




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      :    List[Any]   		=   logging.get_logger(__name__)


__lowerCamelCase      :    List[str]   		=   """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   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[int]     ):
	if key.endswith(".model.1.bias"     ) and len(key.split("."     )     ) > 10:
		snake_case__				:   Optional[int]     =						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__				:   int     =						key.replace(".model.3.weight"  ,						".conv1d_2.weight"     )

	if "conditioner_blocks.0." in key:
		snake_case__				:   List[Any]     =						key.replace("conditioner_blocks.0"  ,						"conditioner_blocks"     )

	if "prime_prior" in key:
		snake_case__				:   Dict     =						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__				:   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   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]  ,						snake_case_      :							str  ,						snake_case_      :							Tuple  ,						snake_case_      :							Tuple     ):
	snake_case__				:   Any     =						{}
	import re

	snake_case__				:   Tuple     =						re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"     )
	snake_case__				:   Tuple     =						re.compile(
	    R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"     )
	snake_case__				:   Optional[int]     =						re.compile(R"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"     )

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

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

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

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

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

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

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

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

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

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

		snake_case__				:   Any     =						replace_key(snake_case_     )

		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__				:   int     =						original_key

		snake_case__				:   List[str]     =						original_key
		snake_case__				:   Tuple     =						value

	return new_dict







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]=None  ,						snake_case_      :							Any=None     ):
	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=snake_case_     )
			os.makedirs(F'''{pytorch_dump_folder_path}/'''  ,						exist_ok=snake_case_     )
			open(F'''{pytorch_dump_folder_path}/{file.split('/'     )[-1]}'''  ,						"wb"     ).write(r.content     )

	snake_case__				:   Union[str, Any]     =						MODEL_MAPPING[model_name.split("/"     )[-1]]

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

	snake_case__				:   Any     =						[]
	snake_case__				:   Union[str, Any]     =						{}
	for i, dict_name in enumerate(snake_case_     ):
		snake_case__				:   Tuple     =						torch.load(F'''{pytorch_dump_folder_path}/{dict_name.split('/'     )[-1]}'''     )["model"]

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

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

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

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

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

	return weight_dict


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











import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


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

__lowerCamelCase      :    Dict   		=   {
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_vision_model"



					def __init__(      self  :						List[Any]							,	__A  :						Dict=1_4_0_8							,	__A  :						Tuple=6_1_4_4							,	__A  :						str=3_9							,	__A  :						int=1_6							,	__A  :						str=2_2_4							,	__A  :						Any=1_4							,	__A  :						Dict="gelu"							,	__A  :						List[Any]=1e-6							,	__A  :						Any=0.0							,	__A  :						List[Any]=1e-1_0							,	__A  :						Union[str, Any]=True							,	**__A  :						Tuple							,	):
						super().__init__(**__A )

						snake_case__				:   List[str]     =						hidden_size
						snake_case__				:   Optional[int]     =						intermediate_size
						snake_case__				:   List[str]     =						num_hidden_layers
						snake_case__				:   List[Any]     =						num_attention_heads
						snake_case__				:   str     =						patch_size
						snake_case__				:   int     =						image_size
						snake_case__				:   int     =						initializer_range
						snake_case__				:   Optional[int]     =						attention_dropout
						snake_case__				:   str     =						layer_norm_eps
						snake_case__				:   Optional[Any]     =						hidden_act
						snake_case__				:   Tuple     =						qkv_bias






					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[Any] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   str     =						cls.get_config_dict(__A							,	**__A )

						# get the vision config dict if we are loading from InstructBlipConfig
						if config_dict.get("model_type" ) == "instructblip":
							snake_case__				:   Union[str, Any]     =						config_dict["vision_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip_qformer"



					def __init__(      self  :						Any							,	__A  :						Union[str, Any]=3_0_5_2_2							,	__A  :						Union[str, Any]=7_6_8							,	__A  :						Optional[int]=1_2							,	__A  :						Dict=1_2							,	__A  :						Dict=3_0_7_2							,	__A  :						List[str]="gelu"							,	__A  :						Union[str, Any]=0.1							,	__A  :						Tuple=0.1							,	__A  :						Any=5_1_2							,	__A  :						Optional[int]=0.0_2							,	__A  :						List[str]=1e-1_2							,	__A  :						Any=0							,	__A  :						Optional[Any]="absolute"							,	__A  :						str=2							,	__A  :						Any=1_4_0_8							,	**__A  :						List[str]							,	):
						super().__init__(pad_token_id=__A							,	**__A )

