



import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


_lowerCamelCase						    =  get_logger(__name__)


_lowerCamelCase						    =  Path(__file__).parent / "model_card_template.md"
_lowerCamelCase						    =  uuida().hex
_lowerCamelCase						    =  os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
_lowerCamelCase						    =  os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
_lowerCamelCase						    =  HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/"






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[Dict, str, None] = None     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Any       =	F"""diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"""
 if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
  return ua + "; telemetry/off"
 if is_torch_available():
  ua += F"""; torch/{_torch_version}"""
 if is_flax_available():
  ua += F"""; jax/{_jax_version}"""
  ua += F"""; flax/{_flax_version}"""
 if is_onnx_available():
  ua += F"""; onnxruntime/{_onnxruntime_version}"""
 # CI will set this value to True
 if os.environ.get("DIFFUSERS_IS_CI"		,	""     ).upper() in ENV_VARS_TRUE_VALUES:
  ua += "; is_ci/true"
 if isinstance(a_		,	a_     ):
  ua += "; " + "; ".join(F"""{k}/{v}""" for k, v in user_agent.items()     )
 elif isinstance(a_		,	a_     ):
  ua += "; " + user_agent
 return ua






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     Optional[str] = None		,	__lowerCamelCase   :     Optional[str] = None     ):



 """simple docstring"""





 if token is None:
  __SCREAMING_SNAKE_CASE       :    Tuple       =	HfFolder.get_token()
 if organization is None:
  __SCREAMING_SNAKE_CASE       :    Any       =	whoami(a_     )['''name''']
  return F"""{username}/{model_id}"""
 else:
  return F"""{organization}/{model_id}"""






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     str     ):



 """simple docstring"""





 if not is_jinja_available():
  raise ValueError(
      "Modelcard rendering is based on Jinja templates."
      " Please make sure to have `jinja` installed before using `create_model_card`."
      " To install it, please run `pip install Jinja2`."     )

 if hasattr(a_		,	"local_rank"     ) and args.local_rank not in [-1, 0]:
  return

 __SCREAMING_SNAKE_CASE       :    Any       =	args.hub_token if hasattr(a_		,	"hub_token"     ) else None
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	get_full_repo_name(a_		,	token=a_     )

 __SCREAMING_SNAKE_CASE       :    List[str]       =	ModelCard.from_template(
     card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
         language="en"		,	license="apache-2.0"		,	library_name="diffusers"		,	tags=[]		,	datasets=args.dataset_name		,	metrics=[]		,	)		,	template_path=a_		,	model_name=a_		,	repo_name=a_		,	dataset_name=args.dataset_name if hasattr(a_		,	"dataset_name"     ) else None		,	learning_rate=args.learning_rate		,	train_batch_size=args.train_batch_size		,	eval_batch_size=args.eval_batch_size		,	gradient_accumulation_steps=(
         args.gradient_accumulation_steps if hasattr(a_		,	"gradient_accumulation_steps"     ) else None
     )		,	adam_betaa=args.adam_betaa if hasattr(a_		,	"adam_beta1"     ) else None		,	adam_betaa=args.adam_betaa if hasattr(a_		,	"adam_beta2"     ) else None		,	adam_weight_decay=args.adam_weight_decay if hasattr(a_		,	"adam_weight_decay"     ) else None		,	adam_epsilon=args.adam_epsilon if hasattr(a_		,	"adam_epsilon"     ) else None		,	lr_scheduler=args.lr_scheduler if hasattr(a_		,	"lr_scheduler"     ) else None		,	lr_warmup_steps=args.lr_warmup_steps if hasattr(a_		,	"lr_warmup_steps"     ) else None		,	ema_inv_gamma=args.ema_inv_gamma if hasattr(a_		,	"ema_inv_gamma"     ) else None		,	ema_power=args.ema_power if hasattr(a_		,	"ema_power"     ) else None		,	ema_max_decay=args.ema_max_decay if hasattr(a_		,	"ema_max_decay"     ) else None		,	mixed_precision=args.mixed_precision		,	)

 __SCREAMING_SNAKE_CASE       :    List[str]       =	os.path.join(args.output_dir		,	"README.md"     )
 model_card.save(a_     )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[str]		,	__lowerCamelCase   :     Optional[str] = None     ):



 """simple docstring"""





 if resolved_file is None or commit_hash is not None:
  return commit_hash
 __SCREAMING_SNAKE_CASE       :    str       =	str(Path(a_     ).as_posix()     )
 __SCREAMING_SNAKE_CASE       :    Any       =	re.search(r"snapshots/([^/]+)/"		,	a_     )
 if search is None:
  return None
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	search.groups()[0]
 return commit_hash if REGEX_COMMIT_HASH.match(a_     ) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_lowerCamelCase						    =  os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
_lowerCamelCase						    =  os.path.join(hf_cache_home, """diffusers""")






