


'''simple docstring'''
from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput

class     _lowerCamelCase       (     nn.Module					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase = 16   ,   __lowercase = 88   ,   __lowercase = None   ,   __lowercase = 1   ,   __lowercase = 0.0   ,   __lowercase = 32   ,   __lowercase = None   ,   __lowercase = False   ,   __lowercase = None   ,   __lowercase = None   ,   __lowercase = "geglu"   ,   __lowercase = None   ,   ):

  """simple docstring"""







  super().__init__()
  __A	:		Optional[Any]	  =      nn.ModuleList(
      [
          TransformeraDModel(
              num_attention_heads=__lowercase   ,   attention_head_dim=__lowercase   ,   in_channels=__lowercase   ,   num_layers=__lowercase   ,   dropout=__lowercase   ,   norm_num_groups=__lowercase   ,   cross_attention_dim=__lowercase   ,   attention_bias=__lowercase   ,   sample_size=__lowercase   ,   num_vector_embeds=__lowercase   ,   activation_fn=__lowercase   ,   num_embeds_ada_norm=__lowercase   ,   )
          for _ in range(2    )
      ]    )

  # Variables that can be set by a pipeline:

  # The ratio of transformer1 to transformer2's output states to be combined during inference
  __A	:		int	  =      0.5

  # The shape of `encoder_hidden_states` is expected to be
  # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
  __A	:		Union[str, Any]	  =      [77, 257]

  # Which transformer to use to encode which condition.
  # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
  __A	:		Any	  =      [1, 0]







 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase=None   ,   __lowercase = True   ,   ):

  """simple docstring"""







  __A	:		Optional[int]	  =      hidden_states

  __A	:		List[Any]	  =      []
  __A	:		Dict	  =      0
  # attention_mask is not used yet
  for i in range(2    ):
   # for each of the two transformers, pass the corresponding condition tokens
   __A	:		str	  =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
   __A	:		Dict	  =      self.transformer_index_for_condition[i]
   __A	:		List[Any]	  =      self.transformers[transformer_index](
       __lowercase   ,   encoder_hidden_states=__lowercase   ,   timestep=__lowercase   ,   cross_attention_kwargs=__lowercase   ,   return_dict=__lowercase   ,   )[0]
   encoded_states.append(encoded_state - input_states    )
   tokens_start += self.condition_lengths[i]

  __A	:		List[Any]	  =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
  __A	:		int	  =      output_states + input_states

  if not return_dict:
   return (output_states,)

  return TransformeraDModelOutput(sample=__lowercase    )










'''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()
UpperCAmelCase_			:  str										=   logging.get_logger(__name__)


UpperCAmelCase_			:  Dict										=   'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_			:  Tuple										=   {
    '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       _lowercase							(					UpperCamelCase__							:       str      ):
 if key.endswith('.model.1.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.1.bias',					'.conv1d_1.bias'      )
 elif key.endswith('.model.1.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Dict	  =      key.replace('.model.1.weight',					'.conv1d_1.weight'      )
 elif key.endswith('.model.3.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Optional[int]	  =      key.replace('.model.3.bias',					'.conv1d_2.bias'      )
 elif key.endswith('.model.3.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.3.weight',					'.conv1d_2.weight'      )

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

 if "prime_prior" in key:
  __A	:		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:
  __A	:		str	  =      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:
  __A	:		str	  =      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       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		Union[str, Any]	  =      {}
 import re

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

 __A	:		List[str]	  =      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
 __A	:		str	  =      re.compile(
     r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
 __A	:		str	  =      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

 __A	:		Optional[int]	  =      re.compile(r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'      )
 __A	:		Union[str, Any]	  =      re.compile(
     r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
 __A	:		List[Any]	  =      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(UpperCamelCase__      ):
   __A	:		Any	  =      re_encoder_block_conv_in.match(UpperCamelCase__      )
   __A	:		int	  =      regex_match.groups()
   __A	:		List[Any]	  =      int(groups[2]      ) * 2 + int(groups[3]      )
   __A	:		Dict	  =      f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
   __A	:		int	  =      re_encoder_block_conv_in.sub(UpperCamelCase__,					UpperCamelCase__      )

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

  elif re_encoder_block_proj_out.fullmatch(UpperCamelCase__      ):
   __A	:		List[str]	  =      re_encoder_block_proj_out.match(UpperCamelCase__      )
   __A	:		Optional[int]	  =      regex_match.groups()
   __A	:		Any	  =      f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __A	:		Optional[Any]	  =      re_encoder_block_proj_out.sub(UpperCamelCase__,					UpperCamelCase__      )

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

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

  elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__      ):
   __A	:		int	  =      re_decoder_block_proj_in.match(UpperCamelCase__      )
   __A	:		List[str]	  =      regex_match.groups()
   __A	:		Tuple	  =      f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __A	:		Optional[Any]	  =      re_decoder_block_proj_in.sub(UpperCamelCase__,					UpperCamelCase__      )

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

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

  elif re_prior_cond_proj_in.fullmatch(UpperCamelCase__      ):
   __A	:		Tuple	  =      re_prior_cond_proj_in.match(UpperCamelCase__      )
   __A	:		Optional[Any]	  =      regex_match.groups()
   __A	:		Optional[int]	  =      f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __A	:		List[Any]	  =      re_prior_cond_proj_in.sub(UpperCamelCase__,					UpperCamelCase__      )

  # keep original key
  else:
   __A	:		List[Any]	  =      original_key

  __A	:		int	  =      replace_key(UpperCamelCase__      )

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

  __A	:		Optional[int]	  =      original_key
  __A	:		Optional[Any]	  =      value

 return new_dict


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

 __A	:		str	  =      MODEL_MAPPING[model_name.split('/'      )[-1]]

 __A	:		Optional[Any]	  =      JukeboxConfig.from_pretrained(UpperCamelCase__      )
 __A	:		Tuple	  =      JukeboxModel(UpperCamelCase__      )

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

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

  __A	:		List[Any]	  =      'vqvae' if i == 0 else f"""priors.{3 - i}"""
  __A	:		Any	  =      fix_jukebox_keys(UpperCamelCase__,					model.state_dict(),					UpperCamelCase__,					UpperCamelCase__      )
  weight_dict.append(UpperCamelCase__      )

 __A	:		Union[str, Any]	  =      weight_dict.pop(0      )
 model.vqvae.load_state_dict(UpperCamelCase__      )
 for i in range(len(UpperCamelCase__      )      ):
  model.priors[i].load_state_dict(weight_dict[2 - i]      )

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

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

 return weight_dict


if __name__ == "__main__":
 UpperCAmelCase_			:  Dict										=   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.',
 )
 UpperCAmelCase_			:  Union[str, Any]										=   parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






