






'''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        =							'base_with_context'


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  Tuple   ):
       """simple docstring"""




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

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

              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']   )   )

              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T   )   )

       a_						     =			nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']   )   )
       return model


def       __UpperCamelCase					(      lowercase_       :  Union[str, Any]   ,       lowercase_       :  str   ):
       """simple docstring"""




       a_						     =			nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T   )   )

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

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

              a_						     =			nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T   )   )
              a_						     =			nn.Parameter(
                  torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']   )   )

              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']   )   )

       a_						     =			nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']   )   )

       return model


def       __UpperCamelCase					(      lowercase_       :  Optional[Any]   ,       lowercase_       :  Tuple   ):
       """simple docstring"""




       a_						     =			nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T   )   )
       a_						     =			nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T   )   )

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

       a_						     =			nn.Parameter(
           torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T   )   )

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

              a_						     =			nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T   )   )

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

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

              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']   )   )
              a_						     =			nn.Parameter(
                  torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T   )   )
              a_						     =			nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T   )   )

       a_						     =			nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']   )   )

       a_						     =			nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T   )   )

       return model


def       __UpperCamelCase					(      lowercase_       :  int   ):
       """simple docstring"""




       a_						     =			checkpoints.load_tax_checkpoint(args.checkpoint_path   )
       a_						     =			jnp.tree_util.tree_map(onp.array   ,       lowercase_   )

       a_						     =			[
           '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_						     =			os.path.join(args.checkpoint_path   ,       '..'   ,       'config.gin'   )
       a_						     =			inference.parse_training_gin_file(lowercase_   ,       lowercase_   )
       a_						     =			inference.InferenceModel(args.checkpoint_path   ,       lowercase_   )

       a_						     =			DDPMScheduler(beta_schedule='squaredcos_cap_v2'   ,       variance_type='fixed_large'   )

       a_						     =			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_						     =			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_						     =			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_						     =			load_notes_encoder(ta_checkpoint['target']['token_encoder']   ,       lowercase_   )
       a_						     =			load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']   ,       lowercase_   )
       a_						     =			load_decoder(ta_checkpoint['target']['decoder']   ,       lowercase_   )

       a_						     =			OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'   )

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


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

  main(args)








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

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


if is_sentencepiece_available():
	from .tokenization_pegasus import PegasusTokenizer
else:
	A			:						List[Any]        =  None


A			:						Optional[Any]        =  logging.get_logger(__name__)


A			:						str        =  '▁'

A			:						Optional[Any]        =  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A			:						Any        =  {
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

A			:						Optional[int]        =  {
    'google/pegasus-xsum': 5_1_2,
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					VOCAB_FILES_NAMES
					A__     =					PRETRAINED_VOCAB_FILES_MAP
					A__     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__     =					PegasusTokenizer
					A__     =					['''input_ids''', '''attention_mask''']

					def __init__(self       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any]=None ,   _UpperCAmelCase       :		List[Any]=None ,   _UpperCAmelCase       :		Union[str, Any]="<pad>" ,   _UpperCAmelCase       :		int="</s>" ,   _UpperCAmelCase       :		int="<unk>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_2>" ,   _UpperCAmelCase       :		Optional[Any]="<mask_1>" ,   _UpperCAmelCase       :		Optional[Any]=None ,   _UpperCAmelCase       :		Dict=103 ,   **_UpperCAmelCase       :		Dict ,   )		->  Dict:




							"""simple docstring"""


							lowercase__	     =						offset

							if additional_special_tokens is not None:
									if not isinstance(_UpperCAmelCase ,   _UpperCAmelCase						):
											raise TypeError(
											    f'''additional_special_tokens should be of type {type(_UpperCAmelCase						)}, but is'''
											    f''' {type(_UpperCAmelCase						)}'''						)

									lowercase__	     =						(
									    ([mask_token_sent] + additional_special_tokens)
									    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
									    else additional_special_tokens
									)
									# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
									additional_special_tokens_extended += [
									    f'''<unk_{i}>''' for i in range(len(_UpperCAmelCase						) ,   self.offset - 1						)
									]

									if len(set(_UpperCAmelCase						)						) != len(_UpperCAmelCase						):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''						)
									lowercase__	     =						additional_special_tokens_extended
							else:
									lowercase__	     =						[mask_token_sent] if mask_token_sent is not None else []
									additional_special_tokens += [f'''<unk_{i}>''' for i in range(2 ,   self.offset						)]

							super().__init__(
							    _UpperCAmelCase ,   tokenizer_file=_UpperCAmelCase ,   pad_token=_UpperCAmelCase ,   eos_token=_UpperCAmelCase ,   unk_token=_UpperCAmelCase ,   mask_token=_UpperCAmelCase ,   mask_token_sent=_UpperCAmelCase ,   offset=_UpperCAmelCase ,   additional_special_tokens=_UpperCAmelCase ,   **_UpperCAmelCase ,   )
							lowercase__	     =						vocab_file
							lowercase__	     =						False if not self.vocab_file else True

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Tuple						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						set(self.all_special_ids						)  # call it once instead of inside list comp
							all_special_ids.remove(self.unk_token_id						)  # <unk> is only sometimes special

							if all_special_ids != set(range(len(self.additional_special_tokens						) + 3						)						):
									raise ValueError(
									    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
									    f''' {len(self.additional_special_tokens						)} additional_special_tokens, but got {all_special_ids}'''						)

							return [1 if x in all_special_ids else 0 for x in seq]

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List ,   _UpperCAmelCase       :		Optional[List] = None ,   _UpperCAmelCase       :		bool = False						)		->  List[int]:




							"""simple docstring"""


							if already_has_special_tokens:
									return self._special_token_mask(_UpperCAmelCase						)
							elif token_ids_a is None:
									return self._special_token_mask(_UpperCAmelCase						) + [1]
							else:
									return self._special_token_mask(token_ids_a + token_ids_a						) + [1]

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any]=None						)		->  List[int]:




							"""simple docstring"""


							if token_ids_a is None:
									return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]


					def     lowerCamelCase__	(self       :		str ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[str] = None						)		->  Tuple[str]:




							"""simple docstring"""


							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(_UpperCAmelCase						):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
									return
							lowercase__	     =						os.path.join(
							    _UpperCAmelCase ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

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

							return (out_vocab_file,)

