

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


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


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

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

												snake_case_  =      [np.array(i.resize((w, h)    ,						resample=PIL_INTERPOLATION['''lanczos''']			)			)[None, :] for i in image]
												snake_case_  =      np.concatenate(SCREAMING_SNAKE_CASE__    ,						axis=0			)
												snake_case_  =      np.array(SCREAMING_SNAKE_CASE__			).astype(np.floataa			) / 255.0
												snake_case_  =      image.transpose(0    ,						3    ,						1    ,						2			)
												snake_case_  =      2.0 * image - 1.0
												snake_case_  =      torch.from_numpy(SCREAMING_SNAKE_CASE__			)
						elif isinstance(image[0]    ,						torch.Tensor			):
												snake_case_  =      torch.cat(SCREAMING_SNAKE_CASE__    ,						dim=0			)
						return image


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__			):
						if isinstance(SCREAMING_SNAKE_CASE__    ,						torch.Tensor			):
												return mask
						elif isinstance(SCREAMING_SNAKE_CASE__    ,						PIL.Image.Image			):
												snake_case_  =      [mask]

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





class       snake_case_  (						__A   ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE	:  UNetaDModel
			SCREAMING_SNAKE_CASE	:  RePaintScheduler







			def __init__(	self					:							str				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Any				) ->Tuple:
									super().__init__()
									self.register_modules(unet=_UpperCamelCase				,  scheduler=_UpperCamelCase				)







			@torch.no_grad()
			def __call__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							Union[torch.Tensor, PIL.Image.Image]				,  _UpperCamelCase					:							Union[torch.Tensor, PIL.Image.Image]				,  _UpperCamelCase					:							int = 2_5_0				,  _UpperCamelCase					:							float = 0.0				,  _UpperCamelCase					:							int = 1_0				,  _UpperCamelCase					:							int = 1_0				,  _UpperCamelCase					:							Optional[Union[torch.Generator, List[torch.Generator]]] = None				,  _UpperCamelCase					:							Optional[str] = "pil"				,  _UpperCamelCase					:							bool = True				,  ) ->Union[ImagePipelineOutput, Tuple]:
									snake_case_  =      image

									snake_case_  =      _preprocess_image(_UpperCamelCase				)
									snake_case_  =      original_image.to(device=self.device				,  dtype=self.unet.dtype				)
									snake_case_  =      _preprocess_mask(_UpperCamelCase				)
									snake_case_  =      mask_image.to(device=self.device				,  dtype=self.unet.dtype				)

									snake_case_  =      original_image.shape[0]

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

									snake_case_  =      original_image.shape
									snake_case_  =      randn_tensor(_UpperCamelCase				,  generator=_UpperCamelCase				,  device=self.device				,  dtype=self.unet.dtype				)

									# set step values
									self.scheduler.set_timesteps(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  self.device				)
									snake_case_  =      eta

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

															else:
																					# compute the reverse: x_t-1 -> x_t
																					snake_case_  =      self.scheduler.undo_step(_UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)
															snake_case_  =      t

									snake_case_  =      (image / 2 + 0.5).clamp(0				,  1				)
									snake_case_  =      image.cpu().permute(0				,  2				,  3				,  1				).numpy()
									if output_type == "pil":
															snake_case_  =      self.numpy_to_pil(_UpperCamelCase				)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=_UpperCamelCase				)



'''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_			:  str										=   logging.get_logger(__name__)


UpperCAmelCase_			:  Dict										=   'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_			:  Tuple										=   {
    '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       _lowercase							(					UpperCamelCase__							:       str      ):
 if key.endswith('.model.1.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.1.bias',					'.conv1d_1.bias'      )
 elif key.endswith('.model.1.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Dict	  =      key.replace('.model.1.weight',					'.conv1d_1.weight'      )
 elif key.endswith('.model.3.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Optional[int]	  =      key.replace('.model.3.bias',					'.conv1d_2.bias'      )
 elif key.endswith('.model.3.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.3.weight',					'.conv1d_2.weight'      )

