


from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros




def    a__    (		snake_case__					:							List[Any]						,   snake_case__					:							List[Any]     ):
  _UpperCAmelCase		:							int							=     k_size // 2
  _UpperCAmelCase,_UpperCAmelCase		:							List[str]							=     mgrid[0 - center : k_size - center, 0 - center : k_size - center]
  _UpperCAmelCase		:							Dict							=     1 / (2 * pi * sigma) * exp(-(square(snake_case__     ) + square(snake_case__     )) / (2 * square(snake_case__     ))     )
  return g




def    a__    (		snake_case__					:							Tuple						,   snake_case__					:							Union[str, Any]						,   snake_case__					:							int     ):
  _UpperCAmelCase,_UpperCAmelCase		:							str							=     image.shape[0], image.shape[1]
  # dst image height and width
  _UpperCAmelCase		:							Tuple							=     height - k_size + 1
  _UpperCAmelCase		:							List[Any]							=     width - k_size + 1

  # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
  _UpperCAmelCase		:							Union[str, Any]							=     zeros((dst_height * dst_width, k_size * k_size)     )
  _UpperCAmelCase		:							Tuple							=     0
  for i, j in product(range(snake_case__     )						,   range(snake_case__     )     ):
    _UpperCAmelCase		:							Dict							=     ravel(image[i : i + k_size, j : j + k_size]     )
    _UpperCAmelCase		:							int							=     window
    row += 1

  #  turn the kernel into shape(k*k, 1)
  _UpperCAmelCase		:							List[str]							=     gen_gaussian_kernel(snake_case__						,   snake_case__     )
  _UpperCAmelCase		:							List[Any]							=     ravel(snake_case__     )

  # reshape and get the dst image
  _UpperCAmelCase		:							str							=     dot(snake_case__						,   snake_case__     ).reshape(snake_case__						,   snake_case__     ).astype(snake_case__     )

  return dst


if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE__					:     List[Any]       =     imread(R'../image_data/lena.jpg')
 # turn image in gray scale value
 SCREAMING_SNAKE_CASE__					:     List[str]       =     cvtColor(img, COLOR_BGR2GRAY)

 # get values with two different mask size
 SCREAMING_SNAKE_CASE__					:     Union[str, Any]       =     gaussian_filter(gray, 3, sigma=1)
 SCREAMING_SNAKE_CASE__					:     List[str]       =     gaussian_filter(gray, 5, sigma=0.8)

 # show result images
 imshow('gaussian filter with 3x3 mask', gaussianaxa)
 imshow('gaussian filter with 5x5 mask', gaussianaxa)
 waitKey()









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()
SCREAMING_SNAKE_CASE__					:     Any       =     logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__					:     Any       =     'https://openaipublic.azureedge.net/jukebox/models/'
SCREAMING_SNAKE_CASE__					:     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    a__    (		snake_case__					:							Union[str, Any]     ):
  if key.endswith(""".model.1.bias"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Dict							=     key.replace(""".model.1.bias"""						,   """.conv1d_1.bias"""     )
  elif key.endswith(""".model.1.weight"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							List[str]							=     key.replace(""".model.1.weight"""						,   """.conv1d_1.weight"""     )
  elif key.endswith(""".model.3.bias"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Union[str, Any]							=     key.replace(""".model.3.bias"""						,   """.conv1d_2.bias"""     )
  elif key.endswith(""".model.3.weight"""     ) and len(key.split("""."""     )     ) > 10:
    _UpperCAmelCase		:							Optional[Any]							=     key.replace(""".model.3.weight"""						,   """.conv1d_2.weight"""     )

  if "conditioner_blocks.0." in key:
    _UpperCAmelCase		:							int							=     key.replace("""conditioner_blocks.0"""						,   """conditioner_blocks"""     )

  if "prime_prior" in key:
    _UpperCAmelCase		:							Union[str, Any]							=     key.replace("""prime_prior"""						,   """encoder"""     )

  if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
    _UpperCAmelCase		:							Union[str, 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:
    _UpperCAmelCase		:							List[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    a__    (		snake_case__					:							List[str]						,   snake_case__					:							Optional[Any]						,   snake_case__					:							Optional[Any]						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							int							=     {}
  import re

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

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

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

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

    elif re_encoder_block_proj_out.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							List[str]							=     re_encoder_block_proj_out.match(snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     regex_match.groups()
      _UpperCAmelCase		:							List[Any]							=     f'''encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'''
      _UpperCAmelCase		:							Tuple							=     re_encoder_block_proj_out.sub(snake_case__						,   snake_case__     )

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

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

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

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

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

    elif re_prior_cond_proj_in.fullmatch(snake_case__     ):
      _UpperCAmelCase		:							Union[str, Any]							=     re_prior_cond_proj_in.match(snake_case__     )
      _UpperCAmelCase		:							Optional[int]							=     regex_match.groups()
      _UpperCAmelCase		:							Optional[Any]							=     f'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
      _UpperCAmelCase		:							Tuple							=     re_prior_cond_proj_in.sub(snake_case__						,   snake_case__     )

    # keep original key
    else:
      _UpperCAmelCase		:							Tuple							=     original_key

    _UpperCAmelCase		:							List[Any]							=     replace_key(snake_case__     )

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

    _UpperCAmelCase		:							List[Any]							=     original_key
    _UpperCAmelCase		:							int							=     value

  return new_dict




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

  _UpperCAmelCase		:							str							=     MODEL_MAPPING[model_name.split("""/"""     )[-1]]

  _UpperCAmelCase		:							Optional[Any]							=     JukeboxConfig.from_pretrained(snake_case__     )
  _UpperCAmelCase		:							int							=     JukeboxModel(snake_case__     )

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

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

    _UpperCAmelCase		:							Tuple							=     """vqvae""" if i == 0 else f'''priors.{3 - i}'''
    _UpperCAmelCase		:							str							=     fix_jukebox_keys(snake_case__						,   model.state_dict()						,   snake_case__						,   snake_case__     )
    weight_dict.append(snake_case__     )

  _UpperCAmelCase		:							Optional[Any]							=     weight_dict.pop(0     )
  model.vqvae.load_state_dict(snake_case__     )
  for i in range(len(snake_case__     )     ):
    model.priors[i].load_state_dict(weight_dict[2 - i]     )

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

  print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''     )
  model.save_pretrained(snake_case__     )

  return weight_dict


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     List[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.',
 )
 SCREAMING_SNAKE_CASE__					:     List[Any]       =     parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





