



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       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Optional[Any]:
 if key.endswith(""".model.1.bias"""				) and len(key.split("""."""				)				) > 10:
  _snake_case    :  Optional[Any]					=					key.replace(""".model.1.bias"""				,       """.conv1d_1.bias"""				)
 elif key.endswith(""".model.1.weight"""				) and len(key.split("""."""				)				) > 10:
  _snake_case    :  List[Any]					=					key.replace(""".model.1.weight"""				,       """.conv1d_1.weight"""				)
 elif key.endswith(""".model.3.bias"""				) and len(key.split("""."""				)				) > 10:
  _snake_case    :  List[Any]					=					key.replace(""".model.3.bias"""				,       """.conv1d_2.bias"""				)
 elif key.endswith(""".model.3.weight"""				) and len(key.split("""."""				)				) > 10:
  _snake_case    :  List[str]					=					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    :  Dict					=					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    :  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:
  _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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Tuple				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     str:
 _snake_case    :  int					=					{}
 import re

 _snake_case    :  Tuple					=					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    :  int					=					re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""				)

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

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

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

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

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

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

  elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  List[str]					=					re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE__				)
   _snake_case    :  int					=					regex_match.groups()
   _snake_case    :  Optional[Any]					=					F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
   _snake_case    :  List[str]					=					re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

  # rename prior cond.model to upsampler.upsample_block and resnet
  elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  int					=					re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Union[str, Any]					=					regex_match.groups()
   _snake_case    :  Any					=					int(groups[1]				) * 2 + int(groups[2]				) - 2
   _snake_case    :  List[Any]					=					F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
   _snake_case    :  Optional[int]					=					re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

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

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

  # keep original key
  else:
   _snake_case    :  Any					=					original_key

  _snake_case    :  List[Any]					=					replace_key(SCREAMING_SNAKE_CASE__				)

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

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

 return new_dict



@torch.no_grad()
def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int=None				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=None				)  ->     str:
 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'				)[-1]}'''				):
   _snake_case    :  List[Any]					=					requests.get(F'''{PREFIX}{file}'''				,       allow_redirects=SCREAMING_SNAKE_CASE__				)
   os.makedirs(F'''{pytorch_dump_folder_path}/'''				,       exist_ok=SCREAMING_SNAKE_CASE__				)
   open(F'''{pytorch_dump_folder_path}/{file.split('/'				)[-1]}'''				,       """wb"""				).write(r.content				)

 _snake_case    :  Optional[Any]					=					MODEL_MAPPING[model_name.split("""/"""				)[-1]]

 _snake_case    :  Union[str, Any]					=					JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Any					=					JukeboxModel(SCREAMING_SNAKE_CASE__				)

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

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

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

 _snake_case    :  List[str]					=					weight_dict.pop(0				)
 model.vqvae.load_state_dict(SCREAMING_SNAKE_CASE__				)
 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  model.priors[i].load_state_dict(weight_dict[2 - i]				)

 Path(SCREAMING_SNAKE_CASE__				).mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
 with open(F'''{pytorch_dump_folder_path}/mapping.json'''				,       """w"""				) as txtfile:
  json.dump(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
 model.save_pretrained(SCREAMING_SNAKE_CASE__				)

 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)





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a__    =	"""src/diffusers"""

# Matches is_xxx_available()
a__    =	re.compile(R"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
a__    =	re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


a__    =	"""
{0} = None
"""

a__    =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


a__    =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Optional[Any]:
 _snake_case    :  str					=					_re_backend.findall(SCREAMING_SNAKE_CASE__				)
 if len(SCREAMING_SNAKE_CASE__				) == 0:
  return None

 return "_and_".join(SCREAMING_SNAKE_CASE__				)



def       lowercase       (   )  ->     Optional[Any]:
 with open(os.path.join(SCREAMING_SNAKE_CASE__				,       """__init__.py"""				)				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
  _snake_case    :  Dict					=					f.readlines()

 # Get to the point we do the actual imports for type checking
 _snake_case    :  Optional[int]					=					0
 _snake_case    :  Union[str, Any]					=					{}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE__				):
  # If the line contains is_backend_available, we grab all objects associated with the `else` block
  _snake_case    :  int					=					find_backend(lines[line_index]				)
  if backend is not None:
   while not lines[line_index].startswith("""else:"""				):
    line_index += 1
   line_index += 1
   _snake_case    :  str					=					[]
   # Until we unindent, add backend objects to the list
   while line_index < len(SCREAMING_SNAKE_CASE__				) and len(lines[line_index]				) > 1:
    _snake_case    :  int					=					lines[line_index]
    _snake_case    :  str					=					_re_single_line_import.search(SCREAMING_SNAKE_CASE__				)
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(""", """				)				)
    elif line.startswith(""" """ * 8				):
     objects.append(line[8:-2]				)
    line_index += 1

   if len(SCREAMING_SNAKE_CASE__				) > 0:
    _snake_case    :  Optional[int]					=					objects
  else:
   line_index += 1

 return backend_specific_objects



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     Dict:
 if name.isupper():
  return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE__				)
 elif name.islower():
  return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
 else:
  return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict=None				)  ->     List[Any]:
 if backend_specific_objects is None:
  _snake_case    :  Dict					=					read_init()
 # For special correspondence backend to module name as used in the function requires_modulename
 _snake_case    :  Union[str, Any]					=					{}

 for backend, objects in backend_specific_objects.items():
  _snake_case    :  List[Any]					=					"""[""" + """, """.join(F'''"{b}"''' for b in backend.split("""_and_"""				)				) + """]"""
  _snake_case    :  Any					=					"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
  dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
  dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				) for o in objects]				)
  _snake_case    :  Any					=					dummy_file

 return dummy_files



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple=False				)  ->     Optional[int]:
 _snake_case    :  str					=					create_dummy_files()
 # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
 _snake_case    :  List[Any]					=					{"""torch""": """pt"""}

 # Locate actual dummy modules and read their content.
 _snake_case    :  Dict					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """utils"""				)
 _snake_case    :  int					=					{
     backend: os.path.join(SCREAMING_SNAKE_CASE__				,       F'''dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py'''				)
     for backend in dummy_files.keys()
 }

 _snake_case    :  int					=					{}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE__				):
   with open(SCREAMING_SNAKE_CASE__				,       """r"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
    _snake_case    :  Union[str, Any]					=					f.read()
  else:
   _snake_case    :  str					=					""""""

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F'''Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py as the main '''
        """__init__ has new objects."""				)
    with open(dummy_file_paths[backend]				,       """w"""				,       encoding="""utf-8"""				,       newline="""\n"""				) as f:
     f.write(dummy_files[backend]				)
   else:
    raise ValueError(
        """The main __init__ has objects that are not present in """
        F'''diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)}_objects.py. Run `make fix-copies` '''
        """to fix this."""				)


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
  a__    =	parser.parse_args()

  check_dummies(args.fix_and_overwrite)
