



"""simple docstring"""




import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


__A       =						random.Random()



def 	__a					(   lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Tuple=1.0      ,lowerCAmelCase_       : Optional[int]=None      ,lowerCAmelCase_       : List[Any]=None    )				->			Union[str, Any]:






						'''simple docstring'''



						if rng is None:
												UpperCAmelCase_=       global_rng

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

						return values






@require_torch
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int=7						,  __UpperCAmelCase     :						Union[str, Any]=400						,  __UpperCAmelCase     :						Optional[int]=2_000						,  __UpperCAmelCase     :						List[str]=1						,  __UpperCAmelCase     :						Union[str, Any]=0.0						,  __UpperCAmelCase     :						Optional[Any]=16_000						,  __UpperCAmelCase     :						List[Any]=True						,  __UpperCAmelCase     :						Dict=80						,  __UpperCAmelCase     :						Union[str, Any]=16						,  __UpperCAmelCase     :						Tuple=64						,  __UpperCAmelCase     :						Any="hann_window"						,  __UpperCAmelCase     :						int=80						,  __UpperCAmelCase     :						List[str]=7_600						,  __UpperCAmelCase     :						Optional[Any]=1E-10						,  __UpperCAmelCase     :						List[Any]=True						,  )      ->			Union[str, Any]:
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       batch_size
							UpperCAmelCase_=       min_seq_length
							UpperCAmelCase_=       max_seq_length
							UpperCAmelCase_=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							UpperCAmelCase_=       feature_size
							UpperCAmelCase_=       padding_value
							UpperCAmelCase_=       sampling_rate
							UpperCAmelCase_=       do_normalize
							UpperCAmelCase_=       num_mel_bins
							UpperCAmelCase_=       hop_length
							UpperCAmelCase_=       win_length
							UpperCAmelCase_=       win_function
							UpperCAmelCase_=       fmin
							UpperCAmelCase_=       fmax
							UpperCAmelCase_=       mel_floor
							UpperCAmelCase_=       return_attention_mask




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			int:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "do_normalize": self.do_normalize,
							    "num_mel_bins": self.num_mel_bins,
							    "hop_length": self.hop_length,
							    "win_length": self.win_length,
							    "win_function": self.win_function,
							    "fmin": self.fmin,
							    "fmax": self.fmax,
							    "mel_floor": self.mel_floor,
							    "return_attention_mask": self.return_attention_mask,
							}




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[str]=False						,  __UpperCAmelCase     :						Dict=False					)      ->			str:
							def _flatten(__UpperCAmelCase     :						Optional[int]					):
													return list(itertools.chain(*__UpperCAmelCase					)					)

							if equal_length:
													UpperCAmelCase_=       floats_list((self.batch_size, self.max_seq_length)					)
							else:
													# make sure that inputs increase in size
													UpperCAmelCase_=       [
													    _flatten(floats_list((x, self.feature_size)					)					)
													    for x in range(self.min_seq_length						,  self.max_seq_length						,  self.seq_length_diff					)
													]

							if numpify:
													UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for x in speech_inputs]

							return speech_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Union[str, Any]=False						,  __UpperCAmelCase     :						int=False					)      ->			Optional[Any]:
							if equal_length:
													UpperCAmelCase_=       [floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
							else:
													# make sure that inputs increase in size
													UpperCAmelCase_=       [
													    floats_list((x, self.num_mel_bins)					)
													    for x in range(self.min_seq_length						,  self.max_seq_length						,  self.seq_length_diff					)
													]

							if numpify:
													UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for x in speech_inputs]

							return speech_inputs






@require_torch
class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: int		       =							SpeechTaFeatureExtractor




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							UpperCAmelCase_=       SpeechTaFeatureExtractionTester(self					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			Optional[Any]:
							self.assertTrue(np.all(np.mean(__UpperCAmelCase						,  axis=0					) < 1E-3					)					)
							self.assertTrue(np.all(np.abs(np.var(__UpperCAmelCase						,  axis=0					) - 1					) < 1E-3					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							# create three inputs of length 800, 1000, and 1200
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for speech_input in speech_inputs]

							# Test not batched input
							UpperCAmelCase_=       feat_extract(speech_inputs[0]						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feat_extract(np_speech_inputs[0]						,  return_tensors="""np"""					).input_values
							self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)

							# Test batched
							UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]

							UpperCAmelCase_=       ["""longest""", """max_length""", """do_not_pad"""]
							UpperCAmelCase_=       [None, 1_600, None]
							for max_length, padding in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  padding=__UpperCAmelCase						,  max_length=__UpperCAmelCase						,  return_tensors="""np"""					)
													UpperCAmelCase_=       processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]					)
													self.assertTrue(input_values[0][800:].sum() < 1E-6					)
													self._check_zero_mean_unit_variance(input_values[1][:1_000]					)
													self.assertTrue(input_values[0][1_000:].sum() < 1E-6					)
													self._check_zero_mean_unit_variance(input_values[2][:1_200]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			str:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       range(800						,  1_400						,  200					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in lengths]

							UpperCAmelCase_=       ["""longest""", """max_length""", """do_not_pad"""]
							UpperCAmelCase_=       [None, 1_600, None]

							for max_length, padding in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  max_length=__UpperCAmelCase						,  padding=__UpperCAmelCase					)
													UpperCAmelCase_=       processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]					)
													self._check_zero_mean_unit_variance(input_values[1][:1_000]					)
													self._check_zero_mean_unit_variance(input_values[2][:1_200]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       feat_extract(
							    __UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=1_000						,  padding="""max_length"""						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]					)
							self._check_zero_mean_unit_variance(input_values[1]					)
							self._check_zero_mean_unit_variance(input_values[2]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       feat_extract(
							    __UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=1_000						,  padding="""longest"""						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]					)
							self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
							self._check_zero_mean_unit_variance(input_values[2]					)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1_000)					)

							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       feat_extract(
							    __UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=2_000						,  padding="""longest"""						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]					)
							self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
							self._check_zero_mean_unit_variance(input_values[2]					)

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1_200)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Any:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       np.random.rand(100					).astype(np.floataa					)
							UpperCAmelCase_=       np_speech_inputs.tolist()

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			str:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							# create three inputs of length 800, 1000, and 1200
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for speech_input in speech_inputs]

							# Test feature size
							UpperCAmelCase_=       feature_extractor(audio_target=__UpperCAmelCase						,  padding=__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							self.assertTrue(input_values.ndim == 3					)
							self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins					)

							# Test not batched input
							UpperCAmelCase_=       feature_extractor(speech_inputs[0]						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feature_extractor(np_speech_inputs[0]						,  return_tensors="""np"""					).input_values
							self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)

							# Test batched
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)

							# Test 2-D numpy arrays are batched.
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in (800, 800, 800)]
							UpperCAmelCase_=       np.asarray(__UpperCAmelCase					)
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Tuple:
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							self.assertTrue(all(len(__UpperCAmelCase					) == len(__UpperCAmelCase					) for x, y in zip(__UpperCAmelCase						,  processed_features[input_name]					)					)					)

							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase					)
							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}						,  tensor_type="""np"""					)

							UpperCAmelCase_=       processed_features[input_name]

							if len(batch_features_input.shape					) < 3:
													UpperCAmelCase_=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)




	@require_torch
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase					)
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}						,  tensor_type="""pt"""					)

							UpperCAmelCase_=       processed_features[input_name]

							if len(batch_features_input.shape					) < 3:
													UpperCAmelCase_=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)




	@require_torch
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			str:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       feat_extract.num_mel_bins  # hack!

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)[input_name]
							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""pt"""					)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_pt.numpy().astype(np.floataa					).sum()					) < 1E-2					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			List[Any]:
							UpperCAmelCase_=       self.feat_extract_dict
							UpperCAmelCase_=       True
							UpperCAmelCase_=       self.feature_extraction_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       [len(__UpperCAmelCase					) for x in speech_inputs]
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       feat_extract.num_mel_bins  # hack!

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)
							self.assertIn("""attention_mask"""						,  __UpperCAmelCase					)
							self.assertListEqual(list(processed.attention_mask.shape					)						,  list(processed[input_name].shape[:2]					)					)
							self.assertListEqual(processed.attention_mask.sum(-1					).tolist()						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[Any]:
							UpperCAmelCase_=       self.feat_extract_dict
							UpperCAmelCase_=       True
							UpperCAmelCase_=       self.feature_extraction_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       [len(__UpperCAmelCase					) for x in speech_inputs]
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)
							UpperCAmelCase_=       min(__UpperCAmelCase					)

							UpperCAmelCase_=       feat_extract.num_mel_bins  # hack!

