





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


lowerCAmelCase       		=      """https://openaipublic.azureedge.net/jukebox/models/"""
lowerCAmelCase       		=      {
    """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						(							a_						:  Union[str, Any]	):
		if key.endswith(".model.1.bias"	) and len(key.split("."	)	) > 1_0:
				lowerCAmelCase     :    Tuple						=   key.replace(".model.1.bias"      ,".conv1d_1.bias"	)
		elif key.endswith(".model.1.weight"	) and len(key.split("."	)	) > 1_0:
				lowerCAmelCase     :    Optional[int]						=   key.replace(".model.1.weight"      ,".conv1d_1.weight"	)
		elif key.endswith(".model.3.bias"	) and len(key.split("."	)	) > 1_0:
				lowerCAmelCase     :    str						=   key.replace(".model.3.bias"      ,".conv1d_2.bias"	)
		elif key.endswith(".model.3.weight"	) and len(key.split("."	)	) > 1_0:
				lowerCAmelCase     :    Dict						=   key.replace(".model.3.weight"      ,".conv1d_2.weight"	)

		if "conditioner_blocks.0." in key:
				lowerCAmelCase     :    Optional[int]						=   key.replace("conditioner_blocks.0"      ,"conditioner_blocks"	)

		if "prime_prior" in key:
				lowerCAmelCase     :    List[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:
				lowerCAmelCase     :    List[Any]						=   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:
				lowerCAmelCase     :    List[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      __A						(							a_						:  List[str]      ,a_						:  List[Any]      ,a_						:  str      ,a_						:  Tuple	):
		lowerCAmelCase     :    Any						=   {}
		import re

		lowerCAmelCase     :    int						=   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"	)
		lowerCAmelCase     :    Union[str, Any]						=   re.compile(
		    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"	)
		lowerCAmelCase     :    Optional[int]						=   re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"	)

		lowerCAmelCase     :    Any						=   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"	)
		lowerCAmelCase     :    Tuple						=   re.compile(
		    R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"	)
		lowerCAmelCase     :    Optional[Any]						=   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"	)

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

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

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

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

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

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

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

				elif re_prior_cond_proj_in.fullmatch(a_	):
						lowerCAmelCase     :    List[str]						=   re_prior_cond_proj_in.match(a_	)
						lowerCAmelCase     :    Optional[int]						=   regex_match.groups()
						lowerCAmelCase     :    List[str]						=   f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
						lowerCAmelCase     :    Optional[int]						=   re_prior_cond_proj_in.sub(a_      ,a_	)

				# keep original key
				else:
						lowerCAmelCase     :    Dict						=   original_key

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

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

		return new_dict






@torch.no_grad()
def      __A						(							a_						:  List[str]=None      ,a_						:  List[Any]=None	):
		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"	)[-1]}'''	):
						lowerCAmelCase     :    List[str]						=   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	)

		lowerCAmelCase     :    Any						=   MODEL_MAPPING[model_name.split("/"	)[-1]]

		lowerCAmelCase     :    List[str]						=   JukeboxConfig.from_pretrained(a_	)
		lowerCAmelCase     :    str						=   JukeboxModel(a_	)

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

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

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

		lowerCAmelCase     :    List[str]						=   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__":
					lowerCAmelCase       		=      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.""",
					)
					lowerCAmelCase       		=      parser.parse_args()
					convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase       		=      logging.get_logger(__name__)
class 		lowerCamelCase  (      _A       ):



	def __init__(   self		,      *a_		,      **a_	):
			warnings.warn(
			    "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
			    " Please use ChineseCLIPImageProcessor instead."		,      a_		,      )
			super().__init__(*a_		,      **a_	)



