


'''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


__lowercase	:						Optional[int]       =	'base_with_context'






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

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

										__a			:       Union[str, Any]					    =  nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale']   )   )

										__a			:       Optional[int]					    =  nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T   )   )
										__a			:       Dict					    =  nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T   )   )
										__a			:       Optional[int]					    =  nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T   )   )

					__a			:       Optional[int]					    =  nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']   )   )
					return model






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   Optional[Any]   ):
					__a			:       List[Any]					    =  nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T   )   )

					__a			:       Dict					    =  nn.Parameter(
					    torch.FloatTensor(weights['Embed_0']['embedding']   )						,      requires_grad=_SCREAMING_SNAKE_CASE   )

					for lyr_num, lyr in enumerate(model.encoders   ):
										__a			:       str					    =  weights[F"""layers_{lyr_num}"""]
										__a			:       Optional[int]					    =  ly_weight['attention']

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

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

					__a			:       Any					    =  nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale']   )   )

					return model






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Tuple						,      _SCREAMING_SNAKE_CASE      :   List[Any]   ):
					__a			:       Dict					    =  nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T   )   )
					__a			:       Union[str, Any]					    =  nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T   )   )

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

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

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

										__a			:       Optional[int]					    =  nn.Parameter(
										    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T   )   )

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

										__a			:       Optional[Any]					    =  ly_weight['MultiHeadDotProductAttention_0']
										__a			:       int					    =  nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T   )   )
										__a			:       Tuple					    =  nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T   )   )
										__a			:       Dict					    =  nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T   )   )
										__a			:       Dict					    =  nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T   )   )
										__a			:       Optional[int]					    =  nn.Parameter(
										    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale']   )   )

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

					__a			:       Tuple					    =  nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale']   )   )

					__a			:       int					    =  nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T   )   )

					return model






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any   ):
					__a			:       Optional[int]					    =  checkpoints.load_tax_checkpoint(args.checkpoint_path   )
					__a			:       Union[str, Any]					    =  jnp.tree_util.tree_map(onp.array						,      _SCREAMING_SNAKE_CASE   )

					__a			:       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()',
					]

					__a			:       Optional[int]					    =  os.path.join(args.checkpoint_path						,      '..'						,      'config.gin'   )
					__a			:       Any					    =  inference.parse_training_gin_file(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE   )
					__a			:       str					    =  inference.InferenceModel(args.checkpoint_path						,      _SCREAMING_SNAKE_CASE   )

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

					__a			:       str					    =  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			:       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'						,      )

					__a			:       Union[str, 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						,      )

					__a			:       int					    =  load_notes_encoder(ta_checkpoint['target']['token_encoder']						,      _SCREAMING_SNAKE_CASE   )
					__a			:       Optional[int]					    =  load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']						,      _SCREAMING_SNAKE_CASE   )
					__a			:       List[str]					    =  load_decoder(ta_checkpoint['target']['decoder']						,      _SCREAMING_SNAKE_CASE   )

					__a			:       Optional[int]					    =  OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder'   )

					__a			:       Optional[int]					    =  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__":
	__lowercase	:						List[str]       =	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	:						int       =	parser.parse_args()

	main(args)







'''simple docstring'''



#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist






def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int   ):
					with open(_SCREAMING_SNAKE_CASE						,      'r'   ) as fh:
										fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_EX   )
										try:
															print(*_SCREAMING_SNAKE_CASE   )
										finally:
															fcntl.flock(_SCREAMING_SNAKE_CASE						,      fcntl.LOCK_UN   )


__lowercase	:						Dict       =	int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
__lowercase	:						Tuple       =	torch.device('cuda', local_rank)
__lowercase	:						Optional[int]       =	socket.gethostname()

__lowercase	:						List[str]       =	f'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	__lowercase	:						str       =	dist.get_rank()
	__lowercase	:						Union[str, Any]       =	dist.get_world_size()

	printflock(f'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(f'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(f'''{gpu} is broken''')
	raise



