




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__			:       Optional[int]         =							logging.get_logger(__name__)


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

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

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

      if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
            SCREAMING_SNAKE_CASE_     						=      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:
            SCREAMING_SNAKE_CASE_     						=      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  UpperCAmelCase_							(				__UpperCAmelCase						:     int      ,      __UpperCAmelCase						:     List[str]      ,      __UpperCAmelCase						:     Union[str, Any]      ,      __UpperCAmelCase						:     str							) ->			List[Any]:
      SCREAMING_SNAKE_CASE_     						=      {}
      import re

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

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

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

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

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

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

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

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

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

            elif re_prior_cond_proj_in.fullmatch(__UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      re_prior_cond_proj_in.match(__UpperCAmelCase							)
                  SCREAMING_SNAKE_CASE_     						=      regex_match.groups()
                  SCREAMING_SNAKE_CASE_     						=      f"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
                  SCREAMING_SNAKE_CASE_     						=      re_prior_cond_proj_in.sub(__UpperCAmelCase      ,      __UpperCAmelCase							)

            # keep original key
            else:
                  SCREAMING_SNAKE_CASE_     						=      original_key

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

            SCREAMING_SNAKE_CASE_     						=      original_key
            SCREAMING_SNAKE_CASE_     						=      value

      return new_dict

@torch.no_grad()
def  UpperCAmelCase_							(				__UpperCAmelCase						:     Union[str, Any]=None      ,      __UpperCAmelCase						:     Any=None							) ->			int:
      for file in MODEL_MAPPING[model_name]:
            if not os.path.isfile(f"{pytorch_dump_folder_path}/{file.split('/'							)[-1]}"							):
                  SCREAMING_SNAKE_CASE_     						=      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							)

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

      SCREAMING_SNAKE_CASE_     						=      JukeboxConfig.from_pretrained(__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_     						=      JukeboxModel(__UpperCAmelCase							)

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

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

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

      SCREAMING_SNAKE_CASE_     						=      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__":
   lowerCamelCase__			:       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.',
   )
   lowerCamelCase__			:       str         =							parser.parse_args()
   convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





import cva
import numpy as np





class 	lowerCamelCase_							:


 '''simple docstring'''







 def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		float    ,    _lowerCAmelCase  :		int						):

       if k in (0.04, 0.06):
             SCREAMING_SNAKE_CASE_     						=      k
             SCREAMING_SNAKE_CASE_     						=      window_size
       else:
             raise ValueError('invalid k value'						)



 def __str__(							self  :		Tuple						):
       return str(self.k						)




 def 				lowerCAmelCase_  (							self  :		int    ,    _lowerCAmelCase  :		str						):
       SCREAMING_SNAKE_CASE_     						=      cva.imread(_lowerCAmelCase    ,    0						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      img.shape
       SCREAMING_SNAKE_CASE_     						=      []
       SCREAMING_SNAKE_CASE_     						=      img.copy()
       SCREAMING_SNAKE_CASE_     						=      cva.cvtColor(_lowerCAmelCase    ,    cva.COLOR_GRAY2RGB						)
       SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      np.gradient(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      dx**2
       SCREAMING_SNAKE_CASE_     						=      dy**2
       SCREAMING_SNAKE_CASE_     						=      dx * dy
       SCREAMING_SNAKE_CASE_     						=      0.04
       SCREAMING_SNAKE_CASE_     						=      self.window_size // 2
       for y in range(_lowerCAmelCase    ,    h - offset						):
             for x in range(_lowerCAmelCase    ,    w - offset						):
                   SCREAMING_SNAKE_CASE_     						=      ixx[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      iyy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()
                   SCREAMING_SNAKE_CASE_     						=      ixy[
                       y - offset : y + offset + 1, x - offset : x + offset + 1
                   ].sum()

                   SCREAMING_SNAKE_CASE_     						=      (wxx * wyy) - (wxy**2)
                   SCREAMING_SNAKE_CASE_     						=      wxx + wyy
                   SCREAMING_SNAKE_CASE_     						=      det - k * (trace**2)
                   # Can change the value
                   if r > 0.5:
                         corner_list.append([x, y, r]						)
                         color_img.itemset((y, x, 0)    ,    0						)
                         color_img.itemset((y, x, 1)    ,    0						)
                         color_img.itemset((y, x, 2)    ,    255						)
       return color_img, corner_list


if __name__ == "__main__":
   lowerCamelCase__			:       Optional[int]         =							HarrisCorner(0.04, 3)
   lowerCamelCase__    ,							lowerCamelCase__			:       str         =							edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)