



'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_snake_case	=						logging.get_logger(__name__)  # pylint: disable=invalid-name






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							warnings.warn(
							    "The preprocess method is deprecated and will be removed in a future version. Please"
							    " use VaeImageProcessor.preprocess instead"       ,				_lowerCamelCase       ,				)
							if isinstance(_lowerCamelCase       ,				torch.Tensor						):
														return image
							elif isinstance(_lowerCamelCase       ,				PIL.Image.Image						):
														_lowerCAmelCase  :							int				     =					[image]

							if isinstance(image[0]       ,				PIL.Image.Image						):
														_lowerCAmelCase  :							Union[str, Any]				     =					image[0].size
														_lowerCAmelCase  :							List[Any]				     =					(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														_lowerCAmelCase  :							Any				     =					[np.array(i.resize((w, h)       ,				resample=PIL_INTERPOLATION["lanczos"]						)						)[None, :] for i in image]
														_lowerCAmelCase  :							Dict				     =					np.concatenate(_lowerCamelCase       ,				axis=0						)
														_lowerCAmelCase  :							Any				     =					np.array(_lowerCamelCase						).astype(np.floataa						) / 255.0
														_lowerCAmelCase  :							Union[str, Any]				     =					image.transpose(0       ,				3       ,				1       ,				2						)
														_lowerCAmelCase  :							Optional[int]				     =					2.0 * image - 1.0
														_lowerCAmelCase  :							List[str]				     =					torch.from_numpy(_lowerCamelCase						)
							elif isinstance(image[0]       ,				torch.Tensor						):
														_lowerCAmelCase  :							Dict				     =					torch.cat(_lowerCamelCase       ,				dim=0						)
							return image






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if isinstance(_lowerCamelCase       ,				torch.Tensor						):
														return mask
							elif isinstance(_lowerCamelCase       ,				PIL.Image.Image						):
														_lowerCAmelCase  :							Any				     =					[mask]

							if isinstance(mask[0]       ,				PIL.Image.Image						):
														_lowerCAmelCase  :							Optional[int]				     =					mask[0].size
														_lowerCAmelCase  :							List[Any]				     =					(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase  :							List[str]				     =					[np.array(m.convert("L"						).resize((w, h)       ,				resample=PIL_INTERPOLATION["nearest"]						)						)[None, :] for m in mask]
														_lowerCAmelCase  :							Dict				     =					np.concatenate(_lowerCamelCase       ,				axis=0						)
														_lowerCAmelCase  :							Any				     =					mask.astype(np.floataa						) / 255.0
														_lowerCAmelCase  :							List[str]				     =					0
														_lowerCAmelCase  :							Dict				     =					1
														_lowerCAmelCase  :							List[Any]				     =					torch.from_numpy(_lowerCamelCase						)
							elif isinstance(mask[0]       ,				torch.Tensor						):
														_lowerCAmelCase  :							Optional[Any]				     =					torch.cat(_lowerCamelCase       ,				dim=0						)
							return mask



class 						UpperCAmelCase_					( __lowercase):
							lowerCamelCase__         =	42
							lowerCamelCase__         =	42







							def __init__(     self,    __a,    __a):

														'''simple docstring'''

														super().__init__()
														self.register_modules(unet=__a,    scheduler=__a)







							@torch.no_grad()
							def __call__(     self,    __a,    __a,    __a = 250,    __a = 0.0,    __a = 10,    __a = 10,    __a = None,    __a = "pil",    __a = True,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					image

														_lowerCAmelCase  :							Optional[Any]				     =					_preprocess_image(__a)
														_lowerCAmelCase  :							Optional[int]				     =					original_image.to(device=self.device,    dtype=self.unet.dtype)
														_lowerCAmelCase  :							Dict				     =					_preprocess_mask(__a)
														_lowerCAmelCase  :							Any				     =					mask_image.to(device=self.device,    dtype=self.unet.dtype)

														_lowerCAmelCase  :							str				     =					original_image.shape[0]

														# sample gaussian noise to begin the loop
														if isinstance(__a,    __a) and len(__a) != batch_size:
																					raise ValueError(
																					    f"You have passed a list of generators of length {len(__a)}, but requested an effective batch"
																					    f" size of {batch_size}. Make sure the batch size matches the length of the generators.")

