



'''simple docstring'''





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__a  			=						logging.get_logger(__name__)

__a  			=						"▁"

__a  			=						{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

__a  			=						{
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

__a  			=						{
    "facebook/m2m100_418M": 1024,
}

# fmt: off
__a  			=						{
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						VOCAB_FILES_NAMES
    lowercase   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase   =						["input_ids", "attention_mask"]

    lowercase   =						[]
    lowercase   =						[]




    def __init__(       self   :	Dict					,	snake_case_   :	Union[str, Any]					,	snake_case_   :	Optional[int]					,	snake_case_   :	Tuple=None					,	snake_case_   :	Any=None					,	snake_case_   :	int="<s>"					,	snake_case_   :	Any="</s>"					,	snake_case_   :	Tuple="</s>"					,	snake_case_   :	List[str]="<pad>"					,	snake_case_   :	Dict="<unk>"					,	snake_case_   :	Optional[int]="m2m100"					,	snake_case_   :	Optional[Dict[str, Any]] = None					,	snake_case_   :	Any=8					,	**snake_case_   :	Any					,	):
          snake_case__    :       str       		= {} if sp_model_kwargs is None else sp_model_kwargs

          snake_case__    :       Tuple       		= language_codes
          snake_case__    :       Any       		= FAIRSEQ_LANGUAGE_CODES[language_codes]
          snake_case__    :       Any       		= {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}

          snake_case__    :       Any       		= kwargs.get("""additional_special_tokens"""					,	[]		)
          kwargs["additional_special_tokens"] += [
              self.get_lang_token(snake_case_		)
              for lang_code in fairseq_language_code
              if self.get_lang_token(snake_case_		) not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              src_lang=snake_case_					,	tgt_lang=snake_case_					,	bos_token=snake_case_					,	eos_token=snake_case_					,	sep_token=snake_case_					,	unk_token=snake_case_					,	pad_token=snake_case_					,	language_codes=snake_case_					,	sp_model_kwargs=self.sp_model_kwargs					,	num_madeup_words=snake_case_					,	**snake_case_					,	)

          snake_case__    :       List[Any]       		= vocab_file
          snake_case__    :       Union[str, Any]       		= load_json(snake_case_		)
          snake_case__    :       Union[str, Any]       		= {v: k for k, v in self.encoder.items()}
          snake_case__    :       Optional[int]       		= spm_file
          snake_case__    :       Tuple       		= load_spm(snake_case_					,	self.sp_model_kwargs		)

          snake_case__    :       int       		= len(self.encoder		)

          snake_case__    :       Optional[Any]       		= {
              self.get_lang_token(snake_case_		): self.encoder_size + i for i, lang_code in enumerate(snake_case_		)
          }
          snake_case__    :       List[Any]       		= {lang_code: self.encoder_size + i for i, lang_code in enumerate(snake_case_		)}
          snake_case__    :       Tuple       		= {v: k for k, v in self.lang_token_to_id.items()}

          snake_case__    :       str       		= src_lang if src_lang is not None else """en"""
          snake_case__    :       int       		= tgt_lang
          snake_case__    :       Dict       		= self.get_lang_id(self._src_lang		)
          self.set_src_lang_special_tokens(self._src_lang		)

          snake_case__    :       Union[str, Any]       		= num_madeup_words




    @property
    def lowerCamelCase			(       self   :	str		):
          return len(self.encoder		) + len(self.lang_token_to_id		)




    @property
    def lowerCamelCase			(       self   :	int		):
          return self._src_lang




    @src_lang.setter
    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	str		):
          snake_case__    :       List[Any]       		= new_src_lang
          self.set_src_lang_special_tokens(self._src_lang		)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	str		):
          return self.sp_model.encode(snake_case_					,	out_type=snake_case_		)




    def lowerCamelCase			(       self   :	Union[str, Any]					,	snake_case_   :	List[Any]		):
          if token in self.lang_token_to_id:
                return self.lang_token_to_id[token]
          return self.encoder.get(snake_case_					,	self.encoder[self.unk_token]		)




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	int		):
          if index in self.id_to_lang_token:
                return self.id_to_lang_token[index]
          return self.decoder.get(snake_case_					,	self.unk_token		)




