


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


__magic_name__			=      'https://openaipublic.azureedge.net/jukebox/models/'
__magic_name__			=      {
    '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					(      lowerCamelCase  :      Dict):
      if key.endswith(""".model.1.bias""") and len(key.split(""".""")) > 10:
            A_						:  Dict          =				key.replace(""".model.1.bias"""							,						""".conv1d_1.bias""")
      elif key.endswith(""".model.1.weight""") and len(key.split(""".""")) > 10:
            A_						:  List[str]          =				key.replace(""".model.1.weight"""							,						""".conv1d_1.weight""")
      elif key.endswith(""".model.3.bias""") and len(key.split(""".""")) > 10:
            A_						:  int          =				key.replace(""".model.3.bias"""							,						""".conv1d_2.bias""")
      elif key.endswith(""".model.3.weight""") and len(key.split(""".""")) > 10:
            A_						:  Union[str, Any]          =				key.replace(""".model.3.weight"""							,						""".conv1d_2.weight""")

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

      if "prime_prior" in key:
            A_						:  Union[str, 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[int]          =				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_						:  List[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      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      List[Any]):
      A_						:  Tuple          =				{}
      import re

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

      A_						:  List[Any]          =				re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""")
      A_						:  Optional[Any]          =				re.compile(
          r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""")
      A_						:  Dict          =				re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""")

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

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

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

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

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

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

            elif re_prior_cond_resnet.fullmatch(__UpperCamelCase):
                  A_						:  Optional[int]          =				re_prior_cond_resnet.match(__UpperCamelCase)
                  A_						:  List[str]          =				regex_match.groups()
                  A_						:  int          =				int(groups[1]) * 2 + int(groups[2]) - 2
                  A_						:  str          =				{"""1""": 1, """3""": 2}[groups[-2]]
                  A_						:  str          =				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_						:  Any          =				re_prior_cond_resnet.sub(__UpperCamelCase							,						__UpperCamelCase)

            elif re_prior_cond_proj_in.fullmatch(__UpperCamelCase):
                  A_						:  Dict          =				re_prior_cond_proj_in.match(__UpperCamelCase)
                  A_						:  Tuple          =				regex_match.groups()
                  A_						:  List[str]          =				F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
                  A_						:  Tuple          =				re_prior_cond_proj_in.sub(__UpperCamelCase							,						__UpperCamelCase)

            # keep original key
            else:
                  A_						:  int          =				original_key

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

            A_						:  List[Any]          =				original_key
            A_						:  Dict          =				value

      return new_dict




@torch.no_grad()
def      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]=None							,						lowerCamelCase  :      Dict=None):
      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=__UpperCamelCase)
                  os.makedirs(F'{pytorch_dump_folder_path}/'							,						exist_ok=__UpperCamelCase)
                  open(F'{pytorch_dump_folder_path}/{file.split("/")[-1]}'							,						"""wb""").write(r.content)

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

      A_						:  Dict          =				JukeboxConfig.from_pretrained(__UpperCamelCase)
      A_						:  int          =				JukeboxModel(__UpperCamelCase)

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

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

            A_						:  Tuple          =				"""vqvae""" if i == 0 else F'priors.{3 - i}'
            A_						:  str          =				fix_jukebox_keys(__UpperCamelCase							,						model.state_dict()							,						__UpperCamelCase							,						__UpperCamelCase)
            weight_dict.append(__UpperCamelCase)

      A_						:  Tuple          =				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__":
     __magic_name__			=      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.',
     )
     __magic_name__			=      parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


__magic_name__			=      logging.get_logger(__name__)



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  def __init__(							self :       Union[str, Any]	,*_a :       Optional[Any]	,**_a :       Optional[int] ):

        '''simple docstring'''




        warnings.warn(
            """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
            """ use OwlViTImageProcessor instead."""	,_a	,)
        super().__init__(*_a	,**_a )
