

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


__lowerCamelCase      :    List[str]   		=   """base_with_context"""







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]  ,						snake_case_      :							Any     ):
	snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]     )     )
	snake_case__				:   List[str]     =						nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"]     )  ,						requires_grad=snake_case_     )
	for lyr_num, lyr in enumerate(model.encoders     ):
		snake_case__				:   str     =						weights[F'''layers_{lyr_num}''']
		snake_case__				:   Union[str, Any]     =						nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]     )     )

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

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

		snake_case__				:   str     =						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__				:   List[str]     =						nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T     )     )

	snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]     )     )
	return model







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							str     ):
	snake_case__				:   int     =						nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T     )     )

	snake_case__				:   Tuple     =						nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"]     )  ,						requires_grad=snake_case_     )

	for lyr_num, lyr in enumerate(model.encoders     ):
		snake_case__				:   int     =						weights[F'''layers_{lyr_num}''']
		snake_case__				:   Any     =						ly_weight["attention"]

		snake_case__				:   int     =						nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T     )     )
		snake_case__				:   Dict     =						nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T     )     )
		snake_case__				:   List[Any]     =						nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T     )     )
		snake_case__				:   Dict     =						nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T     )     )
		snake_case__				:   Any     =						nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]     )     )

		snake_case__				:   Union[str, Any]     =						nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T     )     )
		snake_case__				:   List[Any]     =						nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T     )     )
		snake_case__				:   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__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]     )     )

	return model







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]     ):
	snake_case__				:   Any     =						nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T     )     )
	snake_case__				:   str     =						nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T     )     )

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

	snake_case__				:   List[Any]     =						nn.Parameter(
	    torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T     )     )

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

		snake_case__				:   str     =						nn.Parameter(
		    torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T     )     )

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

		snake_case__				:   List[Any]     =						ly_weight["MultiHeadDotProductAttention_0"]
		snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T     )     )
		snake_case__				:   Optional[int]     =						nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T     )     )
		snake_case__				:   List[Any]     =						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__				:   List[str]     =						nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]     )     )

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

	snake_case__				:   Tuple     =						nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]     )     )

	snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T     )     )

	return model







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]     ):
	snake_case__				:   List[Any]     =						checkpoints.load_tax_checkpoint(args.checkpoint_path     )
	snake_case__				:   Any     =						jnp.tree_util.tree_map(onp.array  ,						snake_case_     )

	snake_case__				:   Union[str, Any]     =						[
	    "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__				:   int     =						os.path.join(args.checkpoint_path  ,						".."  ,						"config.gin"     )
	snake_case__				:   Optional[int]     =						inference.parse_training_gin_file(snake_case_  ,						snake_case_     )
	snake_case__				:   str     =						inference.InferenceModel(args.checkpoint_path  ,						snake_case_     )

	snake_case__				:   str     =						DDPMScheduler(beta_schedule="squaredcos_cap_v2"  ,						variance_type="fixed_large"     )

	snake_case__				:   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"  ,						)

	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__				:   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__				:   Union[str, Any]     =						load_notes_encoder(ta_checkpoint["target"]["token_encoder"]  ,						snake_case_     )
	snake_case__				:   List[str]     =						load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]  ,						snake_case_     )
	snake_case__				:   Union[str, Any]     =						load_decoder(ta_checkpoint["target"]["decoder"]  ,						snake_case_     )

	snake_case__				:   Optional[int]     =						OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"     )

	snake_case__				:   str     =						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__":
	__lowerCamelCase      :    List[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.""",
	)
	__lowerCamelCase      :    List[str]   		=   parser.parse_args()

	main(args)











from __future__ import annotations







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int     ):
	snake_case__				:   str     =						[True] * limit
	snake_case__				:   str     =						False
	snake_case__				:   str     =						False
	snake_case__				:   str     =						True

	for i in range(3  ,						int(limit**0.5 + 1     )  ,						2     ):
		snake_case__				:   Optional[Any]     =						i * 2
		while index < limit:
			snake_case__				:   Union[str, Any]     =						False
			snake_case__				:   Any     =						index + i

	snake_case__				:   Optional[Any]     =						[2]

	for i in range(3  ,						snake_case_  ,						2     ):
		if is_prime[i]:
			primes.append(snake_case_     )

	return primes







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int = 1000000     ):
	snake_case__				:   Optional[int]     =						prime_sieve(snake_case_     )
	snake_case__				:   List[Any]     =						0
	snake_case__				:   List[str]     =						0

	for i in range(len(snake_case_     )     ):
		for j in range(i + length  ,						len(snake_case_     )     ):
			snake_case__				:   Dict     =						sum(primes[i:j]     )
			if sol >= ceiling:
				break

			if sol in primes:
				snake_case__				:   Tuple     =						j - i
				snake_case__				:   str     =						sol

	return largest


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



