




"""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


SCREAMING_SNAKE_CASE_	     =       'base_with_context'





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







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

        UpperCamelCase              =	ly_weight['''attention''']
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

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





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

    UpperCamelCase              =	nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )			,requires_grad=_lowercase    )

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

        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

    UpperCamelCase              =	nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )

    return model





def      __snake_case (				_lowercase			,_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
    UpperCamelCase              =	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

    UpperCamelCase              =	nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )			,requires_grad=_lowercase    )

    UpperCamelCase              =	nn.Parameter(
        torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T    )    )

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

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

        UpperCamelCase              =	ly_weight['''self_attention''']
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

        UpperCamelCase              =	ly_weight['''MultiHeadDotProductAttention_0''']
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']    )    )

        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )
        UpperCamelCase              =	nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
        UpperCamelCase              =	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

    UpperCamelCase              =	nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']    )    )

    UpperCamelCase              =	nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T    )    )

    return model





def      __snake_case (				_lowercase    ):


    """simple docstring"""







    UpperCamelCase              =	checkpoints.load_tax_checkpoint(args.checkpoint_path    )
    UpperCamelCase              =	jnp.tree_util.tree_map(onp.array			,_lowercase    )

    UpperCamelCase              =	[
        '''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()''',
    ]

    UpperCamelCase              =	os.path.join(args.checkpoint_path			,'''..'''			,'''config.gin'''    )
    UpperCamelCase              =	inference.parse_training_gin_file(_lowercase			,_lowercase    )
    UpperCamelCase              =	inference.InferenceModel(args.checkpoint_path			,_lowercase    )

    UpperCamelCase              =	DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''			,variance_type='''fixed_large'''    )

    UpperCamelCase              =	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'''			,)

    UpperCamelCase              =	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'''			,)

    UpperCamelCase              =	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			,)

    UpperCamelCase              =	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']			,_lowercase    )
    UpperCamelCase              =	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']			,_lowercase    )
    UpperCamelCase              =	load_decoder(ta_checkpoint['''target''']['''decoder''']			,_lowercase    )

    UpperCamelCase              =	OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''    )

    UpperCamelCase              =	SpectrogramDiffusionPipeline(
        notes_encoder=_lowercase			,continuous_encoder=_lowercase			,decoder=_lowercase			,scheduler=_lowercase			,melgan=_lowercase			,)
    if args.save:
        pipe.save_pretrained(args.output_path    )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_	     =       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_	     =       parser.parse_args()

  main(args)





from __future__ import annotations

from collections.abc import Callable

lowercase__							:	Optional[Any]											=		list[list[float | int]]


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(size + 1				)] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for row in range(_A				):
		for col in range(_A				):
			snake_case_						       =	matrix[row][col]

		snake_case_						       =	vector[row][0]

	snake_case_						       =	0
	snake_case_						       =	0
	while row < size and col < size:
		# pivoting
		snake_case_						       =	max((abs(augmented[rowa][col]				), rowa) for rowa in range(_A	,      _A				)				)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_       , snake_case_						       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1	,      _A				):
			snake_case_						       =	augmented[rowa][col] / augmented[row][col]
			snake_case_						       =	0
			for cola in range(col + 1	,      size + 1				):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1	,      _A				):
		for row in range(_A				):
			snake_case_						       =	augmented[row][col] / augmented[col][col]
			for cola in range(_A	,      size + 1				):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]	,      10				)] for row in range(_A				)
	]


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(_A				)] for _ in range(_A				)]
	snake_case_						       =	[[0] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for x_val, y_val in enumerate(_A				):
		for col in range(_A				):
			snake_case_						       =	(x_val + 1) ** (size - col - 1)
		snake_case_						       =	y_val

	snake_case_						       =	solve(_A	,      _A				)

	def interpolated_func(_A				) -> int:
		return sum(
		    round(coeffs[x_val][0]				) * (var ** (size - x_val - 1))
		    for x_val in range(_A				)				)

	return interpolated_func


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)


def 		lowerCamelCase__       (						_A = question_function	,      _A = 10				):







	'''simple docstring'''





	snake_case_						       =	[func(_A				) for x_val in range(1	,      order + 1				)]

	snake_case_						       =	[
	    interpolate(data_points[:max_coeff]				) for max_coeff in range(1	,      order + 1				)
	]

	snake_case_						       =	0
	snake_case_						       =	42
	snake_case_						       =	42

	for poly in polynomials:
		snake_case_						       =	1
		while func(_A				) == poly(_A				):
			x_val += 1

		ret += poly(_A				)

	return ret


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