



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__  :  Optional[int]								=					logging.get_logger(__name__)


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

		if "conditioner_blocks.0." in key:
				lowerCAmelCase_			:     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_			:     List[str]									= 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_			:     Union[str, 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(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Any ):
		lowerCAmelCase_			:     Tuple									= {}
		import re

		lowerCAmelCase_			:     Tuple									= re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
		lowerCAmelCase_			:     str									= re.compile(
		    R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
		lowerCAmelCase_			:     str									= re.compile(R'''encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )

		lowerCAmelCase_			:     Dict									= re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)''' )
		lowerCAmelCase_			:     List[str]									= re.compile(
		    R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)''' )
		lowerCAmelCase_			:     Union[str, Any]									= re.compile(R'''decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)''' )

		lowerCAmelCase_			:     str									= 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_			:     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(__UpperCamelCase ):
						lowerCAmelCase_			:     List[Any]									= re_encoder_block_conv_in.match(__UpperCamelCase )
						lowerCAmelCase_			:     str									= regex_match.groups()
						lowerCAmelCase_			:     Tuple									= int(groups[2] ) * 2 + int(groups[3] )
						lowerCAmelCase_			:     Dict									= f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
						lowerCAmelCase_			:     str									= re_encoder_block_conv_in.sub(__UpperCamelCase     ,__UpperCamelCase )

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

				elif re_encoder_block_proj_out.fullmatch(__UpperCamelCase ):
						lowerCAmelCase_			:     str									= re_encoder_block_proj_out.match(__UpperCamelCase )
						lowerCAmelCase_			:     int									= regex_match.groups()
						lowerCAmelCase_			:     Any									= f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
						lowerCAmelCase_			:     List[str]									= re_encoder_block_proj_out.sub(__UpperCamelCase     ,__UpperCamelCase )

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

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

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

				elif re_prior_cond_resnet.fullmatch(__UpperCamelCase ):
						lowerCAmelCase_			:     List[Any]									= re_prior_cond_resnet.match(__UpperCamelCase )
						lowerCAmelCase_			:     int									= regex_match.groups()
						lowerCAmelCase_			:     Tuple									= int(groups[1] ) * 2 + int(groups[2] ) - 2
						lowerCAmelCase_			:     List[Any]									= {'''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_			:     Dict									= prefix + resnet_block
						lowerCAmelCase_			:     List[Any]									= re_prior_cond_resnet.sub(__UpperCamelCase     ,__UpperCamelCase )

				elif re_prior_cond_proj_in.fullmatch(__UpperCamelCase ):
						lowerCAmelCase_			:     int									= re_prior_cond_proj_in.match(__UpperCamelCase )
						lowerCAmelCase_			:     Any									= regex_match.groups()
						lowerCAmelCase_			:     int									= f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
						lowerCAmelCase_			:     int									= re_prior_cond_proj_in.sub(__UpperCamelCase     ,__UpperCamelCase )

				# keep original key
				else:
						lowerCAmelCase_			:     List[Any]									= 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_			:     str									= model_state_dict[f"""{key_prefix}.{key}"""]
						print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match""" )
						lowerCAmelCase_			:     Optional[Any]									= original_key

				lowerCAmelCase_			:     int									= original_key
				lowerCAmelCase_			:     Tuple									= value

		return new_dict


@torch.no_grad()
def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]=None     ,__UpperCamelCase							:		Union[str, Any]=None ):
		for file in MODEL_MAPPING[model_name]:
				if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/" )[-1]}""" ):
						lowerCAmelCase_			:     Union[str, Any]									= 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_			:     Tuple									= MODEL_MAPPING[model_name.split('''/''' )[-1]]

		lowerCAmelCase_			:     Dict									= JukeboxConfig.from_pretrained(__UpperCamelCase )
		lowerCAmelCase_			:     List[Any]									= JukeboxModel(__UpperCamelCase )

		lowerCAmelCase_			:     List[Any]									= []
		lowerCAmelCase_			:     Any									= {}
		for i, dict_name in enumerate(__UpperCamelCase ):
				lowerCAmelCase_			:     List[Any]									= 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_			:     Optional[Any]									= old_dic[k]
						elif k.endswith('''.w''' ):
								lowerCAmelCase_			:     List[str]									= old_dic[k]
						elif "level_2" not in dict_name and "cond.model." in k:
								lowerCAmelCase_			:     str									= old_dic[k]
						else:
								lowerCAmelCase_			:     int									= old_dic[k]

				lowerCAmelCase_			:     List[str]									= '''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_			:     Optional[Any]									= 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__":
						A__  :  List[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[Any]								=					parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

A__  :  Tuple								=					{
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''xlm-roberta-xl'''




							def __init__(       self     :       int				,				A_     :       List[str]=2_5_0_8_8_0				,				A_     :       List[str]=2_5_6_0				,				A_     :       Optional[int]=3_6				,				A_     :       List[Any]=3_2				,				A_     :       Optional[int]=1_0_2_4_0				,				A_     :       Dict="gelu"				,				A_     :       int=0.1				,				A_     :       Optional[Any]=0.1				,				A_     :       int=5_1_4				,				A_     :       Any=1				,				A_     :       Optional[Any]=0.02				,				A_     :       str=1e-05				,				A_     :       Dict=1				,				A_     :       Any=0				,				A_     :       Tuple=2				,				A_     :       str="absolute"				,				A_     :       str=True				,				A_     :       List[str]=None				,				**A_     :       Dict				,				):
									super().__init__(pad_token_id=A_				,				bos_token_id=A_				,				eos_token_id=A_				,				**A_)
									lowerCAmelCase_			:     Tuple									= vocab_size
									lowerCAmelCase_			:     List[str]									= hidden_size
									lowerCAmelCase_			:     int									= num_hidden_layers
									lowerCAmelCase_			:     int									= num_attention_heads
									lowerCAmelCase_			:     Dict									= hidden_act
									lowerCAmelCase_			:     int									= intermediate_size
									lowerCAmelCase_			:     Tuple									= hidden_dropout_prob
									lowerCAmelCase_			:     Optional[Any]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Union[str, Any]									= max_position_embeddings
									lowerCAmelCase_			:     Dict									= type_vocab_size
									lowerCAmelCase_			:     str									= initializer_range
									lowerCAmelCase_			:     str									= layer_norm_eps
									lowerCAmelCase_			:     Optional[Any]									= position_embedding_type
									lowerCAmelCase_			:     Optional[Any]									= use_cache
									lowerCAmelCase_			:     List[str]									= classifier_dropout





class 				__snake_case						(     UpperCamelCase_						):




							@property
							def    UpperCAmelCase__			(       self     :       List[str]):
									if self.task == "multiple-choice":
											lowerCAmelCase_			:     Union[str, Any]									= {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
									else:
											lowerCAmelCase_			:     List[Any]									= {0: '''batch''', 1: '''sequence'''}
									return OrderedDict(
									    [
									        ('''input_ids''', dynamic_axis),
									        ('''attention_mask''', dynamic_axis),
									    ])





