'''simple docstring'''




import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

snake_case	=      random.Random()







def       A_ (			_lowerCamelCase      :				str	,	_lowerCamelCase      :				int=1.0	,	_lowerCamelCase      :				Any=None	,	_lowerCamelCase      :				List[Any]=None     ):
      if rng is None:
            _lowerCAmelCase		 = global_rng

      _lowerCAmelCase		 = []
      for batch_idx in range(shape[0]     ):
            values.append([]     )
            for _ in range(shape[1]     ):
                  values[-1].append(rng.random() * scale     )

      return values





@require_torch
@require_torchaudio
class 							SCREAMING_SNAKE_CASE (					unittest.TestCase			):
  """simple docstring"""







  def __init__(	self :       int       ,       __lowerCAmelCase :       Tuple       ,       __lowerCAmelCase :       Any=7       ,       __lowerCAmelCase :       Dict=400       ,       __lowerCAmelCase :       List[str]=2000       ,       __lowerCAmelCase :       Optional[Any]=10       ,       __lowerCAmelCase :       Optional[Any]=160       ,       __lowerCAmelCase :       int=8       ,       __lowerCAmelCase :       Union[str, Any]=0.0       ,       __lowerCAmelCase :       str=4000       ,       __lowerCAmelCase :       int=False       ,       __lowerCAmelCase :       Any=True       ,       ):


        """simple docstring"""



        _lowerCAmelCase		 = parent
        _lowerCAmelCase		 = batch_size
        _lowerCAmelCase		 = min_seq_length
        _lowerCAmelCase		 = max_seq_length
        _lowerCAmelCase		 = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _lowerCAmelCase		 = padding_value
        _lowerCAmelCase		 = sampling_rate
        _lowerCAmelCase		 = return_attention_mask
        _lowerCAmelCase		 = do_normalize
        _lowerCAmelCase		 = feature_size
        _lowerCAmelCase		 = chunk_length
        _lowerCAmelCase		 = hop_length



  def 	a (	self :       int       ):


        """simple docstring"""



        return {
            "feature_size": self.feature_size,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }



  def 	a (	self :       Optional[Any]       ,       __lowerCAmelCase :       Tuple=False       ,       __lowerCAmelCase :       Tuple=False       ):


        """simple docstring"""



        def _flatten(__lowerCAmelCase :       int       ):
              return list(itertools.chain(*__lowercase       )       )

        if equal_length:
              _lowerCAmelCase		 = [floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
        else:
              # make sure that inputs increase in size
              _lowerCAmelCase		 = [
                  floats_list((x, self.feature_size)       )
                  for x in range(self.min_seq_length       ,       self.max_seq_length       ,       self.seq_length_diff       )
              ]
        if numpify:
              _lowerCAmelCase		 = [np.asarray(__lowercase       ) for x in speech_inputs]
        return speech_inputs





@require_torch
@require_torchaudio
class 							SCREAMING_SNAKE_CASE (					snake_case_						,    unittest.TestCase			):
  """simple docstring"""







  __A  =		WhisperFeatureExtractor if is_speech_available() else None



  def 	a (	self :       str       ):


        """simple docstring"""



        _lowerCAmelCase		 = WhisperFeatureExtractionTester(self       )



  def 	a (	self :       List[Any]       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.feature_extraction_class(**self.feat_extract_dict       )

        with tempfile.TemporaryDirectory() as tmpdirname:
              _lowerCAmelCase		 = feat_extract_first.save_pretrained(__lowercase       )[0]
              check_json_file_has_correct_format(__lowercase       )
              _lowerCAmelCase		 = self.feature_extraction_class.from_pretrained(__lowercase       )

        _lowerCAmelCase		 = feat_extract_first.to_dict()
        _lowerCAmelCase		 = feat_extract_second.to_dict()
        _lowerCAmelCase		 = feat_extract_first.mel_filters
        _lowerCAmelCase		 = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__lowercase       ,       __lowercase       )       )
        self.assertEqual(__lowercase       ,       __lowercase       )



  def 	a (	self :       Union[str, Any]       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.feature_extraction_class(**self.feat_extract_dict       )

        with tempfile.TemporaryDirectory() as tmpdirname:
              _lowerCAmelCase		 = os.path.join(__lowercase       ,       'feat_extract.json'       )
              feat_extract_first.to_json_file(__lowercase       )
              _lowerCAmelCase		 = self.feature_extraction_class.from_json_file(__lowercase       )

        _lowerCAmelCase		 = feat_extract_first.to_dict()
        _lowerCAmelCase		 = feat_extract_second.to_dict()
        _lowerCAmelCase		 = feat_extract_first.mel_filters
        _lowerCAmelCase		 = feat_extract_second.mel_filters
        self.assertTrue(np.allclose(__lowercase       ,       __lowercase       )       )
        self.assertEqual(__lowercase       ,       __lowercase       )



  def 	a (	self :       int       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        # create three inputs of length 800, 1000, and 1200
        _lowerCAmelCase		 = [floats_list((1, x)       )[0] for x in range(800       ,       1400       ,       200       )]
        _lowerCAmelCase		 = [np.asarray(__lowercase       ) for speech_input in speech_inputs]

