



import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)





def 							_A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Any=False					)       ->				Dict:







			'''simple docstring'''
			try:
						_UpperCAmelCase	 =     os.environ[key]
			except KeyError:
						# KEY isn't set, default to `default`.
						_UpperCAmelCase	 =     default
			else:
						# KEY is set, convert it to True or False.
						try:
									_UpperCAmelCase	 =     strtobool(__A					)
						except ValueError:
									# More values are supported, but let's keep the message simple.
									raise ValueError(F"If set, {key} must be yes or no."					)
			return _value


UpperCAmelCase__     = parse_flag_from_env("RUN_SLOW", default=False)





def 							_A			(  _UpperCAmelCase       :     str					)       ->				Optional[Any]:







			'''simple docstring'''
			return unittest.skip('Test was skipped'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Optional[int]:







			'''simple docstring'''
			return unittest.skipUnless(_run_slow_tests							,     'test is slow'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     str					)       ->				Tuple:







			'''simple docstring'''
			return unittest.skipUnless(not torch.cuda.is_available()							,     'test requires only a CPU'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     int					)       ->				int:







			'''simple docstring'''
			return unittest.skipUnless(torch.cuda.is_available()							,     'test requires a GPU'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     str					)       ->				List[Any]:







			'''simple docstring'''
			return unittest.skipUnless(is_xpu_available()							,     'test requires a XPU'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				List[Any]:







			'''simple docstring'''
			return unittest.skipUnless(is_mps_available()							,     'test requires a `mps` backend support in `torch`'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     str					)       ->				Union[str, Any]:







			'''simple docstring'''
			return unittest.skipUnless(
			    is_transformers_available() and is_datasets_available()							,     'test requires the Hugging Face suite'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Any					)       ->				int:







			'''simple docstring'''
			return unittest.skipUnless(is_bnb_available()							,     'test requires the bitsandbytes library'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Optional[Any]					)       ->				Any:







			'''simple docstring'''
			return unittest.skipUnless(is_tpu_available()							,     'test requires TPU'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Dict					)       ->				Optional[Any]:







			'''simple docstring'''
			return unittest.skipUnless(torch.cuda.device_count() == 1							,     'test requires a GPU'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Any					)       ->				List[Any]:







			'''simple docstring'''
			return unittest.skipUnless(torch.xpu.device_count() == 1							,     'test requires a XPU'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Optional[int]					)       ->				List[Any]:







			'''simple docstring'''
			return unittest.skipUnless(torch.cuda.device_count() > 1							,     'test requires multiple GPUs'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Optional[int]					)       ->				List[str]:







			'''simple docstring'''
			return unittest.skipUnless(torch.xpu.device_count() > 1							,     'test requires multiple XPUs'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Union[str, Any]					)       ->				Optional[Any]:







			'''simple docstring'''
			return unittest.skipUnless(is_safetensors_available()							,     'test requires safetensors'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Tuple					)       ->				List[str]:







			'''simple docstring'''
			return unittest.skipUnless(is_deepspeed_available()							,     'test requires DeepSpeed'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Union[str, Any]					)       ->				Tuple:







			'''simple docstring'''
			return unittest.skipUnless(is_torch_version('>='							,     '1.12.0'					)							,     'test requires torch version >= 1.12.0'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Any=None							,     _UpperCAmelCase       :     int=None					)       ->				Dict:







			'''simple docstring'''
			if test_case is None:
						return partial(__A							,     version=__A					)
			return unittest.skipUnless(is_torch_version('>='							,     __A					)							,     F"test requires torch version >= {version}"					)(__A					)





def 							_A			(  _UpperCAmelCase       :     int					)       ->				Dict:







			'''simple docstring'''
			return unittest.skipUnless(is_tensorboard_available()							,     'test requires Tensorboard'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     str					)       ->				Tuple:







			'''simple docstring'''
			return unittest.skipUnless(is_wandb_available()							,     'test requires wandb'					)(__A					)





def 							_A			(  _UpperCAmelCase       :     Optional[int]					)       ->				Tuple:







			'''simple docstring'''
			return unittest.skipUnless(is_comet_ml_available()							,     'test requires comet_ml'					)(__A					)


UpperCAmelCase__     = (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)





def 							_A			(  _UpperCAmelCase       :     Dict					)       ->				Any:







			'''simple docstring'''
			return unittest.skipUnless(
			    _atleast_one_tracker_available							,     'test requires at least one tracker to be available and for `comet_ml` to not be installed'							,     )(__A					)







class     __lowerCAmelCase					(    unittest.TestCase		):
			UpperCamelCase           =  True



			@classmethod
			def 					_lowerCamelCase (		cls     :						Optional[Any])     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     tempfile.mkdtemp()



