

"""simple docstring"""






import random
import unittest

from torch.utils.data import BatchSampler, DataLoader, IterableDataset

from accelerate import Accelerator
from accelerate.data_loader import (
    BatchSamplerShard,
    DataLoaderDispatcher,
    DataLoaderShard,
    IterableDatasetShard,
    SkipBatchSampler,
    SkipDataLoader,
    skip_first_batches,
)







class 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A=0.0_1    ,_A=1000      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    p_stop
      _lowerCAmelCase       :   str  =    max_length







  def __iter__(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    0
      _lowerCAmelCase       :   List[Any]  =    False
      while not stop and count < self.max_length:
          yield count
          count += 1
          _lowerCAmelCase       :   Any  =    random.random() < self.p_stop







class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A=False    ,_A=True      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    [
          BatchSamplerShard(_A    ,2    ,_A    ,split_batches=_A    ,even_batches=_A      )
          for i in range(2      )
      ]
      _lowerCAmelCase       :   Optional[int]  =    [list(_A      ) for batch_sampler_shard in batch_sampler_shards]
      if not split_batches:
          self.assertListEqual([len(_A      ) for shard in batch_sampler_shards]    ,[len(_A      ) for e in expected]      )
      self.assertListEqual(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    BatchSampler(range(24      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   int  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
      ]
      self.check_batch_sampler_shards(_A    ,_A      )

      _lowerCAmelCase       :   int  =    BatchSampler(range(24      )    ,batch_size=3    ,drop_last=_A      )
      # Expected shouldn't change
      self.check_batch_sampler_shards(_A    ,_A      )

      # Check the shards when the dataset is a round multiple of batch size but not total batch size.
      _lowerCAmelCase       :   Union[str, Any]  =    BatchSampler(range(21      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   int  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17], [0, 1, 2]],
      ]
      self.check_batch_sampler_shards(_A    ,_A      )

      _lowerCAmelCase       :   Dict  =    BatchSampler(range(21      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   List[str]  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A      )

      # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
      # num_processes batch.
      _lowerCAmelCase       :   Dict  =    BatchSampler(range(22      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   List[Any]  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 0, 1]],
      ]
      self.check_batch_sampler_shards(_A    ,_A      )

      _lowerCAmelCase       :   int  =    BatchSampler(range(22      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[int]  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A      )

      # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
      # num_processes batch.
      _lowerCAmelCase       :   List[str]  =    BatchSampler(range(20      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[int]  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 0]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17], [1, 2, 3]],
      ]
      self.check_batch_sampler_shards(_A    ,_A      )

      _lowerCAmelCase       :   str  =    BatchSampler(range(20      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Dict  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A      )

      # Check the shards when the dataset is very small.
      _lowerCAmelCase       :   int  =    BatchSampler(range(2      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   int  =    [[[0, 1, 0]], [[1, 0, 1]]]
      self.check_batch_sampler_shards(_A    ,_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    BatchSampler(range(2      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   List[str]  =    [[], []]
      self.check_batch_sampler_shards(_A    ,_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    BatchSampler(range(24      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[int]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

      _lowerCAmelCase       :   List[Any]  =    BatchSampler(range(24      )    ,batch_size=4    ,drop_last=_A      )
      # Expected shouldn't change
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

      # Check the shards when the dataset is not a round multiple of batch size.
      _lowerCAmelCase       :   Tuple  =    BatchSampler(range(22      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [0, 1]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

      _lowerCAmelCase       :   Optional[Any]  =    BatchSampler(range(22      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[int]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

      # Check the shards when the dataset is not a round multiple of batch size or num_processes.
      _lowerCAmelCase       :   Tuple  =    BatchSampler(range(21      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 0]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [1, 2]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

      _lowerCAmelCase       :   int  =    BatchSampler(range(21      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   int  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

      # Check the shards when the dataset is very small.
      _lowerCAmelCase       :   Tuple  =    BatchSampler(range(2      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   List[str]  =    [[[0, 1]], [[0, 1]]]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

      _lowerCAmelCase       :   List[str]  =    BatchSampler(range(2      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Tuple  =    [[], []]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    BatchSampler(range(24      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Any  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21, 22, 23]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      _lowerCAmelCase       :   Dict  =    BatchSampler(range(24      )    ,batch_size=3    ,drop_last=_A      )
      # Expected shouldn't change
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      # Check the shards when the dataset is a round multiple of batch size but not total batch size.
      _lowerCAmelCase       :   Tuple  =    BatchSampler(range(21      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   int  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      _lowerCAmelCase       :   Optional[int]  =    BatchSampler(range(21      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   str  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      # Check the shards when the dataset is not a round multiple of batch size but has a multiple of
      # num_processes batch.
      _lowerCAmelCase       :   str  =    BatchSampler(range(22      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Tuple  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19, 20]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17], [21]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    BatchSampler(range(22      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Dict  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      # Check the shards when the dataset is not a round multiple of batch size but and has not a multiple of
      # num_processes batch.
      _lowerCAmelCase       :   Tuple  =    BatchSampler(range(20      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   str  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14], [18, 19]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      _lowerCAmelCase       :   List[Any]  =    BatchSampler(range(20      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Dict  =    [
          [[0, 1, 2], [6, 7, 8], [12, 13, 14]],
          [[3, 4, 5], [9, 10, 11], [15, 16, 17]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      # Check the shards when the dataset is very small.
      _lowerCAmelCase       :   int  =    BatchSampler(range(2      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   Dict  =    [[[0, 1]], []]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

