





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


lowerCAmelCase :					str 							='''https://openaipublic.azureedge.net/jukebox/models/'''
lowerCAmelCase :					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 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]			):
   if key.endswith(".model.1.bias"			) and len(key.split("."			)			) > 10:
      lowercase_      :Optional[int]         =			key.replace(".model.1.bias"     ,".conv1d_1.bias"			)
   elif key.endswith(".model.1.weight"			) and len(key.split("."			)			) > 10:
      lowercase_      :List[str]         =			key.replace(".model.1.weight"     ,".conv1d_1.weight"			)
   elif key.endswith(".model.3.bias"			) and len(key.split("."			)			) > 10:
      lowercase_      :Dict         =			key.replace(".model.3.bias"     ,".conv1d_2.bias"			)
   elif key.endswith(".model.3.weight"			) and len(key.split("."			)			) > 10:
      lowercase_      :List[Any]         =			key.replace(".model.3.weight"     ,".conv1d_2.weight"			)

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

   if "prime_prior" in key:
      lowercase_      :Tuple         =			key.replace("prime_prior"     ,"encoder"			)

   if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
      lowercase_      :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:
      lowercase_      :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 							UpperCAmelCase_  (						__lowerCamelCase : Union[str, Any]     ,__lowerCamelCase : Any     ,__lowerCamelCase : int     ,__lowerCamelCase : Optional[Any]			):
   lowercase_      :Union[str, Any]         =			{}
   import re

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

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

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

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

      elif re_encoder_block_proj_out.fullmatch(snake_case__			):
         lowercase_      :str         =			re_encoder_block_proj_out.match(snake_case__			)
         lowercase_      :Tuple         =			regex_match.groups()
         lowercase_      :Dict         =			F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
         lowercase_      :str         =			re_encoder_block_proj_out.sub(snake_case__     ,snake_case__			)

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

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

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

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

      elif re_prior_cond_proj_in.fullmatch(snake_case__			):
         lowercase_      :List[Any]         =			re_prior_cond_proj_in.match(snake_case__			)
         lowercase_      :Any         =			regex_match.groups()
         lowercase_      :Dict         =			F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
         lowercase_      :Dict         =			re_prior_cond_proj_in.sub(snake_case__     ,snake_case__			)

      # keep original key
      else:
         lowercase_      :Tuple         =			original_key

      lowercase_      :Dict         =			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:
         lowercase_      :int         =			model_state_dict[F'{key_prefix}.{key}']
         print(F'{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match'			)
         lowercase_      :Optional[int]         =			original_key

      lowercase_      :Dict         =			original_key
      lowercase_      :Any         =			value

   return new_dict
@torch.no_grad()
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]=None     ,__lowerCamelCase : Optional[int]=None			):
   for file in MODEL_MAPPING[model_name]:
      if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"			)[-1]}'			):
         lowercase_      :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			)

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

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

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

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

      lowercase_      :Optional[int]         =			'vqvae' if i == 0 else F'priors.{3 - i}'
      lowercase_      :Optional[int]         =			fix_jukebox_keys(snake_case__     ,model.state_dict()     ,snake_case__     ,snake_case__			)
      weight_dict.append(snake_case__			)

   lowercase_      :List[str]         =			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__":
 lowerCAmelCase :					Any 							=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 :					Any 							=parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase :					Tuple 							=logging.get_logger(__name__)

lowerCAmelCase :					str 							={
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}
class 				a_       (    _lowerCAmelCase							):
     __A								=      "cvt"





     def __init__(			self		: Tuple     ,				lowercase		: str=3     ,				lowercase		: str=[7, 3, 3]     ,				lowercase		: List[str]=[4, 2, 2]     ,				lowercase		: Dict=[2, 1, 1]     ,				lowercase		: int=[64, 192, 384]     ,				lowercase		: Dict=[1, 3, 6]     ,				lowercase		: Dict=[1, 2, 10]     ,				lowercase		: Any=[4.0, 4.0, 4.0]     ,				lowercase		: Tuple=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.0]     ,				lowercase		: List[str]=[0.0, 0.0, 0.1]     ,				lowercase		: Any=[True, True, True]     ,				lowercase		: Any=[False, False, True]     ,				lowercase		: Optional[Any]=["dw_bn", "dw_bn", "dw_bn"]     ,				lowercase		: int=[3, 3, 3]     ,				lowercase		: str=[1, 1, 1]     ,				lowercase		: List[Any]=[2, 2, 2]     ,				lowercase		: Tuple=[1, 1, 1]     ,				lowercase		: Optional[Any]=[1, 1, 1]     ,				lowercase		: str=0.02     ,				lowercase		: str=1e-1_2     ,				**lowercase		: str     ,				):





        """simple docstring"""




        super().__init__(**lowercase						)
        lowercase_      :List[Any]         =			num_channels
        lowercase_      :Union[str, Any]         =			patch_sizes
        lowercase_      :Tuple         =			patch_stride
        lowercase_      :List[Any]         =			patch_padding
        lowercase_      :List[Any]         =			embed_dim
        lowercase_      :Union[str, Any]         =			num_heads
        lowercase_      :Any         =			depth
        lowercase_      :str         =			mlp_ratio
        lowercase_      :List[str]         =			attention_drop_rate
        lowercase_      :List[Any]         =			drop_rate
        lowercase_      :Union[str, Any]         =			drop_path_rate
        lowercase_      :Any         =			qkv_bias
        lowercase_      :Dict         =			cls_token
        lowercase_      :int         =			qkv_projection_method
        lowercase_      :Union[str, Any]         =			kernel_qkv
        lowercase_      :Optional[Any]         =			padding_kv
        lowercase_      :Optional[Any]         =			stride_kv
        lowercase_      :Dict         =			padding_q
        lowercase_      :Any         =			stride_q
        lowercase_      :Dict         =			initializer_range
        lowercase_      :Optional[Any]         =			layer_norm_eps


