






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     a_   (__A				,       __A      )				->						int:



					"""simple docstring"""
					__a					:						Dict      =					nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]      )      )
					__a					:						Any      =					nn.Parameter(
					    torch.FloatTensor(weights["Embed_0"]["embedding"]      )				,       requires_grad=__A      )
					for lyr_num, lyr in enumerate(model.encoders      ):
										__a					:						int      =					weights[f'layers_{lyr_num}']
										__a					:						List[str]      =					nn.Parameter(
										    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]      )      )

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

										__a					:						Any      =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]      )      )

										__a					:						List[Any]      =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T      )      )
										__a					:						Optional[int]      =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T      )      )
										__a					:						Tuple      =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T      )      )

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







def     a_   (__A				,       __A      )				->						int:



					"""simple docstring"""
					__a					:						Tuple      =					nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T      )      )

					__a					:						Union[str, Any]      =					nn.Parameter(
					    torch.FloatTensor(weights["Embed_0"]["embedding"]      )				,       requires_grad=__A      )

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

										__a					:						Any      =					nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T      )      )
										__a					:						str      =					nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T      )      )
										__a					:						Union[str, Any]      =					nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T      )      )
										__a					:						Tuple      =					nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T      )      )
										__a					:						Optional[Any]      =					nn.Parameter(
										    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]      )      )

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

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

					return model







def     a_   (__A				,       __A      )				->						Dict:



					"""simple docstring"""
					__a					:						Optional[int]      =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T      )      )
					__a					:						Union[str, Any]      =					nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T      )      )

					__a					:						int      =					nn.Parameter(
					    torch.FloatTensor(weights["Embed_0"]["embedding"]      )				,       requires_grad=__A      )

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

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

										__a					:						List[Any]      =					nn.Parameter(
										    torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T      )      )

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

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

										__a					:						Dict      =					nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]      )      )
										__a					:						List[str]      =					nn.Parameter(
										    torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T      )      )
										__a					:						Optional[int]      =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T      )      )
										__a					:						str      =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T      )      )
										__a					:						Dict      =					nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T      )      )

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

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

					return model







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



					"""simple docstring"""
					__a					:						Optional[int]      =					checkpoints.load_tax_checkpoint(args.checkpoint_path      )
					__a					:						List[Any]      =					jnp.tree_util.tree_map(onp.array				,       __A      )

					__a					:						Optional[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()",
					]

					__a					:						Any      =					os.path.join(args.checkpoint_path				,       ".."				,       "config.gin"      )
					__a					:						Tuple      =					inference.parse_training_gin_file(__A				,       __A      )
					__a					:						Any      =					inference.InferenceModel(args.checkpoint_path				,       __A      )

					__a					:						Optional[Any]      =					DDPMScheduler(beta_schedule="squaredcos_cap_v2"				,       variance_type="fixed_large"      )

					__a					:						Union[str, 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"				,       )

					__a					:						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"				,       )

					__a					:						List[str]      =					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					:						Any      =					load_notes_encoder(ta_checkpoint["target"]["token_encoder"]				,       __A      )
					__a					:						List[Any]      =					load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]				,       __A      )
					__a					:						Optional[Any]      =					load_decoder(ta_checkpoint["target"]["decoder"]				,       __A      )

					__a					:						List[Any]      =					OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"      )

					__a					:						List[str]      =					SpectrogramDiffusionPipeline(
					    notes_encoder=__A				,       continuous_encoder=__A				,       decoder=__A				,       scheduler=__A				,       melgan=__A				,       )
					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)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase__      			=						{
    '''configuration_poolformer''': [
        '''POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''PoolFormerConfig''',
        '''PoolFormerOnnxConfig''',
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						['''PoolFormerFeatureExtractor''']
			UpperCAmelCase__      			=						['''PoolFormerImageProcessor''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCAmelCase__      			=						[
			    '''POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''PoolFormerForImageClassification''',
			    '''PoolFormerModel''',
			    '''PoolFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_poolformer import (
			    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    PoolFormerConfig,
			    PoolFormerOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_poolformer import PoolFormerFeatureExtractor
						from .image_processing_poolformer import PoolFormerImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_poolformer import (
						    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    PoolFormerForImageClassification,
						    PoolFormerModel,
						    PoolFormerPreTrainedModel,
						)


else:
			import sys

			UpperCAmelCase__      			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure)





