"""simple docstring"""






import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

						if is_vision_available():
												from transformers import MaskaFormerImageProcessor

if is_vision_available():
						from PIL import Image
class        snake_case :




					"""simple docstring"""

					def __init__(       self     :			Tuple					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			List[Any]=2					,lowerCamelCase__     :			List[str]=True					,lowerCamelCase__     :			List[str]=False					,lowerCamelCase__     :			Optional[int]=10					,lowerCamelCase__     :			Any=3					,lowerCamelCase__     :			Any=32 * 8					,lowerCamelCase__     :			List[str]=32 * 8					,lowerCamelCase__     :			Any=4					,lowerCamelCase__     :			Optional[int]=64					,):
									UpperCAmelCase__    =							parent
									UpperCAmelCase__    =							batch_size
									UpperCAmelCase__    =							is_training
									UpperCAmelCase__    =							use_auxiliary_loss
									UpperCAmelCase__    =							num_queries
									UpperCAmelCase__    =							num_channels
									UpperCAmelCase__    =							min_size
									UpperCAmelCase__    =							max_size
									UpperCAmelCase__    =							num_labels
									UpperCAmelCase__    =							hidden_dim
									UpperCAmelCase__    =							hidden_dim







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]    ).to(
									    lowerCamelCase__    )

									UpperCAmelCase__    =							torch.ones([self.batch_size, self.min_size, self.max_size]					,device=lowerCamelCase__    )

									UpperCAmelCase__    =							(
									    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]					,device=lowerCamelCase__    ) > 0.5
									).float()
									UpperCAmelCase__    =							(torch.rand((self.batch_size, self.num_labels)					,device=lowerCamelCase__    ) > 0.5).long()

									UpperCAmelCase__    =							self.get_config()
									return config, pixel_values, pixel_mask, mask_labels, class_labels







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							MaskaFormerConfig(
									    hidden_size=self.hidden_dim					,)
									UpperCAmelCase__    =							self.num_queries
									UpperCAmelCase__    =							self.num_labels

									UpperCAmelCase__    =							[1, 1, 1, 1]
									UpperCAmelCase__    =							self.num_channels

									UpperCAmelCase__    =							64
									UpperCAmelCase__    =							128
									UpperCAmelCase__    =							self.hidden_dim
									UpperCAmelCase__    =							self.hidden_dim
									UpperCAmelCase__    =							self.hidden_dim
									return config







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							self.prepare_config_and_inputs()
									UpperCAmelCase__    =							{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
									return config, inputs_dict







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Union[str, Any]					,lowerCamelCase__     :			int    ):
									UpperCAmelCase__    =							output.encoder_hidden_states
									UpperCAmelCase__    =							output.pixel_decoder_hidden_states
									UpperCAmelCase__    =							output.transformer_decoder_hidden_states

									self.parent.assertTrue(len(lowerCamelCase__    )					,len(config.backbone_config.depths    )    )
									self.parent.assertTrue(len(lowerCamelCase__    )					,len(config.backbone_config.depths    )    )
									self.parent.assertTrue(len(lowerCamelCase__    )					,config.decoder_layers    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Optional[int]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Union[str, Any]=False    ):
									with torch.no_grad():
													UpperCAmelCase__    =							MaskaFormerModel(config=lowerCamelCase__    )
													model.to(lowerCamelCase__    )
													model.eval()

													UpperCAmelCase__    =							model(pixel_values=lowerCamelCase__					,pixel_mask=lowerCamelCase__    )
													UpperCAmelCase__    =							model(lowerCamelCase__					,output_hidden_states=lowerCamelCase__    )

									self.parent.assertEqual(
									    output.transformer_decoder_last_hidden_state.shape					,(self.batch_size, self.num_queries, self.hidden_dim)					,)
									# let's ensure the other two hidden state exists
									self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None    )
									self.parent.assertTrue(output.encoder_last_hidden_state is not None    )

									if output_hidden_states:
													self.check_output_hidden_state(lowerCamelCase__					,lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			int					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			List[Any]					,lowerCamelCase__     :			List[Any]    ):
									UpperCAmelCase__    =							MaskaFormerForUniversalSegmentation(config=lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.eval()

