





import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
       import PIL.Image

       from .features import FeatureType


_A :   Optional[List[str]]  			=  None
_A :   Optional[int]  			=  '<' if sys.byteorder == 'little' else '>'
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
_A :   Tuple  			=  [
    np.dtype('|b1'),
    np.dtype('|u1'),
    np.dtype('<u2'),
    np.dtype('>u2'),
    np.dtype('<i2'),
    np.dtype('>i2'),
    np.dtype('<u4'),
    np.dtype('>u4'),
    np.dtype('<i4'),
    np.dtype('>i4'),
    np.dtype('<f4'),
    np.dtype('>f4'),
    np.dtype('<f8'),
    np.dtype('>f8'),
]


@dataclass
class        __SCREAMING_SNAKE_CASE      :
       _UpperCAmelCase				:  List[str]			   =   True
       _UpperCAmelCase				:  Any			   =   None
       # Automatically constructed
       _UpperCAmelCase				:  str			   =   "PIL.Image.Image"
       _UpperCAmelCase				:  Union[str, Any]			   =   pa.struct({"bytes": pa.binary(), "path": pa.string()} )
       _UpperCAmelCase				:  Union[str, Any]			   =   field(default="Image"					,init=_A					,repr=_A )







       def __call__(				self					:     str      )       ->Optional[int]:
          return self.pa_type







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]      )       ->Tuple:
          if config.PIL_AVAILABLE:
             import PIL.Image
          else:
             raise ImportError('''To support encoding images, please install \'Pillow\'.'''      )

          if isinstance(__SCREAMING_SNAKE_CASE		, __SCREAMING_SNAKE_CASE      ):
             lowerCamelCase__       :						Union[str, Any]        =							np.array(__SCREAMING_SNAKE_CASE      )

          if isinstance(__SCREAMING_SNAKE_CASE		, __SCREAMING_SNAKE_CASE      ):
             return {"path": value, "bytes": None}
          elif isinstance(__SCREAMING_SNAKE_CASE		, __SCREAMING_SNAKE_CASE      ):
             return {"path": None, "bytes": value}
          elif isinstance(__SCREAMING_SNAKE_CASE		, np.ndarray      ):
             # convert the image array to PNG/TIFF bytes
             return encode_np_array(__SCREAMING_SNAKE_CASE      )
          elif isinstance(__SCREAMING_SNAKE_CASE		, PIL.Image.Image      ):
             # convert the PIL image to bytes (default format is PNG/TIFF)
             return encode_pil_image(__SCREAMING_SNAKE_CASE      )
          elif value.get('''path'''      ) is not None and os.path.isfile(value['''path''']      ):
             # we set "bytes": None to not duplicate the data if they're already available locally
             return {"bytes": None, "path": value.get('''path'''      )}
          elif value.get('''bytes'''      ) is not None or value.get('''path'''      ) is not None:
             # store the image bytes, and path is used to infer the image format using the file extension
             return {"bytes": value.get('''bytes'''      ), "path": value.get('''path'''      )}
          else:
             raise ValueError(
                 F"An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}."      )







       def 						__lowerCamelCase   (				self					:     int		, A					:     dict		, A					:     Union[str, Any]=None      )       ->str:
          if not self.decode:
             raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''      )

          if config.PIL_AVAILABLE:
             import PIL.Image
          else:
             raise ImportError('''To support decoding images, please install \'Pillow\'.'''      )

          if token_per_repo_id is None:
             lowerCamelCase__       :						List[Any]        =							{}

          lowerCamelCase__       ,							lowerCamelCase__       :						Optional[Any]        =							value['''path'''], value['''bytes''']
          if bytes_ is None:
             if path is None:
                raise ValueError(F"An image should have one of 'path' or 'bytes' but both are None in {value}."      )
             else:
                if is_local_path(__SCREAMING_SNAKE_CASE      ):
                   lowerCamelCase__       :						str        =							PIL.Image.open(__SCREAMING_SNAKE_CASE      )
                else:
                   lowerCamelCase__       :						Optional[int]        =							path.split('''::'''      )[-1]
                   try:
                      lowerCamelCase__       :						Union[str, Any]        =							string_to_dict(__SCREAMING_SNAKE_CASE		, config.HUB_DATASETS_URL      )['''repo_id''']
                      lowerCamelCase__       :						Optional[Any]        =							token_per_repo_id.get(__SCREAMING_SNAKE_CASE      )
                   except ValueError:
                      lowerCamelCase__       :						List[Any]        =							None
                   with xopen(__SCREAMING_SNAKE_CASE		, '''rb'''		, use_auth_token=__SCREAMING_SNAKE_CASE      ) as f:
                      lowerCamelCase__       :						int        =							BytesIO(f.read()      )
                   lowerCamelCase__       :						int        =							PIL.Image.open(bytes_      )
          else:
             lowerCamelCase__       :						Optional[int]        =							PIL.Image.open(BytesIO(bytes_      )      )
          image.load()  # to avoid "Too many open files" errors
          return image







