





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


UpperCAmelCase_					:   Any 							=						"""https://openaipublic.azureedge.net/jukebox/models/"""
UpperCAmelCase_					:   Union[str, 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 							_lowerCAmelCase (  _a   :					int			)	->			Optional[Any]:
     if key.endswith(""".model.1.bias"""			) and len(key.split("""."""			)			) > 10:
          lowerCAmelCase_					:       Any    =		key.replace(""".model.1.bias"""					,   """.conv1d_1.bias"""			)
     elif key.endswith(""".model.1.weight"""			) and len(key.split("""."""			)			) > 10:
          lowerCAmelCase_					:       str    =		key.replace(""".model.1.weight"""					,   """.conv1d_1.weight"""			)
     elif key.endswith(""".model.3.bias"""			) and len(key.split("""."""			)			) > 10:
          lowerCAmelCase_					:       List[Any]    =		key.replace(""".model.3.bias"""					,   """.conv1d_2.bias"""			)
     elif key.endswith(""".model.3.weight"""			) and len(key.split("""."""			)			) > 10:
          lowerCAmelCase_					:       List[Any]    =		key.replace(""".model.3.weight"""					,   """.conv1d_2.weight"""			)

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

     if "prime_prior" in key:
          lowerCAmelCase_					:       Tuple    =		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_					:       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:
          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 							_lowerCAmelCase (  _a   :					List[Any]					,   _a   :					Any					,   _a   :					Union[str, Any]					,   _a   :					Dict			)	->			str:
     lowerCAmelCase_					:       Optional[int]    =		{}
     import re

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

     lowerCAmelCase_					:       Any    =		re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""			)
     lowerCAmelCase_					:       Any    =		re.compile(
         R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""			)
     lowerCAmelCase_					:       Dict    =		re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""			)

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

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

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

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

          elif re_decoder_block_resnet.fullmatch(_a			):
               lowerCAmelCase_					:       Optional[int]    =		re_decoder_block_resnet.match(_a			)
               lowerCAmelCase_					:       Optional[Any]    =		regex_match.groups()
               lowerCAmelCase_					:       Any    =		int(groups[2]			) * 2 + int(groups[3]			) - 2
               lowerCAmelCase_					:       Any    =		{"""1""": 1, """3""": 2}[groups[-2]]
               lowerCAmelCase_					:       List[Any]    =		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_					:       int    =		re_decoder_block_resnet.sub(_a					,   _a			)

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

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

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

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

          # keep original key
          else:
               lowerCAmelCase_					:       Any    =		original_key

          lowerCAmelCase_					:       int    =		replace_key(_a			)

          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_					:       List[Any]    =		original_key

          lowerCAmelCase_					:       Tuple    =		original_key
          lowerCAmelCase_					:       Optional[int]    =		value

     return new_dict






@torch.no_grad()
def 							_lowerCAmelCase (  _a   :					Tuple=None					,   _a   :					str=None			)	->			List[str]:
     for file in MODEL_MAPPING[model_name]:
          if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"			)[-1]}'			):
               lowerCAmelCase_					:       Optional[int]    =		requests.get(F'{PREFIX}{file}'					,   allow_redirects=_a			)
               os.makedirs(F'{pytorch_dump_folder_path}/'					,   exist_ok=_a			)
               open(F'{pytorch_dump_folder_path}/{file.split("/"			)[-1]}'					,   """wb"""			).write(r.content			)

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

     lowerCAmelCase_					:       Any    =		JukeboxConfig.from_pretrained(_a			)
     lowerCAmelCase_					:       List[str]    =		JukeboxModel(_a			)

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

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

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

     lowerCAmelCase_					:       List[str]    =		weight_dict.pop(0			)
     model.vqvae.load_state_dict(_a			)
     for i in range(len(_a			)			):
          model.priors[i].load_state_dict(weight_dict[2 - i]			)

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

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

     return weight_dict


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







from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  ["""vqvae"""]







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						_lowercase    ,						):
       super().__init__()
       self.register_modules(unet=_lowercase    ,						scheduler=_lowercase    ,						mel=_lowercase    ,						vqvae=_lowercase						)







  def 	UpperCAmelCase__		(	self						):
       return 50 if isinstance(self.scheduler    ,						_lowercase						) else 1_000







  @torch.no_grad()
  def __call__(	self    ,						_lowercase = 1    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = 0    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase=True    ,						):
       lowerCAmelCase_					:       Tuple    =		steps or self.get_default_steps()
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       Union[str, Any]    =		step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size						) == int:
            lowerCAmelCase_					:       Optional[Any]    =		(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
            lowerCAmelCase_					:       Tuple    =		randn_tensor(
                (
                    batch_size,
                    self.unet.config.in_channels,
                    self.unet.config.sample_size[0],
                    self.unet.config.sample_size[1],
                )    ,						generator=_lowercase    ,						device=self.device    ,						)
       lowerCAmelCase_					:       List[str]    =		noise
       lowerCAmelCase_					:       List[str]    =		None

