



import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
			import torch.distributed.checkpoint as dist_cp
			from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
			from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
			from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
			from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


SCREAMING_SNAKE_CASE     				=						get_logger(__name__)







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=0 )		->							int:
		os.makedirs(__SCREAMING_SNAKE_CASE			,     exist_ok=__SCREAMING_SNAKE_CASE )
		with FSDP.state_dict_type(
		    __SCREAMING_SNAKE_CASE			,     fsdp_plugin.state_dict_type			,     fsdp_plugin.state_dict_config			,     fsdp_plugin.optim_state_dict_config ):
				UpperCAmelCase_						=				model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						UpperCAmelCase_						=				f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
						if accelerator.process_index == 0:
								logger.info(f'''Saving model to {output_model_file}''' )
								torch.save(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
								logger.info(f'''Model saved to {output_model_file}''' )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						UpperCAmelCase_						=				(
						    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
						logger.info(f'''Saving model to {output_model_file}''' )
						torch.save(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
						logger.info(f'''Model saved to {output_model_file}''' )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     f'''{MODEL_NAME}_{model_index}''' )
						os.makedirs(__SCREAMING_SNAKE_CASE			,     exist_ok=__SCREAMING_SNAKE_CASE )
						logger.info(f'''Saving model to {ckpt_dir}''' )
						UpperCAmelCase_						=				{"model": state_dict}

						dist_cp.save_state_dict(
						    state_dict=__SCREAMING_SNAKE_CASE			,     storage_writer=dist_cp.FileSystemWriter(__SCREAMING_SNAKE_CASE )			,     planner=DefaultSavePlanner()			,     )
						logger.info(f'''Model saved to {ckpt_dir}''' )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=0 )		->							List[Any]:
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    __SCREAMING_SNAKE_CASE			,     fsdp_plugin.state_dict_type			,     fsdp_plugin.state_dict_config			,     fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if type(__SCREAMING_SNAKE_CASE ) != FSDP and accelerator.process_index != 0:
								if not fsdp_plugin.sync_module_states:
										raise ValueError(
										    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
										    "initializing FSDP object" )
								return
						UpperCAmelCase_						=				f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
						logger.info(f'''Loading model from {input_model_file}''' )
						UpperCAmelCase_						=				torch.load(__SCREAMING_SNAKE_CASE )
						logger.info(f'''Model loaded from {input_model_file}''' )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						UpperCAmelCase_						=				(
						    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
						logger.info(f'''Loading model from {input_model_file}''' )
						UpperCAmelCase_						=				torch.load(__SCREAMING_SNAKE_CASE )
						logger.info(f'''Model loaded from {input_model_file}''' )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						UpperCAmelCase_						=				(
						    os.path.join(__SCREAMING_SNAKE_CASE			,     f'''{MODEL_NAME}_{model_index}''' )
						    if f'''{MODEL_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(f'''Loading model from {ckpt_dir}''' )
						UpperCAmelCase_						=				{"model": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=__SCREAMING_SNAKE_CASE			,     storage_reader=dist_cp.FileSystemReader(__SCREAMING_SNAKE_CASE )			,     planner=DefaultLoadPlanner()			,     )
						UpperCAmelCase_						=				state_dict["model"]
						logger.info(f'''Model loaded from {ckpt_dir}''' )
				model.load_state_dict(__SCREAMING_SNAKE_CASE )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=0 )		->							Optional[Any]:
		os.makedirs(__SCREAMING_SNAKE_CASE			,     exist_ok=__SCREAMING_SNAKE_CASE )
		with FSDP.state_dict_type(
		    __SCREAMING_SNAKE_CASE			,     fsdp_plugin.state_dict_type			,     fsdp_plugin.state_dict_config			,     fsdp_plugin.optim_state_dict_config ):
				UpperCAmelCase_						=				FSDP.optim_state_dict(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								UpperCAmelCase_						=				(
								    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
								)
								UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
								logger.info(f'''Saving Optimizer state to {output_optimizer_file}''' )
								torch.save(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
								logger.info(f'''Optimizer state saved in {output_optimizer_file}''' )
				else:
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     f'''{OPTIMIZER_NAME}_{optimizer_index}''' )
						os.makedirs(__SCREAMING_SNAKE_CASE			,     exist_ok=__SCREAMING_SNAKE_CASE )
						logger.info(f'''Saving Optimizer state to {ckpt_dir}''' )
						dist_cp.save_state_dict(
						    state_dict={"optimizer": optim_state}			,     storage_writer=dist_cp.FileSystemWriter(__SCREAMING_SNAKE_CASE )			,     planner=DefaultSavePlanner()			,     )
						logger.info(f'''Optimizer state saved in {ckpt_dir}''' )







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=0 )		->							Optional[Any]:
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    __SCREAMING_SNAKE_CASE			,     fsdp_plugin.state_dict_type			,     fsdp_plugin.state_dict_config			,     fsdp_plugin.optim_state_dict_config ):
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						UpperCAmelCase_						=				None
						# below check should work but currently it isn't working (mostly opytorch issue),
						# in the meantime disabling it at the cost of excess memory usage
						# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
						UpperCAmelCase_						=				(
						    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
						)
						UpperCAmelCase_						=				os.path.join(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
						logger.info(f'''Loading Optimizer state from {input_optimizer_file}''' )
						UpperCAmelCase_						=				torch.load(__SCREAMING_SNAKE_CASE )
						logger.info(f'''Optimizer state loaded from {input_optimizer_file}''' )
				else:
						UpperCAmelCase_						=				(
						    os.path.join(__SCREAMING_SNAKE_CASE			,     f'''{OPTIMIZER_NAME}_{optimizer_index}''' )
						    if f'''{OPTIMIZER_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(f'''Loading Optimizer from {ckpt_dir}''' )
						UpperCAmelCase_						=				load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()			,     optimizer_key="optimizer"			,     storage_reader=dist_cp.FileSystemReader(__SCREAMING_SNAKE_CASE )			,     )
						UpperCAmelCase_						=				optim_state["optimizer"]
						logger.info(f'''Optimizer loaded from {ckpt_dir}''' )
				UpperCAmelCase_						=				FSDP.optim_state_dict_to_load(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
				optimizer.load_state_dict(__SCREAMING_SNAKE_CASE )






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

