



import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


UpperCAmelCase__     = logging.get_logger(__name__)





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     Dict							,     _UpperCAmelCase       :     Dict=False					)       ->				Optional[Any]:







			'''simple docstring'''
			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    'Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'
						    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
						    ' instructions.'					)
						raise

			if not is_sharded:
						_UpperCAmelCase	 =     os.path.abspath(_UpperCAmelCase					)
						logger.info(F"Loading PyTorch weights from {pt_path}"					)

						_UpperCAmelCase	 =     torch.load(_UpperCAmelCase							,     map_location='cpu'					)
						logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()					):,} parameters."					)

						_UpperCAmelCase	 =     convert_pytorch_state_dict_to_flax(_UpperCAmelCase							,     _UpperCAmelCase					)
			else:
						# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
						_UpperCAmelCase	 =     convert_pytorch_sharded_state_dict_to_flax(_UpperCAmelCase							,     _UpperCAmelCase					)
			return flax_state_dict





def 							A			(  _UpperCAmelCase       :     Tuple[str]							,     _UpperCAmelCase       :     np.ndarray							,     _UpperCAmelCase       :     Dict[str, jnp.ndarray]							,     _UpperCAmelCase       :     str							,     )       ->				(Tuple[str], np.ndarray):







			'''simple docstring'''

			def is_key_or_prefix_key_in_dict(_UpperCAmelCase       :     Tuple[str]					) -> bool:
						return len(set(_UpperCAmelCase					) & {key, (model_prefix,) + key}					) > 0

			# layer norm
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('scale',)
			if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_UpperCAmelCase					):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer mean
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('mean',)
			if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase					):
						return renamed_pt_tuple_key, pt_tensor

			# batch norm layer var
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('var',)
			if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase					):
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('embedding',)
			if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_UpperCAmelCase					):
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('kernel',)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_UpperCAmelCase					):
						_UpperCAmelCase	 =     pt_tensor.transpose(2							,     3							,     1							,     0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('kernel',)
			if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_UpperCAmelCase					):
						_UpperCAmelCase	 =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('weight',)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			_UpperCAmelCase	 =     pt_tuple_key[:-1] + ('bias',)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
			_UpperCAmelCase	 =     None
			if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
						_UpperCAmelCase	 =     pt_tuple_key[-2] + '_g'
			elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
						_UpperCAmelCase	 =     pt_tuple_key[-2] + '_v'
			if name is not None:
						_UpperCAmelCase	 =     pt_tuple_key[:-3] + (name,)
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor





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







			'''simple docstring'''
			# convert pytorch tensor to numpy
			_UpperCAmelCase	 =     {k: v.numpy() for k, v in pt_state_dict.items()}

			_UpperCAmelCase	 =     flax_model.base_model_prefix

			# use params dict if the model contains batch norm layers
			if "params" in flax_model.params:
						_UpperCAmelCase	 =     flax_model.params['params']
			else:
						_UpperCAmelCase	 =     flax_model.params
			_UpperCAmelCase	 =     flatten_dict(_UpperCAmelCase					)

			# add batch_stats keys,values to dict
			if "batch_stats" in flax_model.params:
						_UpperCAmelCase	 =     flatten_dict(flax_model.params['batch_stats']					)
						random_flax_state_dict.update(_UpperCAmelCase					)

			_UpperCAmelCase	 =     {}

			_UpperCAmelCase	 =     (model_prefix not in flax_model_params) and (
			    model_prefix in {k.split('.'					)[0] for k in pt_state_dict.keys()}
			)
			_UpperCAmelCase	 =     (model_prefix in flax_model_params) and (
			    model_prefix not in {k.split('.'					)[0] for k in pt_state_dict.keys()}
			)

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						_UpperCAmelCase	 =     tuple(pt_key.split('.'					)					)

						# remove base model prefix if necessary
						_UpperCAmelCase	 =     pt_tuple_key[0] == model_prefix
						if load_model_with_head_into_base_model and has_base_model_prefix:
									_UpperCAmelCase	 =     pt_tuple_key[1:]

						# Correctly rename weight parameters
						_UpperCAmelCase ,  _UpperCAmelCase	 =     rename_key_and_reshape_tensor(
						    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

						# add model prefix if necessary
						_UpperCAmelCase	 =     (model_prefix,) + flax_key in random_flax_state_dict
						if load_base_model_into_model_with_head and require_base_model_prefix:
									_UpperCAmelCase	 =     (model_prefix,) + flax_key

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
												    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."					)

        # add batch stats if the model contains batchnorm layers
						if "batch_stats" in flax_model.params:
									if "mean" in flax_key[-1] or "var" in flax_key[-1]:
												_UpperCAmelCase	 =     jnp.asarray(_UpperCAmelCase					)
												continue
									# remove num_batches_tracked key
									if "num_batches_tracked" in flax_key[-1]:
												flax_state_dict.pop(_UpperCAmelCase							,     _UpperCAmelCase					)
												continue

