


'''simple docstring'''






from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt







def     UpperCamelCase_      (   A__ :       Sequence[float]							,   A__ :       int							,   A__ :       int ):
		'''simple docstring'''





		if not arr:
				return None, None, 0
		if low == high:
				return low, high, arr[low]

		lowerCAmelCase_      :				Any      =       (low + high) // 2
		lowerCAmelCase_      :				Union[str, Any]      =       max_subarray(A__							,   A__							,   A__ )
		lowerCAmelCase_      :				Optional[Any]      =       max_subarray(A__							,   mid + 1							,   A__ )
		lowerCAmelCase_      :				int      =       max_cross_sum(A__							,   A__							,   A__							,   A__ )
		if left_sum >= right_sum and left_sum >= cross_sum:
				return left_low, left_high, left_sum
		elif right_sum >= left_sum and right_sum >= cross_sum:
				return right_low, right_high, right_sum
		return cross_left, cross_right, cross_sum







def     UpperCamelCase_      (   A__ :       Sequence[float]							,   A__ :       int							,   A__ :       int							,   A__ :       int ):
		'''simple docstring'''





		lowerCAmelCase_      :				Any      =       float("""-inf""" ), -1
		lowerCAmelCase_      :				Any      =       float("""-inf""" ), -1

		lowerCAmelCase_      :				int | float      =       0
		for i in range(A__							,   low - 1							,   -1 ):
				summ += arr[i]
				if summ > left_sum:
						lowerCAmelCase_      :				Any      =       summ
						lowerCAmelCase_      :				str      =       i

		lowerCAmelCase_      :				List[str]      =       0
		for i in range(mid + 1							,   high + 1 ):
				summ += arr[i]
				if summ > right_sum:
						lowerCAmelCase_      :				List[Any]      =       summ
						lowerCAmelCase_      :				Tuple      =       i

		return max_left, max_right, (left_sum + right_sum)







def     UpperCamelCase_      (   A__ :       int ):
		'''simple docstring'''





		lowerCAmelCase_      :				Optional[int]      =       [randint(1							,   A__ ) for _ in range(A__ )]
		lowerCAmelCase_      :				List[str]      =       time.time()
		max_subarray(A__							,   0							,   input_size - 1 )
		lowerCAmelCase_      :				str      =       time.time()
		return end - start







def     UpperCamelCase_      (   ):
		'''simple docstring'''





		lowerCAmelCase_      :				str      =       [10, 1_00, 10_00, 1_00_00, 5_00_00, 10_00_00, 20_00_00, 30_00_00, 40_00_00, 50_00_00]
		lowerCAmelCase_      :				Tuple      =       [time_max_subarray(A__ ) for input_size in input_sizes]
		print("""No of Inputs\t\tTime Taken""" )
		for input_size, runtime in zip(A__							,   A__ ):
				print(A__							,   """\t\t"""							,   A__ )
		plt.plot(A__							,   A__ )
		plt.xlabel("""Number of Inputs""" )
		plt.ylabel("""Time taken in seconds""" )
		plt.show()


if __name__ == "__main__":
		from doctest import testmod

		testmod()







'''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()
__A :  List[str]    							=				logging.get_logger(__name__)


__A :  str    							=				"https://openaipublic.azureedge.net/jukebox/models/"
__A :  Any    							=				{
    "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     UpperCamelCase_      (   A__ :       int ):
  '''simple docstring'''





  if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.1.bias"""							,   """.conv1d_1.bias""" )
  elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.1.weight"""							,   """.conv1d_1.weight""" )
  elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				Union[str, Any]      =       key.replace(""".model.3.bias"""							,   """.conv1d_2.bias""" )
  elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 10:
    lowerCAmelCase_      :				int      =       key.replace(""".model.3.weight"""							,   """.conv1d_2.weight""" )

  if "conditioner_blocks.0." in key:
    lowerCAmelCase_      :				Any      =       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_      :				int      =       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_      :				List[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     UpperCamelCase_      (   A__ :       Dict							,   A__ :       Optional[Any]							,   A__ :       Tuple							,   A__ :       Any ):
  '''simple docstring'''





  lowerCAmelCase_      :				Union[str, Any]      =       {}
  import re

  lowerCAmelCase_      :				Union[str, Any]      =       re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
  lowerCAmelCase_      :				str      =       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_      :				int      =       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
  lowerCAmelCase_      :				Optional[int]      =       re.compile(
      R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
  lowerCAmelCase_      :				Union[str, Any]      =       re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )

  lowerCAmelCase_      :				Any      =       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_      :				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(A__ ):
      lowerCAmelCase_      :				Dict      =       re_encoder_block_conv_in.match(A__ )
      lowerCAmelCase_      :				Optional[int]      =       regex_match.groups()
      lowerCAmelCase_      :				Tuple      =       int(groups[2] ) * 2 + int(groups[3] )
      lowerCAmelCase_      :				Optional[Any]      =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}'
      lowerCAmelCase_      :				List[str]      =       re_encoder_block_conv_in.sub(A__							,   A__ )

