


'''simple docstring'''






import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		import PIL.Image

		from .features import FeatureType


__A :  Optional[List[str]]    							=				None
__A :  List[Any]    							=				"<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
__A :  List[str]    							=				[
    np.dtype("|b1"),
    np.dtype("|u1"),
    np.dtype("<u2"),
    np.dtype(">u2"),
    np.dtype("<i2"),
    np.dtype(">i2"),
    np.dtype("<u4"),
    np.dtype(">u4"),
    np.dtype("<i4"),
    np.dtype(">i4"),
    np.dtype("<f4"),
    np.dtype(">f4"),
    np.dtype("<f8"),
    np.dtype(">f8"),
]






@dataclass
class        __snake_case	:







				"""simple docstring"""



				lowercase       			= True
				lowercase       			= None
				# Automatically constructed
				lowercase       			= 'PIL.Image.Image'
				lowercase       			= pa.struct({'bytes': pa.binary(), 'path': pa.string()})
				lowercase       			= field(default='Image'     ,init=_SCREAMING_SNAKE_CASE     ,repr=_SCREAMING_SNAKE_CASE)

				def __call__(       self   :  Union[str, Any]		)       ->  Dict:
						return self.pa_type

				def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]		)       ->  dict:
						if config.PIL_AVAILABLE:
								import PIL.Image
						else:
								raise ImportError("""To support encoding images, please install 'Pillow'."""		)

						if isinstance(lowerCamelCase   ,						lowerCamelCase		):
								lowerCAmelCase_      :				Union[str, Any]      =       np.array(lowerCamelCase		)

						if isinstance(lowerCamelCase   ,						lowerCamelCase		):
								return {"path": value, "bytes": None}
						elif isinstance(lowerCamelCase   ,						lowerCamelCase		):
								return {"path": None, "bytes": value}
						elif isinstance(lowerCamelCase   ,						np.ndarray		):
								# convert the image array to PNG/TIFF bytes
								return encode_np_array(lowerCamelCase		)
						elif isinstance(lowerCamelCase   ,						PIL.Image.Image		):
								# convert the PIL image to bytes (default format is PNG/TIFF)
								return encode_pil_image(lowerCamelCase		)
						elif value.get("""path"""		) is not None and os.path.isfile(value["""path"""]		):
								# we set "bytes": None to not duplicate the data if they're already available locally
								return {"bytes": None, "path": value.get("""path"""		)}
						elif value.get("""bytes"""		) is not None or value.get("""path"""		) is not None:
								# store the image bytes, and path is used to infer the image format using the file extension
								return {"bytes": value.get("""bytes"""		), "path": value.get("""path"""		)}
						else:
								raise ValueError(
								    F'An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'		)

				def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  dict   ,						lowerCamelCase   :  Optional[int]=None		)       ->  "PIL.Image.Image":
						if not self.decode:
								raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead."""		)

						if config.PIL_AVAILABLE:
								import PIL.Image
						else:
								raise ImportError("""To support decoding images, please install 'Pillow'."""		)

						if token_per_repo_id is None:
								lowerCAmelCase_      :				Dict      =       {}

						lowerCAmelCase_      :				Union[str, Any]      =       value["""path"""], value["""bytes"""]
						if bytes_ is None:
								if path is None:
										raise ValueError(F'An image should have one of \'path\' or \'bytes\' but both are None in {value}.'		)
								else:
										if is_local_path(lowerCamelCase		):
												lowerCAmelCase_      :				List[Any]      =       PIL.Image.open(lowerCamelCase		)
										else:
												lowerCAmelCase_      :				Union[str, Any]      =       path.split("""::"""		)[-1]
												try:
														lowerCAmelCase_      :				Dict      =       string_to_dict(lowerCamelCase   ,						config.HUB_DATASETS_URL		)["""repo_id"""]
														lowerCAmelCase_      :				Optional[Any]      =       token_per_repo_id.get(lowerCamelCase		)
												except ValueError:
														lowerCAmelCase_      :				int      =       None
												with xopen(lowerCamelCase   ,						"""rb"""   ,						use_auth_token=lowerCamelCase		) as f:
														lowerCAmelCase_      :				Optional[Any]      =       BytesIO(f.read()		)
												lowerCAmelCase_      :				Union[str, Any]      =       PIL.Image.open(bytes_		)
						else:
								lowerCAmelCase_      :				Union[str, Any]      =       PIL.Image.open(BytesIO(bytes_		)		)
						image.load()  # to avoid "Too many open files" errors
						return image

