
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    :	Union[str, Any]						=						"base_with_context"


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Tuple:
    __lowercase						:	Tuple					=				nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']		)		)
    __lowercase						:	int					=				nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)		,    requires_grad=UpperCamelCase__		)
    for lyr_num, lyr in enumerate(model.encoders		):
        __lowercase						:	Tuple					=				weights[F'layers_{lyr_num}']
        __lowercase						:	Any					=				nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']		)		)

        __lowercase						:	Optional[int]					=				ly_weight["attention"]
        __lowercase						:	str					=				nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
        __lowercase						:	Optional[int]					=				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						:	List[str]					=				nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']		)		)

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

    __lowercase						:	str					=				nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']		)		)
    return model


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Union[str, Any]:
    __lowercase						:	Any					=				nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T		)		)

    __lowercase						:	List[Any]					=				nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)		,    requires_grad=UpperCamelCase__		)

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

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

        __lowercase						:	Union[str, Any]					=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
        __lowercase						:	Tuple					=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
        __lowercase						:	str					=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)
        __lowercase						:	Dict					=				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 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Any:
    __lowercase						:	Optional[int]					=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T		)		)
    __lowercase						:	Any					=				nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T		)		)

    __lowercase						:	List[str]					=				nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)		,    requires_grad=UpperCamelCase__		)

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

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

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

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

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

        __lowercase						:	int					=				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						:	List[Any]					=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
        __lowercase						:	Optional[int]					=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
        __lowercase						:	int					=				nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)

    __lowercase						:	Tuple					=				nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']		)		)

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

    return model


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Optional[Any]:
    __lowercase						:	Dict					=				checkpoints.load_tax_checkpoint(args.checkpoint_path		)
    __lowercase						:	Dict					=				jnp.tree_util.tree_map(onp.array		,    UpperCamelCase__		)

    __lowercase						:	List[str]					=				[
        "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						:	str					=				os.path.join(args.checkpoint_path		,    '''..'''		,    '''config.gin'''		)
    __lowercase						:	Tuple					=				inference.parse_training_gin_file(UpperCamelCase__		,    UpperCamelCase__		)
    __lowercase						:	int					=				inference.InferenceModel(args.checkpoint_path		,    UpperCamelCase__		)

    __lowercase						:	List[str]					=				DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		,    variance_type='''fixed_large'''		)

    __lowercase						:	List[str]					=				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						:	Any					=				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						:	Dict					=				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						:	List[Any]					=				load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']		,    UpperCamelCase__		)
    __lowercase						:	List[Any]					=				load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']		,    UpperCamelCase__		)
    __lowercase						:	int					=				load_decoder(ta_checkpoint['''target''']['''decoder''']		,    UpperCamelCase__		)

    __lowercase						:	Tuple					=				OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''		)

    __lowercase						:	List[Any]					=				SpectrogramDiffusionPipeline(
        notes_encoder=UpperCamelCase__		,    continuous_encoder=UpperCamelCase__		,    decoder=UpperCamelCase__		,    scheduler=UpperCamelCase__		,    melgan=UpperCamelCase__		,    )
    if args.save:
        pipe.save_pretrained(args.output_path		)


if __name__ == "__main__":
 __lowerCAmelCase    :	Union[str, 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.",
 )
 __lowerCAmelCase    :	Any						=						parser.parse_args()

 main(args)






# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCAmelCase__      		= logging.get_logger(__name__)

lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
lowerCAmelCase__      		= {}
def 							_UpperCAmelCase				(UpperCamelCase__     :  type				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None				,							):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       if format_type in _FORMAT_TYPES:
              logger.warning(
                  f"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"						)
       _A      :       Dict				    = formatter_cls
       for alias in set(aliases + [format_type]						):
              if alias in _FORMAT_TYPES_ALIASES:
                     logger.warning(
                         f"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"						)
              _A      :       Dict				    = format_type
def 							_UpperCAmelCase				(UpperCamelCase__     :  Exception				,							UpperCamelCase__     :  Optional[str]				,							UpperCamelCase__     :  Optional[List[str]] = None						):
       _A      :       Union[str, Any]				    = aliases if aliases is not None else []
       for alias in set(aliases + [format_type]						):
              _A      :       Union[str, Any]				    = unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
    from .torch_formatter import TorchFormatter

    _register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
    lowerCAmelCase__      		= ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
    _register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
    from .tf_formatter import TFFormatter

    _register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
    lowerCAmelCase__      		= ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
    _register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
    from .jax_formatter import JaxFormatter

    _register_formatter(JaxFormatter, 'jax', aliases=[])
else:
    lowerCAmelCase__      		= ValueError('JAX needs to be installed to be able to return JAX arrays.')
    _register_unavailable_formatter(_jax_error, 'jax', aliases=[])
def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]						):
       if format_type in _FORMAT_TYPES_ALIASES:
              return _FORMAT_TYPES_ALIASES[format_type]
       else:
              return format_type






def 							_UpperCAmelCase				(UpperCamelCase__     :  Optional[str]				,							**UpperCamelCase__     :  List[Any]						):
       _A      :       List[str]				    = get_format_type_from_alias(UpperCamelCase__						)
       if format_type in _FORMAT_TYPES:
              return _FORMAT_TYPES[format_type](**UpperCamelCase__						)
       if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
              raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
       else:
              raise ValueError(
                  f"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None						)}, but got '{format_type}'"						)
