





'''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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]    )    )
      _lowerCAmelCase					=				nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]    )				,      requires_grad=lowerCAmelCase    )
      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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


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

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

      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   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """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=lowerCAmelCase    )

      _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   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				checkpoints.load_tax_checkpoint(args.checkpoint_path    )
      _lowerCAmelCase					=				jnp.tree_util.tree_map(onp.array				,      lowerCAmelCase    )

      _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(lowerCAmelCase				,      lowerCAmelCase    )
      _lowerCAmelCase					=				inference.InferenceModel(args.checkpoint_path				,      lowerCAmelCase    )

      _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"""]				,      lowerCAmelCase    )
      _lowerCAmelCase					=				load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]				,      lowerCAmelCase    )
      _lowerCAmelCase					=				load_decoder(ta_checkpoint["""target"""]["""decoder"""]				,      lowerCAmelCase    )

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

      _lowerCAmelCase					=				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__":
      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__							:  List[str]    =parser.parse_args()

      main(args)













'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
      from .tokenization_xlnet import XLNetTokenizer
else:
      A__							:  Any    =None


A__							:  Optional[int]    =logging.get_logger(__name__)

A__							:  Union[str, Any]    ={'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

A__							:  List[str]    ={
    '''vocab_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json''',
        '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json''',
    },
}

A__							:  List[str]    ={
    '''xlnet-base-cased''': None,
    '''xlnet-large-cased''': None,
}

A__							:  List[str]    ='''▁'''

# Segments (not really needed)
A__							:  str    =0
A__							:  str    =1
A__							:  List[Any]    =2
A__							:  str    =3
A__							:  Optional[Any]    =4




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Optional[int]			=  VOCAB_FILES_NAMES
      _lowercase:     Optional[int]			=  PRETRAINED_VOCAB_FILES_MAP
      _lowercase:     Dict			=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowercase:     Optional[Any]			=  '''left'''
      _lowercase:     Dict			=  XLNetTokenizer




      def __init__(     self     :					List[str]     ,     __snake_case     :					Optional[Any]=None     ,     __snake_case     :					str=None     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					str=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					List[Any]="<s>"     ,     __snake_case     :					List[Any]="</s>"     ,     __snake_case     :					str="<unk>"     ,     __snake_case     :					int="<sep>"     ,     __snake_case     :					int="<pad>"     ,     __snake_case     :					Dict="<cls>"     ,     __snake_case     :					int="<mask>"     ,     __snake_case     :					Optional[int]=["<eop>", "<eod>"]     ,     **__snake_case     :					List[str]     ,     )							-> List[str]:
            # Mask token behave like a normal word, i.e. include the space before it
            _lowerCAmelCase					=				AddedToken(__snake_case     ,     lstrip=__snake_case     ,     rstrip=__snake_case    ) if isinstance(__snake_case     ,     __snake_case    ) else mask_token

            super().__init__(
                vocab_file=__snake_case     ,     tokenizer_file=__snake_case     ,     do_lower_case=__snake_case     ,     remove_space=__snake_case     ,     keep_accents=__snake_case     ,     bos_token=__snake_case     ,     eos_token=__snake_case     ,     unk_token=__snake_case     ,     sep_token=__snake_case     ,     pad_token=__snake_case     ,     cls_token=__snake_case     ,     mask_token=__snake_case     ,     additional_special_tokens=__snake_case     ,     **__snake_case     ,     )

            _lowerCAmelCase					=				3
            _lowerCAmelCase					=				do_lower_case
            _lowerCAmelCase					=				remove_space
            _lowerCAmelCase					=				keep_accents
            _lowerCAmelCase					=				vocab_file
            _lowerCAmelCase					=				False if not self.vocab_file else True




      def 							lowercase__       (     self     :					Optional[int]     ,     __snake_case     :					List[int]     ,     __snake_case     :					Optional[List[int]] = None    )							-> List[int]:
            _lowerCAmelCase					=				[self.sep_token_id]
            _lowerCAmelCase					=				[self.cls_token_id]
            if token_ids_a is None:
                  return token_ids_a + sep + cls
            return token_ids_a + sep + token_ids_a + sep + cls




      def 							lowercase__       (     self     :					str     ,     __snake_case     :					List[int]     ,     __snake_case     :					Optional[List[int]] = None    )							-> List[int]:
            _lowerCAmelCase					=				[self.sep_token_id]
            _lowerCAmelCase					=				[2]

            if token_ids_a is None:
                  return len(token_ids_a + sep    ) * [0] + cls_segment_id
            return len(token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1] + cls_segment_id




      def 							lowercase__       (     self     :					Optional[Any]     ,     __snake_case     :					str     ,     __snake_case     :					Optional[str] = None    )							-> Tuple[str]:
            if not self.can_save_slow_tokenizer:
                  raise ValueError(
                      """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                      """tokenizer."""    )

            if not os.path.isdir(__snake_case    ):
                  logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
                  return
            _lowerCAmelCase					=				os.path.join(
                __snake_case     ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

            if os.path.abspath(self.vocab_file    ) != os.path.abspath(__snake_case    ):
                  copyfile(self.vocab_file     ,     __snake_case    )

            return (out_vocab_file,)