				def __lowercase       (       self   :  int		)       ->  Union["FeatureType", Dict[str, "FeatureType"]]:
						from .features import Value

						return (
						    self
						    if self.decode
						    else {
						        "bytes": Value("""binary"""		),
						        "path": Value("""string"""		),
						    }
						)

				def __lowercase       (       self   :  List[str]   ,						lowerCamelCase   :  Union[pa.StringArray, pa.StructArray, pa.ListArray]		)       ->  pa.StructArray:
						if pa.types.is_string(storage.type		):
								lowerCAmelCase_      :				Dict      =       pa.array([None] * len(lowerCamelCase		)   ,						type=pa.binary()		)
								lowerCAmelCase_      :				Optional[Any]      =       pa.StructArray.from_arrays([bytes_array, storage]   ,						["""bytes""", """path"""]   ,						mask=storage.is_null()		)
						elif pa.types.is_binary(storage.type		):
								lowerCAmelCase_      :				Optional[Any]      =       pa.array([None] * len(lowerCamelCase		)   ,						type=pa.string()		)
								lowerCAmelCase_      :				Optional[Any]      =       pa.StructArray.from_arrays([storage, path_array]   ,						["""bytes""", """path"""]   ,						mask=storage.is_null()		)
						elif pa.types.is_struct(storage.type		):
								if storage.type.get_field_index("""bytes"""		) >= 0:
										lowerCAmelCase_      :				Optional[int]      =       storage.field("""bytes"""		)
								else:
										lowerCAmelCase_      :				List[str]      =       pa.array([None] * len(lowerCamelCase		)   ,						type=pa.binary()		)
								if storage.type.get_field_index("""path"""		) >= 0:
										lowerCAmelCase_      :				str      =       storage.field("""path"""		)
								else:
										lowerCAmelCase_      :				Optional[Any]      =       pa.array([None] * len(lowerCamelCase		)   ,						type=pa.string()		)
								lowerCAmelCase_      :				List[str]      =       pa.StructArray.from_arrays([bytes_array, path_array]   ,						["""bytes""", """path"""]   ,						mask=storage.is_null()		)
						elif pa.types.is_list(storage.type		):
								lowerCAmelCase_      :				Union[str, Any]      =       pa.array(
								    [encode_np_array(np.array(lowerCamelCase		)		)["""bytes"""] if arr is not None else None for arr in storage.to_pylist()]   ,						type=pa.binary()   ,						)
								lowerCAmelCase_      :				List[str]      =       pa.array([None] * len(lowerCamelCase		)   ,						type=pa.string()		)
								lowerCAmelCase_      :				List[Any]      =       pa.StructArray.from_arrays(
								    [bytes_array, path_array]   ,						["""bytes""", """path"""]   ,						mask=bytes_array.is_null()		)
						return array_cast(lowerCamelCase   ,						self.pa_type		)





				def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  pa.StructArray		)       ->  pa.StructArray:

						@no_op_if_value_is_null
						def path_to_bytes(lowerCamelCase   :  List[str]		):
								with xopen(lowerCamelCase   ,						"""rb"""		) as f:
										lowerCAmelCase_      :				Union[str, Any]      =       f.read()
								return bytes_

						lowerCAmelCase_      :				List[str]      =       pa.array(
						    [
						        (path_to_bytes(x["""path"""]		) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
						        for x in storage.to_pylist()
						    ]   ,						type=pa.binary()   ,						)
						lowerCAmelCase_      :				List[str]      =       pa.array(
						    [os.path.basename(lowerCamelCase		) if path is not None else None for path in storage.field("""path"""		).to_pylist()]   ,						type=pa.string()   ,						)
						lowerCAmelCase_      :				List[str]      =       pa.StructArray.from_arrays([bytes_array, path_array]   ,						["""bytes""", """path"""]   ,						mask=bytes_array.is_null()		)
						return array_cast(lowerCamelCase   ,						self.pa_type		)







def     UpperCamelCase_      (   ):
		'''simple docstring'''





