

"""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()
__lowerCamelCase					    =		logging.get_logger(__name__)


__lowerCamelCase					    =		"""https://openaipublic.azureedge.net/jukebox/models/"""
__lowerCamelCase					    =		{
    """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    )			->   Optional[Any]:


 if key.endswith('''.model.1.bias'''    ) and len(key.split('''.'''    )    ) > 10:
  __magic_name__	 =   key.replace('''.model.1.bias'''   , '''.conv1d_1.bias'''    )
 elif key.endswith('''.model.1.weight'''    ) and len(key.split('''.'''    )    ) > 10:
  __magic_name__	 =   key.replace('''.model.1.weight'''   , '''.conv1d_1.weight'''    )
 elif key.endswith('''.model.3.bias'''    ) and len(key.split('''.'''    )    ) > 10:
  __magic_name__	 =   key.replace('''.model.3.bias'''   , '''.conv1d_2.bias'''    )
 elif key.endswith('''.model.3.weight'''    ) and len(key.split('''.'''    )    ) > 10:
  __magic_name__	 =   key.replace('''.model.3.weight'''   , '''.conv1d_2.weight'''    )

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

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

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __magic_name__	 =   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:
  __magic_name__	 =   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   , __UpperCamelCase   , __UpperCamelCase   , __UpperCamelCase    )			->   Any:
 __magic_name__	 =   {}
 import re

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

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

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

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

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

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

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

  elif re_decoder_block_proj_in.fullmatch(_A    ):
   __magic_name__	 =   re_decoder_block_proj_in.match(_A    )
   __magic_name__	 =   regex_match.groups()
   __magic_name__	 =   f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
   __magic_name__	 =   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    ):
   __magic_name__	 =   re_prior_cond_conv_out.match(_A    )
   __magic_name__	 =   regex_match.groups()
   __magic_name__	 =   int(groups[1]    ) * 2 + int(groups[2]    ) - 2
   __magic_name__	 =   f'''conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}'''
   __magic_name__	 =   re_prior_cond_conv_out.sub(_A   , _A    )

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

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

  # keep original key
  else:
   __magic_name__	 =   original_key

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

  __magic_name__	 =   original_key
  __magic_name__	 =   value

 return new_dict





@torch.no_grad()
def 						lowercase   ( __UpperCamelCase=None   , __UpperCamelCase=None    )			->   List[str]:


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

 __magic_name__	 =   MODEL_MAPPING[model_name.split('''/'''    )[-1]]

 __magic_name__	 =   JukeboxConfig.from_pretrained(_A    )
 __magic_name__	 =   JukeboxModel(_A    )

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

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

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

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


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.get_logger(__name__)
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      ["""pixel_values"""]

  def __init__(		self			:   Any  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float] = 1 / 2_55  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = True  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)
   __a							=	size if size is not None else {"height": 3_84, "width": 3_84}
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	do_resize
   __a							=	size
   __a							=	resample
   __a							=	do_rescale
   __a							=	rescale_factor
   __a							=	do_normalize
   __a							=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   __a							=	image_std if image_std is not None else OPENAI_CLIP_STD
   __a							=	do_convert_rgb

  def 		_UpperCAmelCase (		self			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Dict[str, int]  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = PILImageResampling.BICUBIC  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)
   if "height" not in size or "width" not in size:
    raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""							)
   __a							=	(size["height"], size["width"])
   return resize(__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[int, float]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   str  ,				):
   return rescale(__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   np.ndarray  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Union[float, List[float]]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, ChannelDimension]] = None  ,				**__SCREAMING_SNAKE_CASE			:   int  ,				):
   return normalize(__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE  ,				data_format=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   ImageInput  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Dict[str, int]] = None  ,				__SCREAMING_SNAKE_CASE			:   PILImageResampling = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[float] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[bool] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[float, List[float]]] = None  ,				__SCREAMING_SNAKE_CASE			:   Optional[Union[str, TensorType]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = None  ,				__SCREAMING_SNAKE_CASE			:   ChannelDimension = ChannelDimension.FIRST  ,				**__SCREAMING_SNAKE_CASE			:   Any  ,				):
   __a							=	do_resize if do_resize is not None else self.do_resize
   __a							=	resample if resample is not None else self.resample
   __a							=	do_rescale if do_rescale is not None else self.do_rescale
   __a							=	rescale_factor if rescale_factor is not None else self.rescale_factor
   __a							=	do_normalize if do_normalize is not None else self.do_normalize
   __a							=	image_mean if image_mean is not None else self.image_mean
   __a							=	image_std if image_std is not None else self.image_std
   __a							=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __a							=	size if size is not None else self.size
   __a							=	get_size_dict(__SCREAMING_SNAKE_CASE  ,				default_to_square=__SCREAMING_SNAKE_CASE							)

   __a							=	make_list_of_images(__SCREAMING_SNAKE_CASE							)

   if not valid_images(__SCREAMING_SNAKE_CASE							):
    raise ValueError(
        "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
        "torch.Tensor, tf.Tensor or jax.ndarray."							)

   if do_resize and size is None or resample is None:
    raise ValueError("Size and resample must be specified if do_resize is True."							)

   if do_rescale and rescale_factor is None:
    raise ValueError("Rescale factor must be specified if do_rescale is True."							)

   if do_normalize and (image_mean is None or image_std is None):
    raise ValueError("Image mean and std must be specified if do_normalize is True."							)

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
    __a							=	[convert_to_rgb(__SCREAMING_SNAKE_CASE							) for image in images]

   # All transformations expect numpy arrays.
   __a							=	[to_numpy_array(__SCREAMING_SNAKE_CASE							) for image in images]

   if do_resize:
    __a							=	[self.resize(image=__SCREAMING_SNAKE_CASE  ,				size=__SCREAMING_SNAKE_CASE  ,				resample=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_rescale:
    __a							=	[self.rescale(image=__SCREAMING_SNAKE_CASE  ,				scale=__SCREAMING_SNAKE_CASE							) for image in images]

   if do_normalize:
    __a							=	[self.normalize(image=__SCREAMING_SNAKE_CASE  ,				mean=__SCREAMING_SNAKE_CASE  ,				std=__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	[to_channel_dimension_format(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							) for image in images]

   __a							=	BatchFeature(data={"pixel_values": images}  ,				tensor_type=__SCREAMING_SNAKE_CASE							)

   return encoded_outputs


