



'''simple docstring'''




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
						from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self     : Dict							,     lowerCAmelCase__     : Dict							,     lowerCAmelCase__     : List[str]=1_3							,     lowerCAmelCase__     : Any=3_2							,     lowerCAmelCase__     : Any=3							,     lowerCAmelCase__     : int=4							,     lowerCAmelCase__     : int=[1_0, 2_0, 3_0, 4_0]							,     lowerCAmelCase__     : List[Any]=[2, 2, 3, 2]							,     lowerCAmelCase__     : Any=True							,     lowerCAmelCase__     : int=True							,     lowerCAmelCase__     : Tuple=3_7							,     lowerCAmelCase__     : List[Any]="gelu"							,     lowerCAmelCase__     : List[Any]=1_0							,     lowerCAmelCase__     : Optional[int]=0.02							,     lowerCAmelCase__     : Union[str, Any]=["stage2", "stage3", "stage4"]							,     lowerCAmelCase__     : Tuple=[2, 3, 4]							,     lowerCAmelCase__     : int=None							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		parent
					__SCREAMING_SNAKE_CASE       :		Dict           =		batch_size
					__SCREAMING_SNAKE_CASE       :		List[str]           =		image_size
					__SCREAMING_SNAKE_CASE       :		str           =		num_channels
					__SCREAMING_SNAKE_CASE       :		str           =		num_stages
					__SCREAMING_SNAKE_CASE       :		Dict           =		hidden_sizes
					__SCREAMING_SNAKE_CASE       :		Tuple           =		depths
					__SCREAMING_SNAKE_CASE       :		Any           =		is_training
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		use_labels
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		intermediate_size
					__SCREAMING_SNAKE_CASE       :		List[str]           =		hidden_act
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		num_labels
					__SCREAMING_SNAKE_CASE       :		str           =		initializer_range
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		out_features
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		out_indices
					__SCREAMING_SNAKE_CASE       :		Any           =		scope

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					__SCREAMING_SNAKE_CASE       :		List[str]           =		None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		ids_tensor([self.batch_size]							,     self.num_labels						)

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.get_config()

					return config, pixel_values, labels

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					return ConvNextVaConfig(
					    num_channels=self.num_channels							,     hidden_sizes=self.hidden_sizes							,     depths=self.depths							,     num_stages=self.num_stages							,     hidden_act=self.hidden_act							,     is_decoder=lowerCAmelCase__							,     initializer_range=self.initializer_range							,     out_features=self.out_features							,     out_indices=self.out_indices							,     num_labels=self.num_labels							,     )

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		ConvNextVaModel(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Dict           =		model(lowerCAmelCase__						)
					# expected last hidden states: B, C, H // 32, W // 32
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)							,     )

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		ConvNextVaForImageClassification(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		int           =		model(lowerCAmelCase__							,     labels=lowerCAmelCase__						)
					self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)						)

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[str]           =		ConvNextVaBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		Dict           =		model(lowerCAmelCase__						)

					# verify hidden states
					self.parent.assertEqual(len(result.feature_maps						)							,     len(config.out_features						)						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,     [self.batch_size, self.hidden_sizes[1], 4, 4]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,     len(config.out_features						)						)
					self.parent.assertListEqual(model.channels							,     config.hidden_sizes[1:]						)

					# verify backbone works with out_features=None
					__SCREAMING_SNAKE_CASE       :		Dict           =		None
					__SCREAMING_SNAKE_CASE       :		Any           =		ConvNextVaBackbone(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE       :		int           =		model(lowerCAmelCase__						)

					# verify feature maps
					self.parent.assertEqual(len(result.feature_maps						)							,     1						)
					self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,     [self.batch_size, self.hidden_sizes[-1], 1, 1]						)

					# verify channels
					self.parent.assertEqual(len(model.channels						)							,     1						)
					self.parent.assertListEqual(model.channels							,     [config.hidden_sizes[-1]]						)

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Any           =		self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		config_and_inputs
					__SCREAMING_SNAKE_CASE       :		List[str]           =		{"""pixel_values""": pixel_values}
					return config, inputs_dict



			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		config_and_inputs
					__SCREAMING_SNAKE_CASE       :		Tuple           =		{"""pixel_values""": pixel_values, """labels""": labels}
					return config, inputs_dict






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


			'''simple docstring'''