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

    elif re_encoder_block_proj_out.fullmatch(A__ ):
      lowerCAmelCase_      :				int      =       re_encoder_block_proj_out.match(A__ )
      lowerCAmelCase_      :				Tuple      =       regex_match.groups()
      lowerCAmelCase_      :				Optional[Any]      =       f'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
      lowerCAmelCase_      :				str      =       re_encoder_block_proj_out.sub(A__							,   A__ )

    # rename vqvae.decoder keys
    elif re_decoder_block_conv_out.fullmatch(A__ ):
      lowerCAmelCase_      :				List[Any]      =       re_decoder_block_conv_out.match(A__ )
      lowerCAmelCase_      :				Tuple      =       regex_match.groups()
      lowerCAmelCase_      :				str      =       int(groups[2] ) * 2 + int(groups[3] ) - 2
      lowerCAmelCase_      :				Any      =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'
      lowerCAmelCase_      :				Tuple      =       re_decoder_block_conv_out.sub(A__							,   A__ )

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

    elif re_decoder_block_proj_in.fullmatch(A__ ):
      lowerCAmelCase_      :				str      =       re_decoder_block_proj_in.match(A__ )
      lowerCAmelCase_      :				Optional[int]      =       regex_match.groups()
      lowerCAmelCase_      :				str      =       f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
      lowerCAmelCase_      :				Union[str, Any]      =       re_decoder_block_proj_in.sub(A__							,   A__ )

    # rename prior cond.model to upsampler.upsample_block and resnet
    elif re_prior_cond_conv_out.fullmatch(A__ ):
      lowerCAmelCase_      :				Optional[Any]      =       re_prior_cond_conv_out.match(A__ )
      lowerCAmelCase_      :				Union[str, Any]      =       regex_match.groups()
      lowerCAmelCase_      :				Union[str, Any]      =       int(groups[1] ) * 2 + int(groups[2] ) - 2
      lowerCAmelCase_      :				Optional[int]      =       f'conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'
      lowerCAmelCase_      :				Dict      =       re_prior_cond_conv_out.sub(A__							,   A__ )

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

    elif re_prior_cond_proj_in.fullmatch(A__ ):
      lowerCAmelCase_      :				List[str]      =       re_prior_cond_proj_in.match(A__ )
      lowerCAmelCase_      :				Optional[Any]      =       regex_match.groups()
      lowerCAmelCase_      :				Any      =       f'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
      lowerCAmelCase_      :				List[str]      =       re_prior_cond_proj_in.sub(A__							,   A__ )

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

    lowerCAmelCase_      :				Optional[Any]      =       replace_key(A__ )

    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_      :				str      =       original_key

    lowerCAmelCase_      :				Dict      =       original_key
    lowerCAmelCase_      :				Optional[int]      =       value

  return new_dict







@torch.no_grad()
def     UpperCamelCase_      (   A__ :       Optional[Any]=None							,   A__ :       str=None ):
  '''simple docstring'''





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

  lowerCAmelCase_      :				Optional[int]      =       MODEL_MAPPING[model_name.split("""/""" )[-1]]

  lowerCAmelCase_      :				List[str]      =       JukeboxConfig.from_pretrained(A__ )
  lowerCAmelCase_      :				Dict      =       JukeboxModel(A__ )

  lowerCAmelCase_      :				Optional[Any]      =       []
  lowerCAmelCase_      :				Dict      =       {}
  for i, dict_name in enumerate(A__ ):
    lowerCAmelCase_      :				List[Any]      =       torch.load(f'{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}' )["""model"""]

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

    lowerCAmelCase_      :				Union[str, Any]      =       """vqvae""" if i == 0 else f'priors.{3 - i}'
    lowerCAmelCase_      :				str      =       fix_jukebox_keys(A__							,   model.state_dict()							,   A__							,   A__ )
    weight_dict.append(A__ )

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

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

  print(f'Saving model {model_name} to {pytorch_dump_folder_path}' )
  model.save_pretrained(A__ )

  return weight_dict


if __name__ == "__main__":
  __A :  str    							=				argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--model_name",
      default="jukebox-5b-lyrics",
      type=str,
      help="Name of the model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="jukebox-5b-lyrics-converted",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  __A :  Optional[Any]    							=				parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