									# also add unexpected weight so that warning is thrown
									_UpperCAmelCase	 =     jnp.asarray(_UpperCAmelCase					)

						else:
									# also add unexpected weight so that warning is thrown
									_UpperCAmelCase	 =     jnp.asarray(_UpperCAmelCase					)

			return unflatten_dict(_UpperCAmelCase					)





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







			'''simple docstring'''
			import torch

			# Load the index
			_UpperCAmelCase	 =     {}
			for shard_file in shard_filenames:
						# load using msgpack utils
						_UpperCAmelCase	 =     torch.load(_UpperCAmelCase					)
						_UpperCAmelCase	 =     {k: v.numpy() for k, v in pt_state_dict.items()}

						_UpperCAmelCase	 =     flax_model.base_model_prefix

						# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
						if "batch_stats" in flax_model.params:
									_UpperCAmelCase	 =     flax_model.params['params']

									_UpperCAmelCase	 =     flatten_dict(_UpperCAmelCase					)
									random_flax_state_dict.update(flatten_dict(flax_model.params['batch_stats']					)					)
						else:
									_UpperCAmelCase	 =     flax_model.params
									_UpperCAmelCase	 =     flatten_dict(_UpperCAmelCase					)

						_UpperCAmelCase	 =     (model_prefix not in flax_model_params) and (
						    model_prefix in {k.split('.'					)[0] for k in pt_state_dict.keys()}
						)
						_UpperCAmelCase	 =     (model_prefix in flax_model_params) and (
						    model_prefix not in {k.split('.'					)[0] for k in pt_state_dict.keys()}
						)
						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
									_UpperCAmelCase	 =     tuple(pt_key.split('.'					)					)

									# remove base model prefix if necessary
									_UpperCAmelCase	 =     pt_tuple_key[0] == model_prefix
									if load_model_with_head_into_base_model and has_base_model_prefix:
												_UpperCAmelCase	 =     pt_tuple_key[1:]

									# Correctly rename weight parameters
									_UpperCAmelCase ,  _UpperCAmelCase	 =     rename_key_and_reshape_tensor(
									    _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)
									# add model prefix if necessary
									_UpperCAmelCase	 =     (model_prefix,) + flax_key in random_flax_state_dict
									if load_base_model_into_model_with_head and require_base_model_prefix:
												_UpperCAmelCase	 =     (model_prefix,) + flax_key

									if flax_key in random_flax_state_dict:
												if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
															raise ValueError(
															    F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
															    F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."					)

            # add batch stats if the model contains batchnorm layers
									if "batch_stats" in flax_model.params:
												if "mean" in flax_key[-1]:
															_UpperCAmelCase	 =     jnp.asarray(_UpperCAmelCase					)
															continue
												if "var" in flax_key[-1]:
															_UpperCAmelCase	 =     jnp.asarray(_UpperCAmelCase					)
															continue
												# remove num_batches_tracked key
												if "num_batches_tracked" in flax_key[-1]:
															flax_state_dict.pop(_UpperCAmelCase							,     _UpperCAmelCase					)
															continue

												# also add unexpected weight so that warning is thrown
												_UpperCAmelCase	 =     jnp.asarray(_UpperCAmelCase					)

									else:
												# also add unexpected weight so that warning is thrown
												_UpperCAmelCase	 =     jnp.asarray(_UpperCAmelCase					)
			return unflatten_dict(_UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     Optional[int]							,     _UpperCAmelCase       :     Any					)       ->				str:







			'''simple docstring'''
			_UpperCAmelCase	 =     os.path.abspath(_UpperCAmelCase					)
			logger.info(F"Loading Flax weights from {flax_checkpoint_path}"					)

			# import correct flax class
			_UpperCAmelCase	 =     getattr(_UpperCAmelCase							,     'Flax' + model.__class__.__name__					)

			# load flax weight dict
			with open(_UpperCAmelCase							,     'rb'					) as state_f:
						try:
									_UpperCAmelCase	 =     from_bytes(_UpperCAmelCase							,     state_f.read()					)
						except UnpicklingError:
									raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. "					)

			return load_flax_weights_in_pytorch_model(_UpperCAmelCase							,     _UpperCAmelCase					)





def 							A			(  _UpperCAmelCase       :     int							,     _UpperCAmelCase       :     Tuple					)       ->				str:







			'''simple docstring'''

			try:
						import torch  # noqa: F401
			except ImportError:
						logger.error(
						    'Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'
						    ' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'
						    ' instructions.'					)
						raise

			# check if we have bf16 weights
			_UpperCAmelCase	 =     flatten_dict(jax.tree_util.tree_map(lambda _UpperCAmelCase					: x.dtype == jnp.bfloataa							,     _UpperCAmelCase					)					).values()
			if any(_UpperCAmelCase					):
						# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
						# and bf16 is not fully supported in PT yet.
						logger.warning(
						    'Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '
						    'before loading those in PyTorch model.'					)
						_UpperCAmelCase	 =     jax.tree_util.tree_map(
						    lambda _UpperCAmelCase					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params							,     _UpperCAmelCase					)

