



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


_lowercase : int      						=						"https://openaipublic.azureedge.net/jukebox/models/"
_lowercase : 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      snake_case__   (		__lowerCamelCase				:    Tuple	):
			"""simple docstring"""
			if key.endswith('''.model.1.bias'''	) and len(key.split('''.'''	)	) > 10:
						lowerCamelCase__		:		Optional[int]			=key.replace('''.model.1.bias'''							,		'''.conv1d_1.bias'''	)
			elif key.endswith('''.model.1.weight'''	) and len(key.split('''.'''	)	) > 10:
						lowerCamelCase__		:		List[str]			=key.replace('''.model.1.weight'''							,		'''.conv1d_1.weight'''	)
			elif key.endswith('''.model.3.bias'''	) and len(key.split('''.'''	)	) > 10:
						lowerCamelCase__		:		Any			=key.replace('''.model.3.bias'''							,		'''.conv1d_2.bias'''	)
			elif key.endswith('''.model.3.weight'''	) and len(key.split('''.'''	)	) > 10:
						lowerCamelCase__		:		Optional[Any]			=key.replace('''.model.3.weight'''							,		'''.conv1d_2.weight'''	)

			if "conditioner_blocks.0." in key:
						lowerCamelCase__		:		List[str]			=key.replace('''conditioner_blocks.0'''							,		'''conditioner_blocks'''	)

			if "prime_prior" in key:
						lowerCamelCase__		:		List[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:
						lowerCamelCase__		:		Tuple			=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__		:		Dict			=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      snake_case__   (		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    Optional[Any]	):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]			={}
			import re

			lowerCamelCase__		:		Dict			=re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''	)
			lowerCamelCase__		:		int			=re.compile(
			    R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''	)
			lowerCamelCase__		:		int			=re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''	)

			lowerCamelCase__		:		int			=re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'''	)
			lowerCamelCase__		:		Any			=re.compile(
			    R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'''	)
			lowerCamelCase__		:		Any			=re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'''	)

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

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

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

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

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

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

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

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

						# keep original key
						else:
									lowerCamelCase__		:		List[str]			=original_key

						lowerCamelCase__		:		Union[str, Any]			=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:
									lowerCamelCase__		:		Tuple			=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__		:		List[str]			=original_key
						lowerCamelCase__		:		int			=value

			return new_dict






@torch.no_grad()
def      snake_case__   (		__lowerCamelCase				:    Union[str, Any]=None							,		__lowerCamelCase				:    Optional[Any]=None	):
			"""simple docstring"""
			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"	)[-1]}'''	):
									lowerCamelCase__		:		Dict			=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	)

			lowerCamelCase__		:		Dict			=MODEL_MAPPING[model_name.split('''/'''	)[-1]]

			lowerCamelCase__		:		List[Any]			=JukeboxConfig.from_pretrained(__lowerCamelCase	)
			lowerCamelCase__		:		str			=JukeboxModel(__lowerCamelCase	)

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

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

						lowerCamelCase__		:		int			='''vqvae''' if i == 0 else f'''priors.{3 - i}'''
						lowerCamelCase__		:		int			=fix_jukebox_keys(__lowerCamelCase							,		model.state_dict()							,		__lowerCamelCase							,		__lowerCamelCase	)
						weight_dict.append(__lowerCamelCase	)

			lowerCamelCase__		:		str			=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__":
		_lowercase : int      						=						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.",
		)
		_lowercase : Union[str, Any]      						=						parser.parse_args()
		convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




