






'''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   :					int   							=					logging.get_logger(__name__)


__lowerCamelCase   :					List[Any]   							=					"https://openaipublic.azureedge.net/jukebox/models/"
__lowerCamelCase   :					Optional[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       ( __magic_name__						)->    List[Any]:



			"""simple docstring"""



			if key.endswith(".model.1.bias"						) and len(key.split("."						)						) > 10:
						snake_case_				:					List[str]   =       key.replace(".model.1.bias"     ,".conv1d_1.bias"						)
			elif key.endswith(".model.1.weight"						) and len(key.split("."						)						) > 10:
						snake_case_				:					str   =       key.replace(".model.1.weight"     ,".conv1d_1.weight"						)
			elif key.endswith(".model.3.bias"						) and len(key.split("."						)						) > 10:
						snake_case_				:					List[Any]   =       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_				:					int   =       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_				:					Optional[int]   =       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       ( __magic_name__     ,__magic_name__     ,__magic_name__     ,__magic_name__						)->    int:



			"""simple docstring"""



			snake_case_				:					Tuple   =       {}
			import re

			snake_case_				:					Optional[int]   =       re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
			snake_case_				:					Dict   =       re.compile(
			    r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
			snake_case_				:					str   =       re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

			snake_case_				:					List[Any]   =       re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
			snake_case_				:					List[Any]   =       re.compile(
			    r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
			snake_case_				:					str   =       re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

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

						elif re_encoder_block_resnet.fullmatch(__magic_name__						):
									snake_case_				:					Dict   =       re_encoder_block_resnet.match(__magic_name__						)
									snake_case_				:					Dict   =       regex_match.groups()
									snake_case_				:					List[Any]   =       int(groups[2]						) * 2 + int(groups[3]						)
									snake_case_				:					Tuple   =       {"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_				:					Dict   =       prefix + resnet_block
									snake_case_				:					List[str]   =       re_encoder_block_resnet.sub(__magic_name__     ,__magic_name__						)

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

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

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

						elif re_decoder_block_proj_in.fullmatch(__magic_name__						):
									snake_case_				:					Optional[Any]   =       re_decoder_block_proj_in.match(__magic_name__						)
									snake_case_				:					Dict   =       regex_match.groups()
									snake_case_				:					List[Any]   =       F'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
									snake_case_				:					Tuple   =       re_decoder_block_proj_in.sub(__magic_name__     ,__magic_name__						)

						# rename prior cond.model to upsampler.upsample_block and resnet
						elif re_prior_cond_conv_out.fullmatch(__magic_name__						):
									snake_case_				:					str   =       re_prior_cond_conv_out.match(__magic_name__						)
									snake_case_				:					List[Any]   =       regex_match.groups()
									snake_case_				:					Optional[Any]   =       int(groups[1]						) * 2 + int(groups[2]						) - 2
									snake_case_				:					List[Any]   =       F'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
									snake_case_				:					List[str]   =       re_prior_cond_conv_out.sub(__magic_name__     ,__magic_name__						)

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

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

						# keep original key
						else:
									snake_case_				:					Union[str, Any]   =       original_key

						snake_case_				:					List[Any]   =       replace_key(__magic_name__						)

						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_				:					List[str]   =       original_key

						snake_case_				:					str   =       original_key
						snake_case_				:					Union[str, Any]   =       value

			return new_dict

@torch.no_grad()
def 	__UpperCAmelCase       ( __magic_name__=None     ,__magic_name__=None						)->    Any:



			"""simple docstring"""



			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split('/'						)[-1]}'''						):
									snake_case_				:					Tuple   =       requests.get(F'''{PREFIX}{file}'''     ,allow_redirects=__magic_name__						)
									os.makedirs(F'''{pytorch_dump_folder_path}/'''     ,exist_ok=__magic_name__						)
									open(F'''{pytorch_dump_folder_path}/{file.split('/'						)[-1]}'''     ,"wb"						).write(r.content						)

			snake_case_				:					int   =       MODEL_MAPPING[model_name.split("/"						)[-1]]

			snake_case_				:					List[str]   =       JukeboxConfig.from_pretrained(__magic_name__						)
			snake_case_				:					Tuple   =       JukeboxModel(__magic_name__						)

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

						snake_case_				:					List[Any]   =       {}
						for k in old_dic.keys():
									if k.endswith(".b"						):
												snake_case_				:					Any   =       old_dic[k]
									elif k.endswith(".w"						):
												snake_case_				:					int   =       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_				:					Dict   =       old_dic[k]

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

			snake_case_				:					Dict   =       weight_dict.pop(0						)
			model.vqvae.load_state_dict(__magic_name__						)
			for i in range(len(__magic_name__						)						):
						model.priors[i].load_state_dict(weight_dict[2 - i]						)

			Path(__magic_name__						).mkdir(exist_ok=__magic_name__						)
			with open(F'''{pytorch_dump_folder_path}/mapping.json'''     ,"w"						) as txtfile:
						json.dump(__magic_name__     ,__magic_name__						)

			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''						)
			model.save_pretrained(__magic_name__						)

			return weight_dict


if __name__ == "__main__":
						__lowerCamelCase   :					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.''',
						)
						__lowerCamelCase   :					Dict   							=					parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					int   							=					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''cvt'''

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[int]=3       ,							lowerCAmelCase__			:Any=[7, 3, 3]       ,							lowerCAmelCase__			:Dict=[4, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[2, 1, 1]       ,							lowerCAmelCase__			:Any=[64, 192, 384]       ,							lowerCAmelCase__			:List[str]=[1, 3, 6]       ,							lowerCAmelCase__			:str=[1, 2, 10]       ,							lowerCAmelCase__			:Any=[4.0, 4.0, 4.0]       ,							lowerCAmelCase__			:int=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Optional[Any]=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Dict=[0.0, 0.0, 0.1]       ,							lowerCAmelCase__			:List[Any]=[True, True, True]       ,							lowerCAmelCase__			:List[Any]=[False, False, True]       ,							lowerCAmelCase__			:Dict=["dw_bn", "dw_bn", "dw_bn"]       ,							lowerCAmelCase__			:Any=[3, 3, 3]       ,							lowerCAmelCase__			:Tuple=[1, 1, 1]       ,							lowerCAmelCase__			:Optional[int]=[2, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[1, 1, 1]       ,							lowerCAmelCase__			:Any=[1, 1, 1]       ,							lowerCAmelCase__			:List[str]=0.0_2       ,							lowerCAmelCase__			:Dict=1E-1_2       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  str:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					int   =       num_channels
										snake_case_				:					int   =       patch_sizes
										snake_case_				:					Optional[Any]   =       patch_stride
										snake_case_				:					Dict   =       patch_padding
										snake_case_				:					Tuple   =       embed_dim
										snake_case_				:					Optional[int]   =       num_heads
										snake_case_				:					Union[str, Any]   =       depth
										snake_case_				:					Optional[int]   =       mlp_ratio
										snake_case_				:					Tuple   =       attention_drop_rate
										snake_case_				:					str   =       drop_rate
										snake_case_				:					Tuple   =       drop_path_rate
										snake_case_				:					Any   =       qkv_bias
										snake_case_				:					Union[str, Any]   =       cls_token
										snake_case_				:					int   =       qkv_projection_method
										snake_case_				:					Any   =       kernel_qkv
										snake_case_				:					Union[str, Any]   =       padding_kv
										snake_case_				:					str   =       stride_kv
										snake_case_				:					Dict   =       padding_q
										snake_case_				:					Tuple   =       stride_q
										snake_case_				:					Any   =       initializer_range
										snake_case_				:					Any   =       layer_norm_eps