														_lowerCAmelCase  :							List[str]				     =					original_image.shape
														_lowerCAmelCase  :							List[Any]				     =					randn_tensor(__a,    generator=__a,    device=self.device,    dtype=self.unet.dtype)

														# set step values
														self.scheduler.set_timesteps(__a,    __a,    __a,    self.device)
														_lowerCAmelCase  :							Tuple				     =					eta

														_lowerCAmelCase  :							List[str]				     =					self.scheduler.timesteps[0] + 1
														_lowerCAmelCase  :							str				     =					generator[0] if isinstance(__a,    __a) else generator
														for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
																					if t < t_last:
																												# predict the noise residual
																												_lowerCAmelCase  :							Union[str, Any]				     =					self.unet(__a,    __a).sample
																												# compute previous image: x_t -> x_t-1
																												_lowerCAmelCase  :							Tuple				     =					self.scheduler.step(__a,    __a,    __a,    __a,    __a,    __a).prev_sample

																					else:
																												# compute the reverse: x_t-1 -> x_t
																												_lowerCAmelCase  :							List[str]				     =					self.scheduler.undo_step(__a,    __a,    __a)
																					_lowerCAmelCase  :							Dict				     =					t

														_lowerCAmelCase  :							Union[str, Any]				     =					(image / 2 + 0.5).clamp(0,    1)
														_lowerCAmelCase  :							Tuple				     =					image.cpu().permute(0,    2,    3,    1).numpy()
														if output_type == "pil":
																					_lowerCAmelCase  :							str				     =					self.numpy_to_pil(__a)

														if not return_dict:
																					return (image,)

														return ImagePipelineOutput(images=__a)





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()
_snake_case	=						logging.get_logger(__name__)


_snake_case	=						"https://openaipublic.azureedge.net/jukebox/models/"
_snake_case	=						{
    "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        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if key.endswith(".model.1.bias"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							int				     =					key.replace(".model.1.bias"       ,				".conv1d_1.bias"						)
							elif key.endswith(".model.1.weight"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							Optional[int]				     =					key.replace(".model.1.weight"       ,				".conv1d_1.weight"						)
							elif key.endswith(".model.3.bias"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							Union[str, Any]				     =					key.replace(".model.3.bias"       ,				".conv1d_2.bias"						)
							elif key.endswith(".model.3.weight"						) and len(key.split("."						)						) > 10:
														_lowerCAmelCase  :							int				     =					key.replace(".model.3.weight"       ,				".conv1d_2.weight"						)

							if "conditioner_blocks.0." in key:
														_lowerCAmelCase  :							List[str]				     =					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  :							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:
														_lowerCAmelCase  :							Tuple				     =					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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					{}
							import re

							_lowerCAmelCase  :							Union[str, Any]				     =					re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"						)
							_lowerCAmelCase  :							List[str]				     =					re.compile(
							    r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"						)
							_lowerCAmelCase  :							List[Any]				     =					re.compile(r"encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"						)

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

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

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

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

														# rename vqvae.decoder keys
														elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							List[str]				     =					re_decoder_block_conv_out.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							Union[str, Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Any				     =					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  :							str				     =					re_decoder_block_conv_out.sub(_lowerCamelCase       ,				_lowerCamelCase						)

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

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

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

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

														# keep original key
														else:
																					_lowerCAmelCase  :							Optional[int]				     =					original_key

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

														_lowerCAmelCase  :							List[Any]				     =					original_key
														_lowerCAmelCase  :							Optional[int]				     =					value

							return new_dict






@torch.no_grad()
def        A       (      _lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(F"{pytorch_dump_folder_path}/{file.split('/'						)[-1]}"						):
																					_lowerCAmelCase  :							List[Any]				     =					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						)

							_lowerCAmelCase  :							Optional[Any]				     =					MODEL_MAPPING[model_name.split("/"						)[-1]]

							_lowerCAmelCase  :							Tuple				     =					JukeboxConfig.from_pretrained(_lowerCamelCase						)
							_lowerCAmelCase  :							Optional[int]				     =					JukeboxModel(_lowerCamelCase						)

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

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

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

							_lowerCAmelCase  :							Optional[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__":
						_snake_case	=						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.",
						)
						_snake_case	=						parser.parse_args()
						convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
