






'''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[str]   				=    """base_with_context"""





def 	lowerCAmelCase_		(lowercase__						:       List[str]     ,					lowercase__						:       Any       )		->    List[str]:


				'''simple docstring'''




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

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

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

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

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





def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       str       )		->    List[str]:


				'''simple docstring'''




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

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

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

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

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

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

				return model





def 	lowerCAmelCase_		(lowercase__						:       List[Any]     ,					lowercase__						:       Optional[Any]       )		->    str:


				'''simple docstring'''




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

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

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

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

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

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

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

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

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

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

				return model





def 	lowerCAmelCase_		(lowercase__						:       Union[str, Any]       )		->    Union[str, Any]:


				'''simple docstring'''




				lowerCAmelCase__      			=						checkpoints.load_tax_checkpoint(args.checkpoint_path       )
				lowerCAmelCase__      			=						jnp.tree_util.tree_map(onp.array     ,					snake_case_       )

				lowerCAmelCase__      			=						[
				    "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()",
				]

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

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

				lowerCAmelCase__      			=						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'''     ,					)

				lowerCAmelCase__      			=						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'''     ,					)

				lowerCAmelCase__      			=						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     ,					)

				lowerCAmelCase__      			=						load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']     ,					snake_case_       )
				lowerCAmelCase__      			=						load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']     ,					snake_case_       )
				lowerCAmelCase__      			=						load_decoder(ta_checkpoint['''target''']['''decoder''']     ,					snake_case_       )

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

				lowerCAmelCase__      			=						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__":
			_UpperCAmelCase			:		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.",
			)
			_UpperCAmelCase			:		List[str]   				=    parser.parse_args()

			main(args)













from math import isclose, sqrt





def 	lowerCAmelCase_		(lowercase__						:       float     ,					lowercase__						:       float     ,					lowercase__						:       float       )		->    tuple[float, float, float]:


				'''simple docstring'''




				lowerCAmelCase__      			=						point_y / 4 / point_x
				lowerCAmelCase__      			=						2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				lowerCAmelCase__      			=						(1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				lowerCAmelCase__      			=						(sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				lowerCAmelCase__      			=						outgoing_gradient**2 + 4
				lowerCAmelCase__      			=						2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				lowerCAmelCase__      			=						(point_y - outgoing_gradient * point_x) ** 2 - 1_00

				lowerCAmelCase__      			=						(
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
				) / (2 * quadratic_term)
				lowerCAmelCase__      			=						(
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term       )
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				lowerCAmelCase__      			=						x_minus if isclose(lowercase__     ,					lowercase__       ) else x_plus
				lowerCAmelCase__      			=						point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient





def 	lowerCAmelCase_		(lowercase__						:       float = 1.4     ,					lowercase__						:       float = -9.6       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						0
				lowerCAmelCase__      			=						first_x_coord
				lowerCAmelCase__      			=						first_y_coord
				lowerCAmelCase__      			=						(10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						next_point(lowercase__     ,					lowercase__     ,					lowercase__       )
								num_reflections += 1

				return num_reflections


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