									def comm_check_on_output(lowerCamelCase__     :			Optional[int]    ):
													# let's still check that all the required stuff is there
													self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None    )
													self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None    )
													self.parent.assertTrue(result.encoder_last_hidden_state is not None    )
													# okay, now we need to check the logits shape
													# due to the encoder compression, masks have a //4 spatial size
													self.parent.assertEqual(
													    result.masks_queries_logits.shape					,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)					,)
													# + 1 for null class
													self.parent.assertEqual(
													    result.class_queries_logits.shape					,(self.batch_size, self.num_queries, self.num_labels + 1)    )

									with torch.no_grad():
													UpperCAmelCase__    =							model(pixel_values=lowerCamelCase__					,pixel_mask=lowerCamelCase__    )
													UpperCAmelCase__    =							model(lowerCamelCase__    )

													comm_check_on_output(lowerCamelCase__    )

													UpperCAmelCase__    =							model(
													    pixel_values=lowerCamelCase__					,pixel_mask=lowerCamelCase__					,mask_labels=lowerCamelCase__					,class_labels=lowerCamelCase__    )

									comm_check_on_output(lowerCamelCase__    )

									self.parent.assertTrue(result.loss is not None    )
									self.parent.assertEqual(result.loss.shape					,torch.Size([1]    )    )






@require_torch
class        snake_case (				__UpperCAmelCase   ,					__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
					snake_case__       					=				{"feature-extraction": MaskaFormerModel} if is_torch_available() else {}

					snake_case__       					=				False
					snake_case__       					=				False
					snake_case__       					=				False
					snake_case__       					=				False







					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							MaskaFormerModelTester(self    )
									UpperCAmelCase__    =							ConfigTester(self					,config_class=lowerCamelCase__					,has_text_modality=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Any    ):
									self.config_tester.run_common_tests()







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__					,**lowerCamelCase__					,output_hidden_states=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase__    )







					@unittest.skip(reason='Mask2Former does not use inputs_embeds'    )
					def 	__lowerCAmelCase		(       self     :			int    ):
									pass







					@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'    )
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									pass







					@unittest.skip(reason='Mask2Former is not a generative model'    )
					def 	__lowerCAmelCase		(       self     :			Dict    ):
									pass







					@unittest.skip(reason='Mask2Former does not use token embeddings'    )
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									pass







					@require_torch_multi_gpu
					@unittest.skip(
					    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'    )
					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									pass







					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									pass







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__    =							model_class(lowerCamelCase__    )
													UpperCAmelCase__    =							inspect.signature(model.forward    )
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													UpperCAmelCase__    =							[*signature.parameters.keys()]

													UpperCAmelCase__    =							['pixel_values']
													self.assertListEqual(arg_names[:1]					,lowerCamelCase__    )







					@slow
					def 	__lowerCAmelCase		(       self     :			int    ):
									for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
													UpperCAmelCase__    =							MaskaFormerModel.from_pretrained(lowerCamelCase__    )
													self.assertIsNotNone(lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							(self.model_tester.min_size,) * 2
									UpperCAmelCase__    =							{
									    'pixel_values': torch.randn((2, 3, *size)					,device=lowerCamelCase__    ),
									    'mask_labels': torch.randn((2, 10, *size)					,device=lowerCamelCase__    ),
									    'class_labels': torch.zeros(2					,10					,device=lowerCamelCase__    ).long(),
									}
									UpperCAmelCase__    =							self.model_tester.get_config()

									UpperCAmelCase__    =							MaskaFormerForUniversalSegmentation(lowerCamelCase__    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							model(**lowerCamelCase__    )
									self.assertTrue(outputs.loss is not None    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.create_and_check_maskaformer_model(lowerCamelCase__					,**lowerCamelCase__					,output_hidden_states=lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__    =							model_class(lowerCamelCase__    ).to(lowerCamelCase__    )
													UpperCAmelCase__    =							model(**lowerCamelCase__					,output_attentions=lowerCamelCase__    )
													self.assertTrue(outputs.attentions is not None    )







					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									if not self.model_tester.is_training:
													return

									UpperCAmelCase__    =							self.all_model_classes[1]
									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()

									UpperCAmelCase__    =							model_class(lowerCamelCase__    )
									model.to(lowerCamelCase__    )
									model.train()

									UpperCAmelCase__    =							model(lowerCamelCase__					,mask_labels=lowerCamelCase__					,class_labels=lowerCamelCase__    ).loss
									loss.backward()







