"""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   :						List[str]      =    logging.get_logger(__name__)


_UpperCamelCase   :						Union[str, Any]      =    'https://openaipublic.azureedge.net/jukebox/models/'
_UpperCamelCase   :						int      =    {
    '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_     (      _lowerCAmelCase  : Union[str, Any]     ):





       '''simple docstring'''

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

       if "conditioner_blocks.0." in key:
              lowercase__			:		Optional[Any]       =	key.replace('conditioner_blocks.0'   , 'conditioner_blocks'     )

       if "prime_prior" in key:
              lowercase__			:		Any       =	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__			:		Optional[int]       =	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__			:		Tuple       =	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_     (      _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : int   , _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : str     ):





       '''simple docstring'''

       lowercase__			:		str       =	{}
       import re

       lowercase__			:		Optional[Any]       =	re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'     )
       lowercase__			:		Optional[int]       =	re.compile(
           R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'     )
       lowercase__			:		Any       =	re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'     )

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

       lowercase__			:		Optional[Any]       =	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__			:		Dict       =	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__			:		Any       =	re_encoder_block_conv_in.match(a__     )
                     lowercase__			:		Dict       =	regex_match.groups()
                     lowercase__			:		Any       =	int(groups[2]     ) * 2 + int(groups[3]     )
                     lowercase__			:		Dict       =	f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
                     lowercase__			:		Union[str, Any]       =	re_encoder_block_conv_in.sub(a__   , a__     )

              elif re_encoder_block_resnet.fullmatch(a__     ):
                     lowercase__			:		List[str]       =	re_encoder_block_resnet.match(a__     )
                     lowercase__			:		Dict       =	regex_match.groups()
                     lowercase__			:		Any       =	int(groups[2]     ) * 2 + int(groups[3]     )
                     lowercase__			:		Any       =	{'1': 1, '3': 2}[groups[-2]]
                     lowercase__			:		List[str]       =	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__			:		int       =	re_encoder_block_resnet.sub(a__   , a__     )

              elif re_encoder_block_proj_out.fullmatch(a__     ):
                     lowercase__			:		Tuple       =	re_encoder_block_proj_out.match(a__     )
                     lowercase__			:		int       =	regex_match.groups()
                     lowercase__			:		List[Any]       =	f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
                     lowercase__			:		Dict       =	re_encoder_block_proj_out.sub(a__   , a__     )

              # rename vqvae.decoder keys
              elif re_decoder_block_conv_out.fullmatch(a__     ):
                     lowercase__			:		Any       =	re_decoder_block_conv_out.match(a__     )
                     lowercase__			:		Dict       =	regex_match.groups()
                     lowercase__			:		Optional[Any]       =	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__			:		List[str]       =	re_decoder_block_conv_out.sub(a__   , a__     )

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

              elif re_decoder_block_proj_in.fullmatch(a__     ):
                     lowercase__			:		Any       =	re_decoder_block_proj_in.match(a__     )
                     lowercase__			:		Union[str, Any]       =	regex_match.groups()
                     lowercase__			:		Dict       =	f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
                     lowercase__			:		Any       =	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__			:		Union[str, Any]       =	re_prior_cond_conv_out.match(a__     )
                     lowercase__			:		int       =	regex_match.groups()
                     lowercase__			:		Optional[int]       =	int(groups[1]     ) * 2 + int(groups[2]     ) - 2
                     lowercase__			:		Optional[int]       =	f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
                     lowercase__			:		int       =	re_prior_cond_conv_out.sub(a__   , a__     )

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

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

              # keep original key
              else:
                     lowercase__			:		Dict       =	original_key

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

              lowercase__			:		Union[str, Any]       =	original_key
              lowercase__			:		List[Any]       =	value

       return new_dict





@torch.no_grad()
def 				a_     (      _lowerCAmelCase  : Any=None   , _lowerCAmelCase  : List[str]=None     ):





       '''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=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__			:		int       =	MODEL_MAPPING[model_name.split('/'     )[-1]]

       lowercase__			:		Tuple       =	JukeboxConfig.from_pretrained(a__     )
       lowercase__			:		Optional[Any]       =	JukeboxModel(a__     )

       lowercase__			:		Dict       =	[]
       lowercase__			:		List[str]       =	{}
       for i, dict_name in enumerate(a__     ):
              lowercase__			:		Optional[Any]       =	torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/"     )[-1]}"""     )['model']

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

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

       lowercase__			:		int       =	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__":
       _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   :						List[Any]      =    parser.parse_args()
       convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)



from typing import Dict

from .base import GenericTensor, Pipeline







class 			__A( a			):




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if tokenize_kwargs is None:
												__a										=    {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''				)
												__a										=    truncation

								__a										=    tokenize_kwargs

								__a										=    {}
								if return_tensors is not None:
												__a										=    return_tensors

								return preprocess_params, {}, postprocess_params




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     **_snake_case				)    ->      Dict[str, GenericTensor]:


								'''simple docstring'''




								__a										=    self.framework
								__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)
								return model_inputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    self.model(**_snake_case				)
								return model_outputs




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case		,     _snake_case=False				)    ->      Optional[int]:


								'''simple docstring'''




								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(     self		,     *_snake_case		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								return super().__call__(*_snake_case		,     **_snake_case				)