



'''simple docstring'''




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


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

UpperCamelCase__       :	Tuple    = {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		Tuple			    =		'''switch_transformers'''
			_A      :		Tuple			    =		['''past_key_values''']
			_A      :		int			    =		{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

			def __init__(							self     : List[str]							,     lowerCAmelCase__     : Tuple=3_2_1_2_8							,     lowerCAmelCase__     : Tuple=7_6_8							,     lowerCAmelCase__     : Any=6_4							,     lowerCAmelCase__     : Any=2_0_4_8							,     lowerCAmelCase__     : Dict=6_4							,     lowerCAmelCase__     : Union[str, Any]=1_2							,     lowerCAmelCase__     : Union[str, Any]=3							,     lowerCAmelCase__     : int=1_2							,     lowerCAmelCase__     : Any=3							,     lowerCAmelCase__     : int=1_2							,     lowerCAmelCase__     : str=8							,     lowerCAmelCase__     : List[str]=False							,     lowerCAmelCase__     : Dict=0.01							,     lowerCAmelCase__     : Dict="float32"							,     lowerCAmelCase__     : List[str]=False							,     lowerCAmelCase__     : Any=3_2							,     lowerCAmelCase__     : List[Any]=1_2_8							,     lowerCAmelCase__     : Union[str, Any]=0.1							,     lowerCAmelCase__     : Dict=1E-6							,     lowerCAmelCase__     : Tuple=0.0_01							,     lowerCAmelCase__     : Dict=0.0_01							,     lowerCAmelCase__     : Optional[int]=1.0							,     lowerCAmelCase__     : Union[str, Any]="relu"							,     lowerCAmelCase__     : Union[str, Any]=True							,     lowerCAmelCase__     : Union[str, Any]=False							,     lowerCAmelCase__     : Optional[int]=True							,     lowerCAmelCase__     : List[Any]=0							,     lowerCAmelCase__     : List[Any]=1							,     **lowerCAmelCase__     : Union[str, Any]							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		vocab_size
					__SCREAMING_SNAKE_CASE       :		str           =		d_model
					__SCREAMING_SNAKE_CASE       :		Tuple           =		d_kv
					__SCREAMING_SNAKE_CASE       :		int           =		d_ff

					__SCREAMING_SNAKE_CASE       :		int           =		num_sparse_encoder_layers

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		num_layers
					__SCREAMING_SNAKE_CASE       :		str           =		(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					__SCREAMING_SNAKE_CASE       :		Dict           =		num_sparse_decoder_layers

					# This tells us, each how many encoder layer we'll have to set a sparse layer.
					if self.num_sparse_encoder_layers > 0:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.num_layers // self.num_sparse_encoder_layers
					else:
							__SCREAMING_SNAKE_CASE       :		str           =		self.num_layers  # HACK: this will create 0 sparse layers

					# This tells us, each how many encoder layer we'll have to set a sparse layer.
					if self.num_sparse_decoder_layers > 0:
							__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		self.num_decoder_layers // self.num_sparse_decoder_layers
					else:
							__SCREAMING_SNAKE_CASE       :		int           =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

					__SCREAMING_SNAKE_CASE       :		str           =		num_heads
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		num_experts
					__SCREAMING_SNAKE_CASE       :		List[str]           =		expert_capacity
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		router_bias
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		router_jitter_noise
					if router_dtype not in ["float32", "float16", "bfloat16"]:
							raise ValueError(F"`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		router_dtype

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		router_ignore_padding_tokens
					__SCREAMING_SNAKE_CASE       :		Any           =		relative_attention_num_buckets
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		relative_attention_max_distance

					__SCREAMING_SNAKE_CASE       :		Any           =		dropout_rate
					__SCREAMING_SNAKE_CASE       :		List[str]           =		layer_norm_epsilon
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		initializer_factor
					__SCREAMING_SNAKE_CASE       :		Dict           =		feed_forward_proj
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		use_cache
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		add_router_probs

					__SCREAMING_SNAKE_CASE       :		Any           =		router_z_loss_coef
					__SCREAMING_SNAKE_CASE       :		Tuple           =		router_aux_loss_coef

					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.feed_forward_proj.split("""-"""						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		act_info[-1]
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		act_info[0] == """gated"""

					if len(lowerCAmelCase__						) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__						) > 2:
							raise ValueError(
							    F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
							    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
							    """'gated-gelu' or 'relu'"""						)

					# for backwards compatibility
					if feed_forward_proj == "gated-gelu":
							__SCREAMING_SNAKE_CASE       :		List[str]           =		"""gelu_new"""

					super().__init__(
					    pad_token_id=lowerCAmelCase__							,     eos_token_id=lowerCAmelCase__							,     is_encoder_decoder=lowerCAmelCase__							,     **lowerCAmelCase__							,     )




'''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__       :	List[Any]    = '''base_with_context'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Dict					):
		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)
		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

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


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   List[str]					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]

				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

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

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

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T					)					)
		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T					)					)

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

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T					)					)

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

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

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

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

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

		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]					)					)

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

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		int           =		checkpoints.load_tax_checkpoint(args.checkpoint_path					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		jnp.tree_util.tree_map(onp.array       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		int           =		[
		    """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()""",
		]

		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		os.path.join(args.checkpoint_path       ,      """.."""       ,      """config.gin"""					)
		__SCREAMING_SNAKE_CASE       :		Any           =		inference.parse_training_gin_file(_lowerCamelCase       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		inference.InferenceModel(args.checkpoint_path       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""       ,      variance_type="""fixed_large"""					)

		__SCREAMING_SNAKE_CASE       :		List[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"""       ,      )

		__SCREAMING_SNAKE_CASE       :		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"""       ,      )

		__SCREAMING_SNAKE_CASE       :		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       ,      )

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

		__SCREAMING_SNAKE_CASE       :		str           =		OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""					)

		__SCREAMING_SNAKE_CASE       :		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__       :	Tuple    = 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__       :	List[str]    = parser.parse_args()

						main(args)