



'''simple docstring'''




import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
						from transformers import SpeechaTextFeatureExtractor

UpperCamelCase__       :	Tuple    = random.Random()


def 				lowerCAmelCase_   (	_lowerCamelCase:   int       ,      _lowerCamelCase:   Optional[Any]=1.0       ,      _lowerCamelCase:   Optional[int]=None       ,      _lowerCamelCase:   Any=None					):
		if rng is None:
				__SCREAMING_SNAKE_CASE       :		int           =		global_rng

		__SCREAMING_SNAKE_CASE       :		str           =		[]
		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 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			def __init__(							self     : Any							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : List[Any]=7							,     lowerCAmelCase__     : str=4_0_0							,     lowerCAmelCase__     : Any=2_0_0_0							,     lowerCAmelCase__     : Any=2_4							,     lowerCAmelCase__     : List[Any]=2_4							,     lowerCAmelCase__     : Tuple=0.0							,     lowerCAmelCase__     : Union[str, Any]=1_6_0_0_0							,     lowerCAmelCase__     : List[Any]=True							,     lowerCAmelCase__     : int=True							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		int           =		parent
					__SCREAMING_SNAKE_CASE       :		int           =		batch_size
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		min_seq_length
					__SCREAMING_SNAKE_CASE       :		Tuple           =		max_seq_length
					__SCREAMING_SNAKE_CASE       :		Dict           =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		feature_size
					__SCREAMING_SNAKE_CASE       :		List[str]           =		num_mel_bins
					__SCREAMING_SNAKE_CASE       :		List[str]           =		padding_value
					__SCREAMING_SNAKE_CASE       :		List[str]           =		sampling_rate
					__SCREAMING_SNAKE_CASE       :		str           =		return_attention_mask
					__SCREAMING_SNAKE_CASE       :		Tuple           =		do_normalize

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






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



			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : Dict=False							,     lowerCAmelCase__     : Optional[Any]=False						):





					"""simple docstring"""






					def _flatten(lowerCAmelCase__     : str						):
							return list(itertools.chain(*lowerCAmelCase__						)						)

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






@require_torch
@require_torchaudio
class 						_UpperCamelCase  (							lowerCamelCase__    ,					unittest.TestCase    ):


			'''simple docstring'''

			_A      :		Tuple			    =		SpeechaTextFeatureExtractor if is_speech_available() else None

			def UpperCamelCase__					(							self     : Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		SpeechaTextFeatureExtractionTester(self						)

			def UpperCamelCase__					(							self     : Union[str, Any]							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					self.assertTrue(np.all(np.mean(lowerCAmelCase__							,     axis=0						) < 1E-3						)						)
					self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__							,     axis=0						) - 1						) < 1E-3						)						)

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					# create three inputs of length 800, 1000, and 1200
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[floats_list((1, x)						)[0] for x in range(8_0_0							,     1_4_0_0							,     2_0_0						)]
					__SCREAMING_SNAKE_CASE       :		int           =		[np.asarray(lowerCAmelCase__						) for speech_input in speech_inputs]

					# Test feature size
					__SCREAMING_SNAKE_CASE       :		Tuple           =		feature_extractor(lowerCAmelCase__							,     padding=lowerCAmelCase__							,     return_tensors="""np"""						).input_features
					self.assertTrue(input_features.ndim == 3						)
					self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size						)

					# Test not batched input
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		feature_extractor(speech_inputs[0]							,     return_tensors="""np"""						).input_features
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		feature_extractor(np_speech_inputs[0]							,     return_tensors="""np"""						).input_features
					self.assertTrue(np.allclose(lowerCAmelCase__							,     lowerCAmelCase__							,     atol=1E-3						)						)

					# Test batched
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		feature_extractor(lowerCAmelCase__							,     return_tensors="""np"""						).input_features
					__SCREAMING_SNAKE_CASE       :		Tuple           =		feature_extractor(lowerCAmelCase__							,     return_tensors="""np"""						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__							,     lowerCAmelCase__						):
							self.assertTrue(np.allclose(lowerCAmelCase__							,     lowerCAmelCase__							,     atol=1E-3						)						)

					# Test 2-D numpy arrays are batched.
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		[floats_list((1, x)						)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
					__SCREAMING_SNAKE_CASE       :		List[str]           =		np.asarray(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Any           =		feature_extractor(lowerCAmelCase__							,     return_tensors="""np"""						).input_features
					__SCREAMING_SNAKE_CASE       :		Tuple           =		feature_extractor(lowerCAmelCase__							,     return_tensors="""np"""						).input_features
					for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__							,     lowerCAmelCase__						):
							self.assertTrue(np.allclose(lowerCAmelCase__							,     lowerCAmelCase__							,     atol=1E-3						)						)

