



'''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()
_UpperCamelCase											=		logging.get_logger(__name__)


_UpperCamelCase											=		'https://openaipublic.azureedge.net/jukebox/models/'
_UpperCamelCase											=		{
    '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			) -> str:
  if key.endswith('.model.1.bias'			) and len(key.split('.'			)			) > 10:
    __lowerCamelCase			:			Tuple       			=					key.replace('.model.1.bias'			,'.conv1d_1.bias'			)
  elif key.endswith('.model.1.weight'			) and len(key.split('.'			)			) > 10:
    __lowerCamelCase			:			Union[str, Any]       			=					key.replace('.model.1.weight'			,'.conv1d_1.weight'			)
  elif key.endswith('.model.3.bias'			) and len(key.split('.'			)			) > 10:
    __lowerCamelCase			:			List[str]       			=					key.replace('.model.3.bias'			,'.conv1d_2.bias'			)
  elif key.endswith('.model.3.weight'			) and len(key.split('.'			)			) > 10:
    __lowerCamelCase			:			Union[str, Any]       			=					key.replace('.model.3.weight'			,'.conv1d_2.weight'			)

  if "conditioner_blocks.0." in key:
    __lowerCamelCase			:			Tuple       			=					key.replace('conditioner_blocks.0'			,'conditioner_blocks'			)

  if "prime_prior" in key:
    __lowerCamelCase			:			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:
    __lowerCamelCase			:			List[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			:			str       			=					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			) -> str:
  __lowerCamelCase			:			int       			=					{}
  import re

  __lowerCamelCase			:			str       			=					re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'			)
  __lowerCamelCase			:			Union[str, Any]       			=					re.compile(
      r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'			)
  __lowerCamelCase			:			int       			=					re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'			)

  __lowerCamelCase			:			Tuple       			=					re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'			)
  __lowerCamelCase			:			Dict       			=					re.compile(
      r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'			)
  __lowerCamelCase			:			List[str]       			=					re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'			)

  __lowerCamelCase			:			Dict       			=					re.compile(r'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'			)
  __lowerCamelCase			:			Optional[int]       			=					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			:			Tuple       			=					re_encoder_block_conv_in.match(_lowerCAmelCase			)
      __lowerCamelCase			:			Dict       			=					regex_match.groups()
      __lowerCamelCase			:			Optional[Any]       			=					int(groups[2]			) * 2 + int(groups[3]			)
      __lowerCamelCase			:			Optional[int]       			=					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			:			Union[str, Any]       			=					re_encoder_block_resnet.match(_lowerCAmelCase			)
      __lowerCamelCase			:			Tuple       			=					regex_match.groups()
      __lowerCamelCase			:			Tuple       			=					int(groups[2]			) * 2 + int(groups[3]			)
      __lowerCamelCase			:			int       			=					{'1': 1, '3': 2}[groups[-2]]
      __lowerCamelCase			:			Optional[int]       			=					F'encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.'
      __lowerCamelCase			:			Any       			=					F'resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}'
      __lowerCamelCase			:			int       			=					prefix + resnet_block
      __lowerCamelCase			:			Dict       			=					re_encoder_block_resnet.sub(_lowerCAmelCase			,_lowerCAmelCase			)

    elif re_encoder_block_proj_out.fullmatch(_lowerCAmelCase			):
      __lowerCamelCase			:			List[str]       			=					re_encoder_block_proj_out.match(_lowerCAmelCase			)
      __lowerCamelCase			:			Any       			=					regex_match.groups()
      __lowerCamelCase			:			str       			=					F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
      __lowerCamelCase			:			List[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			:			Union[str, Any]       			=					int(groups[2]			) * 2 + int(groups[3]			) - 2
      __lowerCamelCase			:			Any       			=					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			:			Optional[int]       			=					re_decoder_block_resnet.match(_lowerCAmelCase			)
      __lowerCamelCase			:			int       			=					regex_match.groups()
      __lowerCamelCase			:			int       			=					int(groups[2]			) * 2 + int(groups[3]			) - 2
      __lowerCamelCase			:			Optional[Any]       			=					{'1': 1, '3': 2}[groups[-2]]
      __lowerCamelCase			:			Tuple       			=					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			:			List[Any]       			=					prefix + resnet_block
      __lowerCamelCase			:			Any       			=					re_decoder_block_resnet.sub(_lowerCAmelCase			,_lowerCAmelCase			)

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

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

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

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

    __lowerCamelCase			:			Optional[int]       			=					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			:			int       			=					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			:			Optional[int]       			=					original_key
    __lowerCamelCase			:			List[str]       			=					value

  return new_dict

@torch.no_grad()
def 			a_	(				_lowerCAmelCase=None			,_lowerCAmelCase=None			) -> int:
  for file in MODEL_MAPPING[model_name]:
    if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"			)[-1]}'			):
      __lowerCamelCase			:			List[str]       			=					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[int]       			=					MODEL_MAPPING[model_name.split('/'			)[-1]]

  __lowerCamelCase			:			List[str]       			=					JukeboxConfig.from_pretrained(_lowerCAmelCase			)
  __lowerCamelCase			:			str       			=					JukeboxModel(_lowerCAmelCase			)

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

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

    __lowerCamelCase			:			Tuple       			=					'vqvae' if i == 0 else F'priors.{3 - i}'
    __lowerCamelCase			:			List[str]       			=					fix_jukebox_keys(_lowerCAmelCase			,model.state_dict()			,_lowerCAmelCase			,_lowerCAmelCase			)
    weight_dict.append(_lowerCAmelCase			)

