





'''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					    =	"base_with_context"







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				int:



			"""simple docstring"""


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

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

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

						__snake_case					:      Tuple							      =     nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
						__snake_case					:      str							      =     nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
						__snake_case					:      str							      =     nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )

			__snake_case					:      List[str]							      =     nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
			return model







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				str:



			"""simple docstring"""


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

			__snake_case					:      str							      =     nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] )    ,	requires_grad=_lowerCamelCase )

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

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

						__snake_case					:      Tuple							      =     nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
						__snake_case					:      List[str]							      =     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(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )

			__snake_case					:      Union[str, Any]							      =     nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )

			return model







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



			"""simple docstring"""


			__snake_case					:      Tuple							      =     nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T ) )
			__snake_case					:      Dict							      =     nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T ) )

			__snake_case					:      Any							      =     nn.Parameter(
			    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] )    ,	requires_grad=_lowerCamelCase )

			__snake_case					:      Union[str, Any]							      =     nn.Parameter(
			    torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T ) )

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

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

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

						__snake_case					:      str							      =     ly_weight["""MultiHeadDotProductAttention_0"""]
						__snake_case					:      List[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					:      str							      =     nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
						__snake_case					:      Optional[Any]							      =     nn.Parameter(
						    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""] ) )

						__snake_case					:      Any							      =     nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
						__snake_case					:      Dict							      =     nn.Parameter(
						    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
						__snake_case					:      List[Any]							      =     nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
						__snake_case					:      Any							      =     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					:      int							      =     nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""] ) )

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

			return model







def 					_a						(       _lowerCamelCase ) ->				Optional[int]:



			"""simple docstring"""


			__snake_case					:      Optional[Any]							      =     checkpoints.load_tax_checkpoint(args.checkpoint_path )
			__snake_case					:      Any							      =     jnp.tree_util.tree_map(onp.array    ,	_lowerCamelCase )

			__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					:      str							      =     os.path.join(args.checkpoint_path    ,	""".."""    ,	"""config.gin""" )
			__snake_case					:      Tuple							      =     inference.parse_training_gin_file(_lowerCamelCase    ,	_lowerCamelCase )
			__snake_case					:      Any							      =     inference.InferenceModel(args.checkpoint_path    ,	_lowerCamelCase )

			__snake_case					:      Tuple							      =     DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    ,	variance_type="""fixed_large""" )

			__snake_case					:      Dict							      =     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					:      List[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					:      int							      =     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					:      Any							      =     load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]    ,	_lowerCamelCase )
			__snake_case					:      Optional[Any]							      =     load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]    ,	_lowerCamelCase )
			__snake_case					:      Optional[int]							      =     load_decoder(ta_checkpoint["""target"""]["""decoder"""]    ,	_lowerCamelCase )

			__snake_case					:      Any							      =     OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""" )

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


if __name__ == "__main__":
	__UpperCamelCase					    =	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					    =	parser.parse_args()

	main(args)









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     det - k * (trace**2)
											# Can change the value
											if r > 0.5:
														corner_list.append([x, y, r]			)
														color_img.itemset((y, x, 0)  ,      0			)
														color_img.itemset((y, x, 1)  ,      0			)
														color_img.itemset((y, x, 2)  ,      2_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