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							self.all_model_classes[1]
									UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							self.model_tester.prepare_config_and_inputs()
									UpperCAmelCase__    =							True
									UpperCAmelCase__    =							True

									UpperCAmelCase__    =							model_class(lowerCamelCase__    ).to(lowerCamelCase__    )
									model.train()

									UpperCAmelCase__    =							model(lowerCamelCase__					,mask_labels=lowerCamelCase__					,class_labels=lowerCamelCase__    )

									UpperCAmelCase__    =							outputs.encoder_hidden_states[0]
									encoder_hidden_states.retain_grad()

									UpperCAmelCase__    =							outputs.pixel_decoder_hidden_states[0]
									pixel_decoder_hidden_states.retain_grad()

									UpperCAmelCase__    =							outputs.transformer_decoder_hidden_states[0]
									transformer_decoder_hidden_states.retain_grad()

									UpperCAmelCase__    =							outputs.attentions[0]
									attentions.retain_grad()

									outputs.loss.backward(retain_graph=lowerCamelCase__    )

									self.assertIsNotNone(encoder_hidden_states.grad    )
									self.assertIsNotNone(pixel_decoder_hidden_states.grad    )
									self.assertIsNotNone(transformer_decoder_hidden_states.grad    )
									self.assertIsNotNone(attentions.grad    )






lowerCAmelCase__						:						Optional[Any]		  =			1E-4



def 						a_		(      ):
				UpperCAmelCase__    =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
				return image







@require_vision
@slow
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					@cached_property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									return "facebook/mask2former-swin-small-coco-instance"







					@cached_property
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints    ) if is_vision_available() else None







					def 	__lowerCAmelCase		(       self     :			List[str]    ):
									UpperCAmelCase__    =							MaskaFormerModel.from_pretrained(self.model_checkpoints    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							self.default_image_processor
									UpperCAmelCase__    =							prepare_img()
									UpperCAmelCase__    =							image_processor(lowerCamelCase__					,return_tensors='pt'    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							inputs['pixel_values'].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0    )
									# check size
									self.assertEqual(lowerCamelCase__					,(1, 3, 384, 384)    )

									with torch.no_grad():
													UpperCAmelCase__    =							model(**lowerCamelCase__    )

									UpperCAmelCase__    =							torch.tensor(
									    [[-0.2_7_9_0, -1.0_7_1_7, -1.1_6_6_8], [-0.5_1_2_8, -0.3_1_2_8, -0.4_9_8_7], [-0.5_8_3_2, 0.1_9_7_1, -0.0_1_9_7]]    ).to(lowerCamelCase__    )
									self.assertTrue(
									    torch.allclose(
									        outputs.encoder_last_hidden_state[0, 0, :3, :3]					,lowerCamelCase__					,atol=lowerCamelCase__    )    )

									UpperCAmelCase__    =							torch.tensor(
									    [[0.8_9_7_3, 1.1_8_4_7, 1.1_7_7_6], [1.1_9_3_4, 1.5_0_4_0, 1.5_1_2_8], [1.1_1_5_3, 1.4_4_8_6, 1.4_9_5_1]]    ).to(lowerCamelCase__    )
									self.assertTrue(
									    torch.allclose(
									        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]					,lowerCamelCase__					,atol=lowerCamelCase__    )    )

									UpperCAmelCase__    =							torch.tensor(
									    [[2.1_1_5_2, 1.7_0_0_0, -0.8_6_0_3], [1.5_8_0_8, 1.8_0_0_4, -0.9_3_5_3], [1.6_0_4_3, 1.7_4_9_5, -0.5_9_9_9]]    ).to(lowerCamelCase__    )
									self.assertTrue(
									    torch.allclose(
									        outputs.transformer_decoder_last_hidden_state[0, :3, :3]					,lowerCamelCase__					,atol=lowerCamelCase__    )    )







