
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		: int								=     '''base_with_context'''




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

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

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

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

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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	Optional[Any]       )  ->     List[Any]:
						_lowercase					       =					nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T       )       )

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

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

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

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

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

						return model




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]		,   SCREAMING_SNAKE_CASE_	:	Optional[int]       )  ->     List[str]:
						_lowercase					       =					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T       )       )
						_lowercase					       =					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T       )       )

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

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

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

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

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

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

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

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

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

						return model




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str       )  ->     Union[str, Any]:
						_lowercase					       =					checkpoints.load_tax_checkpoint(args.checkpoint_path       )
						_lowercase					       =					jnp.tree_util.tree_map(onp.array		,   _lowerCAmelCase       )

						_lowercase					       =					[
						    """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					       =					os.path.join(args.checkpoint_path		,   """.."""		,   """config.gin"""       )
						_lowercase					       =					inference.parse_training_gin_file(_lowerCAmelCase		,   _lowerCAmelCase       )
						_lowercase					       =					inference.InferenceModel(args.checkpoint_path		,   _lowerCAmelCase       )

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

						_lowercase					       =					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					       =					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					       =					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					       =					load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]		,   _lowerCAmelCase       )
						_lowercase					       =					load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]		,   _lowerCAmelCase       )
						_lowercase					       =					load_decoder(ta_checkpoint["""target"""]["""decoder"""]		,   _lowerCAmelCase       )

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

						_lowercase					       =					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		: List[Any]								=     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		: Dict								=     parser.parse_args()

		main(args)




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a  			=						{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

       __a  			=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