        # Test feature size
        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       padding='max_length'       ,       return_tensors='np'       ).input_features
        self.assertTrue(input_features.ndim == 3       )
        self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames       )
        self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size       )

        # Test not batched input
        _lowerCAmelCase		 = feature_extractor(speech_inputs[0]       ,       return_tensors='np'       ).input_features
        _lowerCAmelCase		 = feature_extractor(np_speech_inputs[0]       ,       return_tensors='np'       ).input_features
        self.assertTrue(np.allclose(__lowercase       ,       __lowercase       ,       atol=1e-3       )       )

        # Test batched
        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='np'       ).input_features
        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='np'       ).input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase       ,       __lowercase       ):
              self.assertTrue(np.allclose(__lowercase       ,       __lowercase       ,       atol=1e-3       )       )

        # Test 2-D numpy arrays are batched.
        _lowerCAmelCase		 = [floats_list((1, x)       )[0] for x in (800, 800, 800)]
        _lowerCAmelCase		 = np.asarray(__lowercase       )
        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='np'       ).input_features
        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='np'       ).input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase       ,       __lowercase       ):
              self.assertTrue(np.allclose(__lowercase       ,       __lowercase       ,       atol=1e-3       )       )

        # Test truncation required
        _lowerCAmelCase		 = [floats_list((1, x)       )[0] for x in range(200       ,       (feature_extractor.n_samples + 500)       ,       200       )]
        _lowerCAmelCase		 = [np.asarray(__lowercase       ) for speech_input in speech_inputs]

        _lowerCAmelCase		 = [x[: feature_extractor.n_samples] for x in speech_inputs]
        _lowerCAmelCase		 = [np.asarray(__lowercase       ) for speech_input in speech_inputs_truncated]

        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='np'       ).input_features
        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='np'       ).input_features
        for enc_seq_a, enc_seq_a in zip(__lowercase       ,       __lowercase       ):
              self.assertTrue(np.allclose(__lowercase       ,       __lowercase       ,       atol=1e-3       )       )



  def 	a (	self :       int       ):


        """simple docstring"""



        import torch

        _lowerCAmelCase		 = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        _lowerCAmelCase		 = np.random.rand(100       ,       32       ).astype(np.floataa       )
        _lowerCAmelCase		 = np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
              _lowerCAmelCase		 = feature_extractor.pad([{'input_features': inputs}]       ,       return_tensors='np'       )
              self.assertTrue(np_processed.input_features.dtype == np.floataa       )
              _lowerCAmelCase		 = feature_extractor.pad([{'input_features': inputs}]       ,       return_tensors='pt'       )
              self.assertTrue(pt_processed.input_features.dtype == torch.floataa       )



  def 	a (	self :       Tuple       ,       __lowerCAmelCase :       List[str]       ):


        """simple docstring"""



        _lowerCAmelCase		 = load_dataset('hf-internal-testing/librispeech_asr_dummy'       ,       'clean'       ,       split='validation'       )
        # automatic decoding with librispeech
        _lowerCAmelCase		 = ds.sort('id'       ).select(range(__lowercase       )       )[:num_samples]['audio']

        return [x["array"] for x in speech_samples]



  def 	a (	self :       Dict       ):


        """simple docstring"""



        _lowerCAmelCase		 = torch.tensor(
            [
                0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
                0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
                0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
                -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
            ]       )
        # fmt: on

        _lowerCAmelCase		 = self._load_datasamples(1       )
        _lowerCAmelCase		 = WhisperFeatureExtractor()
        _lowerCAmelCase		 = feature_extractor(__lowercase       ,       return_tensors='pt'       ).input_features
        self.assertEqual(input_features.shape       ,       (1, 80, 3000)       )
        self.assertTrue(torch.allclose(input_features[0, 0, :30]       ,       __lowercase       ,       atol=1e-4       )       )



  def 	a (	self :       Tuple       ):


        """simple docstring"""



        _lowerCAmelCase		 = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
        _lowerCAmelCase		 = self._load_datasamples(1       )[0]
        _lowerCAmelCase		 = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
        _lowerCAmelCase		 = feat_extract.zero_mean_unit_var_norm([audio]       ,       attention_mask=__lowercase       )[0]

        self.assertTrue(np.all(np.mean(__lowercase       ) < 1e-3       )       )
        self.assertTrue(np.all(np.abs(np.var(__lowercase       ) - 1       ) < 1e-3       )       )









'''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()
UpperCAmelCase_			:  str										=   logging.get_logger(__name__)