  __lowerCamelCase			:			str       			=					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__":
   _UpperCamelCase											=		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											=		parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)











'''simple docstring'''





from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


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






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  def __init__(					self :      Union[str, Any]     ,      _a :      CLIPSegForImageSegmentation     ,      _a :      CLIPSegProcessor     ,      _a :      AutoencoderKL     ,      _a :      CLIPTextModel     ,      _a :      CLIPTokenizer     ,      _a :      UNetaDConditionModel     ,      _a :      Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]     ,      _a :      StableDiffusionSafetyChecker     ,      _a :      CLIPImageProcessor     ,      )   ->   int:
    super().__init__()

    if hasattr(scheduler.config     ,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
      __lowerCamelCase			:			int       			=					(
          f'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
          f' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
          'to update the config accordingly as leaving `steps_offset` might led to incorrect results'
          ' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'
          ' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'
          ' file'
      )
      deprecate('steps_offset!=1'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			int       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			List[Any]       			=					FrozenDict(_a  )

    if hasattr(scheduler.config     ,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
      __lowerCamelCase			:			str       			=					(
          f'The configuration file of this scheduler: {scheduler} has not set the configuration'
          ' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'
          ' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'
          ' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'
          ' Hub, it would be very nice if you could open a Pull request for the'
          ' `scheduler/scheduler_config.json` file'
      )
      deprecate('skip_prk_steps not set'     ,      '1.0.0'     ,      _a     ,      standard_warn=_a  )
      __lowerCamelCase			:			Dict       			=					dict(scheduler.config  )
      __lowerCamelCase			:			Tuple       			=					True
      __lowerCamelCase			:			Union[str, Any]       			=					FrozenDict(_a  )

    if safety_checker is None:
      logger.warning(
          f'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
          ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
          ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
          ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
          ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
          ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'  )

    self.register_modules(
        segmentation_model=_a     ,      segmentation_processor=_a     ,      vae=_a     ,      text_encoder=_a     ,      tokenizer=_a     ,      unet=_a     ,      scheduler=_a     ,      safety_checker=_a     ,      feature_extractor=_a     ,      )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      Optional[Union[str, int]] = "auto"  )   ->   List[str]:
    if slice_size == "auto":
      # half the attention head size is usually a good trade-off between
      # speed and memory
      __lowerCamelCase			:			List[Any]       			=					self.unet.config.attention_head_dim // 2
    self.unet.set_attention_slice(_a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   int:
    self.enable_attention_slicing(_a  )




  def 		_lowercase	(					self :      str  )   ->   Any:
    if is_accelerate_available():
      from accelerate import cpu_offload
    else:
      raise ImportError('Please install accelerate via `pip install accelerate`'  )

    __lowerCamelCase			:			Dict       			=					torch.device('cuda'  )

    for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
      if cpu_offloaded_model is not None:
        cpu_offload(_a     ,      _a  )




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		_lowercase	(					self :      Dict  )   ->   int:
    if self.device != torch.device('meta'  ) or not hasattr(self.unet     ,      '_hf_hook'  ):
      return self.device
    for module in self.unet.modules():
      if (
          hasattr(_a     ,      '_hf_hook'  )
          and hasattr(module._hf_hook     ,      'execution_device'  )
          and module._hf_hook.execution_device is not None
      ):
        return torch.device(module._hf_hook.execution_device  )
    return self.device




  @torch.no_grad()
  def __call__(					self :      List[str]     ,      _a :      Union[str, List[str]]     ,      _a :      Union[torch.FloatTensor, PIL.Image.Image]     ,      _a :      str     ,      _a :      int = 512     ,      _a :      int = 512     ,      _a :      int = 50     ,      _a :      float = 7.5     ,      _a :      Optional[Union[str, List[str]]] = None     ,      _a :      Optional[int] = 1     ,      _a :      float = 0.0     ,      _a :      Optional[torch.Generator] = None     ,      _a :      Optional[torch.FloatTensor] = None     ,      _a :      Optional[str] = "pil"     ,      _a :      bool = True     ,      _a :      Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,      _a :      int = 1     ,      **_a :      Optional[Any]     ,      )   ->   int:
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_processor(
        text=[text]     ,      images=[image]     ,      padding='max_length'     ,      return_tensors='pt'  ).to(self.device  )
    __lowerCamelCase			:			Union[str, Any]       			=					self.segmentation_model(**_a  )
    __lowerCamelCase			:			List[str]       			=					torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
    __lowerCamelCase			:			Optional[Any]       			=					self.numpy_to_pil(_a  )[0].resize(image.size  )

    # Run inpainting pipeline with the generated mask
    __lowerCamelCase			:			Tuple       			=					StableDiffusionInpaintPipeline(
        vae=self.vae     ,      text_encoder=self.text_encoder     ,      tokenizer=self.tokenizer     ,      unet=self.unet     ,      scheduler=self.scheduler     ,      safety_checker=self.safety_checker     ,      feature_extractor=self.feature_extractor     ,      )
    return inpainting_pipeline(
        prompt=_a     ,      image=_a     ,      mask_image=_a     ,      height=_a     ,      width=_a     ,      num_inference_steps=_a     ,      guidance_scale=_a     ,      negative_prompt=_a     ,      num_images_per_prompt=_a     ,      eta=_a     ,      generator=_a     ,      latents=_a     ,      output_type=_a     ,      return_dict=_a     ,      callback=_a     ,      callback_steps=_a     ,      )






