



import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable

import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm

from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule







class     __lowerCAmelCase					(    UpperCamelCase__		):
			UpperCamelCase           =  4_2
			UpperCamelCase           =  jnp.floataa
			UpperCamelCase           =  True



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					List[Any]:



						"""simple docstring"""






						super().setup()
						_UpperCAmelCase	 =     nn.Dense(5				,	dtype=self.dtype)



			def __call__(		self     :						Union[str, Any]				,	*A     :						Optional[Any]				,	**A     :						Optional[Any])     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     super().__call__(*__A				,	**__A)
						_UpperCAmelCase	 =     self.cls(outputs[2])
						return outputs[:2] + (cls_out,)







class     __lowerCAmelCase					(    UpperCamelCase__		):
			UpperCamelCase           =  FlaxBigBirdForNaturalQuestionsModule





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







			'''simple docstring'''
			def cross_entropy(_UpperCAmelCase       :     List[Any]							,     _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     Union[str, Any]=None					):
						_UpperCAmelCase	 =     logits.shape[-1]
						_UpperCAmelCase	 =     (labels[..., None] == jnp.arange(UpperCamelCase__					)[None]).astype('f4'					)
						_UpperCAmelCase	 =     jax.nn.log_softmax(UpperCamelCase__							,     axis=-1					)
						_UpperCAmelCase	 =     -jnp.sum(labels * logits							,     axis=-1					)
						if reduction is not None:
									_UpperCAmelCase	 =     reduction(UpperCamelCase__					)
						return loss

			_UpperCAmelCase	 =     partial(UpperCamelCase__							,     reduction=jnp.mean					)
			_UpperCAmelCase	 =     cross_entropy(UpperCamelCase__							,     UpperCamelCase__					)
			_UpperCAmelCase	 =     cross_entropy(UpperCamelCase__							,     UpperCamelCase__					)
			_UpperCAmelCase	 =     cross_entropy(UpperCamelCase__							,     UpperCamelCase__					)
			return (start_loss + end_loss + pooled_loss) / 3







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  '''google/bigbird-roberta-base'''
			UpperCamelCase           =  3_0_0_0
			UpperCamelCase           =  1_0_5_0_0

			UpperCamelCase           =  1_2_8
			UpperCamelCase           =  3

			UpperCamelCase           =  1
			UpperCamelCase           =  5

			# tx_args
			UpperCamelCase           =  3e-5
			UpperCamelCase           =  0.0
			UpperCamelCase           =  2_0_0_0_0
			UpperCamelCase           =  0.0_095

			UpperCamelCase           =  '''bigbird-roberta-natural-questions'''
			UpperCamelCase           =  '''training-expt'''
			UpperCamelCase           =  '''data/nq-training.jsonl'''
			UpperCamelCase           =  '''data/nq-validation.jsonl'''



			def 					_lowerCamelCase (		self     :						int)     ->					List[str]:



						"""simple docstring"""






						os.makedirs(self.base_dir				,	exist_ok=__A)
						_UpperCAmelCase	 =     os.path.join(self.base_dir				,	self.save_dir)
						_UpperCAmelCase	 =     self.batch_size_per_device * jax.device_count()







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  4_2
			UpperCamelCase           =  4_0_9_6  # no dynamic padding on TPUs



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



						"""simple docstring"""






						_UpperCAmelCase	 =     self.collate_fn(__A)
						_UpperCAmelCase	 =     jax.tree_util.tree_map(__A				,	__A)
						return batch



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



						"""simple docstring"""






						_UpperCAmelCase ,  _UpperCAmelCase	 =     self.fetch_inputs(features['input_ids'])
						_UpperCAmelCase	 =     {
						    'input_ids': jnp.array(__A				,	dtype=jnp.intaa),
						    'attention_mask': jnp.array(__A				,	dtype=jnp.intaa),
						    'start_labels': jnp.array(features['start_token']				,	dtype=jnp.intaa),
						    'end_labels': jnp.array(features['end_token']				,	dtype=jnp.intaa),
						    'pooled_labels': jnp.array(features['category']				,	dtype=jnp.intaa),
						}
						return batch



			def 					_lowerCamelCase (		self     :						List[Any]				,	A     :						list)     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     [self._fetch_inputs(__A) for ids in input_ids]
						return zip(*__A)



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



						"""simple docstring"""






						_UpperCAmelCase	 =     [1 for _ in range(len(__A))]
						while len(__A) < self.max_length:
									input_ids.append(self.pad_id)
									attention_mask.append(0)
						return input_ids, attention_mask





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







			'''simple docstring'''
			if seed is not None:
						_UpperCAmelCase	 =     dataset.shuffle(seed=UpperCamelCase__					)
			for i in range(len(UpperCamelCase__					) // batch_size					):
						_UpperCAmelCase	 =     dataset[i * batch_size : (i + 1) * batch_size]
						yield dict(UpperCamelCase__					)





