
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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		str:



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

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

			if "prime_prior" in key:
						lowerCAmelCase__      :      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:
						lowerCAmelCase__      :      Dict         =		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__      :      Optional[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  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		Optional[int]:
			lowerCAmelCase__      :      Optional[Any]         =		{}
			import re

			lowerCAmelCase__      :      Optional[Any]         =		re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'     )
			lowerCAmelCase__      :      Optional[int]         =		re.compile(
			    r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'     )
			lowerCAmelCase__      :      Union[str, Any]         =		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__      :      Dict         =		re.compile(
			    r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'     )
			lowerCAmelCase__      :      str         =		re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'     )

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

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

						elif re_encoder_block_proj_out.fullmatch(_UpperCamelCase     ):
									lowerCAmelCase__      :      Tuple         =		re_encoder_block_proj_out.match(_UpperCamelCase     )
									lowerCAmelCase__      :      Optional[int]         =		regex_match.groups()
									lowerCAmelCase__      :      List[Any]         =		F'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
									lowerCAmelCase__      :      Dict         =		re_encoder_block_proj_out.sub(_UpperCamelCase       ,							_UpperCamelCase     )

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

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

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

						elif re_prior_cond_resnet.fullmatch(_UpperCamelCase     ):
									lowerCAmelCase__      :      Optional[int]         =		re_prior_cond_resnet.match(_UpperCamelCase     )
									lowerCAmelCase__      :      Optional[int]         =		regex_match.groups()
									lowerCAmelCase__      :      List[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__      :      Optional[Any]         =		F'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									lowerCAmelCase__      :      List[Any]         =		prefix + resnet_block
									lowerCAmelCase__      :      str         =		re_prior_cond_resnet.sub(_UpperCamelCase       ,							_UpperCamelCase     )

						elif re_prior_cond_proj_in.fullmatch(_UpperCamelCase     ):
									lowerCAmelCase__      :      Optional[int]         =		re_prior_cond_proj_in.match(_UpperCamelCase     )
									lowerCAmelCase__      :      Dict         =		regex_match.groups()
									lowerCAmelCase__      :      Union[str, Any]         =		F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
									lowerCAmelCase__      :      Union[str, Any]         =		re_prior_cond_proj_in.sub(_UpperCamelCase       ,							_UpperCamelCase     )

						# keep original key
						else:
									lowerCAmelCase__      :      Dict         =		original_key

						lowerCAmelCase__      :      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:
									lowerCAmelCase__      :      Tuple         =		model_state_dict[F'''{key_prefix}.{key}''']
									print(F'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''     )
									lowerCAmelCase__      :      Optional[int]         =		original_key

						lowerCAmelCase__      :      List[str]         =		original_key
						lowerCAmelCase__      :      Dict         =		value

			return new_dict
@torch.no_grad()
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_=None       ,							SCREAMING_SNAKE_CASE_=None     )				->		List[Any]:



			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=_UpperCamelCase     )
									os.makedirs(F'''{pytorch_dump_folder_path}/'''       ,							exist_ok=_UpperCamelCase     )
									open(F'''{pytorch_dump_folder_path}/{file.split("/"     )[-1]}'''       ,							'wb'     ).write(r.content     )

			lowerCAmelCase__      :      Optional[Any]         =		MODEL_MAPPING[model_name.split('/'     )[-1]]

			lowerCAmelCase__      :      Dict         =		JukeboxConfig.from_pretrained(_UpperCamelCase     )
			lowerCAmelCase__      :      List[Any]         =		JukeboxModel(_UpperCamelCase     )

			lowerCAmelCase__      :      int         =		[]
			lowerCAmelCase__      :      Any         =		{}
			for i, dict_name in enumerate(_UpperCamelCase     ):
						lowerCAmelCase__      :      int         =		torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split("/"     )[-1]}'''     )["""model"""]

						lowerCAmelCase__      :      Dict         =		{}
						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[str]         =		old_dic[k]
									else:
												lowerCAmelCase__      :      Any         =		old_dic[k]

						lowerCAmelCase__      :      Optional[int]         =		"""vqvae""" if i == 0 else F'''priors.{3 - i}'''
						lowerCAmelCase__      :      Tuple         =		fix_jukebox_keys(_UpperCamelCase       ,							model.state_dict()       ,							_UpperCamelCase       ,							_UpperCamelCase     )
						weight_dict.append(_UpperCamelCase     )

			lowerCAmelCase__      :      Dict         =		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__":
					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"""






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase    :     Union[str, Any]   						=		{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     int   						=		[
							    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MraForMaskedLM',
							    'MraForMultipleChoice',
							    'MraForQuestionAnswering',
							    'MraForSequenceClassification',
							    'MraForTokenClassification',
							    'MraLayer',
							    'MraModel',
							    'MraPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

							UpperCAmelCase    :     Tuple   						=		_LazyModule(__name__, globals()['__file__'], _import_structure)