			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					__SCREAMING_SNAKE_CASE       :		int           =		[floats_list((1, x)						)[0] for x in range(8_0_0							,     1_4_0_0							,     2_0_0						)]

					__SCREAMING_SNAKE_CASE       :		int           =		["""longest""", """max_length""", """do_not_pad"""]
					__SCREAMING_SNAKE_CASE       :		int           =		[None, 1_6, None]
					for max_length, padding in zip(lowerCAmelCase__							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		int           =		feature_extractor(
							    lowerCAmelCase__							,     padding=lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     return_attention_mask=lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Dict           =		inputs.input_features
							__SCREAMING_SNAKE_CASE       :		Dict           =		inputs.attention_mask
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		[np.sum(lowerCAmelCase__						) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]						)
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]						)
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]						)

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[floats_list((1, x)						)[0] for x in range(8_0_0							,     1_4_0_0							,     2_0_0						)]

					__SCREAMING_SNAKE_CASE       :		Tuple           =		["""longest""", """max_length""", """do_not_pad"""]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[None, 1_6, None]
					for max_length, padding in zip(lowerCAmelCase__							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		Any           =		feature_extractor(
							    lowerCAmelCase__							,     max_length=lowerCAmelCase__							,     padding=lowerCAmelCase__							,     return_tensors="""np"""							,     return_attention_mask=lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		int           =		inputs.input_features
							__SCREAMING_SNAKE_CASE       :		Dict           =		inputs.attention_mask
							__SCREAMING_SNAKE_CASE       :		Dict           =		[np.sum(lowerCAmelCase__						) for x in attention_mask]

							self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]						)
							self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6						)
							self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]						)
							self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6						)
							self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]						)

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		[floats_list((1, x)						)[0] for x in range(8_0_0							,     1_4_0_0							,     2_0_0						)]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		feature_extractor(
					    lowerCAmelCase__							,     padding="""max_length"""							,     max_length=4							,     truncation=lowerCAmelCase__							,     return_tensors="""np"""							,     return_attention_mask=lowerCAmelCase__							,     )
					__SCREAMING_SNAKE_CASE       :		Tuple           =		inputs.input_features
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		inputs.attention_mask
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		np.sum(attention_mask == 1							,     axis=1						)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
					self._check_zero_mean_unit_variance(input_features[1]						)
					self._check_zero_mean_unit_variance(input_features[2]						)

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[floats_list((1, x)						)[0] for x in range(8_0_0							,     1_4_0_0							,     2_0_0						)]
					__SCREAMING_SNAKE_CASE       :		int           =		feature_extractor(
					    lowerCAmelCase__							,     padding="""longest"""							,     max_length=4							,     truncation=lowerCAmelCase__							,     return_tensors="""np"""							,     return_attention_mask=lowerCAmelCase__							,     )
					__SCREAMING_SNAKE_CASE       :		Tuple           =		inputs.input_features
					__SCREAMING_SNAKE_CASE       :		Any           =		inputs.attention_mask
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		np.sum(attention_mask == 1							,     axis=1						)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]						)
					self._check_zero_mean_unit_variance(input_features[2]						)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape							,     (3, 4, 2_4)						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[floats_list((1, x)						)[0] for x in range(8_0_0							,     1_4_0_0							,     2_0_0						)]
					__SCREAMING_SNAKE_CASE       :		List[str]           =		feature_extractor(
					    lowerCAmelCase__							,     padding="""longest"""							,     max_length=1_6							,     truncation=lowerCAmelCase__							,     return_tensors="""np"""							,     return_attention_mask=lowerCAmelCase__							,     )
					__SCREAMING_SNAKE_CASE       :		int           =		inputs.input_features
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		inputs.attention_mask
					__SCREAMING_SNAKE_CASE       :		List[str]           =		np.sum(attention_mask == 1							,     axis=1						)

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]						)
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]						)
					self._check_zero_mean_unit_variance(input_features[2]						)

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape							,     (3, 6, 2_4)						)

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





					"""simple docstring"""






					import torch

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					__SCREAMING_SNAKE_CASE       :		int           =		np.random.rand(1_0_0							,     3_2						).astype(np.floataa						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		np_speech_inputs.tolist()

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

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					from datasets import load_dataset