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	Optional[int]		):
          snake_case__    :       List[Any]       		= []
          snake_case__    :       Optional[Any]       		= """"""
          for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                      out_string += self.sp_model.decode(snake_case_		) + token
                      snake_case__    :       str       		= []
                else:
                      current_sub_tokens.append(snake_case_		)
          out_string += self.sp_model.decode(snake_case_		)
          return out_string.strip()




    def lowerCamelCase			(       self   :	List[Any]					,	snake_case_   :	List[int]					,	snake_case_   :	Optional[List[int]] = None					,	snake_case_   :	bool = False		):

          if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=snake_case_					,	token_ids_a=snake_case_					,	already_has_special_tokens=snake_case_		)

          snake_case__    :       Dict       		= [1] * len(self.prefix_tokens		)
          snake_case__    :       Dict       		= [1] * len(self.suffix_tokens		)
          if token_ids_a is None:
                return prefix_ones + ([0] * len(snake_case_		)) + suffix_ones
          return prefix_ones + ([0] * len(snake_case_		)) + ([0] * len(snake_case_		)) + suffix_ones




    def lowerCamelCase			(       self   :	Optional[int]					,	snake_case_   :	List[int]					,	snake_case_   :	Optional[List[int]] = None		):
          if token_ids_a is None:
                return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       Optional[Any]       		= {self.convert_ids_to_tokens(snake_case_		): i for i in range(self.vocab_size		)}
          vocab.update(self.added_tokens_encoder		)
          return vocab




    def __getstate__(       self   :	Any		):
          snake_case__    :       Any       		= self.__dict__.copy()
          snake_case__    :       str       		= None
          return state




    def __setstate__(       self   :	Optional[Any]					,	snake_case_   :	Dict		):
          snake_case__    :       str       		= d

          # for backward compatibility
          if not hasattr(self					,	"""sp_model_kwargs"""		):
                snake_case__    :       Optional[int]       		= {}

          snake_case__    :       List[str]       		= load_spm(self.spm_file					,	self.sp_model_kwargs		)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[str] = None		):
          snake_case__    :       List[Any]       		= Path(snake_case_		)
          if not save_dir.is_dir():
                raise OSError(f"{save_directory} should be a directory"		)
          snake_case__    :       str       		= save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
          )
          snake_case__    :       Any       		= save_dir / (
              (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
          )

          save_json(self.encoder					,	snake_case_		)

          if os.path.abspath(self.spm_file		) != os.path.abspath(snake_case_		) and os.path.isfile(self.spm_file		):
                copyfile(self.spm_file					,	snake_case_		)
          elif not os.path.isfile(self.spm_file		):
                with open(snake_case_					,	"""wb"""		) as fi:
                      snake_case__    :       Tuple       		= self.sp_model.serialized_model_proto()
                      fi.write(snake_case_		)

          return (str(snake_case_		), str(snake_case_		))




    def lowerCamelCase			(       self   :	Any					,	snake_case_   :	List[str]					,	snake_case_   :	str = "en"					,	snake_case_   :	Optional[List[str]] = None					,	snake_case_   :	str = "ro"					,	**snake_case_   :	Dict					,	):
          snake_case__    :       List[Any]       		= src_lang
          snake_case__    :       Union[str, Any]       		= tgt_lang
          self.set_src_lang_special_tokens(self.src_lang		)
          return super().prepare_seqaseq_batch(snake_case_					,	snake_case_					,	**snake_case_		)




    def lowerCamelCase			(       self   :	str					,	snake_case_   :	Optional[Any]					,	snake_case_   :	Optional[str]					,	snake_case_   :	Optional[str]					,	**snake_case_   :	List[Any]		):
          if src_lang is None or tgt_lang is None:
                raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""		)
          snake_case__    :       Tuple       		= src_lang
          snake_case__    :       Optional[int]       		= self(snake_case_					,	add_special_tokens=snake_case_					,	**snake_case_		)
          snake_case__    :       Optional[Any]       		= self.get_lang_id(snake_case_		)
          snake_case__    :       List[Any]       		= tgt_lang_id
          return inputs




