




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						:							Union[str, Any]							=  """base_with_context"""
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]   )   )
			SCREAMING_SNAKE_CASE           =					nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]   )					,	requires_grad=SCREAMING_SNAKE_CASE   )
			for lyr_num, lyr in enumerate(model.encoders   ):
						SCREAMING_SNAKE_CASE           =					weights[f"""layers_{lyr_num}"""]
						SCREAMING_SNAKE_CASE           =					nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]   )   )

						SCREAMING_SNAKE_CASE           =					ly_weight["""attention"""]
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )

						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]   )   )

						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T   )   )

			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]   )   )
			return model
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T   )   )

			SCREAMING_SNAKE_CASE           =					nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]   )					,	requires_grad=SCREAMING_SNAKE_CASE   )

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

						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]   )   )

						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]   )   )

			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]   )   )

			return model
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T   )   )
			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T   )   )

			SCREAMING_SNAKE_CASE           =					nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]   )					,	requires_grad=SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					nn.Parameter(
			    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T   )   )

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

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

						SCREAMING_SNAKE_CASE           =					ly_weight["""self_attention"""]
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )

						SCREAMING_SNAKE_CASE           =					ly_weight["""MultiHeadDotProductAttention_0"""]
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]   )   )

						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(
						    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T   )   )
						SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T   )   )

			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]   )   )

			SCREAMING_SNAKE_CASE           =					nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T   )   )

			return model
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					checkpoints.load_tax_checkpoint(args.checkpoint_path   )
			SCREAMING_SNAKE_CASE           =					jnp.tree_util.tree_map(onp.array					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					[
			    """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()""",
			]

			SCREAMING_SNAKE_CASE           =					os.path.join(args.checkpoint_path					,	""".."""					,	"""config.gin"""   )
			SCREAMING_SNAKE_CASE           =					inference.parse_training_gin_file(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					inference.InferenceModel(args.checkpoint_path					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""					,	variance_type="""fixed_large"""   )

			SCREAMING_SNAKE_CASE           =					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"""					,	)

			SCREAMING_SNAKE_CASE           =					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"""					,	)

			SCREAMING_SNAKE_CASE           =					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					,	)

			SCREAMING_SNAKE_CASE           =					load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]					,	SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]					,	SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					load_decoder(ta_checkpoint["""target"""]["""decoder"""]					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""   )

			SCREAMING_SNAKE_CASE           =					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__":
		__A						:							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.""",
		)
		__A						:							Any							=  parser.parse_args()

		main(args)






import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
		__A						:							Tuple							=  argparse.ArgumentParser()

		parser.add_argument(
		    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
		)
		parser.add_argument(
		    """--original_config_file""",
		    type=str,
		    required=True,
		    help="""The YAML config file corresponding to the original architecture.""",
		)
		parser.add_argument(
		    """--num_in_channels""",
		    default=None,
		    type=int,
		    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
		)
		parser.add_argument(
		    """--image_size""",
		    default=5_1_2,
		    type=int,
		    help=(
		        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
		        """ Base. Use 768 for Stable Diffusion v2."""
		    ),
		)
		parser.add_argument(
		    """--extract_ema""",
		    action="""store_true""",
		    help=(
		        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
		        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
		        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
		    ),
		)
		parser.add_argument(
		    """--upcast_attention""",
		    action="""store_true""",
		    help=(
		        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
		        """ diffusion 2.1."""
		    ),
		)
		parser.add_argument(
		    """--from_safetensors""",
		    action="""store_true""",
		    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
		)
		parser.add_argument(
		    """--to_safetensors""",
		    action="""store_true""",
		    help="""Whether to store pipeline in safetensors format or not.""",
		)
		parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
		parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
		def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
					'''simple docstring'''
					if string == "True":
								return True
					elif string == "False":
								return False
					else:
								raise ValueError(f"""could not parse string as bool {string}"""   )

		parser.add_argument(
		    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
		)

		parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

		__A						:							Union[str, Any]							=  parser.parse_args()

		__A						:							Any							=  download_controlnet_from_original_ckpt(
		    checkpoint_path=args.checkpoint_path,
		    original_config_file=args.original_config_file,
		    image_size=args.image_size,
		    extract_ema=args.extract_ema,
		    num_in_channels=args.num_in_channels,
		    upcast_attention=args.upcast_attention,
		    from_safetensors=args.from_safetensors,
		    device=args.device,
		    use_linear_projection=args.use_linear_projection,
		    cross_attention_dim=args.cross_attention_dim,
		)




		controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)






