
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_						: List[Any]         =  """https://openaipublic.azureedge.net/jukebox/models/"""
lowerCamelCase_						: Optional[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        A__					(						lowerCamelCase						)  ->	List[Any]:







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

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

				if "prime_prior" in key:
								UpperCamelCase_:							Optional[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:
								UpperCamelCase_:							int	=						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:
								UpperCamelCase_:							List[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        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase						)  ->	Any:
				UpperCamelCase_:							Optional[int]	=						{}
				import re

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

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

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

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

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

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

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

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

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

								elif re_prior_cond_proj_in.fullmatch(lowercase__						):
												UpperCamelCase_:							Any	=						re_prior_cond_proj_in.match(lowercase__						)
												UpperCamelCase_:							str	=						regex_match.groups()
												UpperCamelCase_:							Tuple	=						F'''conditioner_blocks.upsampler.proj_in.{groups[-1]}'''
												UpperCamelCase_:							Optional[Any]	=						re_prior_cond_proj_in.sub(lowercase__     ,    lowercase__						)

								# keep original key
								else:
												UpperCamelCase_:							int	=						original_key

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

								UpperCamelCase_:							Optional[Any]	=						original_key
								UpperCamelCase_:							Union[str, Any]	=						value

				return new_dict







@torch.no_grad()
def        A__					(						lowerCamelCase=None     ,    lowerCamelCase=None						)  ->	Any:







				for file in MODEL_MAPPING[model_name]:
								if not os.path.isfile(F'''{pytorch_dump_folder_path}/{file.split("/"						)[-1]}'''						):
												UpperCamelCase_:							List[str]	=						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						)

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

				UpperCamelCase_:							Any	=						JukeboxConfig.from_pretrained(lowercase__						)
				UpperCamelCase_:							List[Any]	=						JukeboxModel(lowercase__						)

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

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

								UpperCamelCase_:							Dict	=						"""vqvae""" if i == 0 else F'''priors.{3 - i}'''
								UpperCamelCase_:							Tuple	=						fix_jukebox_keys(lowercase__     ,    model.state_dict()     ,    lowercase__     ,    lowercase__						)
								weight_dict.append(lowercase__						)

				UpperCamelCase_:							Union[str, Any]	=						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__":
							lowerCamelCase_						: int         =  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_						: Optional[Any]         =  parser.parse_args()
							convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_						: Optional[Any]         =  {
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[Any]         =  ["""DistilBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[int]         =  [
							    """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DistilBertForMaskedLM""",
							    """DistilBertForMultipleChoice""",
							    """DistilBertForQuestionAnswering""",
							    """DistilBertForSequenceClassification""",
							    """DistilBertForTokenClassification""",
							    """DistilBertModel""",
							    """DistilBertPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  [
							    """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDistilBertForMaskedLM""",
							    """TFDistilBertForMultipleChoice""",
							    """TFDistilBertForQuestionAnswering""",
							    """TFDistilBertForSequenceClassification""",
							    """TFDistilBertForTokenClassification""",
							    """TFDistilBertMainLayer""",
							    """TFDistilBertModel""",
							    """TFDistilBertPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """FlaxDistilBertForMaskedLM""",
							    """FlaxDistilBertForMultipleChoice""",
							    """FlaxDistilBertForQuestionAnswering""",
							    """FlaxDistilBertForSequenceClassification""",
							    """FlaxDistilBertForTokenClassification""",
							    """FlaxDistilBertModel""",
							    """FlaxDistilBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase_						: Optional[Any]         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






