import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[Any]      =			''
   __UpperCAmelCase : str      =			(
       None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
   )
   __UpperCAmelCase : str      =			None  # compression type in fsspec. ex: "gzip"
   __UpperCAmelCase : str      =			None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz


   def __init__(self     :						List[str]       ,  __UpperCAmelCase     :						str = ""       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[dict] = None       ,  **__UpperCAmelCase     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         super().__init__(self       ,  **__UpperCAmelCase		)
         # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
         UpperCAmelCase__								=  fsspec.open(
             __UpperCAmelCase       ,  mode="rb"       ,  protocol=__UpperCAmelCase       ,  compression=self.compression       ,  client_kwargs={
                 "requote_redirect_url": False,  # see https://github.com/huggingface/datasets/pull/5459
                 "trust_env": True,  # Enable reading proxy env variables.
                 **(target_options or {}).pop("client_kwargs"       ,  {}		),  # To avoid issues if it was already passed.
             }       ,  **(target_options or {})       ,  )
         UpperCAmelCase__								=  os.path.basename(self.file.path.split("::"		)[0]		)
         UpperCAmelCase__								=  (
             self.compressed_name[: self.compressed_name.rindex("."		)]
             if "." in self.compressed_name
             else self.compressed_name
         )
         UpperCAmelCase__								=  None


   @classmethod
   def 			lowercase_      (cls     :						int       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         return super()._strip_protocol(__UpperCAmelCase		).lstrip("/"		)


   def 			lowercase_      (self     :						Any		)						->				Any:
         """simple docstring"""



         if self.dir_cache is None:
               UpperCAmelCase__								=  {**self.file.fs.info(self.file.path		), "name": self.uncompressed_name}
               UpperCAmelCase__								=  {f["name"]: f}


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						str		)						->				Union[str, Any]:
         """simple docstring"""



         return self.file.open().read()





   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						str = "rb"       ,  __UpperCAmelCase     :						Any=None       ,  __UpperCAmelCase     :						str=True       ,  __UpperCAmelCase     :						Optional[Any]=None       ,  **__UpperCAmelCase     :						str       ,  )						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self._strip_protocol(__UpperCAmelCase		)
         if mode != "rb":
               raise ValueError(f"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"""		)
         return self.file.open()





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[int]      =			'bz2'
   __UpperCAmelCase : Dict      =			'bz2'
   __UpperCAmelCase : Dict      =			'.bz2'





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : Optional[int]      =			'gzip'
   __UpperCAmelCase : Optional[Any]      =			'gzip'
   __UpperCAmelCase : Union[str, Any]      =			'.gz'





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : List[str]      =			'lz4'
   __UpperCAmelCase : str      =			'lz4'
   __UpperCAmelCase : Any      =			'.lz4'





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			'xz'
   __UpperCAmelCase : int      =			'xz'
   __UpperCAmelCase : Union[str, Any]      =			'.xz'





class      A							(				UpperCAmelCase_     ):
   __UpperCAmelCase : str      =			'zstd'
   __UpperCAmelCase : str      =			'zstd'
   __UpperCAmelCase : Optional[Any]      =			'.zst'


   def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						str = "rb"       ,  __UpperCAmelCase     :						Optional[str] = None       ,  __UpperCAmelCase     :						Optional[dict] = None       ,  __UpperCAmelCase     :						int = DEFAULT_BLOCK_SIZE       ,  **__UpperCAmelCase     :						List[str]       ,  )						->				List[str]:
         """simple docstring"""



         super().__init__(
             fo=__UpperCAmelCase       ,  mode=__UpperCAmelCase       ,  target_protocol=__UpperCAmelCase       ,  target_options=__UpperCAmelCase       ,  block_size=__UpperCAmelCase       ,  **__UpperCAmelCase       ,  )
         # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
         #
         # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
         #     out.close = close
         # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
         #
         # see https://github.com/intake/filesystem_spec/issues/725
         UpperCAmelCase__								=  self.file.__enter__





         class      A							:


            def __init__(self     :						List[Any]       ,  __UpperCAmelCase     :						Any		)						->				List[str]:
                  """simple docstring"""



                  UpperCAmelCase__								=  file_


            def __enter__(self     :						List[str]		)						->				Tuple:
                  """simple docstring"""



                  self._file.__enter__()
                  return self


            def __exit__(self     :						int       ,  *__UpperCAmelCase     :						Any       ,  **__UpperCAmelCase     :						Tuple		)						->				str:
                  """simple docstring"""



                  self._file.__exit__(*__UpperCAmelCase       ,  **__UpperCAmelCase		)


            def __iter__(self     :						str		)						->				List[Any]:
                  """simple docstring"""



                  return iter(self._file		)


            def 			lowercase_      (self     :						int		)						->				Any:
                  """simple docstring"""



                  return next(self._file		)





            def __getattr__(self     :						Optional[int]       ,  __UpperCAmelCase     :						Any		)						->				Dict:
                  """simple docstring"""



                  return getattr(self._file       ,  __UpperCAmelCase		)

         def fixed_enter(*__UpperCAmelCase     :						Tuple       ,  **__UpperCAmelCase     :						Union[str, Any]		):
               return WrappedFile(_enter(*__UpperCAmelCase       ,  **__UpperCAmelCase		)		)

         UpperCAmelCase__								=  fixed_enter




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


UpperCamelCase__											=					'base_with_context'




def    lowerCAmelCase_ (     __A,  __A							)     ->				int:







      '''simple docstring'''


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

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

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

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

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




def    lowerCAmelCase_ (     __A,  __A							)     ->				Tuple:







      '''simple docstring'''


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

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

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

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

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

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

      return model




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[Any]:







      '''simple docstring'''


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

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

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

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

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

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

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

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

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

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

      return model




def    lowerCAmelCase_ (     __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  checkpoints.load_tax_checkpoint(args.checkpoint_path							)
      UpperCAmelCase__								=  jnp.tree_util.tree_map(onp.array,  __A							)

      UpperCAmelCase__								=  [
          "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()",
      ]

      UpperCAmelCase__								=  os.path.join(args.checkpoint_path,  "..",  "config.gin"							)
      UpperCAmelCase__								=  inference.parse_training_gin_file(__A,  __A							)
      UpperCAmelCase__								=  inference.InferenceModel(args.checkpoint_path,  __A							)

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

      UpperCAmelCase__								=  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",  )

      UpperCAmelCase__								=  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",  )

      UpperCAmelCase__								=  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,  )

      UpperCAmelCase__								=  load_notes_encoder(ta_checkpoint["target"]["token_encoder"],  __A							)
      UpperCAmelCase__								=  load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"],  __A							)
      UpperCAmelCase__								=  load_decoder(ta_checkpoint["target"]["decoder"],  __A							)

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

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


if __name__ == "__main__":
      UpperCamelCase__											=					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.',
      )
      UpperCamelCase__											=					parser.parse_args()

      main(args)



