



import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_snake_case	=						logging.get_logger(__name__)



@dataclass
class 						UpperCAmelCase_					:
							lowerCamelCase__         =	field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys())})
							lowerCamelCase__         =	field(
							    metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
							lowerCamelCase__         =	field(
							    default=128     ,      metadata={
							        'help': (
							            'The maximum total input sequence length after tokenization. Sequences longer '
							            'than this will be truncated, sequences shorter will be padded.'
							        )
							    }     ,      )
							lowerCamelCase__         =	field(
							    default=a     ,      metadata={'help': 'Overwrite the cached training and evaluation sets'})







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Tuple				     =					self.task_name.lower()



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'train'
							lowerCamelCase__         =	'dev'
							lowerCamelCase__         =	'test'



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	42
							lowerCamelCase__         =	42
							lowerCamelCase__         =	42







							def __init__(     self,    __a,    __a,    __a = None,    __a = Split.train,    __a = None,    ):

														'''simple docstring'''

														warnings.warn(
														    "This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets "
														    "library. You can have a look at this example script for pointers: "
														    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py",    __a,    )
														_lowerCAmelCase  :							Optional[Any]				     =					args
														_lowerCAmelCase  :							Tuple				     =					glue_processors[args.task_name]()
														_lowerCAmelCase  :							Union[str, Any]				     =					glue_output_modes[args.task_name]
														if isinstance(__a,    __a):
																					try:
																												_lowerCAmelCase  :							Union[str, Any]				     =					Split[mode]
																					except KeyError:
																												raise KeyError("mode is not a valid split name")
        # Load data features from cache or dataset file
														_lowerCAmelCase  :							Optional[Any]				     =					os.path.join(
														    cache_dir if cache_dir is not None else args.data_dir,    f"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}",    )
														_lowerCAmelCase  :							List[Any]				     =					self.processor.get_labels()
														if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
														    "RobertaTokenizer",
														    "RobertaTokenizerFast",
														    "XLMRobertaTokenizer",
														    "BartTokenizer",
														    "BartTokenizerFast",
														):
																					# HACK(label indices are swapped in RoBERTa pretrained model)
																					_lowerCAmelCase     ,					_lowerCAmelCase  :							Tuple				     =					label_list[2], label_list[1]
														_lowerCAmelCase  :							Dict				     =					label_list

														# Make sure only the first process in distributed training processes the dataset,
														# and the others will use the cache.
														_lowerCAmelCase  :							List[str]				     =					cached_features_file + ".lock"
														with FileLock(__a):
																					if os.path.exists(__a) and not args.overwrite_cache:
																												_lowerCAmelCase  :							Dict				     =					time.time()
																												_lowerCAmelCase  :							Union[str, Any]				     =					torch.load(__a)
																												logger.info(
																												    f"Loading features from cached file {cached_features_file} [took %.3f s]",    time.time() - start)
																					else:
																												logger.info(f"Creating features from dataset file at {args.data_dir}")

																												if mode == Split.dev:
																																			_lowerCAmelCase  :							Tuple				     =					self.processor.get_dev_examples(args.data_dir)
																												elif mode == Split.test:
																																			_lowerCAmelCase  :							str				     =					self.processor.get_test_examples(args.data_dir)
																												else:
																																			_lowerCAmelCase  :							Optional[Any]				     =					self.processor.get_train_examples(args.data_dir)
																												if limit_length is not None:
																																			_lowerCAmelCase  :							Any				     =					examples[:limit_length]
																												_lowerCAmelCase  :							Any				     =					glue_convert_examples_to_features(
																												    __a,    __a,    max_length=args.max_seq_length,    label_list=__a,    output_mode=self.output_mode,    )
																												_lowerCAmelCase  :							int				     =					time.time()
																												torch.save(self.features,    __a)
																												# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																												logger.info(
																												    f"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]")







							def __len__(     self):

														'''simple docstring'''

														return len(self.features)







							def __getitem__(     self,    __a):

														'''simple docstring'''

														return self.features[i]







							def      snake_case__   (     self):

														'''simple docstring'''

														return self.label_list





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        A       (      _lowerCamelCase						):





							'''simple docstring'''




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

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

							if "prime_prior" in key:
														_lowerCAmelCase  :							int				     =					key.replace("prime_prior"       ,				"encoder"						)

							if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
														_lowerCAmelCase  :							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:
														_lowerCAmelCase  :							Tuple				     =					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						):





							'''simple docstring'''




							_lowerCAmelCase  :							Any				     =					{}
							import re

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

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

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

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

														elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							Union[str, Any]				     =					re_encoder_block_proj_out.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							List[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[Any]				     =					F"encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"
																					_lowerCAmelCase  :							str				     =					re_encoder_block_proj_out.sub(_lowerCamelCase       ,				_lowerCamelCase						)

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

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

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

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

														elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase						):
																					_lowerCAmelCase  :							int				     =					re_prior_cond_proj_in.match(_lowerCamelCase						)
																					_lowerCAmelCase  :							Optional[Any]				     =					regex_match.groups()
																					_lowerCAmelCase  :							Optional[int]				     =					F"conditioner_blocks.upsampler.proj_in.{groups[-1]}"
																					_lowerCAmelCase  :							List[str]				     =					re_prior_cond_proj_in.sub(_lowerCamelCase       ,				_lowerCamelCase						)

														# keep original key
														else:
																					_lowerCAmelCase  :							Optional[int]				     =					original_key

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

														_lowerCAmelCase  :							List[Any]				     =					original_key
														_lowerCAmelCase  :							Optional[int]				     =					value

							return new_dict






@torch.no_grad()
def        A       (      _lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




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

							_lowerCAmelCase  :							Optional[Any]				     =					MODEL_MAPPING[model_name.split("/"						)[-1]]

							_lowerCAmelCase  :							Tuple				     =					JukeboxConfig.from_pretrained(_lowerCamelCase						)
							_lowerCAmelCase  :							Optional[int]				     =					JukeboxModel(_lowerCamelCase						)

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

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

														_lowerCAmelCase  :							Union[str, Any]				     =					"vqvae" if i == 0 else F"priors.{3 - i}"
														_lowerCAmelCase  :							Union[str, Any]				     =					fix_jukebox_keys(_lowerCamelCase       ,				model.state_dict()       ,				_lowerCamelCase       ,				_lowerCamelCase						)
														weight_dict.append(_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[Any]				     =					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__":
						_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)
