"""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


__A	=					'''base_with_context'''


def  lowercase_ ( _lowerCamelCase:						Optional[Any]    , _lowerCamelCase:						str   )			->   List[str]:



							'''simple docstring'''

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

														__lowerCamelCase       :		Any		 =  ly_weight['''attention''']
														__lowerCamelCase       :		List[Any]		 =  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T   )   )
														__lowerCamelCase       :		Any		 =  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T   )   )
														__lowerCamelCase       :		str		 =  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T   )   )
														__lowerCamelCase       :		Dict		 =  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T   )   )

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

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

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


def  lowercase_ ( _lowerCamelCase:						Union[str, Any]    , _lowerCamelCase:						Dict   )			->   Union[str, Any]:



							'''simple docstring'''

							__lowerCamelCase       :		Any		 =  nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T   )   )

							__lowerCamelCase       :		List[str]		 =  nn.Parameter(
							    torch.FloatTensor(weights["Embed_0"]["embedding"]   )    , requires_grad=__A   )

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

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

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

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

							return model


def  lowercase_ ( _lowerCamelCase:						List[Any]    , _lowerCamelCase:						Any   )			->   Optional[Any]:



							'''simple docstring'''

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

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

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

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

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

														__lowerCamelCase       :		Any		 =  ly_weight['''self_attention''']
														__lowerCamelCase       :		List[str]		 =  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T   )   )
														__lowerCamelCase       :		str		 =  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T   )   )
														__lowerCamelCase       :		Optional[Any]		 =  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T   )   )
														__lowerCamelCase       :		Tuple		 =  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T   )   )

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

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

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

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

							return model


def  lowercase_ ( _lowerCamelCase:						Tuple   )			->   Tuple:



							'''simple docstring'''

							__lowerCamelCase       :		str		 =  checkpoints.load_tax_checkpoint(args.checkpoint_path   )
							__lowerCamelCase       :		Optional[Any]		 =  jnp.tree_util.tree_map(onp.array    , __A   )

							__lowerCamelCase       :		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()''',
							]

							__lowerCamelCase       :		int		 =  os.path.join(args.checkpoint_path    , ".."    , "config.gin"   )
							__lowerCamelCase       :		Union[str, Any]		 =  inference.parse_training_gin_file(__A    , __A   )
							__lowerCamelCase       :		Union[str, Any]		 =  inference.InferenceModel(args.checkpoint_path    , __A   )

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

							__lowerCamelCase       :		Optional[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"    , )

							__lowerCamelCase       :		Optional[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"    , )

							__lowerCamelCase       :		Optional[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    , )

							__lowerCamelCase       :		str		 =  load_notes_encoder(ta_checkpoint["target"]["token_encoder"]    , __A   )
							__lowerCamelCase       :		Dict		 =  load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]    , __A   )
							__lowerCamelCase       :		Any		 =  load_decoder(ta_checkpoint["target"]["decoder"]    , __A   )

							__lowerCamelCase       :		Optional[Any]		 =  OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"   )

							__lowerCamelCase       :		int		 =  SpectrogramDiffusionPipeline(
							    notes_encoder=__A    , continuous_encoder=__A    , decoder=__A    , scheduler=__A    , melgan=__A    , )
							if args.save:
														pipe.save_pretrained(args.output_path   )


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

				main(args)







'''simple docstring'''



from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
      from .deepspeed import (
          DeepSpeedEngineWrapper,
          DeepSpeedOptimizerWrapper,
          DeepSpeedSchedulerWrapper,
          DummyOptim,
          DummyScheduler,
          HfDeepSpeedConfig,
      )

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers



