


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


lowerCAmelCase	=							"""base_with_context"""
def       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
		lowercase__						=				nn.Parameter(
		    torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)						,    requires_grad=SCREAMING_SNAKE_CASE_					)
		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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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

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

		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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		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=SCREAMING_SNAKE_CASE_					)

		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       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		lowercase__						=				checkpoints.load_tax_checkpoint(args.checkpoint_path					)
		lowercase__						=				jnp.tree_util.tree_map(onp.array						,    SCREAMING_SNAKE_CASE_					)

		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(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_					)
		lowercase__						=				inference.InferenceModel(args.checkpoint_path						,    SCREAMING_SNAKE_CASE_					)

		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''']						,    SCREAMING_SNAKE_CASE_					)
		lowercase__						=				load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']						,    SCREAMING_SNAKE_CASE_					)
		lowercase__						=				load_decoder(ta_checkpoint['''target''']['''decoder''']						,    SCREAMING_SNAKE_CASE_					)

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

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


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

						main(args)






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


lowerCAmelCase	=							logging.get_logger(__name__)

lowerCAmelCase	=							{
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      List[Any]  				=			'''lxmert'''
							_lowercase     :      Any  				=			{}

							def __init__(							self:     Any   ,						UpperCamelCase_:     List[Any]=30_522   ,						UpperCamelCase_:     int=768   ,						UpperCamelCase_:     Optional[Any]=12   ,						UpperCamelCase_:     Dict=9_500   ,						UpperCamelCase_:     List[Any]=1_600   ,						UpperCamelCase_:     List[Any]=400   ,						UpperCamelCase_:     List[str]=3_072   ,						UpperCamelCase_:     int="gelu"   ,						UpperCamelCase_:     Any=0.1   ,						UpperCamelCase_:     int=0.1   ,						UpperCamelCase_:     List[str]=512   ,						UpperCamelCase_:     Optional[int]=2   ,						UpperCamelCase_:     Union[str, Any]=0.02   ,						UpperCamelCase_:     Dict=1E-1_2   ,						UpperCamelCase_:     List[Any]=9   ,						UpperCamelCase_:     List[Any]=5   ,						UpperCamelCase_:     Optional[Any]=5   ,						UpperCamelCase_:     str=2_048   ,						UpperCamelCase_:     Dict=4   ,						UpperCamelCase_:     Any=6.67   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     List[str]=True   ,						UpperCamelCase_:     Dict=True   ,						UpperCamelCase_:     Any=True   ,						UpperCamelCase_:     Tuple=True   ,						UpperCamelCase_:     Optional[Any]=True   ,						UpperCamelCase_:     Any=True   ,						**UpperCamelCase_:     Union[str, Any]   ,						)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				vocab_size
									lowercase__						=				hidden_size
									lowercase__						=				num_attention_heads
									lowercase__						=				hidden_act
									lowercase__						=				intermediate_size
									lowercase__						=				hidden_dropout_prob
									lowercase__						=				attention_probs_dropout_prob
									lowercase__						=				max_position_embeddings
									lowercase__						=				type_vocab_size
									lowercase__						=				initializer_range
									lowercase__						=				layer_norm_eps
									lowercase__						=				num_qa_labels
									lowercase__						=				num_object_labels
									lowercase__						=				num_attr_labels
									lowercase__						=				l_layers
									lowercase__						=				x_layers
									lowercase__						=				r_layers
									lowercase__						=				visual_feat_dim
									lowercase__						=				visual_pos_dim
									lowercase__						=				visual_loss_normalizer
									lowercase__						=				task_matched
									lowercase__						=				task_mask_lm
									lowercase__						=				task_obj_predict
									lowercase__						=				task_qa
									lowercase__						=				visual_obj_loss
									lowercase__						=				visual_attr_loss
									lowercase__						=				visual_feat_loss
									lowercase__						=				{'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
									super().__init__(**UpperCamelCase_							)


