

'''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 						_A	(       A__ ):






 """simple docstring"""



 if key.endswith('''.model.1.bias''' ) and len(key.split('''.''' ) ) > 10:
  __lowercase				    =					key.replace('''.model.1.bias'''	,							'''.conv1d_1.bias''' )
 elif key.endswith('''.model.1.weight''' ) and len(key.split('''.''' ) ) > 10:
  __lowercase				    =					key.replace('''.model.1.weight'''	,							'''.conv1d_1.weight''' )
 elif key.endswith('''.model.3.bias''' ) and len(key.split('''.''' ) ) > 10:
  __lowercase				    =					key.replace('''.model.3.bias'''	,							'''.conv1d_2.bias''' )
 elif key.endswith('''.model.3.weight''' ) and len(key.split('''.''' ) ) > 10:
  __lowercase				    =					key.replace('''.model.3.weight'''	,							'''.conv1d_2.weight''' )

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

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

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __lowercase				    =					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:
  __lowercase				    =					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	(       A__	,							A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					{}
 import re

 __lowercase				    =					re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
 __lowercase				    =					re.compile(
     R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
 __lowercase				    =					re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )

 __lowercase				    =					re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
 __lowercase				    =					re.compile(
     R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
 __lowercase				    =					re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )

 __lowercase				    =					re.compile(R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)''' )
 __lowercase				    =					re.compile(
     R'''conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
 __lowercase				    =					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__ ):
   __lowercase				    =					re_encoder_block_conv_in.match(A__ )
   __lowercase				    =					regex_match.groups()
   __lowercase				    =					int(groups[2] ) * 2 + int(groups[3] )
   __lowercase				    =					F"encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"
   __lowercase				    =					re_encoder_block_conv_in.sub(A__	,							A__ )

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

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

  # rename vqvae.decoder keys
  elif re_decoder_block_conv_out.fullmatch(A__ ):
   __lowercase				    =					re_decoder_block_conv_out.match(A__ )
   __lowercase				    =					regex_match.groups()
   __lowercase				    =					int(groups[2] ) * 2 + int(groups[3] ) - 2
   __lowercase				    =					F"decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"
   __lowercase				    =					re_decoder_block_conv_out.sub(A__	,							A__ )

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

  elif re_decoder_block_proj_in.fullmatch(A__ ):
   __lowercase				    =					re_decoder_block_proj_in.match(A__ )
   __lowercase				    =					regex_match.groups()
   __lowercase				    =					F"decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"
   __lowercase				    =					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__ ):
   __lowercase				    =					re_prior_cond_conv_out.match(A__ )
   __lowercase				    =					regex_match.groups()
   __lowercase				    =					int(groups[1] ) * 2 + int(groups[2] ) - 2
   __lowercase				    =					F"conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"
   __lowercase				    =					re_prior_cond_conv_out.sub(A__	,							A__ )

  elif re_prior_cond_resnet.fullmatch(A__ ):
   __lowercase				    =					re_prior_cond_resnet.match(A__ )
   __lowercase				    =					regex_match.groups()
   __lowercase				    =					int(groups[1] ) * 2 + int(groups[2] ) - 2
   __lowercase				    =					{'''1''': 1, '''3''': 2}[groups[-2]]
   __lowercase				    =					F"conditioner_blocks.upsampler.upsample_block.{block_index}."
   __lowercase				    =					F"resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"
   __lowercase				    =					prefix + resnet_block
   __lowercase				    =					re_prior_cond_resnet.sub(A__	,							A__ )

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

  # keep original key
  else:
   __lowercase				    =					original_key

  __lowercase				    =					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:
   __lowercase				    =					model_state_dict[F"{key_prefix}.{key}"]
   print(F"{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match" )
   __lowercase				    =					original_key

  __lowercase				    =					original_key
  __lowercase				    =					value

 return new_dict


@torch.no_grad()
def 						_A	(       A__=None	,							A__=None ):






 """simple docstring"""



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

 __lowercase				    =					MODEL_MAPPING[model_name.split('''/''' )[-1]]

 __lowercase				    =					JukeboxConfig.from_pretrained(A__ )
 __lowercase				    =					JukeboxModel(A__ )

 __lowercase				    =					[]
 __lowercase				    =					{}
 for i, dict_name in enumerate(A__ ):
  __lowercase				    =					torch.load(F"{pytorch_dump_folder_path}/{dict_name.split('/' )[-1]}" )['''model''']

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

  __lowercase				    =					'''vqvae''' if i == 0 else F"priors.{3 - i}"
  __lowercase				    =					fix_jukebox_keys(A__	,							model.state_dict()	,							A__	,							A__ )
  weight_dict.append(A__ )

 __lowercase				    =					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__":
 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)









lowercase    				=   {
    "A": ".-", "B": "-...", "C": "-.-.", "D": "-..", "E": ".", "F": "..-.", "G": "--.",
    "H": "....", "I": "..", "J": ".---", "K": "-.-", "L": ".-..", "M": "--", "N": "-.",
    "O": "---", "P": ".--.", "Q": "--.-", "R": ".-.", "S": "...", "T": "-", "U": "..-",
    "V": "...-", "W": ".--", "X": "-..-", "Y": "-.--", "Z": "--..", "1": ".----",
    "2": "..---", "3": "...--", "4": "....-", "5": ".....", "6": "-....", "7": "--...",
    "8": "---..", "9": "----.", "0": "-----", "&": ".-...", "@": ".--.-.",
    ":": "---...", ",": "--..--", ".": ".-.-.-", "'": ".----.", "\"": ".-..-.",
    "?": "..--..", "/": "-..-.", "=": "-...-", "+": ".-.-.", "-": "-....-",
    "(": "-.--.", ")": "-.--.-", "!": "-.-.--", " ": "/"
}  # Exclamation mark is not in ITU-R recommendation
# fmt: on
lowercase    				=   {value: key for key, value in MORSE_CODE_DICT.items()}





def 		__UpperCAmelCase     (	a_):
							return " ".join(MORSE_CODE_DICT[char] for char in message.upper())





def 		__UpperCAmelCase     (	a_):
							return "".join(REVERSE_DICT[char] for char in message.split())





def 		__UpperCAmelCase     (	):
							snake_case_           =				'Morse code here!'
							print(a_)
							snake_case_           =				encrypt(a_)
							print(a_)
							snake_case_           =				decrypt(a_)
							print(a_)


if __name__ == "__main__":
	main()





