"""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					: List[str]        =logging.get_logger(__name__)


__lowerCAmelCase					: Dict        ="""https://openaipublic.azureedge.net/jukebox/models/"""
__lowerCAmelCase					: Optional[int]        ={
    """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__  :Optional[Any]	)				->					Dict:
      '''simple docstring'''


      if key.endswith(""".model.1.bias"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.1.bias"""      ,	""".conv1d_1.bias"""	)
      elif key.endswith(""".model.1.weight"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.1.weight"""      ,	""".conv1d_1.weight"""	)
      elif key.endswith(""".model.3.bias"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.3.bias"""      ,	""".conv1d_2.bias"""	)
      elif key.endswith(""".model.3.weight"""	) and len(key.split("""."""	)	) > 1_0:
            lowercase														=			key.replace(""".model.3.weight"""      ,	""".conv1d_2.weight"""	)

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

      if "prime_prior" in key:
            lowercase														=			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														=			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														=			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__  :Tuple      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Tuple	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			{}
      import re

      lowercase														=			re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""	)
      lowercase														=			re.compile(
          R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""	)
      lowercase														=			re.compile(R"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""	)

      lowercase														=			re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)"""	)
      lowercase														=			re.compile(
          R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)"""	)
      lowercase														=			re.compile(R"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)"""	)

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

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

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

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

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

            elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase__	):
                  lowercase														=			re_decoder_block_proj_in.match(lowerCAmelCase__	)
                  lowercase														=			regex_match.groups()
                  lowercase														=			f'decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'
                  lowercase														=			re_decoder_block_proj_in.sub(lowerCAmelCase__      ,	lowerCAmelCase__	)

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

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

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

            # keep original key
            else:
                  lowercase														=			original_key

            lowercase														=			replace_key(lowerCAmelCase__	)

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

            lowercase														=			original_key
            lowercase														=			value

      return new_dict


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any=None      ,	lowerCAmelCase__  :Dict=None	)				->					Dict:
      '''simple docstring'''


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

      lowercase														=			MODEL_MAPPING[model_name.split("""/"""	)[-1]]

      lowercase														=			JukeboxConfig.from_pretrained(lowerCAmelCase__	)
      lowercase														=			JukeboxModel(lowerCAmelCase__	)

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

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

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

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

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

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

      return weight_dict


if __name__ == "__main__":
       __lowerCAmelCase					: Union[str, 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					: str        =parser.parse_args()
       convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def     UpperCAmelCase__ (       )				->					Tuple:
      '''simple docstring'''


      lowercase														=			ArgumentParser("""Accelerate CLI tool"""      ,	usage="""accelerate <command> [<args>]"""      ,	allow_abbrev=lowerCAmelCase__	)
      lowercase														=			parser.add_subparsers(help="""accelerate command helpers"""	)

      # Register commands
      get_config_parser(subparsers=lowerCAmelCase__	)
      env_command_parser(subparsers=lowerCAmelCase__	)
      launch_command_parser(subparsers=lowerCAmelCase__	)
      tpu_command_parser(subparsers=lowerCAmelCase__	)
      test_command_parser(subparsers=lowerCAmelCase__	)

      # Let's go
      lowercase														=			parser.parse_args()

      if not hasattr(lowerCAmelCase__      ,	"""func"""	):
            parser.print_help()
            exit(1	)

      # Run
      args.func(lowerCAmelCase__	)


if __name__ == "__main__":
       main()