UpperCAmelCase_			:  Dict										=   'https://openaipublic.azureedge.net/jukebox/models/'
UpperCAmelCase_			:  Tuple										=   {
    '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       _lowercase							(					UpperCamelCase__							:       str      ):
 if key.endswith('.model.1.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.1.bias',					'.conv1d_1.bias'      )
 elif key.endswith('.model.1.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Dict	  =      key.replace('.model.1.weight',					'.conv1d_1.weight'      )
 elif key.endswith('.model.3.bias'      ) and len(key.split('.'      )      ) > 10:
  __A	:		Optional[int]	  =      key.replace('.model.3.bias',					'.conv1d_2.bias'      )
 elif key.endswith('.model.3.weight'      ) and len(key.split('.'      )      ) > 10:
  __A	:		str	  =      key.replace('.model.3.weight',					'.conv1d_2.weight'      )

 if "conditioner_blocks.0." in key:
  __A	:		Tuple	  =      key.replace('conditioner_blocks.0',					'conditioner_blocks'      )

 if "prime_prior" in key:
  __A	:		str	  =      key.replace('prime_prior',					'encoder'      )

 if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
  __A	:		str	  =      key.replace('.emb.',					'.'      )

 if key.endswith('k'      ):  # replace vqvae.X.k with vqvae.X.codebook
  return key.replace('.k',					'.codebook'      )
 if "y_emb." in key:
  return key.replace('y_emb.',					'metadata_embedding.'      )

 if "x_emb.emb." in key:
  __A	:		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       _lowercase							(					UpperCamelCase__							:       Any,					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Optional[Any]      ):
 __A	:		Union[str, Any]	  =      {}
 import re

 __A	:		List[Any]	  =      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
 __A	:		Dict	  =      re.compile(
     r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
 __A	:		Optional[int]	  =      re.compile(r'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

 __A	:		List[str]	  =      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'      )
 __A	:		str	  =      re.compile(
     r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'      )
 __A	:		str	  =      re.compile(r'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'      )

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

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

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

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

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

  elif re_decoder_block_proj_in.fullmatch(UpperCamelCase__      ):
   __A	:		int	  =      re_decoder_block_proj_in.match(UpperCamelCase__      )
   __A	:		List[str]	  =      regex_match.groups()
   __A	:		Tuple	  =      f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
   __A	:		Optional[Any]	  =      re_decoder_block_proj_in.sub(UpperCamelCase__,					UpperCamelCase__      )

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

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

  elif re_prior_cond_proj_in.fullmatch(UpperCamelCase__      ):
   __A	:		Tuple	  =      re_prior_cond_proj_in.match(UpperCamelCase__      )
   __A	:		Optional[Any]	  =      regex_match.groups()
   __A	:		Optional[int]	  =      f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
   __A	:		List[Any]	  =      re_prior_cond_proj_in.sub(UpperCamelCase__,					UpperCamelCase__      )

  # keep original key
  else:
   __A	:		List[Any]	  =      original_key

  __A	:		int	  =      replace_key(UpperCamelCase__      )

  if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
   print(f"""failed converting {original_key} to {key}, does not match"""      )

  # handle missmatched shape
  elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
   __A	:		Optional[Any]	  =      model_state_dict[f"""{key_prefix}.{key}"""]
   print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""      )
   __A	:		Union[str, Any]	  =      original_key

  __A	:		Optional[int]	  =      original_key
  __A	:		Optional[Any]	  =      value

 return new_dict


@torch.no_grad()
def       _lowercase							(					UpperCamelCase__							:       Union[str, Any]=None,					UpperCamelCase__							:       Any=None      ):
 for file in MODEL_MAPPING[model_name]:
  if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'      )[-1]}"""      ):
   __A	:		Any	  =      requests.get(f"""{PREFIX}{file}""",					allow_redirects=UpperCamelCase__      )
   os.makedirs(f"""{pytorch_dump_folder_path}/""",					exist_ok=UpperCamelCase__      )
   open(f"""{pytorch_dump_folder_path}/{file.split('/'      )[-1]}""",					'wb'      ).write(r.content      )

 __A	:		str	  =      MODEL_MAPPING[model_name.split('/'      )[-1]]

 __A	:		Optional[Any]	  =      JukeboxConfig.from_pretrained(UpperCamelCase__      )
 __A	:		Tuple	  =      JukeboxModel(UpperCamelCase__      )

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

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

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

 __A	:		Union[str, Any]	  =      weight_dict.pop(0      )
 model.vqvae.load_state_dict(UpperCamelCase__      )
 for i in range(len(UpperCamelCase__      )      ):
  model.priors[i].load_state_dict(weight_dict[2 - i]      )

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

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

 return weight_dict


if __name__ == "__main__":
 UpperCAmelCase_			:  Dict										=   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.',
 )
 UpperCAmelCase_			:  Union[str, Any]										=   parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)