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.set_src_lang_special_tokens(self.src_lang		)




    def lowerCamelCase			(       self   :	Optional[int]		):
          self.set_tgt_lang_special_tokens(self.tgt_lang		)




    def lowerCamelCase			(       self   :	List[str]					,	snake_case_   :	str		):
          snake_case__    :       int       		= self.get_lang_token(snake_case_		)
          snake_case__    :       int       		= self.lang_token_to_id[lang_token]
          snake_case__    :       Optional[int]       		= [self.cur_lang_id]
          snake_case__    :       Dict       		= [self.eos_token_id]




    def lowerCamelCase			(       self   :	Optional[Any]					,	snake_case_   :	str		):
          snake_case__    :       Any       		= self.get_lang_token(snake_case_		)
          snake_case__    :       List[str]       		= self.lang_token_to_id[lang_token]
          snake_case__    :       Union[str, Any]       		= [self.cur_lang_id]
          snake_case__    :       Any       		= [self.eos_token_id]




    def lowerCamelCase			(       self   :	int					,	snake_case_   :	str		):
          return self.lang_code_to_token[lang]




    def lowerCamelCase			(       self   :	Dict					,	snake_case_   :	str		):
          snake_case__    :       str       		= self.get_lang_token(snake_case_		)
          return self.lang_token_to_id[lang_token]





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					sentencepiece.SentencePieceProcessor:
      snake_case__    :       Dict       		= sentencepiece.SentencePieceProcessor(**_lowerCAmelCase						)
      spm.Load(str(_lowerCAmelCase						)						)
      return spm





def 		__snake_case(							_lowerCAmelCase						)						->					Union[Dict, List]:
      with open(_lowerCAmelCase	,  """r"""						) as f:
            return json.load(_lowerCAmelCase						)





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					None:
      with open(_lowerCAmelCase	,  """w"""						) as f:
            json.dump(_lowerCAmelCase	,  _lowerCAmelCase	,  indent=2						)





'''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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
      snake_case__    :       Dict       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)
      for lyr_num, lyr in enumerate(model.encoders						):
            snake_case__    :       List[str]       		= weights[f"layers_{lyr_num}"]
            snake_case__    :       Dict       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       List[str]       		= ly_weight["""attention"""]
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
            snake_case__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Any]:
      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

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

      for lyr_num, lyr in enumerate(model.encoders						):
            snake_case__    :       List[Any]       		= weights[f"layers_{lyr_num}"]
            snake_case__    :       List[str]       		= ly_weight["""attention"""]

            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

      snake_case__    :       str       		= nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)

      return model





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[str]:
      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T						)						)
      snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T						)						)

      snake_case__    :       List[Any]       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

      snake_case__    :       Optional[Any]       		= nn.Parameter(
          torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T						)						)

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

            snake_case__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)

            snake_case__    :       Any       		= ly_weight["""self_attention"""]
            snake_case__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

            snake_case__    :       int       		= ly_weight["""MultiHeadDotProductAttention_0"""]
            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)
            snake_case__    :       Union[str, Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)
            snake_case__    :       str       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T						)						)
            snake_case__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]						)						)

      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T						)						)

      return model





def 		__snake_case(							_lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Tuple       		= checkpoints.load_tax_checkpoint(args.checkpoint_path						)
      snake_case__    :       List[str]       		= jnp.tree_util.tree_map(onp.array	,  _lowerCAmelCase						)

      snake_case__    :       int       		= [
          """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()""",
      ]

      snake_case__    :       Any       		= os.path.join(args.checkpoint_path	,  """.."""	,  """config.gin"""						)
      snake_case__    :       Dict       		= inference.parse_training_gin_file(_lowerCAmelCase	,  _lowerCAmelCase						)
      snake_case__    :       int       		= inference.InferenceModel(args.checkpoint_path	,  _lowerCAmelCase						)

      snake_case__    :       Optional[int]       		= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""	,  variance_type="""fixed_large"""						)

      snake_case__    :       Union[str, Any]       		= 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"""	,  )

      snake_case__    :       Union[str, Any]       		= 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"""	,  )

      snake_case__    :       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	,  )

      snake_case__    :       Optional[int]       		= load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Union[str, Any]       		= load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Optional[Any]       		= load_decoder(ta_checkpoint["""target"""]["""decoder"""]	,  _lowerCAmelCase						)

      snake_case__    :       str       		= OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""						)

      snake_case__    :       Dict       		= 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  			=						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)
