



'''simple docstring'''

import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=1					)				->					Union[str, Any]:





			"""simple docstring"""







			if n_shave_prefix_segments >= 0:
						return ".".join(path.split('.'					)[n_shave_prefix_segments:]					)
			else:
						return ".".join(path.split('.'					)[:n_shave_prefix_segments]					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=0					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    str      							=[]
			for old_item in old_list:
						snake_case:    Any      							=old_item.replace('in_layers.0'				,							'norm1'					)
						snake_case:    Any      							=new_item.replace('in_layers.2'				,							'conv1'					)

						snake_case:    Tuple      							=new_item.replace('out_layers.0'				,							'norm2'					)
						snake_case:    List[Any]      							=new_item.replace('out_layers.3'				,							'conv2'					)

						snake_case:    str      							=new_item.replace('emb_layers.1'				,							'time_emb_proj'					)
						snake_case:    int      							=new_item.replace('skip_connection'				,							'conv_shortcut'					)

						snake_case:    Optional[int]      							=shave_segments(__UpperCAmelCase				,							n_shave_prefix_segments=__UpperCAmelCase					)

						mapping.append({'old': old_item, 'new': new_item}					)

			return mapping







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=0					)				->					List[Any]:





			"""simple docstring"""







			snake_case:    Tuple      							=[]
			for old_item in old_list:
						snake_case:    Dict      							=old_item

						snake_case:    Dict      							=new_item.replace('norm.weight'				,							'group_norm.weight'					)
						snake_case:    Union[str, Any]      							=new_item.replace('norm.bias'				,							'group_norm.bias'					)

						snake_case:    Any      							=new_item.replace('proj_out.weight'				,							'proj_attn.weight'					)
						snake_case:    Optional[int]      							=new_item.replace('proj_out.bias'				,							'proj_attn.bias'					)

						snake_case:    Dict      							=shave_segments(__UpperCAmelCase				,							n_shave_prefix_segments=__UpperCAmelCase					)

						mapping.append({'old': old_item, 'new': new_item}					)

			return mapping







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None					)				->					Any:





			"""simple docstring"""







			assert isinstance(__UpperCAmelCase				,							__UpperCAmelCase					), "Paths should be a list of dicts containing 'old' and 'new' keys."

			# Splits the attention layers into three variables.
			if attention_paths_to_split is not None:
						for path, path_map in attention_paths_to_split.items():
									snake_case:    Any      							=old_checkpoint[path]
									snake_case:    int      							=old_tensor.shape[0] // 3

									snake_case:    int      							=(-1, channels) if len(old_tensor.shape					) == 3 else (-1)

									snake_case:    Union[str, Any]      							=old_tensor.shape[0] // config['num_head_channels'] // 3

									snake_case:    str      							=old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]					)
									snake_case							,  snake_case							,  snake_case:    int      							=old_tensor.split(channels // num_heads				,							dim=1					)

									snake_case:    Tuple      							=query.reshape(__UpperCAmelCase					)
									snake_case:    Optional[int]      							=key.reshape(__UpperCAmelCase					)
									snake_case:    int      							=value.reshape(__UpperCAmelCase					)

			for path in paths:
						snake_case:    List[Any]      							=path['new']

						# These have already been assigned
						if attention_paths_to_split is not None and new_path in attention_paths_to_split:
									continue

						# Global renaming happens here
						snake_case:    Optional[int]      							=new_path.replace('middle_block.0'				,							'mid_block.resnets.0'					)
						snake_case:    int      							=new_path.replace('middle_block.1'				,							'mid_block.attentions.0'					)
						snake_case:    Dict      							=new_path.replace('middle_block.2'				,							'mid_block.resnets.1'					)

						if additional_replacements is not None:
									for replacement in additional_replacements:
												snake_case:    Tuple      							=new_path.replace(replacement['old']				,							replacement['new']					)