			_UpperCAmelCase	 =     flatten_dict(_UpperCAmelCase					)
			_UpperCAmelCase	 =     pt_model.state_dict()

			_UpperCAmelCase	 =     (pt_model.base_model_prefix in flax_state) and (
			    pt_model.base_model_prefix not in {k.split('.'					)[0] for k in pt_model_dict.keys()}
			)
			_UpperCAmelCase	 =     (pt_model.base_model_prefix not in flax_state) and (
			    pt_model.base_model_prefix in {k.split('.'					)[0] for k in pt_model_dict.keys()}
			)

			# keep track of unexpected & missing keys
			_UpperCAmelCase	 =     []
			_UpperCAmelCase	 =     set(pt_model_dict.keys()					)

			for flax_key_tuple, flax_tensor in flax_state_dict.items():
						_UpperCAmelCase	 =     flax_key_tuple[0] == pt_model.base_model_prefix
						_UpperCAmelCase	 =     '.'.join((pt_model.base_model_prefix,) + flax_key_tuple					) in pt_model_dict

						# adapt flax_key to prepare for loading from/to base model only
						if load_model_with_head_into_base_model and has_base_model_prefix:
									_UpperCAmelCase	 =     flax_key_tuple[1:]
						elif load_base_model_into_model_with_head and require_base_model_prefix:
									_UpperCAmelCase	 =     (pt_model.base_model_prefix,) + flax_key_tuple

						# rename flax weights to PyTorch format
						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_UpperCAmelCase					) not in pt_model_dict:
									# conv layer
									_UpperCAmelCase	 =     flax_key_tuple[:-1] + ('weight',)
									_UpperCAmelCase	 =     jnp.transpose(_UpperCAmelCase							,     (3, 2, 0, 1)					)
						elif flax_key_tuple[-1] == "kernel" and ".".join(_UpperCAmelCase					) not in pt_model_dict:
									# linear layer
									_UpperCAmelCase	 =     flax_key_tuple[:-1] + ('weight',)
									_UpperCAmelCase	 =     flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
									_UpperCAmelCase	 =     flax_key_tuple[:-1] + ('weight',)

						# adding batch stats from flax batch norm to pt
						elif "mean" in flax_key_tuple[-1]:
									_UpperCAmelCase	 =     flax_key_tuple[:-1] + ('running_mean',)
						elif "var" in flax_key_tuple[-1]:
									_UpperCAmelCase	 =     flax_key_tuple[:-1] + ('running_var',)

						if "batch_stats" in flax_state:
									_UpperCAmelCase	 =     '.'.join(flax_key_tuple[1:]					)  # Remove the params/batch_stats header
						else:
									_UpperCAmelCase	 =     '.'.join(_UpperCAmelCase					)

						# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
						_UpperCAmelCase	 =     {}
						# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
						for key in pt_model_dict:
									_UpperCAmelCase	 =     key.split('.'					)
									_UpperCAmelCase	 =     None
									if key_components[-3::2] == ["parametrizations", "original0"]:
												_UpperCAmelCase	 =     key_components[-2] + '_g'
									elif key_components[-3::2] == ["parametrizations", "original1"]:
												_UpperCAmelCase	 =     key_components[-2] + '_v'
									if name is not None:
												_UpperCAmelCase	 =     key_components[:-3] + [name]
												_UpperCAmelCase	 =     '.'.join(_UpperCAmelCase					)
												_UpperCAmelCase	 =     key

						if flax_key in special_pt_names:
									_UpperCAmelCase	 =     special_pt_names[flax_key]

						if flax_key in pt_model_dict:
									if flax_tensor.shape != pt_model_dict[flax_key].shape:
												raise ValueError(
												    F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
												    F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."					)
									else:
												# add weight to pytorch dict
												_UpperCAmelCase	 =     np.asarray(_UpperCAmelCase					) if not isinstance(_UpperCAmelCase							,     np.ndarray					) else flax_tensor
												_UpperCAmelCase	 =     torch.from_numpy(_UpperCAmelCase					)
												# remove from missing keys
												missing_keys.remove(_UpperCAmelCase					)
						else:
									# weight is not expected by PyTorch model
									unexpected_keys.append(_UpperCAmelCase					)

			pt_model.load_state_dict(_UpperCAmelCase					)

			# re-transform missing_keys to list
			_UpperCAmelCase	 =     list(_UpperCAmelCase					)

			if len(_UpperCAmelCase					) > 0:
						logger.warning(
						    'Some weights of the Flax model were not used when initializing the PyTorch model'
						    F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
						    F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
						    ' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'
						    F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
						    ' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'
						    ' FlaxBertForSequenceClassification model).'					)
			else:
						logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n"					)
			if len(_UpperCAmelCase					) > 0:
						logger.warning(
						    F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
						    F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
						    ' use it for predictions and inference.'					)
			else:
						logger.warning(
						    F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
						    'If your task is similar to the task the model of the checkpoint was trained on, '
						    F"you can already use {pt_model.__class__.__name__} for predictions without further training."					)

			return pt_model





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)
