


"""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


__snake_case		=       'base_with_context'
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					Union[str, Any]       ):






					"""simple docstring"""

					_a    	=				nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']       )       )
					_a    	=				nn.Parameter(
					    torch.FloatTensor(weights['''Embed_0''']['''embedding''']       ),     requires_grad=_lowercase       )
					for lyr_num, lyr in enumerate(model.encoders       ):
										_a    	=				weights[f'layers_{lyr_num}']
										_a    	=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']       )       )

										_a    	=				ly_weight['''attention''']
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T       )       )

										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']       )       )

										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T       )       )

					_a    	=				nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']       )       )
					return model
def       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[Any]       ):






					"""simple docstring"""

					_a    	=				nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T       )       )

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

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

										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']       )       )

										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']       )       )

					_a    	=				nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']       )       )

					return model
def       A_   (   _lowerCAmelCase     :					Tuple,     _lowerCAmelCase     :					Dict       ):






					"""simple docstring"""

					_a    	=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T       )       )
					_a    	=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T       )       )

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

					_a    	=				nn.Parameter(
					    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T       )       )

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

										_a    	=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T       )       )

										_a    	=				ly_weight['''self_attention''']
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T       )       )

										_a    	=				ly_weight['''MultiHeadDotProductAttention_0''']
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']       )       )

										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']       )       )
										_a    	=				nn.Parameter(
										    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T       )       )
										_a    	=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T       )       )

					_a    	=				nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']       )       )

					_a    	=				nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T       )       )

					return model
def       A_   (   _lowerCAmelCase     :					Tuple       ):






					"""simple docstring"""

					_a    	=				checkpoints.load_tax_checkpoint(args.checkpoint_path       )
					_a    	=				jnp.tree_util.tree_map(onp.array,     _lowercase       )

					_a    	=				[
					    '''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()''',
					]

					_a    	=				os.path.join(args.checkpoint_path,     '''..''',     '''config.gin'''       )
					_a    	=				inference.parse_training_gin_file(_lowercase,     _lowercase       )
					_a    	=				inference.InferenceModel(args.checkpoint_path,     _lowercase       )

					_a    	=				DDPMScheduler(beta_schedule='''squaredcos_cap_v2''',     variance_type='''fixed_large'''       )

					_a    	=				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''',     )

					_a    	=				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''',     )

					_a    	=				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,     )

					_a    	=				load_notes_encoder(ta_checkpoint['''target''']['''token_encoder'''],     _lowercase       )
					_a    	=				load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder'''],     _lowercase       )
					_a    	=				load_decoder(ta_checkpoint['''target''']['''decoder'''],     _lowercase       )

					_a    	=				OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''       )

					_a    	=				SpectrogramDiffusionPipeline(
					    notes_encoder=_lowercase,     continuous_encoder=_lowercase,     decoder=_lowercase,     scheduler=_lowercase,     melgan=_lowercase,     )
					if args.save:
										pipe.save_pretrained(args.output_path       )


if __name__ == "__main__":
			__snake_case		=       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.''',
			)
			__snake_case		=       parser.parse_args()

			main(args)



"""simple docstring"""






import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[Any],     _lowerCAmelCase     :					Union[str, Any]       ):






     """simple docstring"""

     _a    	=				BertConfig.from_json_file(_lowerCAmelCase       )
     print(f'Building PyTorch model from configuration: {config}'       )
     _a    	=				BertForPreTraining(_lowerCAmelCase       )

     # Load weights from tf checkpoint
     load_tf_weights_in_bert(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       )

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'       )
     torch.save(model.state_dict(),     _lowerCAmelCase       )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case		=       parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)