





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()
snake_case_           =      logging.get_logger(__name__)


snake_case_           =      'https://openaipublic.azureedge.net/jukebox/models/'
snake_case_           =      {
    '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__   (							snake_case_     :  Union[str, Any]	)				->    List[str]:
   if key.endswith('''.model.1.bias'''	) and len(key.split('''.'''	)	) > 10:
      __snake_case								=       key.replace('''.model.1.bias'''	,				'''.conv1d_1.bias'''	)
   elif key.endswith('''.model.1.weight'''	) and len(key.split('''.'''	)	) > 10:
      __snake_case								=       key.replace('''.model.1.weight'''	,				'''.conv1d_1.weight'''	)
   elif key.endswith('''.model.3.bias'''	) and len(key.split('''.'''	)	) > 10:
      __snake_case								=       key.replace('''.model.3.bias'''	,				'''.conv1d_2.bias'''	)
   elif key.endswith('''.model.3.weight'''	) and len(key.split('''.'''	)	) > 10:
      __snake_case								=       key.replace('''.model.3.weight'''	,				'''.conv1d_2.weight'''	)

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

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

   if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
      __snake_case								=       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:
      __snake_case								=       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__   (							snake_case_     :  str	,				snake_case_     :  Optional[Any]	,				snake_case_     :  int	,				snake_case_     :  List[str]	)				->    Optional[Any]:
   __snake_case								=       {}
   import re

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

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

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

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

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

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

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

      elif re_decoder_block_proj_in.fullmatch(snake_case_	):
         __snake_case								=       re_decoder_block_proj_in.match(snake_case_	)
         __snake_case								=       regex_match.groups()
         __snake_case								=       f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
         __snake_case								=       re_decoder_block_proj_in.sub(snake_case_	,				snake_case_	)

      # rename prior cond.model to upsampler.upsample_block and resnet
      elif re_prior_cond_conv_out.fullmatch(snake_case_	):
         __snake_case								=       re_prior_cond_conv_out.match(snake_case_	)
         __snake_case								=       regex_match.groups()
         __snake_case								=       int(groups[1]	) * 2 + int(groups[2]	) - 2
         __snake_case								=       f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
         __snake_case								=       re_prior_cond_conv_out.sub(snake_case_	,				snake_case_	)

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

      elif re_prior_cond_proj_in.fullmatch(snake_case_	):
         __snake_case								=       re_prior_cond_proj_in.match(snake_case_	)
         __snake_case								=       regex_match.groups()
         __snake_case								=       f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
         __snake_case								=       re_prior_cond_proj_in.sub(snake_case_	,				snake_case_	)

      # keep original key
      else:
         __snake_case								=       original_key

      __snake_case								=       replace_key(snake_case_	)

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

      __snake_case								=       original_key
      __snake_case								=       value

   return new_dict






@torch.no_grad()
def lowerCamelCase__   (							snake_case_     :  str=None	,				snake_case_     :  Tuple=None	)				->    Any:
   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'	)[-1]}"""	):
         __snake_case								=       requests.get(f"""{PREFIX}{file}"""	,				allow_redirects=snake_case_	)
         os.makedirs(f"""{pytorch_dump_folder_path}/"""	,				exist_ok=snake_case_	)
         open(f"""{pytorch_dump_folder_path}/{file.split('/'	)[-1]}"""	,				'''wb'''	).write(r.content	)

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

   __snake_case								=       JukeboxConfig.from_pretrained(snake_case_	)
   __snake_case								=       JukeboxModel(snake_case_	)

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

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

      __snake_case								=       '''vqvae''' if i == 0 else f"""priors.{3 - i}"""
      __snake_case								=       fix_jukebox_keys(snake_case_	,				model.state_dict()	,				snake_case_	,				snake_case_	)
      weight_dict.append(snake_case_	)

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

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

   print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""	)
   model.save_pretrained(snake_case_	)

   return weight_dict


if __name__ == "__main__":
    snake_case_           =      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.',
    )
    snake_case_           =      parser.parse_args()
    convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








from __future__ import annotations

import collections
import pprint
from pathlib import Path






def lowerCamelCase__   (							snake_case_     :  str	)				->    str:
   return "".join(sorted(snake_case_	)	)






def lowerCamelCase__   (							snake_case_     :  str	)				->    list[str]:
   return word_by_signature[signature(snake_case_	)]


snake_case_           =      Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
snake_case_           =      sorted({word.strip().lower() for word in data.splitlines()})

snake_case_           =      collections.defaultdict(list)
for word in word_list:
    word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
    snake_case_           =      {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

    with open('anagrams.txt', 'w') as file:
        file.write('all_anagrams = \n ')
        file.write(pprint.pformat(all_anagrams))