        # proj_attn.weight has to be converted from conv 1D to linear
						if "proj_attn.weight" in new_path:
									snake_case:    Optional[Any]      							=old_checkpoint[path['old']][:, :, 0]
						else:
									snake_case:    Dict      							=old_checkpoint[path['old']]







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Any      							={}

			snake_case:    Union[str, Any]      							=checkpoint['time_embed.0.weight']
			snake_case:    Any      							=checkpoint['time_embed.0.bias']
			snake_case:    Dict      							=checkpoint['time_embed.2.weight']
			snake_case:    int      							=checkpoint['time_embed.2.bias']

			snake_case:    List[Any]      							=checkpoint['input_blocks.0.0.weight']
			snake_case:    str      							=checkpoint['input_blocks.0.0.bias']

			snake_case:    List[Any]      							=checkpoint['out.0.weight']
			snake_case:    str      							=checkpoint['out.0.bias']
			snake_case:    Union[str, Any]      							=checkpoint['out.2.weight']
			snake_case:    Optional[Any]      							=checkpoint['out.2.bias']

			# Retrieves the keys for the input blocks only
			snake_case:    str      							=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'input_blocks' in layer}					)
			snake_case:    Union[str, Any]      							={
			    layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
			    for layer_id in range(__UpperCAmelCase					)
			}

			# Retrieves the keys for the middle blocks only
			snake_case:    int      							=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'middle_block' in layer}					)
			snake_case:    Optional[Any]      							={
			    layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
			    for layer_id in range(__UpperCAmelCase					)
			}

			# Retrieves the keys for the output blocks only
			snake_case:    Dict      							=len({'.'.join(layer.split('.'					)[:2]					) for layer in checkpoint if 'output_blocks' in layer}					)
			snake_case:    List[str]      							={
			    layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
			    for layer_id in range(__UpperCAmelCase					)
			}

			for i in range(1				,							__UpperCAmelCase					):
						snake_case:    Optional[int]      							=(i - 1) // (config['num_res_blocks'] + 1)
						snake_case:    str      							=(i - 1) % (config['num_res_blocks'] + 1)

						snake_case:    Union[str, Any]      							=[key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
						snake_case:    str      							=[key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]

						if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
									snake_case:    Tuple      							=checkpoint[
									    f'''input_blocks.{i}.0.op.weight'''
									]
									snake_case:    Any      							=checkpoint[
									    f'''input_blocks.{i}.0.op.bias'''
									]
									continue

						snake_case:    Optional[int]      							=renew_resnet_paths(__UpperCAmelCase					)
						snake_case:    Any      							={'old': f'''input_blocks.{i}.0''', 'new': f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
						snake_case:    List[Any]      							={'old': 'resnets.2.op', 'new': 'downsamplers.0.op'}
						assign_to_checkpoint(
						    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							additional_replacements=[meta_path, resnet_op]				,							config=__UpperCAmelCase					)

						if len(__UpperCAmelCase					):
									snake_case:    List[Any]      							=renew_attention_paths(__UpperCAmelCase					)
									snake_case:    Any      							={
									    'old': f'''input_blocks.{i}.1''',
									    'new': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
									}
									snake_case:    Optional[Any]      							={
									    f'''input_blocks.{i}.1.qkv.bias''': {
									        'key': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
									        'query': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
									        'value': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
									    },
									    f'''input_blocks.{i}.1.qkv.weight''': {
									        'key': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
									        'query': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
									        'value': f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
									    },
									}
									assign_to_checkpoint(
									    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							additional_replacements=[meta_path]				,							attention_paths_to_split=__UpperCAmelCase				,							config=__UpperCAmelCase				,							)

			snake_case:    int      							=middle_blocks[0]
			snake_case:    Tuple      							=middle_blocks[1]
			snake_case:    Optional[Any]      							=middle_blocks[2]

			snake_case:    Optional[Any]      							=renew_resnet_paths(__UpperCAmelCase					)
			assign_to_checkpoint(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							config=__UpperCAmelCase					)

