
'''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


_lowercase			:  Union[str, Any]   							=	"base_with_context"
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Dict				,				__SCREAMING_SNAKE_CASE :			Any							):



						"""simple docstring"""





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

												lowercase_     :					int													=	ly_weight['''attention''']
												lowercase_     :					Optional[int]													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T							)							)
												lowercase_     :					Optional[int]													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T							)							)
												lowercase_     :					Any													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T							)							)
												lowercase_     :					Dict													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T							)							)

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

												lowercase_     :					str													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T							)							)
												lowercase_     :					Any													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T							)							)
												lowercase_     :					Any													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T							)							)

						lowercase_     :					List[Any]													=	nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']							)							)
						return model
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[int]				,				__SCREAMING_SNAKE_CASE :			List[str]							):



						"""simple docstring"""





						lowercase_     :					Optional[Any]													=	nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T							)							)

						lowercase_     :					Any													=	nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']							)				,				requires_grad=__SCREAMING_SNAKE_CASE							)

						for lyr_num, lyr in enumerate(model.encoders							):
												lowercase_     :					Tuple													=	weights[F'''layers_{lyr_num}''']
												lowercase_     :					List[str]													=	ly_weight['''attention''']

												lowercase_     :					Union[str, Any]													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T							)							)
												lowercase_     :					List[str]													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T							)							)
												lowercase_     :					List[Any]													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T							)							)
												lowercase_     :					Optional[int]													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T							)							)
												lowercase_     :					int													=	nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']							)							)

												lowercase_     :					List[Any]													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T							)							)
												lowercase_     :					str													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T							)							)
												lowercase_     :					Union[str, Any]													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T							)							)
												lowercase_     :					Any													=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']							)							)

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

						return model
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			Optional[Any]				,				__SCREAMING_SNAKE_CASE :			Optional[Any]							):



						"""simple docstring"""





						lowercase_     :					Optional[Any]													=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T							)							)
						lowercase_     :					Dict													=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T							)							)

						lowercase_     :					Dict													=	nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']							)				,				requires_grad=__SCREAMING_SNAKE_CASE							)

						lowercase_     :					Dict													=	nn.Parameter(
						    torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T							)							)

						for lyr_num, lyr in enumerate(model.decoders							):
												lowercase_     :					List[Any]													=	weights[F'''layers_{lyr_num}''']
												lowercase_     :					Dict													=	nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']							)							)

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

												lowercase_     :					Dict													=	ly_weight['''self_attention''']
												lowercase_     :					List[str]													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T							)							)
												lowercase_     :					int													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T							)							)
												lowercase_     :					Dict													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T							)							)
												lowercase_     :					int													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T							)							)

												lowercase_     :					Dict													=	ly_weight['''MultiHeadDotProductAttention_0''']
												lowercase_     :					Any													=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T							)							)
												lowercase_     :					Dict													=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T							)							)
												lowercase_     :					List[str]													=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T							)							)
												lowercase_     :					List[Any]													=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T							)							)
												lowercase_     :					Union[str, Any]													=	nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']							)							)

												lowercase_     :					Union[str, Any]													=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']							)							)
												lowercase_     :					Any													=	nn.Parameter(
												    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T							)							)
												lowercase_     :					str													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T							)							)
												lowercase_     :					Dict													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T							)							)
												lowercase_     :					Union[str, Any]													=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T							)							)

						lowercase_     :					Optional[int]													=	nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']							)							)

						lowercase_     :					Tuple													=	nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T							)							)

						return model
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			str							):



						"""simple docstring"""





						lowercase_     :					List[str]													=	checkpoints.load_tax_checkpoint(args.checkpoint_path							)
						lowercase_     :					int													=	jnp.tree_util.tree_map(onp.array				,				__SCREAMING_SNAKE_CASE							)

						lowercase_     :					Union[str, Any]													=	[
						    '''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_     :					Optional[int]													=	os.path.join(args.checkpoint_path				,				'''..'''				,				'''config.gin'''							)
						lowercase_     :					Union[str, Any]													=	inference.parse_training_gin_file(__SCREAMING_SNAKE_CASE				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Union[str, Any]													=	inference.InferenceModel(args.checkpoint_path				,				__SCREAMING_SNAKE_CASE							)

						lowercase_     :					Optional[int]													=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''				,				variance_type='''fixed_large'''							)

						lowercase_     :					int													=	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_     :					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'''				,				)

						lowercase_     :					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				,				)

						lowercase_     :					Optional[Any]													=	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					Optional[int]													=	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,				__SCREAMING_SNAKE_CASE							)
						lowercase_     :					str													=	load_decoder(ta_checkpoint['''target''']['''decoder''']				,				__SCREAMING_SNAKE_CASE							)

						lowercase_     :					List[str]													=	OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''							)

						lowercase_     :					int													=	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__":
							_lowercase			:  int   							=	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.",
							)
							_lowercase			:  Dict   							=	parser.parse_args()

							main(args)





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase			:  List[Any]   							=	{
    "configuration_x_clip": [
        "XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XCLIPConfig",
        "XCLIPTextConfig",
        "XCLIPVisionConfig",
    ],
    "processing_x_clip": ["XCLIPProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase			:  int   							=	[
							    "XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "XCLIPModel",
							    "XCLIPPreTrainedModel",
							    "XCLIPTextModel",
							    "XCLIPVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_x_clip import (
							    XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XCLIPConfig,
							    XCLIPTextConfig,
							    XCLIPVisionConfig,
							)
							from .processing_x_clip import XCLIPProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_x_clip import (
														    XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XCLIPModel,
														    XCLIPPreTrainedModel,
														    XCLIPTextModel,
														    XCLIPVisionModel,
														)

else:
							import sys

							_lowercase			:  List[str]   							=	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



