"""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


__SCREAMING_SNAKE_CASE						:   int         =			'base_with_context'
def  snake_case						(__lowercase ,      __lowercase						)	->	Optional[int]:

						'''simple docstring'''



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

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

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

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

						_snake_case						:					Optional[int]    =   nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]						)						)
						return model
def  snake_case						(__lowercase ,      __lowercase						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T						)						)

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

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

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

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

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

						return model
def  snake_case						(__lowercase ,      __lowercase						)	->	List[Any]:

						'''simple docstring'''



						_snake_case						:					Optional[Any]    =   nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T						)						)
						_snake_case						:					Union[str, Any]    =   nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T						)						)

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

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

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

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

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

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

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

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

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

						return model
def  snake_case						(__lowercase						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					str    =   checkpoints.load_tax_checkpoint(args.checkpoint_path						)
						_snake_case						:					List[Any]    =   jnp.tree_util.tree_map(onp.array ,      _lowerCAmelCase						)

						_snake_case						:					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()""",
						]

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

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

						_snake_case						:					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" ,      )

						_snake_case						:					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" ,      )

						_snake_case						:					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 ,      )

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

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

						_snake_case						:					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__":
				__SCREAMING_SNAKE_CASE						:   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.',
				)
				__SCREAMING_SNAKE_CASE						:   Tuple         =			parser.parse_args()

				main(args)
import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						_snake_case						:					int    =   args.pruning_method
						_snake_case						:					List[Any]    =   args.threshold

						_snake_case						:					Optional[Any]    =   args.model_name_or_path.rstrip("/"						)
						_snake_case						:					List[str]    =   args.target_model_path

						print(F"""Load fine-pruned model from {model_name_or_path}"""						)
						_snake_case						:					List[Any]    =   torch.load(os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						_snake_case						:					List[str]    =   {}

						for name, tensor in model.items():
												if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																		_snake_case						:					Tuple    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "classifier" in name or "qa_output" in name:
																		_snake_case						:					Optional[int]    =   tensor
																		print(F"""Copied layer {name}"""						)
												elif "bias" in name:
																		_snake_case						:					List[Any]    =   tensor
																		print(F"""Copied layer {name}"""						)
												else:
																		if pruning_method == "magnitude":
																								_snake_case						:					Tuple    =   MagnitudeBinarizer.apply(inputs=__lowercase ,      threshold=__lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "topK":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					Optional[Any]    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					Tuple    =   TopKBinarizer.apply(__lowercase ,      __lowercase						)
																								_snake_case						:					Optional[Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "sigmoied_threshold":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					List[Any]    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case						:					List[str]    =   ThresholdBinarizer.apply(__lowercase ,      __lowercase ,      __lowercase						)
																								_snake_case						:					List[str]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		elif pruning_method == "l0":
																								if "mask_scores" in name:
																														continue
																								_snake_case						:					int    =   name[:-6]
																								_snake_case						:					Any    =   model[F"""{prefix_}mask_scores"""]
																								_snake_case    ,_snake_case						:					Union[str, Any]    =   -0.1, 1.1
																								_snake_case						:					Dict    =   torch.sigmoid(__lowercase						)
																								_snake_case						:					List[str]    =   s * (r - l) + l
																								_snake_case						:					Tuple    =   s_bar.clamp(min=0.0 ,      max=1.0						)
																								_snake_case						:					Union[str, Any]    =   tensor * mask
																								print(F"""Pruned layer {name}"""						)
																		else:
																								raise ValueError("Unknown pruning method"						)

						if target_model_path is None:
												_snake_case						:					Any    =   os.path.join(
												    os.path.dirname(__lowercase						) ,      F"""bertarized_{os.path.basename(__lowercase						)}"""						)

						if not os.path.isdir(__lowercase						):
												shutil.copytree(__lowercase ,      __lowercase						)
												print(F"""\nCreated folder {target_model_path}"""						)

						torch.save(__lowercase ,      os.path.join(__lowercase ,      "pytorch_model.bin"						)						)
						print("\nPruned model saved! See you later!"						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:   List[Any]         =			argparse.ArgumentParser()

				parser.add_argument(
				    '--pruning_method',
				    choices=['l0', 'magnitude', 'topK', 'sigmoied_threshold'],
				    type=str,
				    required=True,
				    help=(
				        'Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,'
				        ' sigmoied_threshold = Soft movement pruning)'
				    ),
				)
				parser.add_argument(
				    '--threshold',
				    type=float,
				    required=False,
				    help=(
				        'For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
				        'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
				        'Not needed for `l0`'
				    ),
				)
				parser.add_argument(
				    '--model_name_or_path',
				    type=str,
				    required=True,
				    help='Folder containing the model that was previously fine-pruned',
				)
				parser.add_argument(
				    '--target_model_path',
				    default=None,
				    type=str,
				    required=False,
				    help='Folder containing the model that was previously fine-pruned',
				)

				__SCREAMING_SNAKE_CASE						:   Union[str, Any]         =			parser.parse_args()

				main(args)