			snake_case:    Any      							=renew_resnet_paths(__UpperCAmelCase					)
			assign_to_checkpoint(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							config=__UpperCAmelCase					)

			snake_case:    Optional[int]      							=renew_attention_paths(__UpperCAmelCase					)
			snake_case:    Union[str, Any]      							={
			    'middle_block.1.qkv.bias': {
			        'key': 'mid_block.attentions.0.key.bias',
			        'query': 'mid_block.attentions.0.query.bias',
			        'value': 'mid_block.attentions.0.value.bias',
			    },
			    'middle_block.1.qkv.weight': {
			        'key': 'mid_block.attentions.0.key.weight',
			        'query': 'mid_block.attentions.0.query.weight',
			        'value': 'mid_block.attentions.0.value.weight',
			    },
			}
			assign_to_checkpoint(
			    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							attention_paths_to_split=__UpperCAmelCase				,							config=__UpperCAmelCase					)

			for i in range(__UpperCAmelCase					):
						snake_case:    List[Any]      							=i // (config['num_res_blocks'] + 1)
						snake_case:    Any      							=i % (config['num_res_blocks'] + 1)
						snake_case:    List[str]      							=[shave_segments(__UpperCAmelCase				,							2					) for name in output_blocks[i]]
						snake_case:    List[str]      							={}

						for layer in output_block_layers:
									snake_case							,  snake_case:    List[Any]      							=layer.split('.'					)[0], shave_segments(__UpperCAmelCase				,							1					)
									if layer_id in output_block_list:
												output_block_list[layer_id].append(__UpperCAmelCase					)
									else:
												snake_case:    int      							=[layer_name]

						if len(__UpperCAmelCase					) > 1:
									snake_case:    Dict      							=[key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
									snake_case:    str      							=[key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]

									snake_case:    int      							=renew_resnet_paths(__UpperCAmelCase					)
									snake_case:    Optional[int]      							=renew_resnet_paths(__UpperCAmelCase					)

									snake_case:    List[str]      							={'old': f'''output_blocks.{i}.0''', 'new': f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
									assign_to_checkpoint(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							additional_replacements=[meta_path]				,							config=__UpperCAmelCase					)

									if ["conv.weight", "conv.bias"] in output_block_list.values():
												snake_case:    Optional[Any]      							=list(output_block_list.values()					).index(['conv.weight', 'conv.bias']					)
												snake_case:    int      							=checkpoint[
												    f'''output_blocks.{i}.{index}.conv.weight'''
												]
												snake_case:    List[str]      							=checkpoint[
												    f'''output_blocks.{i}.{index}.conv.bias'''
												]

												# Clear attentions as they have been attributed above.
												if len(__UpperCAmelCase					) == 2:
															snake_case:    Union[str, Any]      							=[]

									if len(__UpperCAmelCase					):
												snake_case:    Optional[int]      							=renew_attention_paths(__UpperCAmelCase					)
												snake_case:    str      							={
												    'old': f'''output_blocks.{i}.1''',
												    'new': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
												}
												snake_case:    List[Any]      							={
												    f'''output_blocks.{i}.1.qkv.bias''': {
												        'key': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
												        'query': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
												        'value': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
												    },
												    f'''output_blocks.{i}.1.qkv.weight''': {
												        'key': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
												        'query': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
												        'value': f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
												    },
												}
												assign_to_checkpoint(
												    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							additional_replacements=[meta_path]				,							attention_paths_to_split=to_split if any('qkv' in key for key in attentions					) else None				,							config=__UpperCAmelCase				,							)
						else:
									snake_case:    Union[str, Any]      							=renew_resnet_paths(__UpperCAmelCase				,							n_shave_prefix_segments=1					)
									for path in resnet_0_paths:
												snake_case:    int      							='.'.join(['output_blocks', str(__UpperCAmelCase					), path['old']]					)
												snake_case:    Tuple      							='.'.join(['up_blocks', str(__UpperCAmelCase					), 'resnets', str(__UpperCAmelCase					), path['new']]					)

												snake_case:    List[Any]      							=checkpoint[old_path]

			return new_checkpoint


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)

	parser.add_argument(
	    '--config_file',
	    default=None,
	    type=str,
	    required=True,
	    help='The config json file corresponding to the architecture.',
	)

