



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()
lowercase_						=       logging.get_logger(__name__)


lowercase_						=       """https://openaipublic.azureedge.net/jukebox/models/"""
lowercase_						=       {
    """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_					):
			if key.endswith(".model.1.bias"					) and len(key.split("."					)					) > 10:
						lowercase__   					=			key.replace(".model.1.bias"  ,					".conv1d_1.bias"					)
			elif key.endswith(".model.1.weight"					) and len(key.split("."					)					) > 10:
						lowercase__   					=			key.replace(".model.1.weight"  ,					".conv1d_1.weight"					)
			elif key.endswith(".model.3.bias"					) and len(key.split("."					)					) > 10:
						lowercase__   					=			key.replace(".model.3.bias"  ,					".conv1d_2.bias"					)
			elif key.endswith(".model.3.weight"					) and len(key.split("."					)					) > 10:
						lowercase__   					=			key.replace(".model.3.weight"  ,					".conv1d_2.weight"					)

			if "conditioner_blocks.0." in key:
						lowercase__   					=			key.replace("conditioner_blocks.0"  ,					"conditioner_blocks"					)

			if "prime_prior" in key:
						lowercase__   					=			key.replace("prime_prior"  ,					"encoder"					)

			if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
						lowercase__   					=			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:
						lowercase__   					=			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_					):
			lowercase__   					=			{}
			import re

			lowercase__   					=			re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"					)
			lowercase__   					=			re.compile(
			    r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"					)
			lowercase__   					=			re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"					)

			lowercase__   					=			re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"					)
			lowercase__   					=			re.compile(
			    r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"					)
			lowercase__   					=			re.compile(r"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"					)

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

						elif re_encoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_encoder_block_resnet.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			int(groups[2]					) * 2 + int(groups[3]					)
									lowercase__   					=			{"1": 1, "3": 2}[groups[-2]]
									lowercase__   					=			f'''encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'''
									lowercase__   					=			f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									lowercase__   					=			prefix + resnet_block
									lowercase__   					=			re_encoder_block_resnet.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						elif re_encoder_block_proj_out.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_encoder_block_proj_out.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
									lowercase__   					=			re_encoder_block_proj_out.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						# rename vqvae.decoder keys
						elif re_decoder_block_conv_out.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_decoder_block_conv_out.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			int(groups[2]					) * 2 + int(groups[3]					) - 2
									lowercase__   					=			f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}'''
									lowercase__   					=			re_decoder_block_conv_out.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						elif re_decoder_block_resnet.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_decoder_block_resnet.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			int(groups[2]					) * 2 + int(groups[3]					) - 2
									lowercase__   					=			{"1": 1, "3": 2}[groups[-2]]
									lowercase__   					=			f'''decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.'''
									lowercase__   					=			f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									lowercase__   					=			prefix + resnet_block
									lowercase__   					=			re_decoder_block_resnet.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						elif re_decoder_block_proj_in.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_decoder_block_proj_in.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
									lowercase__   					=			re_decoder_block_proj_in.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						# rename prior cond.model to upsampler.upsample_block and resnet
						elif re_prior_cond_conv_out.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_prior_cond_conv_out.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			int(groups[1]					) * 2 + int(groups[2]					) - 2
									lowercase__   					=			f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
									lowercase__   					=			re_prior_cond_conv_out.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						elif re_prior_cond_resnet.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_prior_cond_resnet.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			int(groups[1]					) * 2 + int(groups[2]					) - 2
									lowercase__   					=			{"1": 1, "3": 2}[groups[-2]]
									lowercase__   					=			f'''conditioner_blocks.upsampler.upsample_block.{block_index}.'''
									lowercase__   					=			f'''resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'''
									lowercase__   					=			prefix + resnet_block
									lowercase__   					=			re_prior_cond_resnet.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						elif re_prior_cond_proj_in.fullmatch(SCREAMING_SNAKE_CASE_					):
									lowercase__   					=			re_prior_cond_proj_in.match(SCREAMING_SNAKE_CASE_					)
									lowercase__   					=			regex_match.groups()
									lowercase__   					=			f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
									lowercase__   					=			re_prior_cond_proj_in.sub(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

						# keep original key
						else:
									lowercase__   					=			original_key

						lowercase__   					=			replace_key(SCREAMING_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:
									lowercase__   					=			model_state_dict[f'''{key_prefix}.{key}''']
									print(f'''{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'''					)
									lowercase__   					=			original_key

						lowercase__   					=			original_key
						lowercase__   					=			value

			return new_dict



@torch.no_grad()
def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_=None  ,					SCREAMING_SNAKE_CASE_=None					):
			for file in MODEL_MAPPING[model_name]:
						if not os.path.isfile(f'''{pytorch_dump_folder_path}/{file.split("/"					)[-1]}'''					):
									lowercase__   					=			requests.get(f'''{PREFIX}{file}'''  ,					allow_redirects=SCREAMING_SNAKE_CASE_					)
									os.makedirs(f'''{pytorch_dump_folder_path}/'''  ,					exist_ok=SCREAMING_SNAKE_CASE_					)
									open(f'''{pytorch_dump_folder_path}/{file.split("/"					)[-1]}'''  ,					"wb"					).write(r.content					)

			lowercase__   					=			MODEL_MAPPING[model_name.split("/"					)[-1]]

			lowercase__   					=			JukeboxConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			JukeboxModel(SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[]
			lowercase__   					=			{}
			for i, dict_name in enumerate(SCREAMING_SNAKE_CASE_					):
						lowercase__   					=			torch.load(f'''{pytorch_dump_folder_path}/{dict_name.split("/"					)[-1]}'''					)["model"]

						lowercase__   					=			{}
						for k in old_dic.keys():
									if k.endswith(".b"					):
												lowercase__   					=			old_dic[k]
									elif k.endswith(".w"					):
												lowercase__   					=			old_dic[k]
									elif "level_2" not in dict_name and "cond.model." in k:
												lowercase__   					=			old_dic[k]
									else:
												lowercase__   					=			old_dic[k]

						lowercase__   					=			"vqvae" if i == 0 else f'''priors.{3 - i}'''
						lowercase__   					=			fix_jukebox_keys(SCREAMING_SNAKE_CASE_  ,					model.state_dict()  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
						weight_dict.append(SCREAMING_SNAKE_CASE_					)

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

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

			print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''					)
			model.save_pretrained(SCREAMING_SNAKE_CASE_					)

			return weight_dict


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









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
else:
							from .modeling_text_unet import UNetFlatConditionModel
							from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
							from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
							from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
							from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline




