"""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   :						List[Any]      =    '''base_with_context'''





def 				a_     (      _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : Dict     ):





       '''simple docstring'''

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

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

              lowercase__			:		Dict       =	nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']     )     )

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

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





def 				a_     (      _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T     )     )

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

       for lyr_num, lyr in enumerate(model.encoders     ):
              lowercase__			:		Tuple       =	weights[f"""layers_{lyr_num}"""]
              lowercase__			:		Optional[int]       =	ly_weight["""attention"""]

              lowercase__			:		List[Any]       =	nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T     )     )
              lowercase__			:		Tuple       =	nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T     )     )
              lowercase__			:		Tuple       =	nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T     )     )
              lowercase__			:		int       =	nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T     )     )
              lowercase__			:		List[Any]       =	nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']     )     )

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

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

       return model





def 				a_     (      _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T     )     )
       lowercase__			:		Any       =	nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T     )     )

       lowercase__			:		Optional[Any]       =	nn.Parameter(
           torch.FloatTensor(weights['Embed_0']['embedding']     )   , requires_grad=_lowerCamelCase     )

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

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

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

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

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

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

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

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

       return model





def 				a_     (      _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       lowercase__			:		int       =	checkpoints.load_tax_checkpoint(args.checkpoint_path     )
       lowercase__			:		Optional[Any]       =	jnp.tree_util.tree_map(onp.array   , _lowerCamelCase     )

       lowercase__			:		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()""",
       ]

       lowercase__			:		Union[str, Any]       =	os.path.join(args.checkpoint_path   , '..'   , 'config.gin'     )
       lowercase__			:		Any       =	inference.parse_training_gin_file(_lowerCamelCase   , _lowerCamelCase     )
       lowercase__			:		Tuple       =	inference.InferenceModel(args.checkpoint_path   , _lowerCamelCase     )

       lowercase__			:		str       =	DDPMScheduler(beta_schedule='squaredcos_cap_v2'   , variance_type='fixed_large'     )

       lowercase__			:		List[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'   , )

       lowercase__			:		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'   , )

       lowercase__			:		Tuple       =	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   , )

       lowercase__			:		int       =	load_notes_encoder(ta_checkpoint['target']['token_encoder']   , _lowerCamelCase     )
       lowercase__			:		Dict       =	load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']   , _lowerCamelCase     )
       lowercase__			:		Union[str, Any]       =	load_decoder(ta_checkpoint['target']['decoder']   , _lowerCamelCase     )

       lowercase__			:		str       =	OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'     )

       lowercase__			:		Optional[Any]       =	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__":
       _UpperCamelCase   :						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.",
       )
       _UpperCamelCase   :						List[str]      =    parser.parse_args()

       main(args)






'''simple docstring'''




def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		str           =		int(_lowerCamelCase					)
		if n_element < 1:
				__SCREAMING_SNAKE_CASE       :		List[str]           =		ValueError("""a should be a positive number"""					)
				raise my_error

		__SCREAMING_SNAKE_CASE       :		List[Any]           =		[1]
		__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Optional[int]           =		(0, 0, 0)
		__SCREAMING_SNAKE_CASE       :		List[str]           =		1
		while index < n_element:
				while hamming_list[i] * 2 <= hamming_list[-1]:
						i += 1
				while hamming_list[j] * 3 <= hamming_list[-1]:
						j += 1
				while hamming_list[k] * 5 <= hamming_list[-1]:
						k += 1
				hamming_list.append(
				    min(hamming_list[i] * 2       ,      hamming_list[j] * 3       ,      hamming_list[k] * 5					)					)
				index += 1
		return hamming_list


if __name__ == "__main__":
						UpperCamelCase__       :	Optional[Any]    = input('''Enter the last number (nth term) of the Hamming Number Series: ''')
						print('''Formula of Hamming Number Series => 2^i * 3^j * 5^k''')
						UpperCamelCase__       :	List[str]    = hamming(int(n))
						print('''-----------------------------------------------------''')
						print(f"The list with nth numbers is: {hamming_numbers}")
						print('''-----------------------------------------------------''')