      _lowerCAmelCase       :   str  =    BatchSampler(range(2      )    ,batch_size=3    ,drop_last=_A      )
      _lowerCAmelCase       :   str  =    [[], []]
      self.check_batch_sampler_shards(_A    ,_A    ,even_batches=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    BatchSampler(range(24      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[int]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

      _lowerCAmelCase       :   str  =    BatchSampler(range(24      )    ,batch_size=4    ,drop_last=_A      )
      # Expected shouldn't change
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

      # Check the shards when the dataset is not a round multiple of batch size.
      _lowerCAmelCase       :   List[str]  =    BatchSampler(range(22      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[Any]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    BatchSampler(range(22      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   List[Any]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

      # Check the shards when the dataset is not a round multiple of batch size or num_processes.
      _lowerCAmelCase       :   int  =    BatchSampler(range(21      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Any  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

      _lowerCAmelCase       :   Any  =    BatchSampler(range(21      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Optional[int]  =    [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

      # Check the shards when the dataset is very small.
      _lowerCAmelCase       :   Dict  =    BatchSampler(range(2      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   int  =    [[[0, 1]], []]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

      _lowerCAmelCase       :   Dict  =    BatchSampler(range(2      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   Tuple  =    [[], []]
      self.check_batch_sampler_shards(_A    ,_A    ,split_batches=_A    ,even_batches=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    [[0, 1, 2], [3, 4], [5, 6, 7, 8], [9, 10, 11], [12, 13]]
      _lowerCAmelCase       :   Tuple  =    [BatchSamplerShard(_A    ,2    ,_A    ,even_batches=_A      ) for i in range(2      )]

      self.assertEqual(len(batch_sampler_shards[0]      )    ,3      )
      self.assertEqual(len(batch_sampler_shards[1]      )    ,2      )

      self.assertListEqual(list(batch_sampler_shards[0]      )    ,[[0, 1, 2], [5, 6, 7, 8], [12, 13]]      )
      self.assertListEqual(list(batch_sampler_shards[1]      )    ,[[3, 4], [9, 10, 11]]      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A=False    ,_A=2    ,_A=False      ):
      '''simple docstring'''

      random.seed(_A      )
      _lowerCAmelCase       :   Tuple  =    list(_A      )

      _lowerCAmelCase       :   List[str]  =    [
          IterableDatasetShard(
              _A    ,batch_size=_A    ,drop_last=_A    ,num_processes=_A    ,process_index=_A    ,split_batches=_A    ,)
          for i in range(_A      )
      ]
      _lowerCAmelCase       :   Any  =    []
      for iterable_dataset_shard in iterable_dataset_shards:
          # Since our random iterable dataset will be... random... we need to use a seed to get reproducible results.
          random.seed(_A      )
          iterable_dataset_lists.append(list(_A      )      )

      _lowerCAmelCase       :   Union[str, Any]  =    batch_size // num_processes if split_batches else batch_size
      # All iterable dataset shard should have the same length, a round multiple of shard_batch_size
      _lowerCAmelCase       :   Dict  =    iterable_dataset_lists[0]
      for l in iterable_dataset_lists[1:]:
          self.assertEqual(len(_A      )    ,len(_A      )      )
          self.assertTrue(len(_A      ) % shard_batch_size == 0      )

      _lowerCAmelCase       :   Union[str, Any]  =    []
      for idx in range(0    ,len(_A      )    ,_A      ):
          for l in iterable_dataset_lists:
              observed += l[idx : idx + shard_batch_size]

      if not drop_last:
          while len(_A      ) < len(_A      ):
              reference += reference
      self.assertListEqual(_A    ,reference[: len(_A      )]      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    42
      _lowerCAmelCase       :   Union[str, Any]  =    RandomIterableDataset()

      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )
      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )
      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )
      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )

      # Edge case with a very small dataset
      _lowerCAmelCase       :   Optional[int]  =    RandomIterableDataset(max_length=2      )