		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError("""To support encoding images, please install 'Pillow'.""" )

		global _IMAGE_COMPRESSION_FORMATS
		if _IMAGE_COMPRESSION_FORMATS is None:
				PIL.Image.init()
				lowerCAmelCase_      :				str      =       list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
		return _IMAGE_COMPRESSION_FORMATS







def     UpperCamelCase_      (   A__ :       "PIL.Image.Image" ):
		'''simple docstring'''





		lowerCAmelCase_      :				Dict      =       BytesIO()
		if image.format in list_image_compression_formats():
				lowerCAmelCase_      :				List[str]      =       image.format
		else:
				lowerCAmelCase_      :				Optional[int]      =       """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
		image.save(A__							,   format=A__ )
		return buffer.getvalue()







def     UpperCamelCase_      (   A__ :       "PIL.Image.Image" ):
		'''simple docstring'''





		if hasattr(A__							,   """filename""" ) and image.filename != "":
				return {"path": image.filename, "bytes": None}
		else:
				return {"path": None, "bytes": image_to_bytes(A__ )}







def     UpperCamelCase_      (   A__ :       np.ndarray ):
		'''simple docstring'''





		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError("""To support encoding images, please install 'Pillow'.""" )

		lowerCAmelCase_      :				Tuple      =       array.dtype
		lowerCAmelCase_      :				Any      =       dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
		lowerCAmelCase_      :				Optional[Any]      =       dtype.kind
		lowerCAmelCase_      :				Any      =       dtype.itemsize

		lowerCAmelCase_      :				Optional[int]      =       None

		# Multi-channel array case (only np.dtype("|u1") is allowed)
		if array.shape[2:]:
				lowerCAmelCase_      :				Union[str, Any]      =       np.dtype("""|u1""" )
				if dtype_kind not in ["u", "i"]:
						raise TypeError(
						    f'Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.' )
				if dtype is not dest_dtype:
						warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'' )
				# Exact match
		elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
				lowerCAmelCase_      :				Dict      =       dtype
		else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
				while dtype_itemsize >= 1:
						lowerCAmelCase_      :				Any      =       dtype_byteorder + dtype_kind + str(A__ )
						lowerCAmelCase_      :				Any      =       np.dtype(A__ )
						if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
								warnings.warn(f'Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'' )
								break
						else:
								dtype_itemsize //= 2

		if dest_dtype is None:
				raise TypeError(
				    f'Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}' )
		lowerCAmelCase_      :				Dict      =       PIL.Image.fromarray(array.astype(A__ ) )
		return {"path": None, "bytes": image_to_bytes(A__ )}







def     UpperCamelCase_      (   A__ :       Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ):
		'''simple docstring'''





		if config.PIL_AVAILABLE:
				import PIL.Image
		else:
				raise ImportError("""To support encoding images, please install 'Pillow'.""" )

		if objs:
				lowerCAmelCase_      :				List[str]      =       first_non_null_value(A__ )
				if isinstance(A__							,   A__ ):
						return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
				if isinstance(A__							,   np.ndarray ):
						lowerCAmelCase_      :				Dict      =       no_op_if_value_is_null(A__ )
						return [obj_to_image_dict_func(A__ ) for obj in objs]
				elif isinstance(A__							,   PIL.Image.Image ):
						lowerCAmelCase_      :				str      =       no_op_if_value_is_null(A__ )
						return [obj_to_image_dict_func(A__ ) for obj in objs]
				else:
						return objs
		else:
				return objs







'''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 :  List[str]    							=				logging.get_logger(__name__)


