



"""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: Optional[int]										=     logging.get_logger(__name__)


_UpperCamelCase: List[Any]										=     '''https://openaipublic.azureedge.net/jukebox/models/'''
_UpperCamelCase: 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      lowercase__   (	_UpperCAmelCase )   ->	Optional[Any]:






      '''simple docstring'''


      if key.endswith('.model.1.bias' ) and len(key.split('.' ) ) > 10:
            lowercase     :					str 		=      key.replace('.model.1.bias'		,					'.conv1d_1.bias' )
      elif key.endswith('.model.1.weight' ) and len(key.split('.' ) ) > 10:
            lowercase     :					Any 		=      key.replace('.model.1.weight'		,					'.conv1d_1.weight' )
      elif key.endswith('.model.3.bias' ) and len(key.split('.' ) ) > 10:
            lowercase     :					Optional[Any] 		=      key.replace('.model.3.bias'		,					'.conv1d_2.bias' )
      elif key.endswith('.model.3.weight' ) and len(key.split('.' ) ) > 10:
            lowercase     :					List[str] 		=      key.replace('.model.3.weight'		,					'.conv1d_2.weight' )

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

      if "prime_prior" in key:
            lowercase     :					int 		=      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     :					List[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:
            lowercase     :					Union[str, 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      lowercase__   (	_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase		,					_UpperCAmelCase )   ->	List[Any]:






      '''simple docstring'''


      lowercase     :					Tuple 		=      {}
      import re

      lowercase     :					Tuple 		=      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)' )
      lowercase     :					str 		=      re.compile(
          R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
      lowercase     :					str 		=      re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)' )

      lowercase     :					Dict 		=      re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)' )
      lowercase     :					List[str] 		=      re.compile(
          R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)' )
      lowercase     :					Union[str, Any] 		=      re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)' )

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

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

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

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

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

            elif re_decoder_block_proj_in.fullmatch(__UpperCamelCase ):
                  lowercase     :					List[Any] 		=      re_decoder_block_proj_in.match(__UpperCamelCase )
                  lowercase     :					Tuple 		=      regex_match.groups()
                  lowercase     :					List[str] 		=      f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
                  lowercase     :					int 		=      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 ):
                  lowercase     :					List[Any] 		=      re_prior_cond_conv_out.match(__UpperCamelCase )
                  lowercase     :					Optional[Any] 		=      regex_match.groups()
                  lowercase     :					List[Any] 		=      int(groups[1] ) * 2 + int(groups[2] ) - 2
                  lowercase     :					Any 		=      f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
                  lowercase     :					Optional[Any] 		=      re_prior_cond_conv_out.sub(__UpperCamelCase		,					__UpperCamelCase )

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

            elif re_prior_cond_proj_in.fullmatch(__UpperCamelCase ):
                  lowercase     :					int 		=      re_prior_cond_proj_in.match(__UpperCamelCase )
                  lowercase     :					Any 		=      regex_match.groups()
                  lowercase     :					int 		=      f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
                  lowercase     :					int 		=      re_prior_cond_proj_in.sub(__UpperCamelCase		,					__UpperCamelCase )

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

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

            lowercase     :					int 		=      original_key
            lowercase     :					Tuple 		=      value

      return new_dict


@torch.no_grad()
def      lowercase__   (	_UpperCAmelCase=None		,					_UpperCAmelCase=None )   ->	str:






      '''simple docstring'''


      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split('/' )[-1]}''' ):
                  lowercase     :					Union[str, 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 )

      lowercase     :					Tuple 		=      MODEL_MAPPING[model_name.split('/' )[-1]]

      lowercase     :					Dict 		=      JukeboxConfig.from_pretrained(__UpperCamelCase )
      lowercase     :					List[Any] 		=      JukeboxModel(__UpperCamelCase )

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

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

            lowercase     :					List[str] 		=      '''vqvae''' if i == 0 else f'''priors.{3 - i}'''
            lowercase     :					Tuple 		=      fix_jukebox_keys(__UpperCamelCase		,					model.state_dict()		,					__UpperCamelCase		,					__UpperCamelCase )
            weight_dict.append(__UpperCamelCase )

      lowercase     :					Optional[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: List[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.',
     )
     _UpperCamelCase: Optional[Any]										=     parser.parse_args()
     convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__  :  Union[str, Any]								=					logging.get_logger(__name__)

A__  :  Tuple								=					{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''xlm-roberta-xl'''




							def __init__(       self     :       int				,				A_     :       List[str]=2_5_0_8_8_0				,				A_     :       List[str]=2_5_6_0				,				A_     :       Optional[int]=3_6				,				A_     :       List[Any]=3_2				,				A_     :       Optional[int]=1_0_2_4_0				,				A_     :       Dict="gelu"				,				A_     :       int=0.1				,				A_     :       Optional[Any]=0.1				,				A_     :       int=5_1_4				,				A_     :       Any=1				,				A_     :       Optional[Any]=0.02				,				A_     :       str=1e-05				,				A_     :       Dict=1				,				A_     :       Any=0				,				A_     :       Tuple=2				,				A_     :       str="absolute"				,				A_     :       str=True				,				A_     :       List[str]=None				,				**A_     :       Dict				,				):
									super().__init__(pad_token_id=A_				,				bos_token_id=A_				,				eos_token_id=A_				,				**A_)
									lowerCAmelCase_			:     Tuple									= vocab_size
									lowerCAmelCase_			:     List[str]									= hidden_size
									lowerCAmelCase_			:     int									= num_hidden_layers
									lowerCAmelCase_			:     int									= num_attention_heads
									lowerCAmelCase_			:     Dict									= hidden_act
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_dropout_prob
									lowerCAmelCase_			:     Optional[Any]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Union[str, Any]									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= type_vocab_size
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     str									= layer_norm_eps
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= use_cache
									lowerCAmelCase_			:     List[str]									= classifier_dropout





class 				__snake_case						(     UpperCamelCase_						):




							@property
							def    UpperCAmelCase__			(       self     :       List[str]):
									if self.task == "multiple-choice":
											lowerCAmelCase_			:     Union[str, Any]									= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_			:     List[Any]									= {0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ])