			@classmethod
			def 					_lowerCamelCase (		cls     :						int)     ->					List[Any]:



						"""simple docstring"""






						if os.path.exists(cls.tmpdir):
									shutil.rmtree(cls.tmpdir)



			def 					_lowerCamelCase (		self     :						Any)     ->					Union[str, Any]:



						"""simple docstring"""






						if self.clear_on_setup:
									for path in Path(self.tmpdir).glob('**/*'):
												if path.is_file():
															path.unlink()
												elif path.is_dir():
															shutil.rmtree(_UpperCamelCase)







class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						int)     ->					Any:



						"""simple docstring"""






						super().tearDown()
						# Reset the state of the AcceleratorState singleton.
						AcceleratorState._reset_state()
						PartialState._reset_state()







class     __lowerCAmelCase					(    unittest.TestCase		):



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						Union[mock.Mock, List[mock.Mock]])     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     mocks if isinstance(_UpperCamelCase				,	(tuple, list)) else [mocks]
						for m in self.mocks:
									m.start()
									self.addCleanup(m.stop)





def 							_A			(  _UpperCAmelCase       :     Any					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     AcceleratorState()
			_UpperCAmelCase	 =     tensor[None].clone().to(state.device					)
			_UpperCAmelCase	 =     gather(__A					).cpu()
			_UpperCAmelCase	 =     tensor[0].cpu()
			for i in range(tensors.shape[0]					):
						if not torch.equal(tensors[i]							,     __A					):
									return False
			return True







class     __lowerCAmelCase					:



			def __init__(		self     :						List[str]				,	A     :						Optional[int]				,	A     :						Optional[int]				,	A     :						Tuple)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     returncode
						_UpperCAmelCase	 =     stdout
						_UpperCAmelCase	 =     stderr





async def 							_A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     Any					)       ->				Tuple:







			'''simple docstring'''
			while True:
						_UpperCAmelCase	 =     await stream.readline()
						if line:
									callback(__A					)
						else:
									break





async def 							_A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Optional[Any]=None							,     _UpperCAmelCase       :     Any=None							,     _UpperCAmelCase       :     List[str]=None							,     _UpperCAmelCase       :     Union[str, Any]=False							,     _UpperCAmelCase       :     Optional[Any]=False					)       ->				List[Any]:







			'''simple docstring'''
			if echo:
						print('\nRunning: '							,     ' '.join(__A					)					)

			_UpperCAmelCase	 =     await asyncio.create_subprocess_exec(
			    cmd[0]							,     *cmd[1:]							,     stdin=__A							,     stdout=asyncio.subprocess.PIPE							,     stderr=asyncio.subprocess.PIPE							,     env=__A							,     )

			# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
			# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
			#
			# If it starts hanging, will need to switch to the following code. The problem is that no data
			# will be seen until it's done and if it hangs for example there will be no debug info.
			# out, err = await p.communicate()
			# return _RunOutput(p.returncode, out, err)

			_UpperCAmelCase	 =     []
			_UpperCAmelCase	 =     []

			def tee(_UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Optional[Any]=""					):
						_UpperCAmelCase	 =     line.decode('utf-8'					).rstrip()
						sink.append(__A					)
						if not quiet:
									print(__A							,     __A							,     file=__A					)

    # XXX: the timeout doesn't seem to make any difference here
			await asyncio.wait(
			    [
			        asyncio.create_task(_read_stream(p.stdout							,     lambda _UpperCAmelCase					: tee(__A							,     __A							,     sys.stdout							,     label='stdout:'					)					)					),
			        asyncio.create_task(_read_stream(p.stderr							,     lambda _UpperCAmelCase					: tee(__A							,     __A							,     sys.stderr							,     label='stderr:'					)					)					),
			    ]							,     timeout=__A							,     )
			return _RunOutput(await p.wait()							,     __A							,     __A					)





def 							_A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     List[str]=None							,     _UpperCAmelCase       :     Union[str, Any]=None							,     _UpperCAmelCase       :     int=180							,     _UpperCAmelCase       :     Dict=False							,     _UpperCAmelCase       :     List[str]=True					)       ->				Dict:







			'''simple docstring'''
			_UpperCAmelCase	 =     asyncio.get_event_loop()
			_UpperCAmelCase	 =     loop.run_until_complete(
			    _stream_subprocess(__A							,     env=__A							,     stdin=__A							,     timeout=__A							,     quiet=__A							,     echo=__A					)					)

			_UpperCAmelCase	 =     """ """.join(__A					)
			if result.returncode > 0:
						_UpperCAmelCase	 =     """\n""".join(result.stderr					)
						raise RuntimeError(
						    F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
						    F"The combined stderr from workers follows:\n{stderr}"					)

			return result







class     __lowerCAmelCase					(    __lowerCAmelCase		):
			pass





def 							_A			(  _UpperCAmelCase       :     List[str]							,     _UpperCAmelCase       :     str=False					)       ->				Tuple:







