


"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__SCREAMING_SNAKE_CASE							:    str		=    datasets.utils.logging.get_logger(__name__)





@dataclass
class      __A     (datasets.BuilderConfig):





							'''simple docstring'''

							__lowercase:		Optional[datasets.Features]							      =    None
							__lowercase:		str							      =    "utf-8"
							__lowercase:		Optional[str]							      =    None
							__lowercase:		Optional[str]							      =    None
							__lowercase:		bool							      =    True  # deprecated
							__lowercase:		Optional[int]							      =    None  # deprecated
							__lowercase:		int							      =    10 << 20  # 10MB
							__lowercase:		Optional[bool]							      =    None





class      __A     (datasets.ArrowBasedBuilder):





							'''simple docstring'''

							__lowercase:		str							      =    JsonConfig



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Tuple:



														"""simple docstring"""



														if self.config.block_size is not None:
																					logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""       )
																					snake_case_           =		self.config.block_size
														if self.config.use_threads is not True:
																					logger.warning(
																					    """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""       )
														if self.config.newlines_in_values is not None:
																					raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""       )
														return datasets.DatasetInfo(features=self.config.features       )



							def 		lowerCAmelCase       (				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Union[str, Any]       )					->List[Any]:



														"""simple docstring"""



														if not self.config.data_files:
																					raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""       )
														snake_case_           =		dl_manager.download_and_extract(self.config.data_files       )
														if isinstance(UpperCAmelCase_		,       (str, list, tuple)       ):
																					snake_case_           =		data_files
																					if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																												snake_case_           =		[files]
																					snake_case_           =		[dl_manager.iter_files(UpperCAmelCase_       ) for file in files]
																					return [datasets.SplitGenerator(name=datasets.Split.TRAIN		,       gen_kwargs={"""files""": files}       )]
														snake_case_           =		[]
														for split_name, files in data_files.items():
																					if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):
																												snake_case_           =		[files]
																					snake_case_           =		[dl_manager.iter_files(UpperCAmelCase_       ) for file in files]
																					splits.append(datasets.SplitGenerator(name=UpperCAmelCase_		,       gen_kwargs={"""files""": files}       )       )
														return splits



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      pa.Table       )					->pa.Table:



														"""simple docstring"""



														if self.config.features is not None:
																					# adding missing columns
																					for column_name in set(self.config.features       ) - set(pa_table.column_names       ):
																												snake_case_           =		self.config.features.arrow_schema.field(UpperCAmelCase_       ).type
																												snake_case_           =		pa_table.append_column(UpperCAmelCase_		,       pa.array([None] * len(UpperCAmelCase_       )		,       type=UpperCAmelCase_       )       )
																					# more expensive cast to support nested structures with keys in a different order
																					# allows str <-> int/float or str to Audio for example
																					snake_case_           =		table_cast(UpperCAmelCase_		,       self.config.features.arrow_schema       )
														return pa_table



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      str       )					->List[str]:



														"""simple docstring"""



														for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_       )       ):
																					# If the file is one json object and if we need to look at the list of items in one specific field
																					if self.config.field is not None:
																												with open(UpperCAmelCase_		,       encoding=self.config.encoding		,       errors=self.config.encoding_errors       ) as f:
																																			snake_case_           =		json.load(UpperCAmelCase_       )

																												# We keep only the field we are interested in
																												snake_case_           =		dataset[self.config.field]

																												# We accept two format: a list of dicts or a dict of lists
																												if isinstance(UpperCAmelCase_		,       (list, tuple)       ):
																																			snake_case_           =		set().union(*[row.keys() for row in dataset]       )
																																			snake_case_           =		{col: [row.get(UpperCAmelCase_       ) for row in dataset] for col in keys}
																												else:
																																			snake_case_           =		dataset
																												snake_case_           =		pa.Table.from_pydict(UpperCAmelCase_       )
																												yield file_idx, self._cast_table(UpperCAmelCase_       )

