
"""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__   :					Dict							=   'base_with_context'




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



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

    _lowercase:			Optional[int]       =     ly_weight['''attention''']
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
    _lowercase:			Any       =     nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)

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

    _lowercase:			Tuple       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
    _lowercase:			Optional[int]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

  _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']						)						)
  return model




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			int       =     nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T						)						)

  _lowercase:			Union[str, Any]       =     nn.Parameter(
      torch.FloatTensor(weights['''Embed_0''']['''embedding''']						) ,				requires_grad=_UpperCamelCase						)

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

    _lowercase:			Dict       =     nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
    _lowercase:			Optional[Any]       =     nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
    _lowercase:			Optional[Any]       =     nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)
    _lowercase:			Optional[Any]       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']						)						)

    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
    _lowercase:			int       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
    _lowercase:			Union[str, Any]       =     nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)
    _lowercase:			List[Any]       =     nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

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

  return model




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Tuple       =     nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T						)						)
  _lowercase:			Dict       =     nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T						)						)

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

  _lowercase:			Optional[int]       =     nn.Parameter(
      torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T						)						)

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

    _lowercase:			List[str]       =     nn.Parameter(
        torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T						)						)

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

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

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

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

  _lowercase:			List[Any]       =     nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T						)						)

  return model




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Union[str, Any]       =     checkpoints.load_tax_checkpoint(args.checkpoint_path						)
  _lowercase:			Any       =     jnp.tree_util.tree_map(onp.array ,				_UpperCamelCase						)

  _lowercase:			List[str]       =     [
      '''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()''',
  ]

  _lowercase:			Union[str, Any]       =     os.path.join(args.checkpoint_path ,				'''..''' ,				'''config.gin'''						)
  _lowercase:			Union[str, Any]       =     inference.parse_training_gin_file(_UpperCamelCase ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     inference.InferenceModel(args.checkpoint_path ,				_UpperCamelCase						)

  _lowercase:			List[str]       =     DDPMScheduler(beta_schedule='''squaredcos_cap_v2''' ,				variance_type='''fixed_large'''						)

  _lowercase:			Dict       =     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''' ,				)

  _lowercase:			Optional[int]       =     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''' ,				)

  _lowercase:			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 ,				)

  _lowercase:			str       =     load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''] ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''] ,				_UpperCamelCase						)
  _lowercase:			List[str]       =     load_decoder(ta_checkpoint['''target''']['''decoder'''] ,				_UpperCamelCase						)

  _lowercase:			List[str]       =     OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''						)

  _lowercase:			Any       =     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__   :					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.',
  )
  A__   :					Optional[Any]							=   parser.parse_args()

  main(args)


"""simple docstring"""




import qiskit




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase						):






  """simple docstring"""



  _lowercase:			Dict       =     qiskit.Aer.get_backend('''aer_simulator'''						)

  # Create a Quantum Circuit acting on the q register
  _lowercase:			str       =     qiskit.QuantumCircuit(_UpperCamelCase ,				_UpperCamelCase						)

  # Apply X (NOT) Gate to Qubits 0 & 1
  circuit.x(0						)
  circuit.x(1						)

  # Map the quantum measurement to the classical bits
  circuit.measure([0, 1] ,				[0, 1]						)

  # Execute the circuit on the qasm simulator
  _lowercase:			Tuple       =     qiskit.execute(_UpperCamelCase ,				_UpperCamelCase ,				shots=1_000						)

  # Return the histogram data of the results of the experiment.
  return job.result().get_counts(_UpperCamelCase						)


if __name__ == "__main__":
  A__   :					Optional[int]							=   single_qubit_measure(2, 2)
  print(f"""Total count for various states are: {counts}""")
