


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


__snake_case             =   """https://openaipublic.azureedge.net/jukebox/models/"""
__snake_case             =   {
    """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   __lowerCAmelCase						(						lowercase  :  Any			)     ->						str:


    """simple docstring"""





    if key.endswith(".model.1.bias"			) and len(key.split("."			)			) > 10:
        snake_case   :				Dict				 =   key.replace(".model.1.bias"						,							".conv1d_1.bias"			)
    elif key.endswith(".model.1.weight"			) and len(key.split("."			)			) > 10:
        snake_case   :				Dict				 =   key.replace(".model.1.weight"						,							".conv1d_1.weight"			)
    elif key.endswith(".model.3.bias"			) and len(key.split("."			)			) > 10:
        snake_case   :				Optional[int]				 =   key.replace(".model.3.bias"						,							".conv1d_2.bias"			)
    elif key.endswith(".model.3.weight"			) and len(key.split("."			)			) > 10:
        snake_case   :				int				 =   key.replace(".model.3.weight"						,							".conv1d_2.weight"			)

    if "conditioner_blocks.0." in key:
        snake_case   :				List[str]				 =   key.replace("conditioner_blocks.0"						,							"conditioner_blocks"			)

    if "prime_prior" in key:
        snake_case   :				Dict				 =   key.replace("prime_prior"						,							"encoder"			)

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        snake_case   :				Optional[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:
        snake_case   :				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   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  List[str]						,							lowercase  :  List[Any]						,							lowercase  :  List[Any]			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				Dict				 =   {}
    import re

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

    snake_case   :				Optional[int]				 =   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"			)
    snake_case   :				List[Any]				 =   re.compile(
        R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"			)
    snake_case   :				List[Any]				 =   re.compile(R"decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"			)

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

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

        elif re_encoder_block_proj_out.fullmatch(lowercase			):
            snake_case   :				List[str]				 =   re_encoder_block_proj_out.match(lowercase			)
            snake_case   :				Dict				 =   regex_match.groups()
            snake_case   :				List[str]				 =   F'encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}'
            snake_case   :				Optional[int]				 =   re_encoder_block_proj_out.sub(lowercase						,							lowercase			)

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

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

        elif re_decoder_block_proj_in.fullmatch(lowercase			):
            snake_case   :				List[str]				 =   re_decoder_block_proj_in.match(lowercase			)
            snake_case   :				Tuple				 =   regex_match.groups()
            snake_case   :				Tuple				 =   F'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
            snake_case   :				Optional[int]				 =   re_decoder_block_proj_in.sub(lowercase						,							lowercase			)

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

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

        elif re_prior_cond_proj_in.fullmatch(lowercase			):
            snake_case   :				str				 =   re_prior_cond_proj_in.match(lowercase			)
            snake_case   :				Tuple				 =   regex_match.groups()
            snake_case   :				List[Any]				 =   F'conditioner_blocks.upsampler.proj_in.{groups[-1]}'
            snake_case   :				int				 =   re_prior_cond_proj_in.sub(lowercase						,							lowercase			)

        # keep original key
        else:
            snake_case   :				int				 =   original_key

        snake_case   :				Any				 =   replace_key(lowercase			)

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

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

    return new_dict







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


    """simple docstring"""





    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(F'{pytorch_dump_folder_path}/{file.split("/"			)[-1]}'			):
            snake_case   :				Optional[Any]				 =   requests.get(F'{PREFIX}{file}'						,							allow_redirects=lowercase			)
            os.makedirs(F'{pytorch_dump_folder_path}/'						,							exist_ok=lowercase			)
            open(F'{pytorch_dump_folder_path}/{file.split("/"			)[-1]}'						,							"wb"			).write(r.content			)

    snake_case   :				Union[str, Any]				 =   MODEL_MAPPING[model_name.split("/"			)[-1]]

    snake_case   :				Optional[int]				 =   JukeboxConfig.from_pretrained(lowercase			)
    snake_case   :				Tuple				 =   JukeboxModel(lowercase			)

    snake_case   :				str				 =   []
    snake_case   :				str				 =   {}
    for i, dict_name in enumerate(lowercase			):
        snake_case   :				List[str]				 =   torch.load(F'{pytorch_dump_folder_path}/{dict_name.split("/"			)[-1]}'			)["model"]

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

        snake_case   :				Any				 =   "vqvae" if i == 0 else F'priors.{3 - i}'
        snake_case   :				Any				 =   fix_jukebox_keys(lowercase						,							model.state_dict()						,							lowercase						,							lowercase			)
        weight_dict.append(lowercase			)

    snake_case   :				Optional[int]				 =   weight_dict.pop(0			)
    model.vqvae.load_state_dict(lowercase			)
    for i in range(len(lowercase			)			):
        model.priors[i].load_state_dict(weight_dict[2 - i]			)

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

    print(F'Saving model {model_name} to {pytorch_dump_folder_path}'			)
    model.save_pretrained(lowercase			)

    return weight_dict


if __name__ == "__main__":
  __snake_case             =   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.""",
  )
  __snake_case             =   parser.parse_args()
  convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase    (				unittest.TestCase     ):







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				int				 =   UNetaDModel(
           block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=("DownBlock2D", "AttnDownBlock2D")						,     up_block_types=("AttnUpBlock2D", "UpBlock2D")						,     )
       return model







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Any				 =   self.dummy_uncond_unet
       snake_case   :				Tuple				 =   KarrasVeScheduler()

       snake_case   :				int				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   torch.manual_seed(0						)
       snake_case   :				List[Any]				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Dict				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						,     return_dict=UpperCamelCase__						)[0]

       snake_case   :				Tuple				 =   image[0, -3:, -3:, -1]
       snake_case   :				Tuple				 =   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case   :				int				 =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2







@slow
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   "google/ncsnpp-celebahq-256"
       snake_case   :				List[str]				 =   UNetaDModel.from_pretrained(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   KarrasVeScheduler()

       snake_case   :				Optional[int]				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Union[str, Any]				 =   pipe(num_inference_steps=20						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				int				 =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       snake_case   :				Any				 =   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





