



#
# 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 snake_case__  (			*__SCREAMING_SNAKE_CASE )		->							Dict:
		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 )


SCREAMING_SNAKE_CASE     				=						int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
SCREAMING_SNAKE_CASE     				=						torch.device("cuda", local_rank)
SCREAMING_SNAKE_CASE     				=						socket.gethostname()

SCREAMING_SNAKE_CASE     				=						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
			SCREAMING_SNAKE_CASE     				=						dist.get_rank()
			SCREAMING_SNAKE_CASE     				=						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






import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							np.array:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		UpperCAmelCase_						=				"f32le"
		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-i",
		    "pipe:0",
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]

		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdin=subprocess.PIPE			,     stdout=subprocess.PIPE ) as ffmpeg_process:
						UpperCAmelCase_						=				ffmpeg_process.communicate(__SCREAMING_SNAKE_CASE )
		except FileNotFoundError as error:
				raise ValueError("ffmpeg was not found but is required to load audio files from filename" ) from error
		UpperCAmelCase_						=				output_stream[0]
		UpperCAmelCase_						=				np.frombuffer(__SCREAMING_SNAKE_CASE			,     np.floataa )
		if audio.shape[0] == 0:
				raise ValueError("Malformed soundfile" )
		return audio







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							Dict:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		UpperCAmelCase_						=				platform.system()
		if system == "Linux":
				UpperCAmelCase_						=				"alsa"
				UpperCAmelCase_						=				"default"
		elif system == "Darwin":
				UpperCAmelCase_						=				"avfoundation"
				UpperCAmelCase_						=				":0"
		elif system == "Windows":
				UpperCAmelCase_						=				"dshow"
				UpperCAmelCase_						=				"default"

		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-f",
		    format_,
		    "-i",
		    input_,
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-fflags",
		    "nobuffer",
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		UpperCAmelCase_						=				_ffmpeg_stream(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		for item in iterator:
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							int:
		if stream_chunk_s is not None:
				UpperCAmelCase_						=				stream_chunk_s
		else:
				UpperCAmelCase_						=				chunk_length_s

		UpperCAmelCase_						=				ffmpeg_microphone(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     format_for_conversion=__SCREAMING_SNAKE_CASE )
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				np.intaa
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				np.floataa
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		if stride_length_s is None:
				UpperCAmelCase_						=				chunk_length_s / 6
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		if isinstance(__SCREAMING_SNAKE_CASE			,     (int, float) ):
				UpperCAmelCase_						=				[stride_length_s, stride_length_s]

		UpperCAmelCase_						=				int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
		UpperCAmelCase_						=				int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
		UpperCAmelCase_						=				datetime.datetime.now()
		UpperCAmelCase_						=				datetime.timedelta(seconds=__SCREAMING_SNAKE_CASE )
		for item in chunk_bytes_iter(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     stride=(stride_left, stride_right)			,     stream=__SCREAMING_SNAKE_CASE ):
				# Put everything back in numpy scale
				UpperCAmelCase_						=				np.frombuffer(item["raw"]			,     dtype=__SCREAMING_SNAKE_CASE )
				UpperCAmelCase_						=				(
				    item["stride"][0] // size_of_sample,
				    item["stride"][1] // size_of_sample,
				)
				UpperCAmelCase_						=				sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False )		->							Dict:
		UpperCAmelCase_						=				B""
		UpperCAmelCase_					,						UpperCAmelCase_						=				stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}''' )
		UpperCAmelCase_						=				0
		for raw in iterator:
				acc += raw
				if stream and len(__SCREAMING_SNAKE_CASE ) < chunk_len:
						UpperCAmelCase_						=				(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(__SCREAMING_SNAKE_CASE ) >= chunk_len:
								# We are flushing the accumulator
								UpperCAmelCase_						=				(_stride_left, stride_right)
								UpperCAmelCase_						=				{"raw": acc[:chunk_len], "stride": stride}
								if stream:
										UpperCAmelCase_						=				False
								yield item
								UpperCAmelCase_						=				stride_left
								UpperCAmelCase_						=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(__SCREAMING_SNAKE_CASE ) > stride_left:
				UpperCAmelCase_						=				{"raw": acc, "stride": (_stride_left, 0)}
				if stream:
						UpperCAmelCase_						=				False
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				2**24  # 16Mo
		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdout=subprocess.PIPE			,     bufsize=__SCREAMING_SNAKE_CASE ) as ffmpeg_process:
						while True:
								UpperCAmelCase_						=				ffmpeg_process.stdout.read(__SCREAMING_SNAKE_CASE )
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError("ffmpeg was not found but is required to stream audio files from filename" ) from error