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[str] = None		,	__lowerCamelCase   :     Optional[str] = None     ):



 """simple docstring"""





 if new_cache_dir is None:
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	DIFFUSERS_CACHE
 if old_cache_dir is None:
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	old_diffusers_cache

 __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	Path(a_     ).expanduser()
 __SCREAMING_SNAKE_CASE       :    List[Any]       =	Path(a_     ).expanduser()
 for old_blob_path in old_cache_dir.glob("**/blobs/*"     ):
  if old_blob_path.is_file() and not old_blob_path.is_symlink():
   __SCREAMING_SNAKE_CASE       :    Dict       =	new_cache_dir / old_blob_path.relative_to(a_     )
   new_blob_path.parent.mkdir(parents=a_		,	exist_ok=a_     )
   os.replace(a_		,	a_     )
   try:
    os.symlink(a_		,	a_     )
   except OSError:
    logger.warning(
        "Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."     )
 # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


_lowerCamelCase						    =  os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
    _lowerCamelCase						    =  0
else:
    with open(cache_version_file) as f:
        try:
            _lowerCamelCase						    =  int(f.read())
        except ValueError:
            _lowerCamelCase						    =  0

if cache_version < 1:
    _lowerCamelCase						    =  os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
    if old_cache_is_not_empty:
        logger.warning(
            """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
            """existing cached models. This is a one-time operation, you can interrupt it or run it """
            """later by calling `diffusers.utils.hub_utils.move_cache()`."""
        )
        try:
            move_cache()
        except Exception as e:
            _lowerCamelCase						    =  "\n".join(traceback.format_tb(e.__traceback__))
            logger.error(
                f'''There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease '''
                """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
                """message and we will do our best to help."""
            )

if cache_version < 1:
    try:
        os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
        with open(cache_version_file, """w""") as f:
            f.write("""1""")
    except Exception:
        logger.warning(
            f'''There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure '''
            """the directory exists and can be written to."""
        )






def 	_lowerCAmelCase						(				__lowerCamelCase   :     str		,	__lowerCamelCase   :     Optional[str] = None     ):



 """simple docstring"""





 if variant is not None:
  __SCREAMING_SNAKE_CASE       :    Optional[int]       =	weights_name.split("."     )
  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	splits[:-1] + [variant] + splits[-1:]
  __SCREAMING_SNAKE_CASE       :    List[str]       =	'''.'''.join(a_     )

 return weights_name






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	*,
    __lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Optional[int]		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     int		,	__lowerCamelCase   :     Tuple		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     int=None		,	):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	str(a_     )
 if os.path.isfile(a_     ):
  return pretrained_model_name_or_path
 elif os.path.isdir(a_     ):
  if os.path.isfile(os.path.join(a_		,	a_     )     ):
   # Load from a PyTorch checkpoint
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	os.path.join(a_		,	a_     )
   return model_file
  elif subfolder is not None and os.path.isfile(
      os.path.join(a_		,	a_		,	a_     )     ):
   __SCREAMING_SNAKE_CASE       :    List[str]       =	os.path.join(a_		,	a_		,	a_     )
   return model_file
  else:
   raise EnvironmentError(
       F"""Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."""     )
 else:
  # 1. First check if deprecated way of loading from branches is used
  if (
      revision in DEPRECATED_REVISION_ARGS
      and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
      and version.parse(version.parse(a_     ).base_version     ) >= version.parse("0.20.0"     )
  ):
   try:
    __SCREAMING_SNAKE_CASE       :    List[str]       =	hf_hub_download(
        a_		,	filename=_add_variant(a_		,	a_     )		,	cache_dir=a_		,	force_download=a_		,	proxies=a_		,	resume_download=a_		,	local_files_only=a_		,	use_auth_token=a_		,	user_agent=a_		,	subfolder=a_		,	revision=revision or commit_hash		,	)
    warnings.warn(
        F"""Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."""		,	a_		,	)
    return model_file
   except:  # noqa: E722
    warnings.warn(
        F"""You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(a_		,	a_     )} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(a_		,	a_     )}' so that the correct variant file can be added."""		,	a_		,	)
  try:
   # 2. Load model file as usual
   __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	hf_hub_download(
       a_		,	filename=a_		,	cache_dir=a_		,	force_download=a_		,	proxies=a_		,	resume_download=a_		,	local_files_only=a_		,	use_auth_token=a_		,	user_agent=a_		,	subfolder=a_		,	revision=revision or commit_hash		,	)
   return model_file

  except RepositoryNotFoundError:
   raise EnvironmentError(
       F"""{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier """
       "listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a "
       "token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
       "login`."     )
  except RevisionNotFoundError:
   raise EnvironmentError(
       F"""{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for """
       "this model name. Check the model page at "
       F"""'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."""     )
  except EntryNotFoundError:
   raise EnvironmentError(
       F"""{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."""     )
  except HTTPError as err:
   raise EnvironmentError(
       F"""There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"""     )
  except ValueError:
   raise EnvironmentError(
       F"""We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"""
       F""" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"""
       F""" directory containing a file named {weights_name} or"""
       " \nCheckout your internet connection or see how to run the library in"
       " offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'."     )
  except EnvironmentError:
   raise EnvironmentError(
       F"""Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from """
       "\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. "
       F"""Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory """
       F"""containing a file named {weights_name}"""     )











