



'''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


UpperCamelCase__:     int										=    "base_with_context"


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

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

              UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

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

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


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]     ,   _lowerCAmelCase						:		str						)   ->     Any:
       UpperCAmelCase				: str											=	nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T						)						)

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

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

              UpperCAmelCase				: Union[str, Any]											=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T						)						)
              UpperCAmelCase				: Union[str, Any]											=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T						)						)
              UpperCAmelCase				: List[str]											=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T						)						)
              UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T						)						)
              UpperCAmelCase				: str											=	nn.Parameter(
                  torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']						)						)

              UpperCAmelCase				: str											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
              UpperCAmelCase				: Union[str, Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
              UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)
              UpperCAmelCase				: Optional[Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)

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

       return model


def 				snake_case_  (      _lowerCAmelCase						:		str     ,   _lowerCAmelCase						:		Tuple						)   ->     Any:
       UpperCAmelCase				: Optional[Any]											=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T						)						)
       UpperCAmelCase				: Dict											=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T						)						)

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

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

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

              UpperCAmelCase				: Optional[int]											=	nn.Parameter(
                  torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T						)						)

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

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

              UpperCAmelCase				: Union[str, Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']						)						)
              UpperCAmelCase				: str											=	nn.Parameter(
                  torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T						)						)
              UpperCAmelCase				: List[Any]											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T						)						)
              UpperCAmelCase				: Tuple											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T						)						)
              UpperCAmelCase				: Dict											=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T						)						)

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

       UpperCAmelCase				: Optional[Any]											=	nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T						)						)

       return model


def 				snake_case_  (      _lowerCAmelCase						:		str						)   ->     Optional[int]:
       UpperCAmelCase				: List[str]											=	checkpoints.load_tax_checkpoint(args.checkpoint_path						)
       UpperCAmelCase				: Optional[Any]											=	jnp.tree_util.tree_map(onp.array     ,   _lowerCAmelCase						)

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

       UpperCAmelCase				: Any											=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''     ,   variance_type='''fixed_large'''						)

       UpperCAmelCase				: 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'''     ,   )

       UpperCAmelCase				: Optional[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'''     ,   )

       UpperCAmelCase				: Tuple											=	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				: Any											=	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']     ,   _lowerCAmelCase						)
       UpperCAmelCase				: int											=	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']     ,   _lowerCAmelCase						)
       UpperCAmelCase				: Dict											=	load_decoder(ta_checkpoint['''target''']['''decoder''']     ,   _lowerCAmelCase						)

       UpperCAmelCase				: List[Any]											=	OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''						)

       UpperCAmelCase				: Optional[Any]											=	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__":
      UpperCamelCase__:     Optional[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.",
      )
      UpperCamelCase__:     Any										=    parser.parse_args()

      main(args)










'''simple docstring'''






from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

UpperCamelCase__:     Tuple										=    {
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """time_series_transformer"""
	lowerCamelCase__    				= {
	    """hidden_size""": """d_model""",
	    """num_attention_heads""": """encoder_attention_heads""",
	    """num_hidden_layers""": """encoder_layers""",
	}







	def __init__(		self    :  str	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  str = "student_t"	,    __snake_case    :  str = "nll"	,    __snake_case    :  int = 1	,    __snake_case    :  List[int] = [1, 2, 3, 4, 5, 6, 7]	,    __snake_case    :  Optional[Union[str, bool]] = "mean"	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  int = 0	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  int = 32	,    __snake_case    :  int = 32	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  int = 2	,    __snake_case    :  bool = True	,    __snake_case    :  str = "gelu"	,    __snake_case    :  int = 64	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  float = 0.1	,    __snake_case    :  int = 100	,    __snake_case    :  float = 0.02	,    __snake_case    :  Optional[Any]=True	,    **__snake_case    :  List[Any]	,    )    ->	List[str]:
								# time series specific configuration
								UpperCAmelCase				: List[Any]											=	prediction_length
								UpperCAmelCase				: List[Any]											=	context_length or prediction_length
								UpperCAmelCase				: Tuple											=	distribution_output
								UpperCAmelCase				: Optional[Any]											=	loss
								UpperCAmelCase				: Tuple											=	input_size
								UpperCAmelCase				: Optional[int]											=	num_time_features
								UpperCAmelCase				: Dict											=	lags_sequence
								UpperCAmelCase				: Any											=	scaling
								UpperCAmelCase				: Tuple											=	num_dynamic_real_features
								UpperCAmelCase				: Any											=	num_static_real_features
								UpperCAmelCase				: Optional[int]											=	num_static_categorical_features
								if cardinality and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The cardinality should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Any											=	cardinality
								else:
															UpperCAmelCase				: Optional[Any]											=	[0]
								if embedding_dimension and num_static_categorical_features > 0:
															if len(__snake_case	) != num_static_categorical_features:
																						raise ValueError(
																						    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''	)
															UpperCAmelCase				: Optional[int]											=	embedding_dimension
								else:
															UpperCAmelCase				: Optional[Any]											=	[min(50	,    (cat + 1) // 2	) for cat in self.cardinality]
								UpperCAmelCase				: List[Any]											=	num_parallel_samples

								# Transformer architecture configuration
								UpperCAmelCase				: int											=	input_size * len(__snake_case	) + self._number_of_features
								UpperCAmelCase				: int											=	d_model
								UpperCAmelCase				: str											=	encoder_attention_heads
								UpperCAmelCase				: str											=	decoder_attention_heads
								UpperCAmelCase				: List[str]											=	encoder_ffn_dim
								UpperCAmelCase				: Any											=	decoder_ffn_dim
								UpperCAmelCase				: Any											=	encoder_layers
								UpperCAmelCase				: str											=	decoder_layers

								UpperCAmelCase				: Optional[int]											=	dropout
								UpperCAmelCase				: Union[str, Any]											=	attention_dropout
								UpperCAmelCase				: List[Any]											=	activation_dropout
								UpperCAmelCase				: Optional[int]											=	encoder_layerdrop
								UpperCAmelCase				: Dict											=	decoder_layerdrop

								UpperCAmelCase				: Tuple											=	activation_function
								UpperCAmelCase				: Dict											=	init_std

								UpperCAmelCase				: Optional[int]											=	use_cache

								super().__init__(is_encoder_decoder=__snake_case	,    **__snake_case	)







	@property
	def   A			(		self    :  Union[str, Any]	)    ->	int:
								return (
								    sum(self.embedding_dimension	)
								    + self.num_dynamic_real_features
								    + self.num_time_features
								    + self.num_static_real_features
								    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
								)