			'''simple docstring'''
			try:
						_UpperCAmelCase	 =     subprocess.check_output(__A							,     stderr=subprocess.STDOUT					)
						if return_stdout:
									if hasattr(__A							,     'decode'					):
												_UpperCAmelCase	 =     output.decode('utf-8'					)
									return output
			except subprocess.CalledProcessError as e:
						raise SubprocessCallException(
						    F"Command `{' '.join(__A					)}` failed with the following error:\n\n{e.output.decode()}"					) from e





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()
UpperCAmelCase__     = logging.get_logger(__name__)


UpperCAmelCase__     = "https://openaipublic.azureedge.net/jukebox/models/"
UpperCAmelCase__     = {
    "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 							A			(  _UpperCAmelCase       :     List[str]					)       ->				Tuple:







			'''simple docstring'''
			if key.endswith('.model.1.bias'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.1.bias'							,     '.conv1d_1.bias'					)
			elif key.endswith('.model.1.weight'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.1.weight'							,     '.conv1d_1.weight'					)
			elif key.endswith('.model.3.bias'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.3.bias'							,     '.conv1d_2.bias'					)
			elif key.endswith('.model.3.weight'					) and len(key.split('.'					)					) > 10:
						_UpperCAmelCase	 =     key.replace('.model.3.weight'							,     '.conv1d_2.weight'					)

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

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

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						_UpperCAmelCase	 =     key.replace('.emb.'							,     '.'					)

			if key.endswith('k'					):  # replace vqvae.X.k with vqvae.X.codebook
						return key.replace('.k'							,     '.codebook'					)
			if "y_emb." in key:
						return key.replace('y_emb.'							,     'metadata_embedding.'					)

			if "x_emb.emb." in key:
						_UpperCAmelCase	 =     key.replace('0.x_emb.emb'							,     'embed_tokens'					)

			if "prime_state_ln" in key:
						return key.replace('prime_state_ln'							,     'encoder.final_layer_norm'					)
			if ".ln" in key:
						return key.replace('.ln'							,     '.layer_norm'					)
			if "_ln" in key:
						return key.replace('_ln'							,     '_layer_norm'					)

			if "prime_state_proj" in key:
						return key.replace('prime_state_proj'							,     'encoder.proj_in'					)
			if "prime_x_out" in key:
						return key.replace('prime_x_out'							,     'encoder.lm_head'					)
			if "prior.x_out" in key:
						return key.replace('x_out'							,     'fc_proj_out'					)
			if "x_emb" in key:
						return key.replace('x_emb'							,     'embed_tokens'					)

			return key





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     List[Any]					)       ->				Tuple:







			'''simple docstring'''
			_UpperCAmelCase	 =     {}
			import re

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

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

			_UpperCAmelCase	 =     re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(
			    R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'					)
			_UpperCAmelCase	 =     re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'					)

			for original_key, value in state_dict.items():
						# rename vqvae.encoder keys
						if re_encoder_block_conv_in.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_encoder_block_conv_in.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     int(groups[2]					) * 2 + int(groups[3]					)
									_UpperCAmelCase	 =     F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
									_UpperCAmelCase	 =     re_encoder_block_conv_in.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

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

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

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

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

						elif re_decoder_block_proj_in.fullmatch(_UpperCAmelCase					):
									_UpperCAmelCase	 =     re_decoder_block_proj_in.match(_UpperCAmelCase					)
									_UpperCAmelCase	 =     regex_match.groups()
									_UpperCAmelCase	 =     F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
									_UpperCAmelCase	 =     re_decoder_block_proj_in.sub(_UpperCAmelCase							,     _UpperCAmelCase					)

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

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

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

						# keep original key
						else:
									_UpperCAmelCase	 =     original_key

						_UpperCAmelCase	 =     replace_key(_UpperCAmelCase					)

						if F"{key_prefix}.{key}" not in model_state_dict or key is None:
									print(F"failed converting {original_key} to {key}, does not match"					)

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

						_UpperCAmelCase	 =     original_key
						_UpperCAmelCase	 =     value

			return new_dict





@torch.no_grad()
def 							A			(  _UpperCAmelCase       :     List[str]=None							,     _UpperCAmelCase       :     Dict=None					)       ->				Dict:







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

			_UpperCAmelCase	 =     MODEL_MAPPING[model_name.split('/'					)[-1]]

			_UpperCAmelCase	 =     JukeboxConfig.from_pretrained(_UpperCAmelCase					)
			_UpperCAmelCase	 =     JukeboxModel(_UpperCAmelCase					)

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

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

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

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

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

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

			return weight_dict


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