




'''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


a       :						List[Any]								=		'base_with_context'


def __UpperCAmelCase	(   _UpperCAmelCase		:   List[str]		,				_UpperCAmelCase		:   Union[str, Any]						)      ->      Any:
			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]						)						)
			__snake_case			=		nn.Parameter(
			    torch.FloatTensor(weights["Embed_0"]["embedding"]						)		,				requires_grad=_UpperCamelCase						)
			for lyr_num, lyr in enumerate(model.encoders						):
						__snake_case			=		weights[F'''layers_{lyr_num}''']
						__snake_case			=		nn.Parameter(
						    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]						)						)

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

						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)
			return model


def __UpperCAmelCase	(   _UpperCAmelCase		:   int		,				_UpperCAmelCase		:   List[str]						)      ->      int:
			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T						)						)

			__snake_case			=		nn.Parameter(
			    torch.FloatTensor(weights["Embed_0"]["embedding"]						)		,				requires_grad=_UpperCamelCase						)

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

						__snake_case			=		nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(
						    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]						)						)

						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)

			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)

			return model


def __UpperCAmelCase	(   _UpperCAmelCase		:   Tuple		,				_UpperCAmelCase		:   str						)      ->      List[str]:
			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T						)						)
			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T						)						)

			__snake_case			=		nn.Parameter(
			    torch.FloatTensor(weights["Embed_0"]["embedding"]						)		,				requires_grad=_UpperCamelCase						)

			__snake_case			=		nn.Parameter(
			    torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T						)						)

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

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

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

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

						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]						)						)
						__snake_case			=		nn.Parameter(
						    torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T						)						)
						__snake_case			=		nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T						)						)

			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]						)						)

			__snake_case			=		nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T						)						)

			return model


def __UpperCAmelCase	(   _UpperCAmelCase		:   Union[str, Any]						)      ->      Dict:
			__snake_case			=		checkpoints.load_tax_checkpoint(args.checkpoint_path						)
			__snake_case			=		jnp.tree_util.tree_map(onp.array		,				_UpperCamelCase						)

			__snake_case			=		[
			    '''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			=		os.path.join(args.checkpoint_path		,				".."		,				"config.gin"						)
			__snake_case			=		inference.parse_training_gin_file(_UpperCamelCase		,				_UpperCamelCase						)
			__snake_case			=		inference.InferenceModel(args.checkpoint_path		,				_UpperCamelCase						)

			__snake_case			=		DDPMScheduler(beta_schedule="squaredcos_cap_v2"		,				variance_type="fixed_large"						)

			__snake_case			=		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			=		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			=		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			=		load_notes_encoder(ta_checkpoint["target"]["token_encoder"]		,				_UpperCamelCase						)
			__snake_case			=		load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]		,				_UpperCamelCase						)
			__snake_case			=		load_decoder(ta_checkpoint["target"]["decoder"]		,				_UpperCamelCase						)

			__snake_case			=		OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"						)

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


if __name__ == "__main__":
						a       :						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.''',
						)
						a       :						str								=		parser.parse_args()

						main(args)







"""simple docstring"""




from __future__ import annotations

import typing
from collections import Counter




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      typing.Counter[int]   			=  Counter()
  for base in range(1			,					max_perimeter + 1    ):
    for perpendicular in range(_UpperCamelCase			,					max_perimeter + 1    ):
      _lowercase:      str   			=  (base * base + perpendicular * perpendicular) ** 0.5
      if hypotenuse == int(_UpperCamelCase    ):
        _lowercase:      str   			=  int(base + perpendicular + hypotenuse    )
        if perimeter > max_perimeter:
          continue
        triplets[perimeter] += 1
  return triplets




def 			_lowerCAmelCase		(		_UpperCamelCase = 1_000    ):






  """simple docstring"""



  _lowercase:      int   			=  pythagorean_triple(_UpperCamelCase    )
  return triplets.most_common(1    )[0][0]


if __name__ == "__main__":
  print(f"""Perimeter {solution()} has maximum solutions""")
