


"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		Dict							      =    ["""input_values""", """attention_mask"""]



							def __init__(				self							:      List[Any]		,       UpperCAmelCase_							:      int = 1		,       UpperCAmelCase_							:      int = 16_000		,       UpperCAmelCase_							:      float = 0.0		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      int = 80		,       UpperCAmelCase_							:      int = 16		,       UpperCAmelCase_							:      int = 64		,       UpperCAmelCase_							:      str = "hann_window"		,       UpperCAmelCase_							:      float = 1.0		,       UpperCAmelCase_							:      float = 80		,       UpperCAmelCase_							:      float = 7_600		,       UpperCAmelCase_							:      float = 1E-10		,       UpperCAmelCase_							:      int = 2		,       UpperCAmelCase_							:      bool = True		,       **UpperCAmelCase_							:      str		,       )					->str:



														"""simple docstring"""



														super().__init__(feature_size=UpperCAmelCase_		,       sampling_rate=UpperCAmelCase_		,       padding_value=UpperCAmelCase_		,       **UpperCAmelCase_       )
														snake_case_           =		do_normalize
														snake_case_           =		return_attention_mask

														snake_case_           =		num_mel_bins
														snake_case_           =		hop_length
														snake_case_           =		win_length
														snake_case_           =		win_function
														snake_case_           =		frame_signal_scale
														snake_case_           =		fmin
														snake_case_           =		fmax
														snake_case_           =		mel_floor
														snake_case_           =		reduction_factor

														snake_case_           =		win_length * sampling_rate // 1_000
														snake_case_           =		hop_length * sampling_rate // 1_000
														snake_case_           =		optimal_fft_length(self.sample_size       )
														snake_case_           =		(self.n_fft // 2) + 1

														snake_case_           =		window_function(window_length=self.sample_size		,       name=self.win_function		,       periodic=UpperCAmelCase_       )

														snake_case_           =		mel_filter_bank(
														    num_frequency_bins=self.n_freqs		,       num_mel_filters=self.num_mel_bins		,       min_frequency=self.fmin		,       max_frequency=self.fmax		,       sampling_rate=self.sampling_rate		,       norm="""slaney"""		,       mel_scale="""slaney"""		,       )

														if frame_signal_scale != 1.0:
																					warnings.warn(
																					    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""		,       UpperCAmelCase_		,       )
														if reduction_factor != 2.0:
																					warnings.warn(
																					    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""		,       UpperCAmelCase_		,       )



							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def 		lowerCAmelCase       (				UpperCAmelCase_							:      List[np.ndarray]		,       UpperCAmelCase_							:      List[np.ndarray]		,       UpperCAmelCase_							:      float = 0.0       )					->List[np.ndarray]:



														"""simple docstring"""



														if attention_mask is not None:
																					snake_case_           =		np.array(UpperCAmelCase_		,       np.intaa       )
																					snake_case_           =		[]

																					for vector, length in zip(UpperCAmelCase_		,       attention_mask.sum(-1       )       ):
																												snake_case_           =		(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7       )
																												if length < normed_slice.shape[0]:
																																			snake_case_           =		padding_value

																												normed_input_values.append(UpperCAmelCase_       )
														else:
																					snake_case_           =		[(x - x.mean()) / np.sqrt(x.var() + 1E-7       ) for x in input_values]

														return normed_input_values



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      np.ndarray		,       )					->np.ndarray:



														"""simple docstring"""



														snake_case_           =		spectrogram(
														    UpperCAmelCase_		,       window=self.window		,       frame_length=self.sample_size		,       hop_length=self.sample_stride		,       fft_length=self.n_fft		,       mel_filters=self.mel_filters		,       mel_floor=self.mel_floor		,       log_mel="""log10"""		,       )
														return log_mel_spec.T



							def __call__(				self							:      int		,       UpperCAmelCase_							:      Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None		,       UpperCAmelCase_							:      Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None		,       UpperCAmelCase_							:      Union[bool, str, PaddingStrategy] = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[Union[str, TensorType]] = None		,       UpperCAmelCase_							:      Optional[int] = None		,       **UpperCAmelCase_							:      Optional[Any]		,       )					->BatchFeature:



