"""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


__lowerCAmelCase					: Tuple        ="""base_with_context"""


def     UpperCAmelCase__ (       lowerCAmelCase__  :int      ,	lowerCAmelCase__  :Union[str, Any]	)				->					List[Any]:
      '''simple docstring'''


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

            lowercase														=			ly_weight["""attention"""]
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

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

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

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


def     UpperCAmelCase__ (       lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str	)				->					Any:
      '''simple docstring'''


      lowercase														=			nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T	)	)

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

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

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

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

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

      return model


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :str	)				->					Optional[Any]:
      '''simple docstring'''


      lowercase														=			nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T	)	)
      lowercase														=			nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T	)	)

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

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

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

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

            lowercase														=			ly_weight["""self_attention"""]
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

            lowercase														=			ly_weight["""MultiHeadDotProductAttention_0"""]
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)
            lowercase														=			nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]	)	)

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

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

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

      return model


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any	)				->					Optional[int]:
      '''simple docstring'''


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

      lowercase														=			[
          """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														=			os.path.join(args.checkpoint_path      ,	""".."""      ,	"""config.gin"""	)
      lowercase														=			inference.parse_training_gin_file(lowerCAmelCase__      ,	lowerCAmelCase__	)
      lowercase														=			inference.InferenceModel(args.checkpoint_path      ,	lowerCAmelCase__	)

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

      lowercase														=			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														=			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														=			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														=			load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]      ,	lowerCAmelCase__	)
      lowercase														=			load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]      ,	lowerCAmelCase__	)
      lowercase														=			load_decoder(ta_checkpoint["""target"""]["""decoder"""]      ,	lowerCAmelCase__	)

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

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


if __name__ == "__main__":
       __lowerCAmelCase					: Union[str, Any]        =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.""",
       )
       __lowerCAmelCase					: int        =parser.parse_args()

       main(args)




"""simple docstring"""






def     UpperCAmelCase__ (       lowerCAmelCase__  :str      ,	lowerCAmelCase__  :int	)				->					list:
      '''simple docstring'''


      lowercase														=			word.split()

      def justify(lowerCAmelCase__  :list      ,	lowerCAmelCase__  :int      ,	lowerCAmelCase__  :int	) -> str:
            lowercase														=			max_width - width
            lowercase														=			len(lowerCAmelCase__	)
            if len(lowerCAmelCase__	) == 1:
                  # if there is only word in line
                  # just insert overall_spaces_count for the remainder of line
                  return line[0] + " " * overall_spaces_count
            else:
                  lowercase														=			words_count - 1
                  # num_spaces_between_words_list[i] : tells you to insert
                  # num_spaces_between_words_list[i] spaces
                  # after word on line[i]
                  lowercase														=			spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  lowercase														=			(
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(lowerCAmelCase__	):
                        num_spaces_between_words_list[i] += 1
                  lowercase														=			[]
                  for i in range(lowerCAmelCase__	):
                        # add the word
                        aligned_words_list.append(line[i]	)
                        # add the spaces to insert
                        aligned_words_list.append(num_spaces_between_words_list[i] * """ """	)
                  # just add the last word to the sentence
                  aligned_words_list.append(line[-1]	)
                  # join the aligned words list to form a justified line
                  return "".join(lowerCAmelCase__	)

      lowercase														=			[]
      lowercase														=			[]
      lowercase														=			0
      for word in words:
            if width + len(lowerCAmelCase__	) + len(lowerCAmelCase__	) <= max_width:
                  # keep adding words until we can fill out max_width
                  # width = sum of length of all words (without overall_spaces_count)
                  # len(word) = length of current word
                  # len(line) = number of overall_spaces_count to insert between words
                  line.append(lowerCAmelCase__	)
                  width += len(lowerCAmelCase__	)
            else:
                  # justify the line and add it to result
                  answer.append(justify(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__	)	)
                  # reset new line and new width
                  lowercase							,							lowercase														=			[word], len(lowerCAmelCase__	)
      lowercase														=			max_width - width - len(lowerCAmelCase__	)
      answer.append(""" """.join(lowerCAmelCase__	) + (remaining_spaces + 1) * """ """	)
      return answer


if __name__ == "__main__":
       from doctest import testmod

       testmod()