@partial(jax.pmap							,     axis_name='batch'					)
def 							_A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     int							,     **_UpperCAmelCase       :     Dict					)       ->				List[Any]:







			'''simple docstring'''
			def loss_fn(_UpperCAmelCase       :     Any					):
						_UpperCAmelCase	 =     model_inputs.pop('start_labels'					)
						_UpperCAmelCase	 =     model_inputs.pop('end_labels'					)
						_UpperCAmelCase	 =     model_inputs.pop('pooled_labels'					)

						_UpperCAmelCase	 =     state.apply_fn(**UpperCamelCase__							,     params=UpperCamelCase__							,     dropout_rng=UpperCamelCase__							,     train=UpperCamelCase__					)
						_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     outputs

						return state.loss_fn(
						    UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     )

			_UpperCAmelCase ,  _UpperCAmelCase	 =     jax.random.split(UpperCamelCase__					)
			_UpperCAmelCase	 =     jax.value_and_grad(UpperCamelCase__					)
			_UpperCAmelCase ,  _UpperCAmelCase	 =     grad_fn(state.params					)
			_UpperCAmelCase	 =     jax.lax.pmean({'loss': loss}							,     axis_name='batch'					)
			_UpperCAmelCase	 =     jax.lax.pmean(UpperCamelCase__							,     'batch'					)

			_UpperCAmelCase	 =     state.apply_gradients(grads=UpperCamelCase__					)
			return state, metrics, new_drp_rng





@partial(jax.pmap							,     axis_name='batch'					)
def 							_A			(  _UpperCAmelCase       :     Optional[Any]							,     **_UpperCAmelCase       :     Optional[Any]					)       ->				Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase	 =     model_inputs.pop('start_labels'					)
			_UpperCAmelCase	 =     model_inputs.pop('end_labels'					)
			_UpperCAmelCase	 =     model_inputs.pop('pooled_labels'					)

			_UpperCAmelCase	 =     state.apply_fn(**UpperCamelCase__							,     params=state.params							,     train=UpperCamelCase__					)
			_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     outputs

			_UpperCAmelCase	 =     state.loss_fn(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					)
			_UpperCAmelCase	 =     jax.lax.pmean({'loss': loss}							,     axis_name='batch'					)
			return metrics







class     __lowerCAmelCase					(    train_state.TrainState		):
			UpperCamelCase           =  struct.field(pytree_node=UpperCamelCase__		)







@dataclass
class     __lowerCAmelCase					:
			UpperCamelCase           =  4_2
			UpperCamelCase           =  4_2
			UpperCamelCase           =  4_2
			UpperCamelCase           =  4_2
			UpperCamelCase           =  4_2
			UpperCamelCase           =  4_2
			UpperCamelCase           =  None



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						int				,	A     :						str				,	A     :						Optional[Any]				,	A     :						Union[str, Any]=None)     ->					str:



						"""simple docstring"""






						_UpperCAmelCase	 =     model.params
						_UpperCAmelCase	 =     TrainState.create(
						    apply_fn=model.__call__				,	params=__A				,	tx=__A				,	loss_fn=__A				,	)
						if ckpt_dir is not None:
									_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     restore_checkpoint(__A				,	__A)
									_UpperCAmelCase	 =     {
									    'lr': args.lr,
									    'init_lr': args.init_lr,
									    'warmup_steps': args.warmup_steps,
									    'num_train_steps': num_train_steps,
									    'weight_decay': args.weight_decay,
									}
									_UpperCAmelCase ,  _UpperCAmelCase	 =     build_tx(**__A)
									_UpperCAmelCase	 =     train_state.TrainState(
									    step=__A				,	apply_fn=model.__call__				,	params=__A				,	tx=__A				,	opt_state=__A				,	)
									_UpperCAmelCase	 =     args
									_UpperCAmelCase	 =     data_collator
									_UpperCAmelCase	 =     lr
									_UpperCAmelCase	 =     params
						_UpperCAmelCase	 =     jax_utils.replicate(__A)
						return state



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



						"""simple docstring"""






						_UpperCAmelCase	 =     self.args
						_UpperCAmelCase	 =     len(__A) // args.batch_size

						_UpperCAmelCase	 =     jax.random.PRNGKey(0)
						_UpperCAmelCase	 =     jax.random.split(__A				,	jax.device_count())
						for epoch in range(args.max_epochs):
									_UpperCAmelCase	 =     jnp.array(0				,	dtype=jnp.floataa)
									_UpperCAmelCase	 =     get_batched_dataset(__A				,	args.batch_size				,	seed=__A)
									_UpperCAmelCase	 =     0
									for batch in tqdm(__A				,	total=__A				,	desc=F"Running EPOCH-{epoch}"):
												_UpperCAmelCase	 =     self.data_collator(__A)
												_UpperCAmelCase ,  _UpperCAmelCase ,  _UpperCAmelCase	 =     self.train_step_fn(__A				,	__A				,	**__A)
												running_loss += jax_utils.unreplicate(metrics['loss'])
												i += 1
												if i % args.logging_steps == 0:
															_UpperCAmelCase	 =     jax_utils.unreplicate(state.step)
															_UpperCAmelCase	 =     running_loss.item() / i
															_UpperCAmelCase	 =     self.scheduler_fn(state_step - 1)

