


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__			 =						logging.get_logger(__name__)


snake_case__			 =						'''https://openaipublic.azureedge.net/jukebox/models/'''
snake_case__			 =						{
    '''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 				lowerCamelCase__	(      a	:   List[Any]				)    ->	List[str]:





   """simple docstring"""




   if key.endswith(".model.1.bias"				) and len(key.split("."				)				) > 10:
      a__ :int						  =    key.replace(".model.1.bias"	,  ".conv1d_1.bias"				)
   elif key.endswith(".model.1.weight"				) and len(key.split("."				)				) > 10:
      a__ :int						  =    key.replace(".model.1.weight"	,  ".conv1d_1.weight"				)
   elif key.endswith(".model.3.bias"				) and len(key.split("."				)				) > 10:
      a__ :Dict						  =    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__ :Optional[Any]						  =    key.replace("conditioner_blocks.0"	,  "conditioner_blocks"				)

   if "prime_prior" in key:
      a__ :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:
      a__ :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:
      a__ :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 				lowerCamelCase__	(      a	:   str	,  a	:   List[str]	,  a	:   Union[str, Any]	,  a	:   Tuple				)    ->	Tuple:





   """simple docstring"""
   a__ :Optional[Any]						  =    {}
   import re

   a__ :Dict						  =    re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"				)
   a__ :Optional[int]						  =    re.compile(
       r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"				)
   a__ :str						  =    re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"				)

   a__ :Optional[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__ :str						  =    re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"				)

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

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

      elif re_encoder_block_proj_out.fullmatch(__lowercase				):
         a__ :Union[str, Any]						  =    re_encoder_block_proj_out.match(__lowercase				)
         a__ :Any						  =    regex_match.groups()
         a__ :Optional[Any]						  =    F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
         a__ :Dict						  =    re_encoder_block_proj_out.sub(__lowercase	,  __lowercase				)

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

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

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

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

      elif re_prior_cond_proj_in.fullmatch(__lowercase				):
         a__ :Any						  =    re_prior_cond_proj_in.match(__lowercase				)
         a__ :int						  =    regex_match.groups()
         a__ :Optional[int]						  =    F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
         a__ :List[Any]						  =    re_prior_cond_proj_in.sub(__lowercase	,  __lowercase				)

      # keep original key
      else:
         a__ :Optional[int]						  =    original_key

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

      a__ :str						  =    original_key
      a__ :int						  =    value

   return new_dict


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   str=None	,  a	:   Tuple=None				)    ->	str:





   """simple docstring"""




   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'				)[-1]}'''				):
         a__ :Union[str, Any]						  =    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				)

   a__ :List[str]						  =    MODEL_MAPPING[model_name.split("/"				)[-1]]

   a__ :Any						  =    JukeboxConfig.from_pretrained(__lowercase				)
   a__ :str						  =    JukeboxModel(__lowercase				)

   a__ :Optional[Any]						  =    []
   a__ :Union[str, Any]						  =    {}
   for i, dict_name in enumerate(__lowercase				):
      a__ :Tuple						  =    torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/'				)[-1]}'''				)['model']

      a__ :Union[str, Any]						  =    {}
      for k in old_dic.keys():
         if k.endswith(".b"				):
            a__ :Union[str, Any]						  =    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__ :Optional[Any]						  =    old_dic[k]
         else:
            a__ :Optional[int]						  =    old_dic[k]

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

   a__ :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__":
  snake_case__			 =						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__			 =						parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
		snake_case__			 =						argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
		)
		# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
		parser.add_argument(
		    '''--original_config_file''',
		    default=None,
		    type=str,
		    help='''The YAML config file corresponding to the original architecture.''',
		)
		parser.add_argument(
		    '''--num_in_channels''',
		    default=None,
		    type=int,
		    help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
		)
		parser.add_argument(
		    '''--scheduler_type''',
		    default='''pndm''',
		    type=str,
		    help='''Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']''',
		)
		parser.add_argument(
		    '''--pipeline_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''''
		        '''. If `None` pipeline will be automatically inferred.'''
		    ),
		)
		parser.add_argument(
		    '''--image_size''',
		    default=None,
		    type=int,
		    help=(
		        '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
		        ''' Base. Use 768 for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--prediction_type''',
		    default=None,
		    type=str,
		    help=(
		        '''The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'''
		        ''' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'''
		    ),
		)
		parser.add_argument(
		    '''--extract_ema''',
		    action='''store_true''',
		    help=(
		        '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
		        ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
		        ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
		    ),
		)
		parser.add_argument(
		    '''--upcast_attention''',
		    action='''store_true''',
		    help=(
		        '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
		        ''' diffusion 2.1.'''
		    ),
		)
		parser.add_argument(
		    '''--from_safetensors''',
		    action='''store_true''',
		    help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
		)
		parser.add_argument(
		    '''--to_safetensors''',
		    action='''store_true''',
		    help='''Whether to store pipeline in safetensors format or not.''',
		)
		parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
		parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')
		parser.add_argument(
		    '''--stable_unclip''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.''',
		)
		parser.add_argument(
		    '''--stable_unclip_prior''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.''',
		)
		parser.add_argument(
		    '''--clip_stats_path''',
		    type=str,
		    help='''Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.''',
		    required=False,
		)
		parser.add_argument(
		    '''--controlnet''', action='''store_true''', default=None, help='''Set flag if this is a controlnet checkpoint.'''
		)
		parser.add_argument('''--half''', action='''store_true''', help='''Save weights in half precision.''')
		parser.add_argument(
		    '''--vae_path''',
		    type=str,
		    default=None,
		    required=False,
		    help='''Set to a path, hub id to an already converted vae to not convert it again.''',
		)
		snake_case__			 =						parser.parse_args()

		snake_case__			 =						download_from_original_stable_diffusion_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    prediction_type=args.prediction_type,
		    model_type=args.pipeline_type,
		    extract_ema=args.extract_ema,
		    scheduler_type=args.scheduler_type,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    stable_unclip=args.stable_unclip,
		    stable_unclip_prior=args.stable_unclip_prior,
		    clip_stats_path=args.clip_stats_path,
		    controlnet=args.controlnet,
		    vae_path=args.vae_path,
		)

		if args.half:
				pipe.to(torch_dtype=torch.floataa)

		if args.controlnet:
				# only save the controlnet model
				pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
		else:
				pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


