
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

__snake_case:  Union[str, Any]								=			{
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"big_bird"


     def __init__(      self							,		lowerCAmelCase_=5_03_58							,		lowerCAmelCase_=7_68							,		lowerCAmelCase_=12							,		lowerCAmelCase_=12							,		lowerCAmelCase_=30_72							,		lowerCAmelCase_="gelu_new"							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=40_96							,		lowerCAmelCase_=2							,		lowerCAmelCase_=0.02							,		lowerCAmelCase_=1E-1_2							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		lowerCAmelCase_=2							,		lowerCAmelCase_=66							,		lowerCAmelCase_="block_sparse"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=False							,		lowerCAmelCase_=64							,		lowerCAmelCase_=3							,		lowerCAmelCase_=None							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		bos_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		sep_token_id=lowerCAmelCase_							,		**lowerCAmelCase_							,		)

       a_      :	Dict      =							vocab_size
       a_      :	Dict      =							max_position_embeddings
       a_      :	Any      =							hidden_size
       a_      :	Optional[Any]      =							num_hidden_layers
       a_      :	Optional[int]      =							num_attention_heads
       a_      :	Dict      =							intermediate_size
       a_      :	Any      =							hidden_act
       a_      :	List[str]      =							hidden_dropout_prob
       a_      :	Any      =							attention_probs_dropout_prob
       a_      :	Optional[Any]      =							initializer_range
       a_      :	Union[str, Any]      =							type_vocab_size
       a_      :	Tuple      =							layer_norm_eps
       a_      :	str      =							use_cache

       a_      :	Tuple      =							rescale_embeddings
       a_      :	Optional[int]      =							attention_type
       a_      :	Any      =							use_bias
       a_      :	Union[str, Any]      =							block_size
       a_      :	Optional[int]      =							num_random_blocks
       a_      :	Optional[Any]      =							classifier_dropout







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       if self.task == "multiple-choice":
         a_      :	Tuple      =							{0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
         a_      :	Tuple      =							{0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ] )




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


__snake_case:  Dict								=			"https://openaipublic.azureedge.net/jukebox/models/"
__snake_case:  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 	_snake_case						(      A_       :    str   ):
  """simple docstring"""







  if key.endswith(""".model.1.bias"""   ) and len(key.split("""."""   )   ) > 10:
    a_      :	Optional[Any]      =							key.replace(""".model.1.bias"""     ,		""".conv1d_1.bias"""   )
  elif key.endswith(""".model.1.weight"""   ) and len(key.split("""."""   )   ) > 10:
    a_      :	Tuple      =							key.replace(""".model.1.weight"""     ,		""".conv1d_1.weight"""   )
  elif key.endswith(""".model.3.bias"""   ) and len(key.split("""."""   )   ) > 10:
    a_      :	Union[str, Any]      =							key.replace(""".model.3.bias"""     ,		""".conv1d_2.bias"""   )
  elif key.endswith(""".model.3.weight"""   ) and len(key.split("""."""   )   ) > 10:
    a_      :	List[str]      =							key.replace(""".model.3.weight"""     ,		""".conv1d_2.weight"""   )

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

  if "prime_prior" in key:
    a_      :	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:
    a_      :	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:
    a_      :	Optional[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 	_snake_case						(      A_       :    str     ,		A_       :    int     ,		A_       :    str     ,		A_       :    List[Any]   ):
  """simple docstring"""







  a_      :	List[str]      =							{}
  import re

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

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

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

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

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

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

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

    elif re_decoder_block_proj_in.fullmatch(A_   ):
      a_      :	Tuple      =							re_decoder_block_proj_in.match(A_   )
      a_      :	Any      =							regex_match.groups()
      a_      :	Dict      =							f'''decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}'''
      a_      :	Tuple      =							re_decoder_block_proj_in.sub(A_     ,		A_   )

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

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

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

    # keep original key
    else:
      a_      :	str      =							original_key

    a_      :	Any      =							replace_key(A_   )

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

    a_      :	Tuple      =							original_key
    a_      :	Union[str, Any]      =							value

  return new_dict






@torch.no_grad()
def 	_snake_case						(      A_       :    Dict=None     ,		A_       :    Optional[Any]=None   ):
  """simple docstring"""







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

  a_      :	List[Any]      =							MODEL_MAPPING[model_name.split("""/"""   )[-1]]

  a_      :	Optional[Any]      =							JukeboxConfig.from_pretrained(A_   )
  a_      :	List[Any]      =							JukeboxModel(A_   )

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

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

    a_      :	List[Any]      =							"""vqvae""" if i == 0 else f'''priors.{3 - i}'''
    a_      :	Any      =							fix_jukebox_keys(A_     ,		model.state_dict()     ,		A_     ,		A_   )
    weight_dict.append(A_   )

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

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

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

  return weight_dict


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