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Any:
          from .features import Value

          return (
              self
              if self.decode
              else {
                  "bytes": Value('''binary'''      ),
                  "path": Value('''string'''      ),
              }
          )







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     Union[pa.StringArray, pa.StructArray, pa.ListArray]      )       ->Any:
          if pa.types.is_string(storage.type      ):
             lowerCamelCase__       :						Union[str, Any]        =							pa.array([None] * len(__SCREAMING_SNAKE_CASE      )		, type=pa.binary()      )
             lowerCamelCase__       :						Optional[Any]        =							pa.StructArray.from_arrays([bytes_array, storage]		, ['''bytes''', '''path''']		, mask=storage.is_null()      )
          elif pa.types.is_binary(storage.type      ):
             lowerCamelCase__       :						List[str]        =							pa.array([None] * len(__SCREAMING_SNAKE_CASE      )		, type=pa.string()      )
             lowerCamelCase__       :						Any        =							pa.StructArray.from_arrays([storage, path_array]		, ['''bytes''', '''path''']		, mask=storage.is_null()      )
          elif pa.types.is_struct(storage.type      ):
             if storage.type.get_field_index('''bytes'''      ) >= 0:
                lowerCamelCase__       :						Tuple        =							storage.field('''bytes'''      )
             else:
                lowerCamelCase__       :						Optional[int]        =							pa.array([None] * len(__SCREAMING_SNAKE_CASE      )		, type=pa.binary()      )
             if storage.type.get_field_index('''path'''      ) >= 0:
                lowerCamelCase__       :						Dict        =							storage.field('''path'''      )
             else:
                lowerCamelCase__       :						Union[str, Any]        =							pa.array([None] * len(__SCREAMING_SNAKE_CASE      )		, type=pa.string()      )
             lowerCamelCase__       :						Dict        =							pa.StructArray.from_arrays([bytes_array, path_array]		, ['''bytes''', '''path''']		, mask=storage.is_null()      )
          elif pa.types.is_list(storage.type      ):
             lowerCamelCase__       :						int        =							pa.array(
                 [encode_np_array(np.array(__SCREAMING_SNAKE_CASE      )      )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]		, type=pa.binary()		, )
             lowerCamelCase__       :						List[str]        =							pa.array([None] * len(__SCREAMING_SNAKE_CASE      )		, type=pa.string()      )
             lowerCamelCase__       :						Tuple        =							pa.StructArray.from_arrays(
                 [bytes_array, path_array]		, ['''bytes''', '''path''']		, mask=bytes_array.is_null()      )
          return array_cast(__SCREAMING_SNAKE_CASE		, self.pa_type      )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     pa.StructArray      )       ->Union[str, Any]:

          @no_op_if_value_is_null
          def path_to_bytes(A					:     str      ):
             with xopen(__SCREAMING_SNAKE_CASE		, '''rb'''      ) as f:
                lowerCamelCase__       :						Dict        =							f.read()
             return bytes_

          lowerCamelCase__       :						str        =							pa.array(
              [
                  (path_to_bytes(x['''path''']      ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
                  for x in storage.to_pylist()
              ]		, type=pa.binary()		, )
          lowerCamelCase__       :						List[Any]        =							pa.array(
              [os.path.basename(__SCREAMING_SNAKE_CASE      ) if path is not None else None for path in storage.field('''path'''      ).to_pylist()]		, type=pa.string()		, )
          lowerCamelCase__       :						Dict        =							pa.StructArray.from_arrays([bytes_array, path_array]		, ['''bytes''', '''path''']		, mask=bytes_array.is_null()      )
          return array_cast(__SCREAMING_SNAKE_CASE		, self.pa_type      )



def 	_a						(   )    ->    Any:







