





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()
__a      : int 				=				logging.get_logger(__name__)


__a      : Any 				=				"""https://openaipublic.azureedge.net/jukebox/models/"""
__a      : Union[str, 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 		__magic_name__				( lowercase_				)		->					str:


						'''simple docstring'''


						if key.endswith(".model.1.bias"				) and len(key.split("."				)				) > 10:
												UpperCamelCase  							= key.replace(".model.1.bias"						,		".conv1d_1.bias"				)
						elif key.endswith(".model.1.weight"				) and len(key.split("."				)				) > 10:
												UpperCamelCase  							= key.replace(".model.1.weight"						,		".conv1d_1.weight"				)
						elif key.endswith(".model.3.bias"				) and len(key.split("."				)				) > 10:
												UpperCamelCase  							= key.replace(".model.3.bias"						,		".conv1d_2.bias"				)
						elif key.endswith(".model.3.weight"				) and len(key.split("."				)				) > 10:
												UpperCamelCase  							= key.replace(".model.3.weight"						,		".conv1d_2.weight"				)

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

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

						if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
												UpperCamelCase  							= 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:
												UpperCamelCase  							= 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 		__magic_name__				( lowercase_						,		lowercase_						,		lowercase_						,		lowercase_				)		->					str:


						'''simple docstring'''


						UpperCamelCase  							= {}
						import re

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

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

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

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

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

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

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

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

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

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

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

												# keep original key
												else:
																		UpperCamelCase  							= original_key

												UpperCamelCase  							= replace_key(_lowerCAmelCase				)

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

												UpperCamelCase  							= original_key
												UpperCamelCase  							= value

						return new_dict
@torch.no_grad()
def 		__magic_name__				( lowercase_=None						,		lowercase_=None				)		->					int:


						'''simple docstring'''


						for file in MODEL_MAPPING[model_name]:
												if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''				):
																		UpperCamelCase  							= requests.get(f'''{PREFIX}{file}'''						,		allow_redirects=_lowerCAmelCase				)
																		os.makedirs(f'''{pytorch_dump_folder_path}/'''						,		exist_ok=_lowerCAmelCase				)
																		open(f'''{pytorch_dump_folder_path}/{file.split("/"				)[-1]}'''						,		"wb"				).write(r.content				)

						UpperCamelCase  							= MODEL_MAPPING[model_name.split("/"				)[-1]]

						UpperCamelCase  							= JukeboxConfig.from_pretrained(_lowerCAmelCase				)
						UpperCamelCase  							= JukeboxModel(_lowerCAmelCase				)

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

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

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

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

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

						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''				)
						model.save_pretrained(_lowerCAmelCase				)

						return weight_dict


if __name__ == "__main__":
		__a      : 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.""",
		)
		__a      : Optional[int] 				=				parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

_UpperCamelCase											=		{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


_UpperCamelCase											=		{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_				   =PRETRAINED_INIT_CONFIGURATION
  a_				   =RoFormerTokenizer




  def __init__(					self :      List[str]     ,      _a :      Optional[Any]=None     ,      _a :      Dict=None     ,      _a :      Dict=True     ,      _a :      Union[str, Any]="[UNK]"     ,      _a :      List[Any]="[SEP]"     ,      _a :      List[Any]="[PAD]"     ,      _a :      Any="[CLS]"     ,      _a :      Any="[MASK]"     ,      _a :      Dict=True     ,      _a :      Any=None     ,      **_a :      Tuple     ,      )   ->   Optional[int]:
    super().__init__(
        _a     ,      tokenizer_file=_a     ,      do_lower_case=_a     ,      unk_token=_a     ,      sep_token=_a     ,      pad_token=_a     ,      cls_token=_a     ,      mask_token=_a     ,      tokenize_chinese_chars=_a     ,      strip_accents=_a     ,      **_a     ,      )

    __lowerCamelCase			:			List[str]       			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()  )
    if (
        pre_tok_state.get('lowercase'     ,      _a  ) != do_lower_case
        or pre_tok_state.get('strip_accents'     ,      _a  ) != strip_accents
    ):
      __lowerCamelCase			:			Dict       			=					getattr(_a     ,      pre_tok_state.pop('type'  )  )
      __lowerCamelCase			:			Tuple       			=					do_lower_case
      __lowerCamelCase			:			int       			=					strip_accents
      __lowerCamelCase			:			Dict       			=					pre_tok_class(**_a  )

    __lowerCamelCase			:			Any       			=					do_lower_case




  def __getstate__(					self :      Union[str, Any]  )   ->   List[Any]:
    __lowerCamelCase			:			Optional[Any]       			=					self.__dict__.copy()
    __lowerCamelCase			:			Any       			=					BertPreTokenizer()
    return state




  def __setstate__(					self :      Optional[Any]     ,      _a :      Optional[int]  )   ->   Tuple:
    __lowerCamelCase			:			Tuple       			=					d
    __lowerCamelCase			:			Optional[Any]       			=					self.__dict__['_tokenizer'].get_vocab()
    __lowerCamelCase			:			Any       			=					PreTokenizer.custom(JiebaPreTokenizer(_a  )  )




  def 		_lowercase	(					self :      Any     ,      _a :      Union[str, Any]     ,      _a :      str=None  )   ->   Any:
    __lowerCamelCase			:			Optional[Any]       			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

    return output




  def 		_lowercase	(					self :      int     ,      _a :      List[int]     ,      _a :      Optional[List[int]] = None  )   ->   List[int]:
    __lowerCamelCase			:			int       			=					[self.sep_token_id]
    __lowerCamelCase			:			int       			=					[self.cls_token_id]
    if token_ids_a is None:
      return len(cls + token_ids_a + sep  ) * [0]
    return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]




  def 		_lowercase	(					self :      List[str]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    __lowerCamelCase			:			Optional[int]       			=					self._tokenizer.model.save(_a     ,      name=_a  )
    return tuple(_a  )




  def 		_lowercase	(					self :      int     ,      _a :      Optional[Any]     ,      _a :      Tuple=None     ,      _a :      List[str]=None     ,      _a :      Optional[int]=False     ,      **_a :      Dict     ,      )   ->   Dict:
    __lowerCamelCase			:			Dict       			=					BertPreTokenizer()
    return super().save_pretrained(_a     ,      _a     ,      _a     ,      _a     ,      **_a  )