	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

	a							   =				parser.parse_args()

	a							   =				torch.load(args.checkpoint_path)

	with open(args.config_file) as f:
		a							   =				json.loads(f.read())

	a							   =				convert_ldm_checkpoint(checkpoint, config)

	if "ldm" in config:
		del config["ldm"]

	a							   =				UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		a							   =				DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
		a							   =				VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

		a							   =				LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)









'''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


a							   =				'base_with_context'







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					str:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']					)					)
			snake_case:    Tuple      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)
			for lyr_num, lyr in enumerate(model.encoders					):
						snake_case:    Dict      							=weights[f'''layers_{lyr_num}''']
						snake_case:    str      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

						snake_case:    Any      							=ly_weight['attention']
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

						snake_case:    List[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T					)					)

			snake_case:    Dict      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)

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

						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']					)					)

						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Tuple      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)
						snake_case:    Any      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)

			snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']					)					)

			return model







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T					)					)
			snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T					)					)

			snake_case:    Tuple      							=nn.Parameter(
			    torch.FloatTensor(weights['Embed_0']['embedding']					)				,							requires_grad=__UpperCAmelCase					)

			snake_case:    Any      							=nn.Parameter(
			    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T					)					)

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

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

						snake_case:    str      							=ly_weight['self_attention']
						snake_case:    str      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)

						snake_case:    Optional[Any]      							=ly_weight['MultiHeadDotProductAttention_0']
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T					)					)
						snake_case:    List[str]      							=nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T					)					)
						snake_case:    Dict      							=nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T					)					)
						snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T					)					)
						snake_case:    Any      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']					)					)

						snake_case:    int      							=nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(
						    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T					)					)
						snake_case:    int      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T					)					)
						snake_case:    Optional[int]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T					)					)
						snake_case:    Union[str, Any]      							=nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T					)					)

			snake_case:    Optional[Any]      							=nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']					)					)

			snake_case:    int      							=nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T					)					)

			return model







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=checkpoints.load_tax_checkpoint(args.checkpoint_path					)
			snake_case:    Tuple      							=jnp.tree_util.tree_map(onp.array				,							__UpperCAmelCase					)

			snake_case:    str      							=[
			    '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()',
			]

			snake_case:    List[Any]      							=os.path.join(args.checkpoint_path				,							'..'				,							'config.gin'					)
			snake_case:    Optional[Any]      							=inference.parse_training_gin_file(__UpperCAmelCase				,							__UpperCAmelCase					)
			snake_case:    List[str]      							=inference.InferenceModel(args.checkpoint_path				,							__UpperCAmelCase					)

			snake_case:    List[Any]      							=DDPMScheduler(beta_schedule='squaredcos_cap_v2'				,							variance_type='fixed_large'					)

			snake_case:    Optional[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'				,							)

			snake_case:    Optional[Any]      							=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'				,							)

			snake_case:    List[Any]      							=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				,							)

			snake_case:    Optional[Any]      							=load_notes_encoder(ta_checkpoint['target']['token_encoder']				,							__UpperCAmelCase					)
			snake_case:    Optional[Any]      							=load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']				,							__UpperCAmelCase					)
			snake_case:    Union[str, Any]      							=load_decoder(ta_checkpoint['target']['decoder']				,							__UpperCAmelCase					)

			snake_case:    int      							=OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'					)

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


if __name__ == "__main__":
	a							   =				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.',
	)
	a							   =				parser.parse_args()

	main(args)




