


'''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__  			=				'''base_with_context'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Dict ):






			"""simple docstring"""




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

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

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

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

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






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			__A=	nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

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

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

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

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

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

			return model






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			__A=	nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
			__A=	nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )

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

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

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

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

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

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

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

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

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

			return model






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	checkpoints.load_tax_checkpoint(args.checkpoint_path )
			__A=	jnp.tree_util.tree_map(onp.array,_SCREAMING_SNAKE_CASE )

			__A=	[
			    '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()',
			]

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

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

			__A=	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',)

			__A=	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',)

			__A=	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,)

			__A=	load_notes_encoder(ta_checkpoint['target']['token_encoder'],_SCREAMING_SNAKE_CASE )
			__A=	load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'],_SCREAMING_SNAKE_CASE )
			__A=	load_decoder(ta_checkpoint['target']['decoder'],_SCREAMING_SNAKE_CASE )

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

			__A=	SpectrogramDiffusionPipeline(
			    notes_encoder=_SCREAMING_SNAKE_CASE,continuous_encoder=_SCREAMING_SNAKE_CASE,decoder=_SCREAMING_SNAKE_CASE,scheduler=_SCREAMING_SNAKE_CASE,melgan=_SCREAMING_SNAKE_CASE,)
			if args.save:
						pipe.save_pretrained(args.output_path )


if __name__ == "__main__":
					UpperCAmelCase__  			=				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__  			=				parser.parse_args()

					main(args)







'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import BridgeTowerImageProcessor
class 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			def __init__(  self							:			str			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Dict[str, int] = None			,    lowerCAmelCase_							:			int = 32			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Union[int, float] = 1 / 255			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,    lowerCAmelCase_							:			Optional[Union[float, List[float]]] = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,    lowerCAmelCase_							:			bool = True			,    lowerCAmelCase_							:			str=7			,    lowerCAmelCase_							:			Union[str, Any]=30			,    lowerCAmelCase_							:			str=400			,    lowerCAmelCase_							:			int=3			,    )      ->		str:
						__A=	parent
						__A=	do_resize
						__A=	size if size is not None else {'shortest_edge': 288}
						__A=	size_divisor
						__A=	do_rescale
						__A=	rescale_factor
						__A=	do_normalize
						__A=	do_center_crop
						__A=	image_mean
						__A=	image_std
						__A=	do_pad
						__A=	batch_size
						__A=	num_channels
						__A=	min_resolution
						__A=	max_resolution
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		int:
						return {
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_normalize": self.do_normalize,
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "size_divisor": self.size_divisor,
						}

			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			str=False	)      ->		str:
						if not batched:
									__A=	self.size['shortest_edge']
									__A=	image_inputs[0]
									if isinstance(lowerCAmelCase_			,    Image.Image	):
												__A,	__A=	image.size
									else:
												__A,	__A=	image.shape[1], image.shape[2]
									__A=	size / min(lowerCAmelCase_			,    lowerCAmelCase_	)
									if h < w:
												__A,	__A=	size, scale * w
									else:
												__A,	__A=	scale * h, size

									__A=	int((1_333 / 800) * size	)
									if max(lowerCAmelCase_			,    lowerCAmelCase_	) > max_size:
												__A=	max_size / max(lowerCAmelCase_			,    lowerCAmelCase_	)
												__A=	newh * scale
												__A=	neww * scale

									__A,	__A=	int(newh + 0.5	), int(neww + 0.5	)
									__A,	__A=	(
									    newh // self.size_divisor * self.size_divisor,
									    neww // self.size_divisor * self.size_divisor,
									)

						else:
									__A=	[]
									for image in image_inputs:
												__A,	__A=	self.get_expected_values([image]	)
												expected_values.append((expected_height, expected_width)	)
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[0]	)[0]
									__A=	max(lowerCAmelCase_			,    key=lambda lowerCAmelCase_	: item[1]	)[1]

						return expected_height, expected_width


@require_torch
@require_vision
class 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				int					      =			BridgeTowerImageProcessor if is_vision_available() else None
			def 					lowerCAmelCase     (  self							:			Dict	)      ->		Optional[int]:
						__A=	BridgeTowerImageProcessingTester(self	)
			@property
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Union[str, Any]:
						return self.image_processor_tester.prepare_image_processor_dict()
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[Any]:
						__A=	self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_mean'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'image_std'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_normalize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'do_resize'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size'	)	)
						self.assertTrue(hasattr(lowerCAmelCase_			,    'size_divisor'	)	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		str:
						pass
			def 					lowerCAmelCase     (  self							:			Union[str, Any]	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    Image.Image	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )
			def 					lowerCAmelCase     (  self							:			Any	)      ->		Any:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    numpify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    np.ndarray	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )

			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Tuple:
						# Initialize image processor
						__A=	self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						__A=	prepare_image_inputs(self.image_processor_tester			,    equal_resolution=lowerCAmelCase_			,    torchify=lowerCAmelCase_	)
						for image in image_inputs:
									self.assertIsInstance(lowerCAmelCase_			,    torch.Tensor	)

						# Test not batched input
						__A=	image_processing(image_inputs[0]			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)			,    )

						# Test batched
						__A=	image_processing(lowerCAmelCase_			,    return_tensors='pt'	).pixel_values

						__A,	__A=	self.image_processor_tester.get_expected_values(lowerCAmelCase_			,    batched=lowerCAmelCase_	)
						self.assertEqual(
						    encoded_images.shape			,    (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,    )