														"""simple docstring"""



														if audio is None and audio_target is None:
																					raise ValueError("""You must provide either `audio` or `audio_target` values."""       )

														if sampling_rate is not None:
																					if sampling_rate != self.sampling_rate:
																												raise ValueError(
																												    F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
																												    F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
																												    F""" {self.sampling_rate} and not {sampling_rate}."""       )
														else:
																					logger.warning(
																					    """It is strongly recommended to pass the ``sampling_rate`` argument to this function. """
																					    """Failing to do so can result in silent errors that might be hard to debug."""       )

														if audio is not None:
																					snake_case_           =		self._process_audio(
																					    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       **UpperCAmelCase_		,       )
														else:
																					snake_case_           =		None

														if audio_target is not None:
																					snake_case_           =		self._process_audio(
																					    UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       UpperCAmelCase_		,       **UpperCAmelCase_		,       )

																					if inputs is None:
																												return inputs_target
																					else:
																												snake_case_           =		inputs_target["""input_values"""]
																												snake_case_           =		inputs_target.get("""attention_mask"""       )
																												if decoder_attention_mask is not None:
																																			snake_case_           =		decoder_attention_mask

														return inputs



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      Union[bool, str, PaddingStrategy] = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      bool = False		,       UpperCAmelCase_							:      Optional[int] = None		,       UpperCAmelCase_							:      Optional[bool] = None		,       UpperCAmelCase_							:      Optional[Union[str, TensorType]] = None		,       **UpperCAmelCase_							:      Optional[int]		,       )					->BatchFeature:



														"""simple docstring"""



														snake_case_           =		isinstance(UpperCAmelCase_		,       np.ndarray       ) and len(speech.shape       ) > 1
														if is_batched_numpy and len(speech.shape       ) > 2:
																					raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""       )
														snake_case_           =		is_batched_numpy or (
														    isinstance(UpperCAmelCase_		,       (list, tuple)       ) and (isinstance(speech[0]		,       (np.ndarray, tuple, list)       ))
														)

														if is_batched:
																					snake_case_           =		[np.asarray(UpperCAmelCase_		,       dtype=np.floataa       ) for speech in speech]
														elif not is_batched and not isinstance(UpperCAmelCase_		,       np.ndarray       ):
																					snake_case_           =		np.asarray(UpperCAmelCase_		,       dtype=np.floataa       )
														elif isinstance(UpperCAmelCase_		,       np.ndarray       ) and speech.dtype is np.dtype(np.floataa       ):
																					snake_case_           =		speech.astype(np.floataa       )

														# always return batch
														if not is_batched:
																					snake_case_           =		[speech]

														# needed to make pad() work on spectrogram inputs
														snake_case_           =		self.feature_size

														# convert into correct format for padding
														if is_target:
																					snake_case_           =		[self._extract_mel_features(UpperCAmelCase_       ) for waveform in speech]
																					snake_case_           =		BatchFeature({"""input_values""": features}       )
																					snake_case_           =		self.num_mel_bins
														else:
																					snake_case_           =		BatchFeature({"""input_values""": speech}       )

														snake_case_           =		self.pad(
														    UpperCAmelCase_		,       padding=UpperCAmelCase_		,       max_length=UpperCAmelCase_		,       truncation=UpperCAmelCase_		,       pad_to_multiple_of=UpperCAmelCase_		,       return_attention_mask=UpperCAmelCase_		,       **UpperCAmelCase_		,       )

														snake_case_           =		feature_size_hack

														# convert input values to correct format
														snake_case_           =		padded_inputs["""input_values"""]
														if not isinstance(input_values[0]		,       np.ndarray       ):
																					snake_case_           =		[np.asarray(UpperCAmelCase_		,       dtype=np.floataa       ) for array in input_values]
														elif (
														    not isinstance(UpperCAmelCase_		,       np.ndarray       )
														    and isinstance(input_values[0]		,       np.ndarray       )
														    and input_values[0].dtype is np.dtype(np.floataa       )
														):
																					snake_case_           =		[array.astype(np.floataa       ) for array in input_values]
														elif isinstance(UpperCAmelCase_		,       np.ndarray       ) and input_values.dtype is np.dtype(np.floataa       ):
																					snake_case_           =		input_values.astype(np.floataa       )

														# convert attention_mask to correct format
														snake_case_           =		padded_inputs.get("""attention_mask"""       )
														if attention_mask is not None:
																					snake_case_           =		[np.asarray(UpperCAmelCase_		,       dtype=np.intaa       ) for array in attention_mask]

														# zero-mean and unit-variance normalization
														if not is_target and self.do_normalize:
																					snake_case_           =		(
																					    attention_mask
																					    if self._get_padding_strategies(UpperCAmelCase_		,       max_length=UpperCAmelCase_       ) is not PaddingStrategy.DO_NOT_PAD
																					    else None
																					)
																					snake_case_           =		self.zero_mean_unit_var_norm(
																					    padded_inputs["""input_values"""]		,       attention_mask=UpperCAmelCase_		,       padding_value=self.padding_value       )

														if return_tensors is not None:
																					snake_case_           =		padded_inputs.convert_to_tensors(UpperCAmelCase_       )

														return padded_inputs



							def 		lowerCAmelCase       (				self							:      Any       )					->Dict[str, Any]:



														"""simple docstring"""



														snake_case_           =		super().to_dict()

														# Don't serialize these as they are derived from the other properties.
														snake_case_           =		["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
														for name in names:
																					if name in output:
																												del output[name]

														return output







"""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)



