





'''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()
_lowercase       						=			logging.get_logger(__name__)


_lowercase       						=			"""https://openaipublic.azureedge.net/jukebox/models/"""
_lowercase       						=			{
    """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    : Tuple     ) ->int:
     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    __UpperCamelCase	(	a    : Tuple					,   a    : List[str]					,   a    : Optional[Any]					,   a    : int     ) ->Union[str, 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(a     ):
               snake_case				=			re_encoder_block_conv_in.match(a     )
               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(a					,   a     )

          elif re_encoder_block_resnet.fullmatch(a     ):
               snake_case				=			re_encoder_block_resnet.match(a     )
               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(a					,   a     )

          elif re_encoder_block_proj_out.fullmatch(a     ):
               snake_case				=			re_encoder_block_proj_out.match(a     )
               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(a					,   a     )

          # rename vqvae.decoder keys
          elif re_decoder_block_conv_out.fullmatch(a     ):
               snake_case				=			re_decoder_block_conv_out.match(a     )
               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(a					,   a     )

          elif re_decoder_block_resnet.fullmatch(a     ):
               snake_case				=			re_decoder_block_resnet.match(a     )
               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(a					,   a     )

          elif re_decoder_block_proj_in.fullmatch(a     ):
               snake_case				=			re_decoder_block_proj_in.match(a     )
               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(a					,   a     )

          # rename prior cond.model to upsampler.upsample_block and resnet
          elif re_prior_cond_conv_out.fullmatch(a     ):
               snake_case				=			re_prior_cond_conv_out.match(a     )
               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(a					,   a     )

          elif re_prior_cond_resnet.fullmatch(a     ):
               snake_case				=			re_prior_cond_resnet.match(a     )
               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(a					,   a     )

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

          # keep original key
          else:
               snake_case				=			original_key

          snake_case				=			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:
               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    __UpperCamelCase	(	a    : List[str]=None					,   a    : Tuple=None     ) ->Tuple:
     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=a     )
               os.makedirs(f"""{pytorch_dump_folder_path}/"""					,   exist_ok=a     )
               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(a     )
     snake_case				=			JukeboxModel(a     )

     snake_case				=			[]
     snake_case				=			{}
     for i, dict_name in enumerate(a     ):
          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(a					,   model.state_dict()					,   a					,   a     )
          weight_dict.append(a     )

     snake_case				=			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__":
   _lowercase       						=			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.',
   )
   _lowercase       						=			parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Dict:
 snake_case				=			[
     '''encoder.version''',
     '''decoder.version''',
     '''model.encoder.version''',
     '''model.decoder.version''',
     '''decoder.output_projection.weight''',
     '''_float_tensor''',
     '''encoder.embed_positions._float_tensor''',
     '''decoder.embed_positions._float_tensor''',
 ]
 for k in ignore_keys:
  state_dict.pop(a					,   a     )

def    __UpperCamelCase	(	a    : Optional[Any]     ) ->int:
 snake_case				=			list(s_dict.keys()     )
 for key in keys:
  if "transformer_layers" in key:
   snake_case				=			s_dict.pop(a     )
  elif "subsample" in key:
   snake_case				=			s_dict.pop(a     )

def    __UpperCamelCase	(	a    : Optional[int]     ) ->Optional[int]:
 snake_case				,		snake_case				=			emb.weight.shape
 snake_case				=			nn.Linear(a					,   a					,   bias=a     )
 snake_case				=			emb.weight.data
 return lin_layer

def    __UpperCamelCase	(	a    : Any					,   a    : Tuple     ) ->Tuple:
 snake_case				=			torch.load(a					,   map_location='''cpu'''     )
 snake_case				=			mam_aaa['''args''']
 snake_case				=			mam_aaa['''model''']
 snake_case				=			state_dict['''decoder.output_projection.weight''']

 remove_ignore_keys_(a     )
 rename_keys(a     )

 snake_case				=			state_dict['''decoder.embed_tokens.weight'''].shape[0]

 snake_case				=			args.share_decoder_input_output_embed

 snake_case				=			[int(a     ) for i in args.conv_kernel_sizes.split(''','''     )]
 snake_case				=			SpeechaTextConfig(
     vocab_size=a					,   max_source_positions=args.max_source_positions					,   max_target_positions=args.max_target_positions					,   encoder_layers=args.encoder_layers					,   decoder_layers=args.decoder_layers					,   encoder_attention_heads=args.encoder_attention_heads					,   decoder_attention_heads=args.decoder_attention_heads					,   encoder_ffn_dim=args.encoder_ffn_embed_dim					,   decoder_ffn_dim=args.decoder_ffn_embed_dim					,   d_model=args.encoder_embed_dim					,   dropout=args.dropout					,   attention_dropout=args.attention_dropout					,   activation_dropout=args.activation_dropout					,   activation_function='''relu'''					,   num_conv_layers=len(a     )					,   conv_channels=args.conv_channels					,   conv_kernel_sizes=a					,   input_feat_per_channel=args.input_feat_per_channel					,   input_channels=args.input_channels					,   tie_word_embeddings=a					,   num_beams=5					,   max_length=200					,   use_cache=a					,   decoder_start_token_id=2					,   early_stopping=a					,   )

 snake_case				=			SpeechaTextForConditionalGeneration(a     )
 snake_case				,		snake_case				=			model.model.load_state_dict(a					,   strict=a     )
 if len(a     ) > 0 and not set(a     ) <= {
     "encoder.embed_positions.weights",
     "decoder.embed_positions.weights",
 }:
  raise ValueError(
      '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
      f""" but all the following weights are missing {missing}"""     )

 if tie_embeds:
  snake_case				=			make_linear_from_emb(model.model.decoder.embed_tokens     )
 else:
  snake_case				=			lm_head_weights

 model.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
   parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   _lowercase       						=			parser.parse_args()
   convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

