



import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple					)      ->			Dict:
							if isinstance(__UpperCAmelCase						,  __UpperCAmelCase					):
													# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
													# modified it, it will not be accepted here again, since `auto` values would have been overridden
													UpperCAmelCase_=       deepcopy(__UpperCAmelCase					)
							elif os.path.exists(__UpperCAmelCase					):
													with io.open(__UpperCAmelCase						,  """r"""						,  encoding="""utf-8"""					) as f:
																			UpperCAmelCase_=       json.load(__UpperCAmelCase					)
							else:
													try:
																			UpperCAmelCase_=       baseaa.urlsafe_baadecode(__UpperCAmelCase					).decode("""utf-8"""					)
																			UpperCAmelCase_=       json.loads(__UpperCAmelCase					)
													except (UnicodeDecodeError, AttributeError, ValueError):
																			raise ValueError(
																			    F"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""					)

							UpperCAmelCase_=       config

							self.set_stage_and_offload()




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[int]:
							# zero stage - this is done as early as possible, before model is created, to allow
							# ``is_deepspeed_zero3_enabled`` query and getting to the early deepspeed config object
							# during ``zero.Init()`` which needs to know the dtype, and some other hparams.
							UpperCAmelCase_=       self.get_value("""zero_optimization.stage"""						,  -1					)

							# offload
							UpperCAmelCase_=       False
							if self.is_zeroa() or self.is_zeroa():
													UpperCAmelCase_=       set(["""cpu""", """nvme"""]					)
													UpperCAmelCase_=       set(
													    [
													        self.get_value("""zero_optimization.offload_optimizer.device"""					),
													        self.get_value("""zero_optimization.offload_param.device"""					),
													    ]					)
													if len(offload_devices & offload_devices_valid					) > 0:
																			UpperCAmelCase_=       True




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			Union[str, Any]:
							UpperCAmelCase_=       self.config

							# find the config node of interest if it exists
							UpperCAmelCase_=       ds_key_long.split("""."""					)
							UpperCAmelCase_=       nodes.pop()
							for node in nodes:
													UpperCAmelCase_=       config.get(__UpperCAmelCase					)
													if config is None:
																			return None, ds_key

							return config, ds_key




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Optional[int]						,  __UpperCAmelCase     :						Union[str, Any]=None					)      ->			Any:
							UpperCAmelCase_,       UpperCAmelCase_=       self.find_config_node(__UpperCAmelCase					)
							if config is None:
													return default
							return config.get(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						List[Any]						,  __UpperCAmelCase     :						Dict=False					)      ->			Dict:
							UpperCAmelCase_=       self.config

							# find the config node of interest if it exists
							UpperCAmelCase_=       ds_key_long.split("""."""					)
							for node in nodes:
													UpperCAmelCase_=       config
													UpperCAmelCase_=       config.get(__UpperCAmelCase					)
													if config is None:
																			if must_exist:
																									raise ValueError(F"""Can't find {ds_key_long} entry in the config: {self.config}"""					)
																			else:
																									return

        # if found remove it
							if parent_config is not None:
													parent_config.pop(__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Optional[Any]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.get_value(__UpperCAmelCase					)
							return False if value is None else bool(__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						str					)      ->			List[Any]:
							UpperCAmelCase_=       self.get_value(__UpperCAmelCase					)
							return False if value is None else not bool(__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Union[str, Any]:
							return self._stage == 2




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[Any]:
							return self._stage == 3




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return self._offload






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       engine




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Union[str, Any]						,  **__UpperCAmelCase     :						List[str]					)      ->			Dict:
							# runs backpropagation and handles mixed precision
							self.engine.backward(__UpperCAmelCase						,  **__UpperCAmelCase					)

							# Deepspeed's `engine.step` performs the following operations:
							# - gradient accumulation check
							# - gradient clipping
							# - optimizer step
							# - zero grad
							# - checking overflow
							# - lr_scheduler step (only if engine.lr_scheduler is not None)
							self.engine.step()
							# and this plugin overrides the above calls with no-ops when Accelerate runs under
							# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
							# training loop that works transparently under many training regimes.






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Optional[int]						,  __UpperCAmelCase     :						str					)      ->			Dict:
							super().__init__(__UpperCAmelCase						,  device_placement=__UpperCAmelCase						,  scaler=__UpperCAmelCase					)
							UpperCAmelCase_=       hasattr(self.optimizer						,  """overflow"""					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Any=None					)      ->			Optional[int]:
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			Optional[Any]:
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			str:
							if self.__has_overflow__:
													return self.optimizer.overflow
							return False






class 		lowercase						(			snake_case__):






	"""simple docstring"""


	def __init__(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Any						,  __UpperCAmelCase     :						str					)      ->			List[Any]:
							super().__init__(__UpperCAmelCase						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			str:
							pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						str=0.001						,  __UpperCAmelCase     :						int=0						,  **__UpperCAmelCase     :						str					)      ->			Dict:
							UpperCAmelCase_=       params
							UpperCAmelCase_=       lr
							UpperCAmelCase_=       weight_decay
							UpperCAmelCase_=       kwargs






class 		lowercase						:






	"""simple docstring"""


	def __init__(     self     :						Optional[Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						str=None						,  __UpperCAmelCase     :						Union[str, Any]=0						,  **__UpperCAmelCase     :						Any					)      ->			Dict:
							UpperCAmelCase_=       optimizer
							UpperCAmelCase_=       total_num_steps
							UpperCAmelCase_=       warmup_num_steps
							UpperCAmelCase_=       kwargs









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 	__a					(   lowerCAmelCase_       : List[Any]    )				->			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					(   lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any    )				->			Optional[Any]:






						'''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(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_encoder_block_conv_in.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_encoder_block_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_encoder_block_resnet.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												# rename vqvae.decoder keys
												elif re_decoder_block_conv_out.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_conv_out.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

												elif re_decoder_block_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_resnet.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												elif re_prior_cond_resnet.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_prior_cond_resnet.match(lowerCAmelCase_    )
																		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(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												# keep original key
												else:
																		UpperCAmelCase_=       original_key

												UpperCAmelCase_=       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:
																		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					(   lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : str=None    )				->			List[str]:






						'''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=lowerCAmelCase_    )
																		os.makedirs(F"""{pytorch_dump_folder_path}/"""      ,exist_ok=lowerCAmelCase_    )
																		open(F"""{pytorch_dump_folder_path}/{file.split("/"    )[-1]}"""      ,"""wb"""    ).write(r.content    )

						UpperCAmelCase_=       MODEL_MAPPING[model_name.split("""/"""    )[-1]]

						UpperCAmelCase_=       JukeboxConfig.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       JukeboxModel(lowerCAmelCase_    )

						UpperCAmelCase_=       []
						UpperCAmelCase_=       {}
						for i, dict_name in enumerate(lowerCAmelCase_    ):
												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(lowerCAmelCase_      ,model.state_dict()      ,lowerCAmelCase_      ,lowerCAmelCase_    )
												weight_dict.append(lowerCAmelCase_    )

						UpperCAmelCase_=       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__":
	__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)




