






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      :    List[str]   		=   """base_with_context"""







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]  ,						snake_case_      :							Any     ):
	snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]     )     )
	snake_case__				:   List[str]     =						nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"]     )  ,						requires_grad=snake_case_     )
	for lyr_num, lyr in enumerate(model.encoders     ):
		snake_case__				:   str     =						weights[F'''layers_{lyr_num}''']
		snake_case__				:   Union[str, Any]     =						nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]     )     )

		snake_case__				:   Union[str, Any]     =						ly_weight["attention"]
		snake_case__				:   Optional[int]     =						nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T     )     )
		snake_case__				:   Optional[int]     =						nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T     )     )
		snake_case__				:   Tuple     =						nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T     )     )
		snake_case__				:   Tuple     =						nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T     )     )

		snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]     )     )

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

	snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]     )     )
	return model







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							str     ):
	snake_case__				:   int     =						nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T     )     )

	snake_case__				:   Tuple     =						nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"]     )  ,						requires_grad=snake_case_     )

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

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

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

	snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]     )     )

	return model







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]  ,						snake_case_      :							Optional[Any]     ):
	snake_case__				:   Any     =						nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T     )     )
	snake_case__				:   str     =						nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T     )     )

	snake_case__				:   Union[str, Any]     =						nn.Parameter(
	    torch.FloatTensor(weights["Embed_0"]["embedding"]     )  ,						requires_grad=snake_case_     )

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

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

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

		snake_case__				:   Optional[Any]     =						ly_weight["self_attention"]
		snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T     )     )
		snake_case__				:   int     =						nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T     )     )
		snake_case__				:   List[Any]     =						nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T     )     )
		snake_case__				:   Dict     =						nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T     )     )

		snake_case__				:   List[Any]     =						ly_weight["MultiHeadDotProductAttention_0"]
		snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T     )     )
		snake_case__				:   Optional[int]     =						nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T     )     )
		snake_case__				:   List[Any]     =						nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T     )     )
		snake_case__				:   Union[str, Any]     =						nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T     )     )
		snake_case__				:   List[str]     =						nn.Parameter(
		    torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]     )     )

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

	snake_case__				:   Tuple     =						nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]     )     )

	snake_case__				:   Optional[Any]     =						nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T     )     )

	return model







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Union[str, Any]     ):
	snake_case__				:   List[Any]     =						checkpoints.load_tax_checkpoint(args.checkpoint_path     )
	snake_case__				:   Any     =						jnp.tree_util.tree_map(onp.array  ,						snake_case_     )

	snake_case__				:   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()",
	]

	snake_case__				:   int     =						os.path.join(args.checkpoint_path  ,						".."  ,						"config.gin"     )
	snake_case__				:   Optional[int]     =						inference.parse_training_gin_file(snake_case_  ,						snake_case_     )
	snake_case__				:   str     =						inference.InferenceModel(args.checkpoint_path  ,						snake_case_     )

	snake_case__				:   str     =						DDPMScheduler(beta_schedule="squaredcos_cap_v2"  ,						variance_type="fixed_large"     )

	snake_case__				:   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"  ,						)

	snake_case__				:   Optional[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"  ,						)

	snake_case__				:   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  ,						)

	snake_case__				:   Union[str, Any]     =						load_notes_encoder(ta_checkpoint["target"]["token_encoder"]  ,						snake_case_     )
	snake_case__				:   List[str]     =						load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]  ,						snake_case_     )
	snake_case__				:   Union[str, Any]     =						load_decoder(ta_checkpoint["target"]["decoder"]  ,						snake_case_     )

	snake_case__				:   Optional[int]     =						OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"     )

	snake_case__				:   str     =						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__":
	__lowerCamelCase      :    List[str]   		=   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      :    List[str]   		=   parser.parse_args()

	main(args)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase      :    List[str]   		=   {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   ["""XLNetTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   ["""XLNetTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    str   		=   [
	    """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLNetForMultipleChoice""",
	    """XLNetForQuestionAnswering""",
	    """XLNetForQuestionAnsweringSimple""",
	    """XLNetForSequenceClassification""",
	    """XLNetForTokenClassification""",
	    """XLNetLMHeadModel""",
	    """XLNetModel""",
	    """XLNetPreTrainedModel""",
	    """load_tf_weights_in_xlnet""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase      :    Dict   		=   [
	    """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLNetForMultipleChoice""",
	    """TFXLNetForQuestionAnsweringSimple""",
	    """TFXLNetForSequenceClassification""",
	    """TFXLNetForTokenClassification""",
	    """TFXLNetLMHeadModel""",
	    """TFXLNetMainLayer""",
	    """TFXLNetModel""",
	    """TFXLNetPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet import XLNetTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlnet_fast import XLNetTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlnet import (
		    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLNetForMultipleChoice,
		    XLNetForQuestionAnswering,
		    XLNetForQuestionAnsweringSimple,
		    XLNetForSequenceClassification,
		    XLNetForTokenClassification,
		    XLNetLMHeadModel,
		    XLNetModel,
		    XLNetPreTrainedModel,
		    load_tf_weights_in_xlnet,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlnet import (
		    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLNetForMultipleChoice,
		    TFXLNetForQuestionAnsweringSimple,
		    TFXLNetForSequenceClassification,
		    TFXLNetForTokenClassification,
		    TFXLNetLMHeadModel,
		    TFXLNetMainLayer,
		    TFXLNetModel,
		    TFXLNetPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase      :    int   		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



