





'''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 :					Any 							='''base_with_context'''
def 							UpperCAmelCase_  (						__lowerCamelCase : Dict     ,__lowerCamelCase : List[Any]			):
   lowercase_      :Union[str, Any]         =			nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]			)			)
   lowercase_      :Optional[int]         =			nn.Parameter(
       torch.FloatTensor(weights["Embed_0"]["embedding"]			)     ,requires_grad=_lowerCAmelCase			)
   for lyr_num, lyr in enumerate(model.encoders			):
      lowercase_      :Union[str, Any]         =			weights[F'layers_{lyr_num}']
      lowercase_      :Union[str, Any]         =			nn.Parameter(
          torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]			)			)

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

      lowercase_      :List[Any]         =			nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]			)			)

      lowercase_      :Tuple         =			nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T			)			)
      lowercase_      :Optional[Any]         =			nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T			)			)
      lowercase_      :Tuple         =			nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T			)			)

   lowercase_      :Optional[int]         =			nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]			)			)
   return model
def 							UpperCAmelCase_  (						__lowerCamelCase : int     ,__lowerCamelCase : Union[str, Any]			):
   lowercase_      :Union[str, Any]         =			nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T			)			)

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

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

      lowercase_      :Optional[Any]         =			nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T			)			)
      lowercase_      :int         =			nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T			)			)
      lowercase_      :Union[str, Any]         =			nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T			)			)
      lowercase_      :Tuple         =			nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T			)			)
      lowercase_      :Any         =			nn.Parameter(
          torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]			)			)

      lowercase_      :int         =			nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T			)			)
      lowercase_      :List[str]         =			nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T			)			)
      lowercase_      :Optional[Any]         =			nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T			)			)
      lowercase_      :List[str]         =			nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]			)			)

   lowercase_      :Optional[int]         =			nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]			)			)

   return model
def 							UpperCAmelCase_  (						__lowerCamelCase : Optional[Any]     ,__lowerCamelCase : int			):
   lowercase_      :Optional[Any]         =			nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T			)			)
   lowercase_      :Union[str, Any]         =			nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T			)			)

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

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

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

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

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

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

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

   lowercase_      :int         =			nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]			)			)

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

   return model
def 							UpperCAmelCase_  (						__lowerCamelCase : Dict			):
   lowercase_      :str         =			checkpoints.load_tax_checkpoint(args.checkpoint_path			)
   lowercase_      :List[Any]         =			jnp.tree_util.tree_map(onp.array     ,_lowerCAmelCase			)

   lowercase_      :Dict         =			[
       """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()""",
   ]

   lowercase_      :Optional[Any]         =			os.path.join(args.checkpoint_path     ,".."     ,"config.gin"			)
   lowercase_      :List[str]         =			inference.parse_training_gin_file(_lowerCAmelCase     ,_lowerCAmelCase			)
   lowercase_      :Dict         =			inference.InferenceModel(args.checkpoint_path     ,_lowerCAmelCase			)

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

   lowercase_      :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"     ,)

   lowercase_      :Dict         =			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"     ,)

   lowercase_      :List[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     ,)

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

   lowercase_      :Tuple         =			OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"			)

   lowercase_      :Tuple         =			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__":
 lowerCAmelCase :					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.''',
 )
 lowerCAmelCase :					Dict 							=parser.parse_args()

 main(args)









'''simple docstring'''


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase :					Any 							={
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase :					int 							=logging.get_logger(__name__)
class 				a_       (    _lowerCAmelCase							):
     __A								=      "maskformer"
     __A								=      {"hidden_size": "mask_feature_size"}
     __A								=      ["resnet", "swin"]
     __A								=      ["detr"]





     def __init__(			self		: List[Any]     ,				lowercase		: int = 256     ,				lowercase		: int = 256     ,				lowercase		: float = 0.1     ,				lowercase		: bool = False     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: Optional[Dict] = None     ,				lowercase		: float = 0.02     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 1.0     ,				lowercase		: float = 20.0     ,				lowercase		: Optional[bool] = None     ,				**lowercase		: Any     ,				):





        """simple docstring"""




        if backbone_config is None:
           # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
           lowercase_      :Any         =			SwinConfig(
               image_size=384     ,				in_channels=3     ,				patch_size=4     ,				embed_dim=128     ,				depths=[2, 2, 18, 2]     ,				num_heads=[4, 8, 16, 32]     ,				window_size=12     ,				drop_path_rate=0.3     ,				out_features=["stage1", "stage2", "stage3", "stage4"]     ,				)

        if isinstance(lowercase     ,				lowercase						):
           lowercase_      :Optional[int]         =			backbone_config.pop("model_type"						)
           lowercase_      :Optional[int]         =			CONFIG_MAPPING[backbone_model_type]
           lowercase_      :int         =			config_class.from_dict(lowercase						)

        # verify that the backbone is supported
        if backbone_config.model_type not in self.backbones_supported:
           logger.warning_once(
               F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. '
               F'Supported model types: {",".join(self.backbones_supported						)}'						)

        if decoder_config is None:
           # fall back to https://huggingface.co/facebook/detr-resnet-50
           lowercase_      :Optional[Any]         =			DetrConfig()
        else:
           # verify that the decoder is supported
           lowercase_      :Tuple         =			(
               decoder_config.pop("model_type"						) if isinstance(lowercase     ,				lowercase						) else decoder_config.model_type
           )
           if decoder_type not in self.decoders_supported:
              raise ValueError(
                  F'Transformer Decoder {decoder_type} not supported, please use one of'
                  F' {",".join(self.decoders_supported						)}'						)
           if isinstance(lowercase     ,				lowercase						):
              lowercase_      :str         =			CONFIG_MAPPING[decoder_type]
              lowercase_      :List[str]         =			config_class.from_dict(lowercase						)

        lowercase_      :str         =			backbone_config
        lowercase_      :Union[str, Any]         =			decoder_config
        # main feature dimension for the model
        lowercase_      :Any         =			fpn_feature_size
        lowercase_      :Optional[int]         =			mask_feature_size
        # initializer
        lowercase_      :List[Any]         =			init_std
        lowercase_      :Union[str, Any]         =			init_xavier_std
        # Hungarian matcher && loss
        lowercase_      :List[str]         =			cross_entropy_weight
        lowercase_      :int         =			dice_weight
        lowercase_      :List[str]         =			mask_weight
        lowercase_      :Optional[Any]         =			use_auxiliary_loss
        lowercase_      :str         =			no_object_weight
        lowercase_      :int         =			output_auxiliary_logits

        lowercase_      :Optional[Any]         =			self.decoder_config.encoder_attention_heads
        lowercase_      :int         =			self.decoder_config.num_hidden_layers
        super().__init__(**lowercase						)





     @classmethod
     def 							lowercase__				(			cls		: Tuple     ,				lowercase		: PretrainedConfig     ,				lowercase		: PretrainedConfig     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        return cls(
            backbone_config=lowercase     ,				decoder_config=lowercase     ,				**lowercase     ,				)







     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :str         =			copy.deepcopy(self.__dict__						)
        lowercase_      :int         =			self.backbone_config.to_dict()
        lowercase_      :List[Any]         =			self.decoder_config.to_dict()
        lowercase_      :Optional[Any]         =			self.__class__.model_type
        return output


