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      a_      :	Union[str, Any]      =							re_prior_cond_proj_in.sub(A_     ,		A_   )

    # keep original key
    else:
      a_      :	str      =							original_key

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

    a_      :	Tuple      =							original_key
    a_      :	Union[str, Any]      =							value

  return new_dict






@torch.no_grad()
def 	_snake_case						(      A_       :    Dict=None     ,		A_       :    Optional[Any]=None   ):
  """simple docstring"""







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

  a_      :	List[Any]      =							MODEL_MAPPING[model_name.split("""/"""   )[-1]]

  a_      :	Optional[Any]      =							JukeboxConfig.from_pretrained(A_   )
  a_      :	List[Any]      =							JukeboxModel(A_   )

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

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

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

  a_      :	str      =							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__":
    __snake_case:  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.",
    )
    __snake_case:  Any								=			parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)