			_A      :		Any			    =		(
			    (
			        ConvNextVaModel,
			        ConvNextVaForImageClassification,
			        ConvNextVaBackbone,
			    )
			    if is_torch_available()
			    else ()
			)
			_A      :		Optional[int]			    =		(
			    {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_A      :		Union[str, Any]			    =		False
			_A      :		int			    =		False
			_A      :		List[str]			    =		False
			_A      :		List[str]			    =		False
			_A      :		List[str]			    =		False

			def UpperCamelCase__					(							self     : str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		ConvNextVaModelTester(self						)
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ConfigTester(self							,     config_class=lowerCAmelCase__							,     has_text_modality=lowerCAmelCase__							,     hidden_size=3_7						)

			def UpperCamelCase__					(							self     : Optional[Any]						):





					"""simple docstring"""






					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()

			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					return

			@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds"""						)
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings"""						)
			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					pass

			@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking"""						)
			def UpperCamelCase__					(							self     : List[Any]						):





					"""simple docstring"""






					pass

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





					"""simple docstring"""






					if not self.model_tester.is_training:
							return

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Dict           =		self.model_tester.prepare_config_and_inputs_with_labels()
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		True

							if model_class.__name__ in [
							    *get_values(lowerCAmelCase__						),
							    *get_values(lowerCAmelCase__						),
							]:
									continue

							__SCREAMING_SNAKE_CASE       :		Tuple           =		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.train()
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self._prepare_for_class(lowerCAmelCase__							,     lowerCAmelCase__							,     return_labels=lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		model(**lowerCAmelCase__						).loss
							loss.backward()

			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					if not self.model_tester.is_training:
							return

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		self.model_tester.prepare_config_and_inputs_with_labels()
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		False
							__SCREAMING_SNAKE_CASE       :		int           =		True

							if (
							    model_class.__name__
							    in [*get_values(lowerCAmelCase__						), *get_values(lowerCAmelCase__						)]
							    or not model_class.supports_gradient_checkpointing
							):
									continue

							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.gradient_checkpointing_enable()
							model.train()
							__SCREAMING_SNAKE_CASE       :		Dict           =		self._prepare_for_class(lowerCAmelCase__							,     lowerCAmelCase__							,     return_labels=lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		model(**lowerCAmelCase__						).loss
							loss.backward()

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		model_class(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Dict           =		inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE       :		List[str]           =		[*signature.parameters.keys()]

							__SCREAMING_SNAKE_CASE       :		List[Any]           =		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					def check_hidden_states_output(lowerCAmelCase__     : Optional[Any]							,     lowerCAmelCase__     : Optional[int]							,     lowerCAmelCase__     : Tuple						):
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()

							with torch.no_grad():
									__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model(**self._prepare_for_class(lowerCAmelCase__							,     lowerCAmelCase__						)						)

							__SCREAMING_SNAKE_CASE       :		List[str]           =		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.model_tester.num_stages
							self.assertEqual(len(lowerCAmelCase__						)							,     expected_num_stages + 1						)

							# ConvNextV2's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:]						)							,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,     )

					__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE       :		str           =		True
							check_hidden_states_output(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE       :		str           =		True

							check_hidden_states_output(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__						)



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





					"""simple docstring"""






					for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		ConvNextVaModel.from_pretrained(lowerCAmelCase__						)
							self.assertIsNotNone(lowerCAmelCase__						)


def 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Dict           =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
		return image






@require_torch
@require_vision
class 						_UpperCamelCase  (							unittest.TestCase    ):


			'''simple docstring'''

			@cached_property
			def UpperCamelCase__					(							self     : Dict						):





					"""simple docstring"""






					return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224"""						) if is_vision_available() else None



			@slow
			def UpperCamelCase__					(							self     : Any						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Dict           =		ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224"""						).to(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		Tuple           =		self.default_image_processor
					__SCREAMING_SNAKE_CASE       :		Dict           =		prepare_img()
					__SCREAMING_SNAKE_CASE       :		List[str]           =		preprocessor(images=lowerCAmelCase__							,     return_tensors="""pt"""						).to(lowerCAmelCase__						)

					# forward pass
					with torch.no_grad():
							__SCREAMING_SNAKE_CASE       :		str           =		model(**lowerCAmelCase__						)

					# verify the logits
					__SCREAMING_SNAKE_CASE       :		List[str]           =		torch.Size((1, 1_0_0_0)						)
					self.assertEqual(outputs.logits.shape							,     lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE       :		int           =		torch.tensor([0.99_96, 0.19_66, -0.43_86]						).to(lowerCAmelCase__						)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,     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)