


"""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


snake_case__						:   str			 = '''base_with_context'''


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

            lowerCAmelCase    :		Dict				=      ly_weight['''attention''']
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Dict				=      nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Optional[int]				=      nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

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

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

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


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Optional[int]	):
      lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T	)	)

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

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

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

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

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

      return model


def    _snake_case  (  _snake_case :	List[str]       ,      _snake_case :	str	):
      lowerCAmelCase    :		Any				=      nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T	)	)
      lowerCAmelCase    :		Optional[Any]				=      nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T	)	)

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

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

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

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

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

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

            lowerCAmelCase    :		List[Any]				=      nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)
            lowerCAmelCase    :		Any				=      nn.Parameter(
                torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T	)	)
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
            lowerCAmelCase    :		str				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
            lowerCAmelCase    :		Union[str, Any]				=      nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

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

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

      return model


def    _snake_case  (  _snake_case :	Tuple	):
      lowerCAmelCase    :		int				=      checkpoints.load_tax_checkpoint(args.checkpoint_path	)
      lowerCAmelCase    :		Any				=      jnp.tree_util.tree_map(onp.array       ,      _snake_case	)

      lowerCAmelCase    :		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()''',
      ]

      lowerCAmelCase    :		List[Any]				=      os.path.join(args.checkpoint_path       ,      '''..'''       ,      '''config.gin'''	)
      lowerCAmelCase    :		List[Any]				=      inference.parse_training_gin_file(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		List[Any]				=      inference.InferenceModel(args.checkpoint_path       ,      _snake_case	)

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

      lowerCAmelCase    :		Optional[Any]				=      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    :		Tuple				=      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    :		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       ,      )

      lowerCAmelCase    :		Union[str, Any]				=      load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']       ,      _snake_case	)
      lowerCAmelCase    :		Dict				=      load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']       ,      _snake_case	)
      lowerCAmelCase    :		Tuple				=      load_decoder(ta_checkpoint['''target''']['''decoder''']       ,      _snake_case	)

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

      lowerCAmelCase    :		Tuple				=      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__":
 snake_case__						:   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.''',
 )
 snake_case__						:   Dict			 = parser.parse_args()

 main(args)










import cva
import numpy as np
class     a    :



      """simple docstring"""


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

        if k in (0.04, 0.06):
          __UpperCAmelCase							:	int           =  k
          __UpperCAmelCase							:	List[str]           =  window_size
        else:
          raise ValueError("""invalid k value"""					)
      def __str__(      self					:			Union[str, Any]					)							->      str:
        return str(self.k					)




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

            __UpperCAmelCase							:	int           =  (wxx * wyy) - (wxy**2)
            __UpperCAmelCase							:	Any           =  wxx + wyy
            __UpperCAmelCase							:	Optional[Any]           =  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)		,      255					)
        return color_img, corner_list


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