					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(lowerCamelCase__    ).eval()
									UpperCAmelCase__    =							self.default_image_processor
									UpperCAmelCase__    =							prepare_img()
									UpperCAmelCase__    =							image_processor(lowerCamelCase__					,return_tensors='pt'    ).to(lowerCamelCase__    )
									UpperCAmelCase__    =							inputs['pixel_values'].shape
									# check size is divisible by 32
									self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0    )
									# check size
									self.assertEqual(lowerCamelCase__					,(1, 3, 384, 384)    )

									with torch.no_grad():
													UpperCAmelCase__    =							model(**lowerCamelCase__    )
									# masks_queries_logits
									UpperCAmelCase__    =							outputs.masks_queries_logits
									self.assertEqual(
									    masks_queries_logits.shape					,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)    )
									UpperCAmelCase__    =							[
									    [-8.7_8_3_9, -9.0_0_5_6, -8.8_1_2_1],
									    [-7.4_1_0_4, -7.0_3_1_3, -6.5_4_0_1],
									    [-6.6_1_0_5, -6.3_4_2_7, -6.4_6_7_5],
									]
									UpperCAmelCase__    =							torch.tensor(lowerCamelCase__    ).to(lowerCamelCase__    )
									self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]					,lowerCamelCase__					,atol=lowerCamelCase__    )    )
									# class_queries_logits
									UpperCAmelCase__    =							outputs.class_queries_logits
									self.assertEqual(class_queries_logits.shape					,(1, model.config.num_queries, model.config.num_labels + 1)    )
									UpperCAmelCase__    =							torch.tensor(
									    [
									        [1.8_3_2_4, -8.0_8_3_5, -4.1_9_2_2],
									        [0.8_4_5_0, -9.0_0_5_0, -3.6_0_5_3],
									        [0.3_0_4_5, -7.7_2_9_3, -3.0_2_7_5],
									    ]    ).to(lowerCamelCase__    )
									self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]					,lowerCamelCase__					,atol=lowerCamelCase__    )    )







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints    ).to(lowerCamelCase__    ).eval()
									UpperCAmelCase__    =							self.default_image_processor

									UpperCAmelCase__    =							image_processor(
									    [np.zeros((3, 800, 1_333)    ), np.zeros((3, 800, 1_333)    )]					,segmentation_maps=[np.zeros((384, 384)    ).astype(np.floataa    ), np.zeros((384, 384)    ).astype(np.floataa    )]					,return_tensors='pt'					,)

									UpperCAmelCase__    =							inputs['pixel_values'].to(lowerCamelCase__    )
									UpperCAmelCase__    =							[el.to(lowerCamelCase__    ) for el in inputs['mask_labels']]
									UpperCAmelCase__    =							[el.to(lowerCamelCase__    ) for el in inputs['class_labels']]

									with torch.no_grad():
													UpperCAmelCase__    =							model(**lowerCamelCase__    )

									self.assertTrue(outputs.loss is not None    )




"""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


lowerCAmelCase__						:						str		  =			'base_with_context'



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )
				for lyr_num, lyr in enumerate(model.encoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							ly_weight['attention']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
				return model



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )

				for lyr_num, lyr in enumerate(model.encoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							ly_weight['attention']

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )

				return model



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T ) )

				for lyr_num, lyr in enumerate(model.decoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T ) )

								UpperCAmelCase__    =							ly_weight['self_attention']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

								UpperCAmelCase__    =							ly_weight['MultiHeadDotProductAttention_0']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale'] ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T ) )

				return model



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							checkpoints.load_tax_checkpoint(args.checkpoint_path )
				UpperCAmelCase__    =							jnp.tree_util.tree_map(onp.array      ,    lowerCamelCase )

				UpperCAmelCase__    =							[
				    '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()',
				]

				UpperCAmelCase__    =							os.path.join(args.checkpoint_path      ,    '..'      ,    'config.gin' )
				UpperCAmelCase__    =							inference.parse_training_gin_file(lowerCamelCase      ,    lowerCamelCase )
				UpperCAmelCase__    =							inference.InferenceModel(args.checkpoint_path      ,    lowerCamelCase )

				UpperCAmelCase__    =							DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,    variance_type='fixed_large' )

				UpperCAmelCase__    =							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'      ,    )

				UpperCAmelCase__    =							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'      ,    )

				UpperCAmelCase__    =							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      ,    )

				UpperCAmelCase__    =							load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_decoder(ta_checkpoint['target']['decoder']      ,    lowerCamelCase )

				UpperCAmelCase__    =							OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder' )

				UpperCAmelCase__    =							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__":
						lowerCAmelCase__						:						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.',
						)
						lowerCAmelCase__						:						List[str]		  =			parser.parse_args()

						main(args)



