





"""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 			=  """base_with_context"""

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

        __a :						Dict			       =  ly_weight['''attention''']
        __a :						Dict			       =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T))
        __a :						Dict			       =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T))
        __a :						Dict			       =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T))
        __a :						List[Any]			       =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T))

        __a :						str			       =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']))

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

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

def     __A      (			a_						:Any   ,       a_						:Optional[Any])		->		int:
    __a :						List[Any]			       =  nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T))

    __a :						Dict			       =  nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding'''])   ,       requires_grad=a_)

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

        __a :						List[str]			       =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T))
        __a :						List[str]			       =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T))
        __a :						str			       =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T))
        __a :						Any			       =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T))
        __a :						Union[str, Any]			       =  nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']))

        __a :						Optional[Any]			       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T))
        __a :						Dict			       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T))
        __a :						Dict			       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T))
        __a :						Any			       =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']))

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

    return model

def     __A      (			a_						:Dict   ,       a_						:Union[str, Any])		->		Optional[Any]:
    __a :						List[str]			       =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T))
    __a :						int			       =  nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T))

    __a :						Any			       =  nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding'''])   ,       requires_grad=a_)

    __a :						Union[str, Any]			       =  nn.Parameter(
        torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T))

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

        __a :						Optional[Any]			       =  nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T))

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

        __a :						str			       =  ly_weight['''MultiHeadDotProductAttention_0''']
        __a :						Any			       =  nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T))
        __a :						Union[str, Any]			       =  nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T))
        __a :						List[Any]			       =  nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T))
        __a :						Tuple			       =  nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T))
        __a :						Dict			       =  nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']))

        __a :						List[Any]			       =  nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']))
        __a :						Tuple			       =  nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T))
        __a :						Optional[Any]			       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T))
        __a :						List[str]			       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T))
        __a :						str			       =  nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T))

    __a :						Union[str, Any]			       =  nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']))

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

    return model

def     __A      (			a_						:str)		->		Tuple:
    __a :						str			       =  checkpoints.load_tax_checkpoint(args.checkpoint_path)
    __a :						str			       =  jnp.tree_util.tree_map(onp.array   ,       a_)

    __a :						Dict			       =  [
        '''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()''',
    ]

    __a :						List[str]			       =  os.path.join(args.checkpoint_path   ,       '''..'''   ,       '''config.gin''')
    __a :						int			       =  inference.parse_training_gin_file(a_   ,       a_)
    __a :						Optional[int]			       =  inference.InferenceModel(args.checkpoint_path   ,       a_)

    __a :						Tuple			       =  DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''   ,       variance_type='''fixed_large''')

    __a :						Tuple			       =  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'''   ,       )

    __a :						List[str]			       =  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'''   ,       )

    __a :						str			       =  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   ,       )

    __a :						Tuple			       =  load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']   ,       a_)
    __a :						Union[str, Any]			       =  load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']   ,       a_)
    __a :						Optional[int]			       =  load_decoder(ta_checkpoint['''target''']['''decoder''']   ,       a_)

    __a :						Any			       =  OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder''')

    __a :						Any			       =  SpectrogramDiffusionPipeline(
        notes_encoder=a_   ,       continuous_encoder=a_   ,       decoder=a_   ,       scheduler=a_   ,       melgan=a_   ,       )
    if args.save:
        pipe.save_pretrained(args.output_path)


if __name__ == "__main__":
  A 			=  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 			=  parser.parse_args()

  main(args)






"""simple docstring"""






from math import isqrt, loga

def     __A      (			a_						:int)		->		list[int]:
				__a :						int			       =  [True] * max_number
				for i in range(2   ,       isqrt(max_number - 1) + 1):
								if is_prime[i]:
												for j in range(i**2   ,       a_   ,       a_):
																__a :						int			       =  False

				return [i for i in range(2   ,       a_) if is_prime[i]]

def     __A      (			a_						:int = 80_08_00   ,       a_						:int = 80_08_00)		->		int:
				__a :						str			       =  degree * loga(a_)
				__a :						Tuple			       =  int(a_)
				__a :						int			       =  calculate_prime_numbers(a_)

				__a :						List[Any]			       =  0
				__a :						Optional[Any]			       =  0
				__a :						Dict			       =  len(a_) - 1
				while left < right:
								while (
								    prime_numbers[right] * loga(prime_numbers[left])
								    + prime_numbers[left] * loga(prime_numbers[right])
								    > upper_bound
								):
												right -= 1
								hybrid_integers_count += right - left
								left += 1

				return hybrid_integers_count


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