      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )
      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )
      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )
      self.check_iterable_dataset_shards(_A    ,_A    ,batch_size=4    ,drop_last=_A    ,split_batches=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Dict  =    BatchSampler(range(16      )    ,batch_size=4    ,drop_last=_A      )
      _lowerCAmelCase       :   int  =    SkipBatchSampler(_A    ,2      )
      self.assertListEqual(list(_A      )    ,[[8, 9, 10, 11], [12, 13, 14, 15]]      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    SkipDataLoader(list(range(16      )      )    ,batch_size=4    ,skip_batches=2      )
      self.assertListEqual([t.tolist() for t in dataloader]    ,[[8, 9, 10, 11], [12, 13, 14, 15]]      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    DataLoader(list(range(16      )      )    ,batch_size=4      )
      _lowerCAmelCase       :   int  =    skip_first_batches(_A    ,num_batches=2      )
      self.assertListEqual([t.tolist() for t in new_dataloader]    ,[[8, 9, 10, 11], [12, 13, 14, 15]]      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    DataLoaderShard(list(range(16      )      )    ,batch_size=4      )
      for idx, _ in enumerate(_A      ):
          self.assertEqual(dataloader.end_of_dataloader    ,idx == 3      )

      # Test it also works on the second iteration
      for idx, _ in enumerate(_A      ):
          self.assertEqual(dataloader.end_of_dataloader    ,idx == 3      )







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      Accelerator()
      _lowerCAmelCase       :   Tuple  =    DataLoaderDispatcher(range(16      )    ,batch_size=4      )
      for idx, _ in enumerate(_A      ):
          self.assertEqual(dataloader.end_of_dataloader    ,idx == 3      )

      # Test it also works on the second iteration
      for idx, _ in enumerate(_A      ):
          self.assertEqual(dataloader.end_of_dataloader    ,idx == 3      )








"""simple docstring"""






import argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase						=		logging.get_logger(__name__)


_lowerCAmelCase						=		"""https://openaipublic.azureedge.net/jukebox/models/"""
_lowerCAmelCase						=		{
    """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 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if key.endswith('.model.1.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.bias' ,   '.conv1d_1.bias'   )
    elif key.endswith('.model.1.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.weight' ,   '.conv1d_1.weight'   )
    elif key.endswith('.model.3.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   int  =    key.replace('.model.3.bias' ,   '.conv1d_2.bias'   )
    elif key.endswith('.model.3.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Tuple  =    key.replace('.model.3.weight' ,   '.conv1d_2.weight'   )

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

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

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        _lowerCAmelCase       :   Optional[Any]  =    key.replace('.emb.' ,   '.'   )

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

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

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

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

    return key

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {}
    import re

    _lowerCAmelCase       :   Optional[Any]  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Optional[int]  =    re.compile(
        R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Union[str, Any]  =    re.compile(
        R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Optional[int]  =    re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(
        R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   List[str]  =    re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'   )

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

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

        elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   str  =    re_encoder_block_proj_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
            _lowerCAmelCase       :   Any  =    re_encoder_block_proj_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Optional[int]  =    re_decoder_block_conv_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            _lowerCAmelCase       :   Dict  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_decoder_block_conv_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

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

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Any  =    re_decoder_block_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   str  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   str  =    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   ):
            _lowerCAmelCase       :   List[Any]  =    re_prior_cond_conv_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Any  =    regex_match.groups()
            _lowerCAmelCase       :   Optional[int]  =    int(groups[1]   ) * 2 + int(groups[2]   ) - 2
            _lowerCAmelCase       :   Any  =    f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   List[str]  =    re_prior_cond_conv_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

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

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[str]  =    regex_match.groups()
            _lowerCAmelCase       :   List[Any]  =    f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

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

        _lowerCAmelCase       :   List[Any]  =    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:
            _lowerCAmelCase       :   Dict  =    model_state_dict[f"""{key_prefix}.{key}"""]
            print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""   )
            _lowerCAmelCase       :   Optional[int]  =    original_key

        _lowerCAmelCase       :   Union[str, Any]  =    original_key
        _lowerCAmelCase       :   Optional[Any]  =    value

    return new_dict

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase=None ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'   )[-1]}"""   ):
            _lowerCAmelCase       :   str  =    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   )

    _lowerCAmelCase       :   Union[str, Any]  =    MODEL_MAPPING[model_name.split('/'   )[-1]]

    _lowerCAmelCase       :   Optional[Any]  =    JukeboxConfig.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    JukeboxModel(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    _lowerCAmelCase       :   Any  =    {}
    for i, dict_name in enumerate(_lowerCamelCase   ):
        _lowerCAmelCase       :   Union[str, Any]  =    torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'   )[-1]}"""   )['model']

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

        _lowerCAmelCase       :   List[str]  =    'vqvae' if i == 0 else f"""priors.{3 - i}"""
        _lowerCAmelCase       :   Tuple  =    fix_jukebox_keys(_lowerCamelCase ,   model.state_dict() ,   _lowerCamelCase ,   _lowerCamelCase   )
        weight_dict.append(_lowerCamelCase   )

    _lowerCAmelCase       :   List[Any]  =    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__":
 _lowerCAmelCase						=		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.""",
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