   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('''To support encoding images, please install \'Pillow\'.'''					)

   global _IMAGE_COMPRESSION_FORMATS
   if _IMAGE_COMPRESSION_FORMATS is None:
      PIL.Image.init()
      lowerCamelCase__       :						Dict        =							list(set(PIL.Image.OPEN.keys()					) & set(PIL.Image.SAVE.keys()					)					)
   return _IMAGE_COMPRESSION_FORMATS



def 	_a						(   UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							BytesIO()
   if image.format in list_image_compression_formats():
      lowerCamelCase__       :						Optional[Any]        =							image.format
   else:
      lowerCamelCase__       :						Tuple        =							'''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
   image.save(lowercase__      ,  format=lowercase__					)
   return buffer.getvalue()



def 	_a						(   UpperCAmelCase					)    ->    int:







   """simple docstring"""
   if hasattr(lowercase__      ,  '''filename'''					) and image.filename != "":
      return {"path": image.filename, "bytes": None}
   else:
      return {"path": None, "bytes": image_to_bytes(lowercase__					)}



def 	_a						(   UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('''To support encoding images, please install \'Pillow\'.'''					)

   lowerCamelCase__       :						Any        =							array.dtype
   lowerCamelCase__       :						Tuple        =							dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
   lowerCamelCase__       :						str        =							dtype.kind
   lowerCamelCase__       :						List[str]        =							dtype.itemsize

   lowerCamelCase__       :						Dict        =							None

   # Multi-channel array case (only np.dtype("|u1") is allowed)
   if array.shape[2:]:
      lowerCamelCase__       :						Any        =							np.dtype('''|u1'''					)
      if dtype_kind not in ["u", "i"]:
         raise TypeError(
             f"Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays."					)
      if dtype is not dest_dtype:
         warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"					)
    # Exact match
   elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
      lowerCamelCase__       :						Tuple        =							dtype
   else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
      while dtype_itemsize >= 1:
         lowerCamelCase__       :						List[Any]        =							dtype_byteorder + dtype_kind + str(lowercase__					)
         lowerCamelCase__       :						int        =							np.dtype(lowercase__					)
         if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
            warnings.warn(f"Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'"					)
            break
         else:
            dtype_itemsize //= 2

   if dest_dtype is None:
      raise TypeError(
          f"Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}"					)
   lowerCamelCase__       :						Optional[int]        =							PIL.Image.fromarray(array.astype(lowercase__					)					)
   return {"path": None, "bytes": image_to_bytes(lowercase__					)}




def 	_a						(   UpperCAmelCase					)    ->    Dict:







   """simple docstring"""
   if config.PIL_AVAILABLE:
      import PIL.Image
   else:
      raise ImportError('''To support encoding images, please install \'Pillow\'.'''					)

   if objs:
      lowerCamelCase__       ,							lowerCamelCase__       :						str        =							first_non_null_value(lowercase__					)
      if isinstance(lowercase__      ,  lowercase__					):
         return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
      if isinstance(lowercase__      ,  np.ndarray					):
         lowerCamelCase__       :						str        =							no_op_if_value_is_null(lowercase__					)
         return [obj_to_image_dict_func(lowercase__					) for obj in objs]
      elif isinstance(lowercase__      ,  PIL.Image.Image					):
         lowerCamelCase__       :						List[str]        =							no_op_if_value_is_null(lowercase__					)
         return [obj_to_image_dict_func(lowercase__					) for obj in objs]
      else:
         return objs
   else:
      return objs









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 :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







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

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

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

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

   lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T					)					)

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

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

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

      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)

   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

   lowerCamelCase__       :						str        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(
       torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T					)					)

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

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

      lowerCamelCase__       :						Optional[Any]        =							ly_weight['''self_attention''']
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

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

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

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T					)					)

   return model



def 	_a						(   UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						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()''',
   ]

   lowerCamelCase__       :						List[Any]        =							os.path.join(args.checkpoint_path      ,  '''..'''      ,  '''config.gin'''					)
   lowerCamelCase__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

   lowerCamelCase__       :						int        =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      ,  variance_type='''fixed_large'''					)

   lowerCamelCase__       :						str        =							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__       :						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'''      ,  )

   lowerCamelCase__       :						Optional[int]        =							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__       :						Optional[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

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

   lowerCamelCase__       :						List[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 :   int  			=  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 :   Tuple  			=  parser.parse_args()

       main(args)