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=__UpperCAmelCase						,  truncation=__UpperCAmelCase						,  return_tensors="""np"""					)
							self.assertIn("""attention_mask"""						,  __UpperCAmelCase					)
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape					)						,  [processed_pad[input_name].shape[0], max_length]					)
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1					).tolist()						,  [max_length for x in speech_inputs]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			List[str]:
							from datasets import load_dataset

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Optional[Any]:
							# fmt: off
							UpperCAmelCase_=       torch.tensor(
							    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
							     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
							     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
							     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
							     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
							     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]					)
							# fmt: on

							UpperCAmelCase_=       self._load_datasamples(1					)
							UpperCAmelCase_=       SpeechTaFeatureExtractor()
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""pt"""					).input_values
							self.assertEquals(input_values.shape						,  (1, 93_680)					)
							self.assertTrue(torch.allclose(input_values[0, :30]						,  __UpperCAmelCase						,  atol=1E-6					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			str:
							# fmt: off
							UpperCAmelCase_=       torch.tensor(
							    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
							     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
							     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
							     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]					)
							# fmt: on

							UpperCAmelCase_=       self._load_datasamples(1					)
							UpperCAmelCase_=       SpeechTaFeatureExtractor()
							UpperCAmelCase_=       feature_extractor(audio_target=__UpperCAmelCase						,  return_tensors="""pt"""					).input_values
							self.assertEquals(input_values.shape						,  (1, 366, 80)					)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]						,  __UpperCAmelCase						,  atol=1E-4					)					)









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()
__A       =						logging.get_logger(__name__)


__A       =						'''https://openaipublic.azureedge.net/jukebox/models/'''
__A       =						{
    '''jukebox-1b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''1b_lyrics/prior_level_2.pth.tar''',
    ],
    '''jukebox-5b-lyrics''': [
        '''5b/vqvae.pth.tar''',
        '''5b/prior_level_0.pth.tar''',
        '''5b/prior_level_1.pth.tar''',
        '''5b_lyrics/prior_level_2.pth.tar''',
    ],
}



def 	__a					(   lowerCAmelCase_       : List[Any]    )				->			Tuple:






						'''simple docstring'''



						if key.endswith(""".model.1.bias"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.1.bias"""      ,""".conv1d_1.bias"""    )
						elif key.endswith(""".model.1.weight"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.1.weight"""      ,""".conv1d_1.weight"""    )
						elif key.endswith(""".model.3.bias"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.3.bias"""      ,""".conv1d_2.bias"""    )
						elif key.endswith(""".model.3.weight"""    ) and len(key.split("""."""    )    ) > 10:
												UpperCAmelCase_=       key.replace(""".model.3.weight"""      ,""".conv1d_2.weight"""    )

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

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

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

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

						if "x_emb.emb." in key:
												UpperCAmelCase_=       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_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : str      ,lowerCAmelCase_       : Any    )				->			Optional[Any]:






						'''simple docstring'''



						UpperCAmelCase_=       {}
						import re

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

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

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

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

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

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

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

												elif re_decoder_block_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_decoder_block_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       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_    ):
																		UpperCAmelCase_=       re_prior_cond_conv_out.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       int(groups[1]    ) * 2 + int(groups[2]    ) - 2
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_conv_out.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

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

												elif re_prior_cond_proj_in.fullmatch(lowerCAmelCase_    ):
																		UpperCAmelCase_=       re_prior_cond_proj_in.match(lowerCAmelCase_    )
																		UpperCAmelCase_=       regex_match.groups()
																		UpperCAmelCase_=       F"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
																		UpperCAmelCase_=       re_prior_cond_proj_in.sub(lowerCAmelCase_      ,lowerCAmelCase_    )

												# keep original key
												else:
																		UpperCAmelCase_=       original_key

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

												UpperCAmelCase_=       original_key
												UpperCAmelCase_=       value

						return new_dict



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : List[Any]=None      ,lowerCAmelCase_       : str=None    )				->			List[str]:






						'''simple docstring'''



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

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

						UpperCAmelCase_=       JukeboxConfig.from_pretrained(lowerCAmelCase_    )
						UpperCAmelCase_=       JukeboxModel(lowerCAmelCase_    )

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

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

												UpperCAmelCase_=       """vqvae""" if i == 0 else F"""priors.{3 - i}"""
												UpperCAmelCase_=       fix_jukebox_keys(lowerCAmelCase_      ,model.state_dict()      ,lowerCAmelCase_      ,lowerCAmelCase_    )
												weight_dict.append(lowerCAmelCase_    )

						UpperCAmelCase_=       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__":
	__A       =						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.''',
	)
	__A       =						parser.parse_args()
	convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)




