

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


lowercase_   							=  'base_with_context'
def 	a (     A__      :  List[Any] ,  A__      :  Dict	)       -> Optional[int]:





      """simple docstring"""



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

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

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

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

      _lowercase      			=nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']	)	)
      return model
def 	a (     A__      :  Union[str, Any] ,  A__      :  Dict	)       -> Any:





      """simple docstring"""



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

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

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

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

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

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

      return model
def 	a (     A__      :  Dict ,  A__      :  Any	)       -> Union[str, Any]:





      """simple docstring"""



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

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

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

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

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

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

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

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

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

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

      return model
def 	a (     A__      :  List[Any]	)       -> Optional[Any]:





      """simple docstring"""



      _lowercase      			=checkpoints.load_tax_checkpoint(args.checkpoint_path	)
      _lowercase      			=jnp.tree_util.tree_map(onp.array ,  UpperCAmelCase__	)

      _lowercase      			=[
          '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()',
      ]

      _lowercase      			=os.path.join(args.checkpoint_path ,  '..' ,  'config.gin'	)
      _lowercase      			=inference.parse_training_gin_file(UpperCAmelCase__ ,  UpperCAmelCase__	)
      _lowercase      			=inference.InferenceModel(args.checkpoint_path ,  UpperCAmelCase__	)

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

      _lowercase      			=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' ,  )

      _lowercase      			=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' ,  )

      _lowercase      			=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 ,  )

      _lowercase      			=load_notes_encoder(ta_checkpoint['target']['token_encoder'] ,  UpperCAmelCase__	)
      _lowercase      			=load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'] ,  UpperCAmelCase__	)
      _lowercase      			=load_decoder(ta_checkpoint['target']['decoder'] ,  UpperCAmelCase__	)

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

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


if __name__ == "__main__":
  lowercase_   							=  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.',
  )
  lowercase_   							=  parser.parse_args()

  main(args)




from __future__ import annotations

lowercase   =	'''Muhammad Umer Farooq'''
lowercase   =	'''MIT'''
lowercase   =	'''1.0.0'''
lowercase   =	'''Muhammad Umer Farooq'''
lowercase   =	'''contact@muhammadumerfarooq.me'''
lowercase   =	'''Alpha'''

import re
from html.parser import HTMLParser
from urllib import parse

import requests





class    __A(					UpperCAmelCase							):

					def __init__(						self       :  Tuple ,       __UpperCamelCase       :  str ):
												super().__init__()
												lowerCamelCase_					=       []
												lowerCamelCase_					=       domain



					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  list[tuple[str, str | None]] ):
												# Only parse the 'anchor' tag.
												if tag == "a":
																			# Check the list of defined attributes.
																			for name, value in attrs:
																										# If href is defined, and not empty nor # print it.
																										if name == "href" and value != "#" and value != "":
																																	# If not already in urls.
																																	if value not in self.urls:
																																								lowerCamelCase_					=       parse.urljoin(self.domain ,       __UpperCamelCase )
																																								self.urls.append(__UpperCamelCase )



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							return ".".join(get_sub_domain_name(UpperCAmelCase__							).split("""."""							)[-2:]							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							return parse.urlparse(UpperCAmelCase__							).netloc



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "https://github.com"							)	->     list[str]:
							lowerCamelCase_					=       get_domain_name(UpperCAmelCase__							)

							# Initialize the parser
							lowerCamelCase_					=       Parser(UpperCAmelCase__							)

							try:
														# Open URL
														lowerCamelCase_					=       requests.get(UpperCAmelCase__							)

														# pass the raw HTML to the parser to get links
														parser.feed(r.text							)

														# Get links and loop through
														lowerCamelCase_					=       set()
														for link in parser.urls:
																					# open URL.
																					# read = requests.get(link)
																					try:
																												lowerCamelCase_					=       requests.get(UpperCAmelCase__							)
																												# Get the valid email.
																												lowerCamelCase_					=       re.findall("""[a-zA-Z0-9]+@""" + domain      ,					read.text							)
																												# If not in list then append it.
																												for email in emails:
																																			valid_emails.add(UpperCAmelCase__							)
																					except ValueError:
																												pass
							except ValueError:
														raise SystemExit(1							)

							# Finally return a sorted list of email addresses with no duplicates.
							return sorted(UpperCAmelCase__							)


if __name__ == "__main__":
		lowercase   =	emails_from_url('''https://github.com''')
		print(F"""{len(emails)} emails found:""")
		print('''\n'''.join(sorted(emails)))





