

'''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


_UpperCamelCase						:  int							       =				"""base_with_context"""


def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : List[str]			) ->  List[Any]:


    """simple docstring"""


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

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

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

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

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


def       snake_case   (	snake_case      : Optional[int]					,		snake_case      : Any			) ->  Union[str, Any]:


    """simple docstring"""


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

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

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

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

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

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

    return model


def       snake_case   (	snake_case      : Any					,		snake_case      : List[Any]			) ->  str:


    """simple docstring"""


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

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

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

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

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

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

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

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

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

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

    return model


def       snake_case   (	snake_case      : Tuple			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							checkpoints.load_tax_checkpoint(args.checkpoint_path			)
    lowerCAmelCase 						=							jnp.tree_util.tree_map(onp.array					,		snake_case			)

    lowerCAmelCase 						=							[
        """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 						=							os.path.join(args.checkpoint_path					,		'..'					,		'config.gin'			)
    lowerCAmelCase 						=							inference.parse_training_gin_file(snake_case					,		snake_case			)
    lowerCAmelCase 						=							inference.InferenceModel(args.checkpoint_path					,		snake_case			)

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

    lowerCAmelCase 						=							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 						=							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 						=							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 						=							load_notes_encoder(ta_checkpoint['target']['token_encoder']					,		snake_case			)
    lowerCAmelCase 						=							load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']					,		snake_case			)
    lowerCAmelCase 						=							load_decoder(ta_checkpoint['target']['decoder']					,		snake_case			)

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

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


if __name__ == "__main__":
     _UpperCamelCase						:  List[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.",
     )
     _UpperCamelCase						:  int							       =				parser.parse_args()

     main(args)












"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__magic_name__       :					int						=			{
    """configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
    """configuration_data2vec_text""": [
        """DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecTextConfig""",
        """Data2VecTextOnnxConfig""",
    ],
    """configuration_data2vec_vision""": [
        """DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Data2VecVisionConfig""",
        """Data2VecVisionOnnxConfig""",
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__magic_name__       :					List[Any]						=			[
		    """DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecAudioForAudioFrameClassification""",
		    """Data2VecAudioForCTC""",
		    """Data2VecAudioForSequenceClassification""",
		    """Data2VecAudioForXVector""",
		    """Data2VecAudioModel""",
		    """Data2VecAudioPreTrainedModel""",
		]
		__magic_name__       :					Optional[int]						=			[
		    """DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecTextForCausalLM""",
		    """Data2VecTextForMaskedLM""",
		    """Data2VecTextForMultipleChoice""",
		    """Data2VecTextForQuestionAnswering""",
		    """Data2VecTextForSequenceClassification""",
		    """Data2VecTextForTokenClassification""",
		    """Data2VecTextModel""",
		    """Data2VecTextPreTrainedModel""",
		]
		__magic_name__       :					str						=			[
		    """DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Data2VecVisionForImageClassification""",
		    """Data2VecVisionForMaskedImageModeling""",
		    """Data2VecVisionForSemanticSegmentation""",
		    """Data2VecVisionModel""",
		    """Data2VecVisionPreTrainedModel""",
		]

if is_tf_available():
		__magic_name__       :					Optional[Any]						=			[
		    """TFData2VecVisionForImageClassification""",
		    """TFData2VecVisionForSemanticSegmentation""",
		    """TFData2VecVisionModel""",
		    """TFData2VecVisionPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
		from .configuration_dataavec_text import (
		    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecTextConfig,
		    DataaVecTextOnnxConfig,
		)
		from .configuration_dataavec_vision import (
		    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DataaVecVisionConfig,
		    DataaVecVisionOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_dataavec_audio import (
				    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecAudioForAudioFrameClassification,
				    DataaVecAudioForCTC,
				    DataaVecAudioForSequenceClassification,
				    DataaVecAudioForXVector,
				    DataaVecAudioModel,
				    DataaVecAudioPreTrainedModel,
				)
				from .modeling_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecTextForCausalLM,
				    DataaVecTextForMaskedLM,
				    DataaVecTextForMultipleChoice,
				    DataaVecTextForQuestionAnswering,
				    DataaVecTextForSequenceClassification,
				    DataaVecTextForTokenClassification,
				    DataaVecTextModel,
				    DataaVecTextPreTrainedModel,
				)
				from .modeling_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DataaVecVisionForImageClassification,
				    DataaVecVisionForMaskedImageModeling,
				    DataaVecVisionForSemanticSegmentation,
				    DataaVecVisionModel,
				    DataaVecVisionPreTrainedModel,
				)
		if is_tf_available():
				from .modeling_tf_dataavec_vision import (
				    TFDataaVecVisionForImageClassification,
				    TFDataaVecVisionForSemanticSegmentation,
				    TFDataaVecVisionModel,
				    TFDataaVecVisionPreTrainedModel,
				)

else:
		import sys

		__magic_name__       :					Optional[Any]						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