__A :  str    							=				"https://openaipublic.azureedge.net/jukebox/models/"
__A :  Any    							=				{
    "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     UpperCamelCase_      (   A__ :       int ):
  '''simple docstring'''





  if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.1.bias"""							,   """.conv1d_1.bias""" )
  elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.1.weight"""							,   """.conv1d_1.weight""" )
  elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.3.bias"""							,   """.conv1d_2.bias""" )
  elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				int      =       key.replace(""".model.3.weight"""							,   """.conv1d_2.weight""" )

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

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

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    lowerCAmelCase_      :				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:
    lowerCAmelCase_      :				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     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Optional[Any]							,   A__ :       Tuple							,   A__ :       Any ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       {}
  import re

  lowerCAmelCase_      :				Union[str, Any]      =       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
  lowerCAmelCase_      :				str      =       re.compile(
      R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
  lowerCAmelCase_      :				Dict      =       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )

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

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

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

    elif re_encoder_block_proj_out.fullmatch(A__ ):
      lowerCAmelCase_      :				int      =       re_encoder_block_proj_out.match(A__ )
      lowerCAmelCase_      :				Tuple      =       regex_match.groups()
      lowerCAmelCase_      :				Optional[Any]      =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
      lowerCAmelCase_      :				str      =       re_encoder_block_proj_out.sub(A__							,   A__ )

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

    elif re_decoder_block_resnet.fullmatch(A__ ):
      lowerCAmelCase_      :				Optional[Any]      =       re_decoder_block_resnet.match(A__ )
      lowerCAmelCase_      :				Optional[Any]      =       regex_match.groups()
      lowerCAmelCase_      :				Optional[Any]      =       int(groups[2] ) * 2 + int(groups[3] ) - 2
      lowerCAmelCase_      :				Any      =       {"""1""": 1, """3""": 2}[groups[-2]]
      lowerCAmelCase_      :				Optional[int]      =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'
      lowerCAmelCase_      :				Union[str, Any]      =       f'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
      lowerCAmelCase_      :				Dict      =       prefix + resnet_block
      lowerCAmelCase_      :				Any      =       re_decoder_block_resnet.sub(A__							,   A__ )

    elif re_decoder_block_proj_in.fullmatch(A__ ):
      lowerCAmelCase_      :				str      =       re_decoder_block_proj_in.match(A__ )
      lowerCAmelCase_      :				Optional[int]      =       regex_match.groups()
      lowerCAmelCase_      :				str      =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
      lowerCAmelCase_      :				Union[str, Any]      =       re_decoder_block_proj_in.sub(A__							,   A__ )

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

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

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

    # keep original key
    else:
      lowerCAmelCase_      :				Optional[Any]      =       original_key

    lowerCAmelCase_      :				Optional[Any]      =       replace_key(A__ )

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

    lowerCAmelCase_      :				Dict      =       original_key
    lowerCAmelCase_      :				Optional[int]      =       value

  return new_dict







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]=None							,   A__ :       str=None ):
  '''simple docstring'''





  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(f'{pytorch_dump_folder_path}/{file.split("/" )[-1]}' ):
      lowerCAmelCase_      :				List[Any]      =       requests.get(f'{PREFIX}{file}'							,   allow_redirects=A__ )
      os.makedirs(f'{pytorch_dump_folder_path}/'							,   exist_ok=A__ )
      open(f'{pytorch_dump_folder_path}/{file.split("/" )[-1]}'							,   """wb""" ).write(r.content )

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

  lowerCAmelCase_      :				List[str]      =       JukeboxConfig.from_pretrained(A__ )
  lowerCAmelCase_      :				Dict      =       JukeboxModel(A__ )

  lowerCAmelCase_      :				Optional[Any]      =       []
  lowerCAmelCase_      :				Dict      =       {}
  for i, dict_name in enumerate(A__ ):
    lowerCAmelCase_      :				List[Any]      =       torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}' )["""model"""]

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

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

  lowerCAmelCase_      :				int      =       weight_dict.pop(0 )
  model.vqvae.load_state_dict(A__ )
  for i in range(len(A__ ) ):
    model.priors[i].load_state_dict(weight_dict[2 - i] )

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

  print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )

  return weight_dict


if __name__ == "__main__":
  __A :  str    							=				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 :  Optional[Any]    							=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