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()
_lowerCamelCase						    =  logging.get_logger(__name__)


_lowerCamelCase						    =  """https://openaipublic.azureedge.net/jukebox/models/"""
_lowerCamelCase						    =  {
    """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   :     Any     ):



 """simple docstring"""





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

 if "conditioner_blocks.0." in key:
  __SCREAMING_SNAKE_CASE       :    List[Any]       =	key.replace("conditioner_blocks.0"		,	"conditioner_blocks"     )

 if "prime_prior" in key:
  __SCREAMING_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:
  __SCREAMING_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:
  __SCREAMING_SNAKE_CASE       :    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   :     Tuple		,	__lowerCamelCase   :     Union[str, Any]		,	__lowerCamelCase   :     List[str]		,	__lowerCamelCase   :     Optional[Any]     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    str       =	{}
 import re

 __SCREAMING_SNAKE_CASE       :    str       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(
     r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Tuple       =	re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

 __SCREAMING_SNAKE_CASE       :    int       =	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	re.compile(
     r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
 __SCREAMING_SNAKE_CASE       :    List[str]       =	re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

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

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

  elif re_encoder_block_proj_out.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Any       =	re_encoder_block_proj_out.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    int       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Optional[int]       =	F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    Dict       =	re_encoder_block_proj_out.sub(__lowerCamelCase		,	__lowerCamelCase     )

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

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

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

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

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

  elif re_prior_cond_proj_in.fullmatch(__lowerCamelCase     ):
   __SCREAMING_SNAKE_CASE       :    Tuple       =	re_prior_cond_proj_in.match(__lowerCamelCase     )
   __SCREAMING_SNAKE_CASE       :    Dict       =	regex_match.groups()
   __SCREAMING_SNAKE_CASE       :    Dict       =	F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __SCREAMING_SNAKE_CASE       :    int       =	re_prior_cond_proj_in.sub(__lowerCamelCase		,	__lowerCamelCase     )

  # keep original key
  else:
   __SCREAMING_SNAKE_CASE       :    Tuple       =	original_key

  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	replace_key(__lowerCamelCase     )

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

  __SCREAMING_SNAKE_CASE       :    List[str]       =	original_key
  __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	value

 return new_dict






@torch.no_grad()
def 	_lowerCAmelCase						(				__lowerCamelCase   :     Optional[int]=None		,	__lowerCamelCase   :     List[Any]=None     ):



 """simple docstring"""





 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split("/"     )[-1]}"""     ):
   __SCREAMING_SNAKE_CASE       :    Dict       =	requests.get(F"""{PREFIX}{file}"""		,	allow_redirects=__lowerCamelCase     )
   os.makedirs(F"""{pytorch_dump_folder_path}/"""		,	exist_ok=__lowerCamelCase     )
   open(F"""{pytorch_dump_folder_path}/{file.split("/"     )[-1]}"""		,	"wb"     ).write(r.content     )

 __SCREAMING_SNAKE_CASE       :    int       =	MODEL_MAPPING[model_name.split("/"     )[-1]]

 __SCREAMING_SNAKE_CASE       :    List[str]       =	JukeboxConfig.from_pretrained(__lowerCamelCase     )
 __SCREAMING_SNAKE_CASE       :    str       =	JukeboxModel(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    int       =	[]
 __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{}
 for i, dict_name in enumerate(__lowerCamelCase     ):
  __SCREAMING_SNAKE_CASE       :    str       =	torch.load(F"""{pytorch_dump_folder_path}/{dict_name.split("/"     )[-1]}"""     )["model"]

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

  __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	"vqvae" if i == 0 else F"""priors.{3 - i}"""
  __SCREAMING_SNAKE_CASE       :    int       =	fix_jukebox_keys(__lowerCamelCase		,	model.state_dict()		,	__lowerCamelCase		,	__lowerCamelCase     )
  weight_dict.append(__lowerCamelCase     )

 __SCREAMING_SNAKE_CASE       :    Dict       =	weight_dict.pop(0     )
 model.vqvae.load_state_dict(__lowerCamelCase     )
 for i in range(len(__lowerCamelCase     )     ):
  model.priors[i].load_state_dict(weight_dict[2 - i]     )

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

 print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""     )
 model.save_pretrained(__lowerCamelCase     )

 return weight_dict


if __name__ == "__main__":
      _lowerCamelCase						    =  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.""",
      )
      _lowerCamelCase						    =  parser.parse_args()
      convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