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

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



			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		np.array([
					    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
					    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
					    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
					]						)
					# fmt: on

					__SCREAMING_SNAKE_CASE       :		Dict           =		self._load_datasamples(1						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		feature_extractor(lowerCAmelCase__							,     return_tensors="""pt"""						).input_features
					self.assertEquals(input_features.shape							,     (1, 5_8_4, 2_4)						)
					self.assertTrue(np.allclose(input_features[0, 0, :3_0]							,     lowerCAmelCase__							,     atol=1E-4						)						)




'''simple docstring'''




import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


UpperCamelCase__       :	List[Any]    = '''base_with_context'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Dict					):
		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)
		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]					)					)
		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   List[str]					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]					)					)

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T					)					)
		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T					)					)

		for lyr_num, lyr in enumerate(model.decoders					):
				__SCREAMING_SNAKE_CASE       :		str           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		ly_weight["""self_attention"""]
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""MultiHeadDotProductAttention_0"""]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T					)					)

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		int           =		checkpoints.load_tax_checkpoint(args.checkpoint_path					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		jnp.tree_util.tree_map(onp.array       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		int           =		[
		    """from __gin__ import dynamic_registration""",
		    """from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
		    """diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
		    """diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
		]

		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		os.path.join(args.checkpoint_path       ,      """.."""       ,      """config.gin"""					)
		__SCREAMING_SNAKE_CASE       :		Any           =		inference.parse_training_gin_file(_lowerCamelCase       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		inference.InferenceModel(args.checkpoint_path       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""       ,      variance_type="""fixed_large"""					)

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		SpectrogramNotesEncoder(
		    max_length=synth_model.sequence_length["""inputs"""]       ,      vocab_size=synth_model.model.module.config.vocab_size       ,      d_model=synth_model.model.module.config.emb_dim       ,      dropout_rate=synth_model.model.module.config.dropout_rate       ,      num_layers=synth_model.model.module.config.num_encoder_layers       ,      num_heads=synth_model.model.module.config.num_heads       ,      d_kv=synth_model.model.module.config.head_dim       ,      d_ff=synth_model.model.module.config.mlp_dim       ,      feed_forward_proj="""gated-gelu"""       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		SpectrogramContEncoder(
		    input_dims=synth_model.audio_codec.n_dims       ,      targets_context_length=synth_model.sequence_length["""targets_context"""]       ,      d_model=synth_model.model.module.config.emb_dim       ,      dropout_rate=synth_model.model.module.config.dropout_rate       ,      num_layers=synth_model.model.module.config.num_encoder_layers       ,      num_heads=synth_model.model.module.config.num_heads       ,      d_kv=synth_model.model.module.config.head_dim       ,      d_ff=synth_model.model.module.config.mlp_dim       ,      feed_forward_proj="""gated-gelu"""       ,      )

		__SCREAMING_SNAKE_CASE       :		Tuple           =		TaFilmDecoder(
		    input_dims=synth_model.audio_codec.n_dims       ,      targets_length=synth_model.sequence_length["""targets_context"""]       ,      max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time       ,      d_model=synth_model.model.module.config.emb_dim       ,      num_layers=synth_model.model.module.config.num_decoder_layers       ,      num_heads=synth_model.model.module.config.num_heads       ,      d_kv=synth_model.model.module.config.head_dim       ,      d_ff=synth_model.model.module.config.mlp_dim       ,      dropout_rate=synth_model.model.module.config.dropout_rate       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		load_decoder(ta_checkpoint["""target"""]["""decoder"""]       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		SpectrogramDiffusionPipeline(
		    notes_encoder=_lowerCamelCase       ,      continuous_encoder=_lowerCamelCase       ,      decoder=_lowerCamelCase       ,      scheduler=_lowerCamelCase       ,      melgan=_lowerCamelCase       ,      )
		if args.save:
				pipe.save_pretrained(args.output_path					)


if __name__ == "__main__":
						UpperCamelCase__       :	Tuple    = argparse.ArgumentParser()

						parser.add_argument('''--output_path''', default=None, type=str, required=True, help='''Path to the converted model.''')
						parser.add_argument(
						    '''--save''', default=True, type=bool, required=False, help='''Whether to save the converted model or not.'''
						)
						parser.add_argument(
						    '''--checkpoint_path''',
						    default=f"{MODEL}/checkpoint_500000",
						    type=str,
						    required=False,
						    help='''Path to the original jax model checkpoint.''',
						)
						UpperCamelCase__       :	List[str]    = parser.parse_args()

						main(args)