






'''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 					lowercase__				(   __UpperCamelCase					)->				Dict:
      if key.endswith(""".model.1.bias"""					) and len(key.split("""."""					)					) > 10:
            UpperCamelCase       						=      key.replace(""".model.1.bias"""	,		""".conv1d_1.bias"""					)
      elif key.endswith(""".model.1.weight"""					) and len(key.split("""."""					)					) > 10:
            UpperCamelCase       						=      key.replace(""".model.1.weight"""	,		""".conv1d_1.weight"""					)
      elif key.endswith(""".model.3.bias"""					) and len(key.split("""."""					)					) > 10:
            UpperCamelCase       						=      key.replace(""".model.3.bias"""	,		""".conv1d_2.bias"""					)
      elif key.endswith(""".model.3.weight"""					) and len(key.split("""."""					)					) > 10:
            UpperCamelCase       						=      key.replace(""".model.3.weight"""	,		""".conv1d_2.weight"""					)

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

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

      if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
            UpperCamelCase       						=      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:
            UpperCamelCase       						=      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 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				Tuple:
      UpperCamelCase       						=      {}
      import re

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

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

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

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

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

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

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

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

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

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

            # keep original key
            else:
                  UpperCamelCase       						=      original_key

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

            UpperCamelCase       						=      original_key
            UpperCamelCase       						=      value

      return new_dict







@torch.no_grad()
def 					lowercase__				(   __UpperCamelCase=None	,		__UpperCamelCase=None					)->				Optional[Any]:
      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'					)[-1]}"					):
                  UpperCamelCase       						=      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					)

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

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

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

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

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

      UpperCamelCase       						=      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__":
     __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'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


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

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     SCREAMING_SNAKE_CASE__											=     parser.parse_args()

     SCREAMING_SNAKE_CASE__											=     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     SCREAMING_SNAKE_CASE__											=     CLIPImageProcessor()
     SCREAMING_SNAKE_CASE__											=     CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     SCREAMING_SNAKE_CASE__											=     UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)
