






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


SCREAMING_SNAKE_CASE__   :      Optional[int]				=		"""base_with_context"""

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






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

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

								__magic_name__		:int   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']			)			)

								__magic_name__		:List[str]   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T			)			)
								__magic_name__		:Tuple   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T			)			)
								__magic_name__		:List[Any]   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T			)			)

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

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T			)			)

				__magic_name__		:Union[str, Any]   =   nn.Parameter(
				    torch.FloatTensor(weights['''Embed_0''']['''embedding''']			),	requires_grad=snake_case			)

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

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

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

				__magic_name__		:int   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']			)			)

				return model

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T			)			)
				__magic_name__		:str   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T			)			)

				__magic_name__		:List[str]   =   nn.Parameter(
				    torch.FloatTensor(weights['''Embed_0''']['''embedding''']			),	requires_grad=snake_case			)

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

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

								__magic_name__		:Optional[Any]   =   nn.Parameter(
								    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T			)			)

								__magic_name__		:Tuple   =   ly_weight['''self_attention''']
								__magic_name__		:Optional[Any]   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T			)			)
								__magic_name__		:str   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T			)			)
								__magic_name__		:Any   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T			)			)
								__magic_name__		:int   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T			)			)

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

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

				__magic_name__		:Union[str, Any]   =   nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']			)			)

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

				return model

def   __lowercase       (			snake_case			):







				"""simple docstring"""






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

				__magic_name__		: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()''',
				]

				__magic_name__		:Dict   =   os.path.join(args.checkpoint_path,	'''..''',	'''config.gin'''			)
				__magic_name__		:Dict   =   inference.parse_training_gin_file(snake_case,	snake_case			)
				__magic_name__		:Optional[int]   =   inference.InferenceModel(args.checkpoint_path,	snake_case			)

				__magic_name__		:List[str]   =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2''',	variance_type='''fixed_large'''			)

				__magic_name__		:List[str]   =   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''',	)

				__magic_name__		:str   =   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''',	)

				__magic_name__		:Dict   =   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,	)

				__magic_name__		:int   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''],	snake_case			)
				__magic_name__		:Any   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],	snake_case			)
				__magic_name__		:int   =   load_decoder(ta_checkpoint['''target''']['''decoder'''],	snake_case			)

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

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      str				=		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.""",
					)
					SCREAMING_SNAKE_CASE__   :      Dict				=		parser.parse_args()

					main(args)














import sys

SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		(
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)

def   __lowercase       (			snake_case = N			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   -sys.maxsize - 1
				for i in range(len(snake_case			) - 1_2			):
								__magic_name__		:List[Any]   =   1
								for j in range(1_3			):
												product *= int(n[i + j]			)
								if product > largest_product:
												__magic_name__		:str   =   product
				return largest_product


if __name__ == "__main__":
					print(f"{solution() = }")