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

 if "prime_prior" in key:
  __A	:		str	  =      key.replace('prime_prior',					'encoder'      )

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __A	:		str	  =      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:
  __A	:		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       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		Union[str, Any]	  =      {}
 import re

 __A	:		List[Any]	  =      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
 __A	:		Dict	  =      re.compile(
     r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
 __A	:		Optional[int]	  =      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

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

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

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

  elif re_encoder_block_proj_out.fullmatch(UpperCamelCase__      ):
   __A	:		List[str]	  =      re_encoder_block_proj_out.match(UpperCamelCase__      )
   __A	:		Optional[int]	  =      regex_match.groups()
   __A	:		Any	  =      f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
   __A	:		Optional[Any]	  =      re_encoder_block_proj_out.sub(UpperCamelCase__,					UpperCamelCase__      )

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

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

  elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__      ):
   __A	:		int	  =      re_decoder_block_proj_in.match(UpperCamelCase__      )
   __A	:		List[str]	  =      regex_match.groups()
   __A	:		Tuple	  =      f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __A	:		Optional[Any]	  =      re_decoder_block_proj_in.sub(UpperCamelCase__,					UpperCamelCase__      )

  # rename prior cond.model to upsampler.upsample_block and resnet
  elif re_prior_cond_conv_out.fullmatch(UpperCamelCase__      ):
   __A	:		Optional[int]	  =      re_prior_cond_conv_out.match(UpperCamelCase__      )
   __A	:		Union[str, Any]	  =      regex_match.groups()
   __A	:		Dict	  =      int(groups[1]      ) * 2 + int(groups[2]      ) - 2
   __A	:		List[str]	  =      f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
   __A	:		List[str]	  =      re_prior_cond_conv_out.sub(UpperCamelCase__,					UpperCamelCase__      )

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

  elif re_prior_cond_proj_in.fullmatch(UpperCamelCase__      ):
   __A	:		Tuple	  =      re_prior_cond_proj_in.match(UpperCamelCase__      )
   __A	:		Optional[Any]	  =      regex_match.groups()
   __A	:		Optional[int]	  =      f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __A	:		List[Any]	  =      re_prior_cond_proj_in.sub(UpperCamelCase__,					UpperCamelCase__      )

  # keep original key
  else:
   __A	:		List[Any]	  =      original_key

  __A	:		int	  =      replace_key(UpperCamelCase__      )

  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:
   __A	:		Optional[Any]	  =      model_state_dict[f"""{key_prefix}.{key}"""]
   print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""      )
   __A	:		Union[str, Any]	  =      original_key

  __A	:		Optional[int]	  =      original_key
  __A	:		Optional[Any]	  =      value

 return new_dict


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

 __A	:		str	  =      MODEL_MAPPING[model_name.split('/'      )[-1]]

 __A	:		Optional[Any]	  =      JukeboxConfig.from_pretrained(UpperCamelCase__      )
 __A	:		Tuple	  =      JukeboxModel(UpperCamelCase__      )

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

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

  __A	:		List[Any]	  =      'vqvae' if i == 0 else f"""priors.{3 - i}"""
  __A	:		Any	  =      fix_jukebox_keys(UpperCamelCase__,					model.state_dict(),					UpperCamelCase__,					UpperCamelCase__      )
  weight_dict.append(UpperCamelCase__      )

 __A	:		Union[str, Any]	  =      weight_dict.pop(0      )
 model.vqvae.load_state_dict(UpperCamelCase__      )
 for i in range(len(UpperCamelCase__      )      ):
  model.priors[i].load_state_dict(weight_dict[2 - i]      )

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

 print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""      )
 model.save_pretrained(UpperCamelCase__      )

 return weight_dict


if __name__ == "__main__":
 UpperCAmelCase_			:  Dict										=   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_			:  Union[str, Any]										=   parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