       if audio_file is not None or raw_audio is not None:
            self.mel.load_audio(_lowercase    ,						_lowercase						)
            lowerCAmelCase_					:       Tuple    =		self.mel.audio_slice_to_image(_lowercase						)
            lowerCAmelCase_					:       List[Any]    =		np.frombuffer(input_image.tobytes()    ,						dtype="""uint8"""						).reshape(
                (input_image.height, input_image.width)						)
            lowerCAmelCase_					:       Optional[Any]    =		(input_image / 255) * 2 - 1
            lowerCAmelCase_					:       Optional[Any]    =		torch.tensor(input_image[np.newaxis, :, :]    ,						dtype=torch.float						).to(self.device						)

            if self.vqvae is not None:
                 lowerCAmelCase_					:       List[Any]    =		self.vqvae.encode(torch.unsqueeze(_lowercase    ,						0						)						).latent_dist.sample(
                     generator=_lowercase						)[0]
                 lowerCAmelCase_					:       str    =		self.vqvae.config.scaling_factor * input_images

            if start_step > 0:
                 lowerCAmelCase_					:       str    =		self.scheduler.add_noise(_lowercase    ,						_lowercase    ,						self.scheduler.timesteps[start_step - 1]						)

            lowerCAmelCase_					:       Dict    =		(
                self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
            )
            lowerCAmelCase_					:       Union[str, Any]    =		int(mask_start_secs * pixels_per_second						)
            lowerCAmelCase_					:       str    =		int(mask_end_secs * pixels_per_second						)
            lowerCAmelCase_					:       List[Any]    =		self.scheduler.add_noise(_lowercase    ,						_lowercase    ,						torch.tensor(self.scheduler.timesteps[start_step:]						)						)

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]						)						):
            if isinstance(self.unet    ,						_lowercase						):
                 lowerCAmelCase_					:       List[Any]    =		self.unet(_lowercase    ,						_lowercase    ,						_lowercase						)["""sample"""]
            else:
                 lowerCAmelCase_					:       Any    =		self.unet(_lowercase    ,						_lowercase						)["""sample"""]

            if isinstance(self.scheduler    ,						_lowercase						):
                 lowerCAmelCase_					:       str    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						eta=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]
            else:
                 lowerCAmelCase_					:       List[str]    =		self.scheduler.step(
                     model_output=_lowercase    ,						timestep=_lowercase    ,						sample=_lowercase    ,						generator=_lowercase    ,						)["""prev_sample"""]

            if mask is not None:
                 if mask_start > 0:
                      lowerCAmelCase_					:       Optional[Any]    =		mask[:, step, :, :mask_start]
                 if mask_end > 0:
                      lowerCAmelCase_					:       Optional[Any]    =		mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
            # 0.18215 was scaling factor used in training to ensure unit variance
            lowerCAmelCase_					:       Dict    =		1 / self.vqvae.config.scaling_factor * images
            lowerCAmelCase_					:       Dict    =		self.vqvae.decode(_lowercase						)["""sample"""]

       lowerCAmelCase_					:       List[Any]    =		(images / 2 + 0.5).clamp(0    ,						1						)
       lowerCAmelCase_					:       List[Any]    =		images.cpu().permute(0    ,						2    ,						3    ,						1						).numpy()
       lowerCAmelCase_					:       Optional[Any]    =		(images * 255).round().astype("""uint8"""						)
       lowerCAmelCase_					:       str    =		list(
           (Image.fromarray(_[:, :, 0]						) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(_lowercase    ,						mode="""RGB"""						).convert("""L"""						) for _ in images)						)

       lowerCAmelCase_					:       str    =		[self.mel.image_to_audio(_lowercase						) for _ in images]
       if not return_dict:
            return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(_lowercase						)[:, np.newaxis, :]						)    ,						**ImagePipelineOutput(_lowercase						)						)







  @torch.no_grad()
  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = 50						):
       assert isinstance(self.scheduler    ,						_lowercase						)
       self.scheduler.set_timesteps(_lowercase						)
       lowerCAmelCase_					:       List[Any]    =		np.array(
           [np.frombuffer(image.tobytes()    ,						dtype="""uint8"""						).reshape((1, image.height, image.width)						) for image in images]						)
       lowerCAmelCase_					:       List[str]    =		(sample / 255) * 2 - 1
       lowerCAmelCase_					:       Optional[Any]    =		torch.Tensor(_lowercase						).to(self.device						)

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps    ,						(0,)						)						):
            lowerCAmelCase_					:       int    =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
            lowerCAmelCase_					:       Optional[int]    =		self.scheduler.alphas_cumprod[t]
            lowerCAmelCase_					:       Any    =		(
                self.scheduler.alphas_cumprod[prev_timestep]
                if prev_timestep >= 0
                else self.scheduler.final_alpha_cumprod
            )
            lowerCAmelCase_					:       Optional[int]    =		1 - alpha_prod_t
            lowerCAmelCase_					:       Union[str, Any]    =		self.unet(_lowercase    ,						_lowercase						)["""sample"""]
            lowerCAmelCase_					:       int    =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
            lowerCAmelCase_					:       Dict    =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
            lowerCAmelCase_					:       Optional[Any]    =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample







  @staticmethod
  def 	UpperCAmelCase__		(	_lowercase    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		acos(torch.dot(torch.flatten(_lowercase						)    ,						torch.flatten(_lowercase						)						) / torch.norm(_lowercase						) / torch.norm(_lowercase						)						)
       return sin((1 - alpha) * theta						) * xa / sin(_lowercase						) + sin(alpha * theta						) * xa / sin(_lowercase						)
