


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						:				Any             =      "base_with_context"







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		str:







		"""simple docstring"""





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

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

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

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

		A					       = nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']	)	)
		return model







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Any:







		"""simple docstring"""





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

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

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

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

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

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

		return model







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





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

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

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

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

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

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

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

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

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

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

		return model







def  __snake_case       (   UpperCamelCase__	)      ->		Optional[Any]:







		"""simple docstring"""





		A					       = checkpoints.load_tax_checkpoint(args.checkpoint_path	)
		A					       = jnp.tree_util.tree_map(onp.array ,			__UpperCAmelCase	)

		A					       = [
		    '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()',
		]

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

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

		A					       = 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' ,			)

		A					       = 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' ,			)

		A					       = 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 ,			)

		A					       = load_notes_encoder(ta_checkpoint['target']['token_encoder'] ,			__UpperCAmelCase	)
		A					       = load_continuous_encoder(ta_checkpoint['target']['continuous_encoder'] ,			__UpperCAmelCase	)
		A					       = load_decoder(ta_checkpoint['target']['decoder'] ,			__UpperCAmelCase	)

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

		A					       = 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__":
	UpperCamelCase						:				Optional[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.",
	)
	UpperCamelCase						:				List[str]             =      parser.parse_args()

	main(args)









"""simple docstring"""


from unittest.mock import Mock, patch

from file_transfer.send_file import send_file







@patch('socket.socket'	)
@patch('builtins.open'	)
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = Mock()
		A					       = conn, Mock()
		A					       = iter([1, None]	)
		A					       = lambda UpperCamelCase__	: next(UpperCamelCase__	)

		# ===== invoke =====
		send_file(filename='mytext.txt' ,			testing=UpperCamelCase__	)

		# ===== ensurance =====
		sock.assert_called_once()
		sock.return_value.bind.assert_called_once()
		sock.return_value.listen.assert_called_once()
		sock.return_value.accept.assert_called_once()
		conn.recv.assert_called_once()

		file.return_value.__enter__.assert_called_once()
		file.return_value.__enter__.return_value.read.assert_called()

		conn.send.assert_called_once()
		conn.close.assert_called_once()
		sock.return_value.shutdown.assert_called_once()
		sock.return_value.close.assert_called_once()





