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__     :   Union[str, Any]		=   logging.get_logger(__name__)


lowerCAmelCase__     :   Any		=   '''https://openaipublic.azureedge.net/jukebox/models/'''
lowerCAmelCase__     :   Optional[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  UpperCamelCase__						(				A__    )   ->					str:
				if key.endswith('.model.1.bias'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	Optional[Any]												=      key.replace('.model.1.bias'			, '.conv1d_1.bias'    )
				elif key.endswith('.model.1.weight'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	List[Any]												=      key.replace('.model.1.weight'			, '.conv1d_1.weight'    )
				elif key.endswith('.model.3.bias'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	Dict												=      key.replace('.model.3.bias'			, '.conv1d_2.bias'    )
				elif key.endswith('.model.3.weight'    ) and len(key.split('.'    )    ) > 10:
								snake_case__					:	Any												=      key.replace('.model.3.weight'			, '.conv1d_2.weight'    )

				if "conditioner_blocks.0." in key:
								snake_case__					:	str												=      key.replace('conditioner_blocks.0'			, 'conditioner_blocks'    )

				if "prime_prior" in key:
								snake_case__					:	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:
								snake_case__					:	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:
								snake_case__					:	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  UpperCamelCase__						(				A__			, A__			, A__			, A__    )   ->					Union[str, Any]:
				snake_case__					:	int												=      {}
				import re

				snake_case__					:	List[str]												=      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'    )
				snake_case__					:	List[Any]												=      re.compile(
				    r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'    )
				snake_case__					:	Optional[Any]												=      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'    )

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

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

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

								elif re_encoder_block_proj_out.fullmatch(lowerCAmelCase_    ):
												snake_case__					:	Union[str, Any]												=      re_encoder_block_proj_out.match(lowerCAmelCase_    )
												snake_case__					:	Dict												=      regex_match.groups()
												snake_case__					:	Any												=      F"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
												snake_case__					:	Tuple												=      re_encoder_block_proj_out.sub(lowerCAmelCase_			, lowerCAmelCase_    )

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

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

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

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

								elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_    ):
												snake_case__					:	Dict												=      re_prior_cond_proj_in.match(lowerCAmelCase_    )
												snake_case__					:	Tuple												=      regex_match.groups()
												snake_case__					:	Optional[int]												=      F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
												snake_case__					:	List[str]												=      re_prior_cond_proj_in.sub(lowerCAmelCase_			, lowerCAmelCase_    )

								# keep original key
								else:
												snake_case__					:	Tuple												=      original_key

								snake_case__					:	int												=      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:
												snake_case__					:	Dict												=      model_state_dict[F"""{key_prefix}.{key}"""]
												print(F"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""    )
												snake_case__					:	Union[str, Any]												=      original_key

								snake_case__					:	Any												=      original_key
								snake_case__					:	Any												=      value

				return new_dict
@torch.no_grad()
def  UpperCamelCase__						(				A__=None			, A__=None    )   ->					Optional[int]:
				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(F"""{pytorch_dump_folder_path}/{file.split('/'    )[-1]}"""    ):
												snake_case__					:	List[str]												=      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    )

				snake_case__					:	str												=      MODEL_MAPPING[model_name.split('/'    )[-1]]

				snake_case__					:	List[Any]												=      JukeboxConfig.from_pretrained(lowerCAmelCase_    )
				snake_case__					:	Tuple												=      JukeboxModel(lowerCAmelCase_    )

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

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

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

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







'''simple docstring'''

import math



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      bool:
		_a     :      Optional[int]        =							math.loga(math.sqrt(4 * positive_integer + 1  ) / 2 + 1 / 2  )

		return exponent == int(lowerCAmelCase_  )



def      __lowerCamelCase     (      lowerCAmelCase_ = 1 / 12345  )			->      int:
		_a     :      int        =							0
		_a     :      Optional[Any]        =							0

		_a     :      int        =							3
		while True:
				_a     :      Tuple        =							(integer**2 - 1) / 4
				# if candidate is an integer, then there is a partition for k
				if partition_candidate == int(lowerCAmelCase_  ):
						_a     :      Union[str, Any]        =							int(lowerCAmelCase_  )
						total_partitions += 1
						if check_partition_perfect(lowerCAmelCase_  ):
								perfect_partitions += 1
				if perfect_partitions > 0:
						if perfect_partitions / total_partitions < max_proportion:
								return int(lowerCAmelCase_  )
				integer += 1


if __name__ == "__main__":
							print(f"""{solution() = }""")