																					# If the file has one json object per line
																					else:
																												with open(UpperCAmelCase_		,       """rb"""       ) as f:
																																			snake_case_           =		0
																																			# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																																			# Set a default minimum value of 16kB if the chunk size is really small
																																			snake_case_           =		max(self.config.chunksize // 32		,       16 << 10       )
																																			snake_case_           =		(
																																			    self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
																																			)
																																			while True:
																																										snake_case_           =		f.read(self.config.chunksize       )
																																										if not batch:
																																																	break
																																										# Finish current line
																																										try:
																																																	batch += f.readline()
																																										except (AttributeError, io.UnsupportedOperation):
																																																	batch += readline(UpperCAmelCase_       )
																																										# PyArrow only accepts utf-8 encoded bytes
																																										if self.config.encoding != "utf-8":
																																																	snake_case_           =		batch.decode(self.config.encoding		,       errors=UpperCAmelCase_       ).encode("""utf-8"""       )
																																										try:
																																																	while True:
																																																								try:
																																																															snake_case_           =		paj.read_json(
																																																															    io.BytesIO(UpperCAmelCase_       )		,       read_options=paj.ReadOptions(block_size=UpperCAmelCase_       )       )
																																																															break
																																																								except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																															if (
																																																															    isinstance(UpperCAmelCase_		,       pa.ArrowInvalid       )
																																																															    and "straddling" not in str(UpperCAmelCase_       )
																																																															    or block_size > len(UpperCAmelCase_       )
																																																															):
																																																																						raise
																																																															else:
																																																																						# Increase the block size in case it was too small.
																																																																						# The block size will be reset for the next file.
																																																																						logger.debug(
																																																																						    F"""Batch of {len(UpperCAmelCase_       )} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""       )
																																																																						block_size *= 2
																																										except pa.ArrowInvalid as e:
																																																	try:
																																																								with open(
																																																								    UpperCAmelCase_		,       encoding=self.config.encoding		,       errors=self.config.encoding_errors       ) as f:
																																																															snake_case_           =		json.load(UpperCAmelCase_       )
																																																	except json.JSONDecodeError:
																																																								logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_       )}: {e}"""       )
																																																								raise e
																																																	# If possible, parse the file as a list of json objects and exit the loop
																																																	if isinstance(UpperCAmelCase_		,       UpperCAmelCase_       ):  # list is the only sequence type supported in JSON
																																																								try:
																																																															snake_case_           =		set().union(*[row.keys() for row in dataset]       )
																																																															snake_case_           =		{col: [row.get(UpperCAmelCase_       ) for row in dataset] for col in keys}
																																																															snake_case_           =		pa.Table.from_pydict(UpperCAmelCase_       )
																																																								except (pa.ArrowInvalid, AttributeError) as e:
																																																															logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_       )}: {e}"""       )
																																																															raise ValueError(F"""Not able to read records in the JSON file at {file}."""       ) from None
																																																								yield file_idx, self._cast_table(UpperCAmelCase_       )
																																																								break
																																																	else:
																																																								logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_       )}: {e}"""       )
																																																								raise ValueError(
																																																								    F"""Not able to read records in the JSON file at {file}. """
																																																								    F"""You should probably indicate the field of the JSON file containing your records. """
																																																								    F"""This JSON file contain the following fields: {str(list(dataset.keys()       )       )}. """
																																																								    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """       ) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																										yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase_       )
																																										batch_idx += 1







"""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()
__SCREAMING_SNAKE_CASE							:    str		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Optional[int]		=    'https://openaipublic.azureedge.net/jukebox/models/'
__SCREAMING_SNAKE_CASE							:    List[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				(      _SCREAMING_SNAKE_CASE				)    ->				int:
							if key.endswith(""".model.1.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.bias"""				, """.conv1d_1.bias"""				)
							elif key.endswith(""".model.1.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.1.weight"""				, """.conv1d_1.weight"""				)
							elif key.endswith(""".model.3.bias"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.bias"""				, """.conv1d_2.bias"""				)
							elif key.endswith(""".model.3.weight"""				) and len(key.split("""."""				)				) > 10:
														snake_case_           =		key.replace(""".model.3.weight"""				, """.conv1d_2.weight"""				)

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

							if "prime_prior" in key:
														snake_case_           =		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_           =		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_           =		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				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							snake_case_           =		{}
							import re

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

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

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

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

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

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

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

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

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

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

														elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE				):
																					snake_case_           =		re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE				)
																					snake_case_           =		regex_match.groups()
																					snake_case_           =		f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																					snake_case_           =		re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)

														# keep original key
														else:
																					snake_case_           =		original_key

														snake_case_           =		replace_key(_SCREAMING_SNAKE_CASE				)

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

														snake_case_           =		original_key
														snake_case_           =		value

							return new_dict







@torch.no_grad()
def 						_a				(      _SCREAMING_SNAKE_CASE=None				, _SCREAMING_SNAKE_CASE=None				)    ->				Optional[int]:
							for file in MODEL_MAPPING[model_name]:
														if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				):
																					snake_case_           =		requests.get(f"""{PREFIX}{file}"""				, allow_redirects=_SCREAMING_SNAKE_CASE				)
																					os.makedirs(f"""{pytorch_dump_folder_path}/"""				, exist_ok=_SCREAMING_SNAKE_CASE				)
																					open(f"""{pytorch_dump_folder_path}/{file.split("/"				)[-1]}"""				, """wb"""				).write(r.content				)

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

							snake_case_           =		JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		JukeboxModel(_SCREAMING_SNAKE_CASE				)

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

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

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

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

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

							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
							model.save_pretrained(_SCREAMING_SNAKE_CASE				)

							return weight_dict


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