															_UpperCAmelCase	 =     self.evaluate(__A				,	__A)
															_UpperCAmelCase	 =     {
															    'step': state_step.item(),
															    'eval_loss': eval_loss.item(),
															    'tr_loss': tr_loss,
															    'lr': lr.item(),
															}
															tqdm.write(str(__A))
															self.logger.log(__A				,	commit=__A)

												if i % args.save_steps == 0:
															self.save_checkpoint(args.save_dir + F"-e{epoch}-s{i}"				,	state=__A)



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



						"""simple docstring"""






						_UpperCAmelCase	 =     get_batched_dataset(__A				,	self.args.batch_size)
						_UpperCAmelCase	 =     len(__A) // self.args.batch_size
						_UpperCAmelCase	 =     jnp.array(0				,	dtype=jnp.floataa)
						_UpperCAmelCase	 =     0
						for batch in tqdm(__A				,	total=__A				,	desc='Evaluating ... '):
									_UpperCAmelCase	 =     self.data_collator(__A)
									_UpperCAmelCase	 =     self.val_step_fn(__A				,	**__A)
									running_loss += jax_utils.unreplicate(metrics['loss'])
									i += 1
						return running_loss / i



			def 					_lowerCamelCase (		self     :						int				,	A     :						int				,	A     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     jax_utils.unreplicate(__A)
						print(F"SAVING CHECKPOINT IN {save_dir}"				,	end=' ... ')
						self.model_save_fn(__A				,	params=state.params)
						with open(os.path.join(__A				,	'opt_state.msgpack')				,	'wb') as f:
									f.write(to_bytes(state.opt_state))
						joblib.dump(self.args				,	os.path.join(__A				,	'args.joblib'))
						joblib.dump(self.data_collator				,	os.path.join(__A				,	'data_collator.joblib'))
						with open(os.path.join(__A				,	'training_state.json')				,	'w') as f:
									json.dump({'step': state.step.item()}				,	__A)
						print('DONE')





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







			'''simple docstring'''
			print(F"RESTORING CHECKPOINT FROM {save_dir}"							,     end=' ... '					)
			with open(os.path.join(UpperCamelCase__							,     'flax_model.msgpack'					)							,     'rb'					) as f:
						_UpperCAmelCase	 =     from_bytes(state.params							,     f.read()					)

			with open(os.path.join(UpperCamelCase__							,     'opt_state.msgpack'					)							,     'rb'					) as f:
						_UpperCAmelCase	 =     from_bytes(state.opt_state							,     f.read()					)

			_UpperCAmelCase	 =     joblib.load(os.path.join(UpperCamelCase__							,     'args.joblib'					)					)
			_UpperCAmelCase	 =     joblib.load(os.path.join(UpperCamelCase__							,     'data_collator.joblib'					)					)

			with open(os.path.join(UpperCamelCase__							,     'training_state.json'					)							,     'r'					) as f:
						_UpperCAmelCase	 =     json.load(UpperCamelCase__					)
			_UpperCAmelCase	 =     training_state['step']

			print('DONE'					)
			return params, opt_state, step, args, data_collator





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







			'''simple docstring'''
			_UpperCAmelCase	 =     num_train_steps - warmup_steps
			_UpperCAmelCase	 =     optax.linear_schedule(init_value=UpperCamelCase__							,     end_value=UpperCamelCase__							,     transition_steps=UpperCamelCase__					)
			_UpperCAmelCase	 =     optax.linear_schedule(init_value=UpperCamelCase__							,     end_value=1E-7							,     transition_steps=UpperCamelCase__					)
			_UpperCAmelCase	 =     optax.join_schedules(schedules=[warmup_fn, decay_fn]							,     boundaries=[warmup_steps]					)
			return lr





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







			'''simple docstring'''
			def weight_decay_mask(_UpperCAmelCase       :     Optional[Any]					):
						_UpperCAmelCase	 =     traverse_util.flatten_dict(UpperCamelCase__					)
						_UpperCAmelCase	 =     {k: (v[-1] != 'bias' and v[-2:] != ('LayerNorm', 'scale')) for k, v in params.items()}
						return traverse_util.unflatten_dict(UpperCamelCase__					)

			_UpperCAmelCase	 =     scheduler_fn(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					)

			_UpperCAmelCase	 =     optax.adamw(learning_rate=UpperCamelCase__							,     weight_decay=UpperCamelCase__							,     mask=UpperCamelCase__					)
			return tx, lr





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)