						snake_case__				:   Dict     =						vocab_size
						snake_case__				:   Optional[int]     =						hidden_size
						snake_case__				:   Optional[Any]     =						num_hidden_layers
						snake_case__				:   str     =						num_attention_heads
						snake_case__				:   int     =						hidden_act
						snake_case__				:   Optional[Any]     =						intermediate_size
						snake_case__				:   Union[str, Any]     =						hidden_dropout_prob
						snake_case__				:   List[Any]     =						attention_probs_dropout_prob
						snake_case__				:   List[Any]     =						max_position_embeddings
						snake_case__				:   int     =						initializer_range
						snake_case__				:   Dict     =						layer_norm_eps
						snake_case__				:   str     =						position_embedding_type
						snake_case__				:   Dict     =						cross_attention_frequency
						snake_case__				:   List[str]     =						encoder_hidden_size






					@classmethod
					def  _lowercase  (      cls  :						List[Any]							,	__A  :						Union[str, os.PathLike]							,	**__A  :						Optional[int] ):
						cls._set_token_in_kwargs(__A )

						snake_case__,		snake_case__				:   Tuple     =						cls.get_config_dict(__A							,	**__A )

						# get the qformer config dict if we are loading from InstructBlipConfig
						if config_dict.get("model_type" ) == "instructblip":
							snake_case__				:   List[Any]     =						config_dict["qformer_config"]

						if "model_type" in config_dict and hasattr(cls							,	"model_type" ) and config_dict["model_type"] != cls.model_type:
							logger.warning(
							    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
							    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

						return cls.from_dict(__A							,	**__A )



class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	"instructblip"
					a_			=	True



					def __init__(      self  :						List[str]							,	__A  :						Optional[Any]=None							,	__A  :						Tuple=None							,	__A  :						Optional[int]=None							,	__A  :						Optional[Any]=3_2							,	**__A  :						Optional[int] ):
						super().__init__(**__A )

						if vision_config is None:
							snake_case__				:   Any     =						{}
							logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values." )

						if qformer_config is None:
							snake_case__				:   Optional[Any]     =						{}
							logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values." )

						if text_config is None:
							snake_case__				:   Optional[int]     =						{}
							logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )

						snake_case__				:   List[Any]     =						InstructBlipVisionConfig(**__A )
						snake_case__				:   Union[str, Any]     =						InstructBlipQFormerConfig(**__A )
						snake_case__				:   Dict     =						text_config["model_type"] if "model_type" in text_config else "opt"
						snake_case__				:   List[Any]     =						CONFIG_MAPPING[text_model_type](**__A )

						snake_case__				:   Union[str, Any]     =						self.text_config.tie_word_embeddings
						snake_case__				:   Tuple     =						self.text_config.is_encoder_decoder

						snake_case__				:   str     =						num_query_tokens
						snake_case__				:   Dict     =						self.vision_config.hidden_size
						snake_case__				:   List[Any]     =						self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
						snake_case__				:   int     =						1.0
						snake_case__				:   Optional[int]     =						0.0_2



					@classmethod
					def  _lowercase  (      cls  :						List[str]							,	__A  :						InstructBlipVisionConfig							,	__A  :						InstructBlipQFormerConfig							,	__A  :						PretrainedConfig							,	**__A  :						int							,	):
						return cls(
						    vision_config=vision_config.to_dict()							,	qformer_config=qformer_config.to_dict()							,	text_config=text_config.to_dict()							,	**__A							,	)






					def  _lowercase  (      self  :						Optional[int] ):
						snake_case__				:   Any     =						copy.deepcopy(self.__dict__ )
						snake_case__				:   Optional[Any]     =						self.vision_config.to_dict()
						snake_case__				:   List[str]     =						self.qformer_config.to_dict()
						snake_case__				:   List[Any]     =						self.text_config.to_dict()
						snake_case__				:   List[Any]     =						self.__class__.model_type
						return output



