



"""simple docstring"""






import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
		import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


_lowercase					:					Union[str, Any] 		=		get_logger(__name__)






def 	snake_case__  (						__lowerCamelCase							:       str				,						__lowerCamelCase							:       List[dict]				,						__lowerCamelCase							:       list				,						__lowerCamelCase							:       list				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       GradScaler = None				,						):
			"""simple docstring"""
			# Model states
			for i, state in enumerate(__lowerCamelCase      ):
						lowerCamelCase__						:						List[Any]						      =f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
						lowerCamelCase__						:						str						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						save(__lowerCamelCase				,						__lowerCamelCase      )
						logger.info(f'''Model weights saved in {output_model_file}'''      )
			# Optimizer states
			for i, opt in enumerate(__lowerCamelCase      ):
						lowerCamelCase__						:						List[str]						      =opt.state_dict()
						lowerCamelCase__						:						Union[str, Any]						      =f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
						lowerCamelCase__						:						Dict						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						save(__lowerCamelCase				,						__lowerCamelCase      )
						logger.info(f'''Optimizer state saved in {output_optimizer_file}'''      )
			# Scheduler states
			for i, scheduler in enumerate(__lowerCamelCase      ):
						lowerCamelCase__						:						List[Any]						      =scheduler.state_dict()
						lowerCamelCase__						:						Tuple						      =f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
						lowerCamelCase__						:						str						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						save(__lowerCamelCase				,						__lowerCamelCase      )
						logger.info(f'''Scheduler state saved in {output_scheduler_file}'''      )
			# GradScaler state
			if scaler is not None:
						lowerCamelCase__						:						List[str]						      =scaler.state_dict()
						lowerCamelCase__						:						Tuple						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						torch.save(__lowerCamelCase				,						__lowerCamelCase      )
						logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''      )
			# Random number generator states
			lowerCamelCase__						:						Optional[int]						      ={}
			lowerCamelCase__						:						Dict						      =f'''{RNG_STATE_NAME}_{process_index}.pkl'''
			lowerCamelCase__						:						List[Any]						      =random.getstate()
			lowerCamelCase__						:						List[Any]						      =np.random.get_state()
			lowerCamelCase__						:						Optional[int]						      =torch.get_rng_state()
			if is_xpu_available():
						lowerCamelCase__						:						List[Any]						      =torch.xpu.get_rng_state_all()
			else:
						lowerCamelCase__						:						Any						      =torch.cuda.get_rng_state_all()
			if is_tpu_available():
						lowerCamelCase__						:						str						      =xm.get_rng_state()
			lowerCamelCase__						:						Optional[Any]						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
			torch.save(__lowerCamelCase				,						__lowerCamelCase      )
			logger.info(f'''Random states saved in {output_states_file}'''      )
			return output_dir






def 	snake_case__  (						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       Union[str, Any]=None				,						__lowerCamelCase							:       Optional[Any]=None				,						**__lowerCamelCase							:       Optional[Any]				,						):
			"""simple docstring"""
			if map_location not in [None, "cpu", "on_device"]:
						raise TypeError(
						    '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''      )
			if map_location is None:
						lowerCamelCase__						:						Union[str, Any]						      ='''cpu'''
			elif map_location == "on_device":
						lowerCamelCase__						:						Dict						      =PartialState().device
			# Model states
			for i, model in enumerate(__lowerCamelCase      ):
						lowerCamelCase__						:						Optional[Any]						      =f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
						lowerCamelCase__						:						Any						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						models[i].load_state_dict(torch.load(__lowerCamelCase				,						map_location=__lowerCamelCase      )				,						**__lowerCamelCase      )
			logger.info('''All model weights loaded successfully'''      )

			# Optimizer states
			for i, opt in enumerate(__lowerCamelCase      ):
						lowerCamelCase__						:						Dict						      =f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
						lowerCamelCase__						:						Dict						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						lowerCamelCase__						:						int						      =torch.load(__lowerCamelCase				,						map_location=__lowerCamelCase      )
						optimizers[i].load_state_dict(__lowerCamelCase      )
			logger.info('''All optimizer states loaded successfully'''      )

			# Scheduler states
			for i, scheduler in enumerate(__lowerCamelCase      ):
						lowerCamelCase__						:						Any						      =f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
						lowerCamelCase__						:						List[str]						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						scheduler.load_state_dict(torch.load(__lowerCamelCase      )      )
			logger.info('''All scheduler states loaded successfully'''      )

			# GradScaler state
			if scaler is not None:
						lowerCamelCase__						:						List[Any]						      =os.path.join(__lowerCamelCase				,						__lowerCamelCase      )
						scaler.load_state_dict(torch.load(__lowerCamelCase      )      )
						logger.info('''GradScaler state loaded successfully'''      )

			# Random states
			try:
						lowerCamelCase__						:						Dict						      =torch.load(os.path.join(__lowerCamelCase				,						f'''{RNG_STATE_NAME}_{process_index}.pkl'''      )      )
						random.setstate(states['''random_state''']      )
						np.random.set_state(states['''numpy_random_seed''']      )
						torch.set_rng_state(states['''torch_manual_seed''']      )
						if is_xpu_available():
									torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']      )
						else:
									torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']      )
						if is_tpu_available():
									xm.set_rng_state(states['''xm_seed''']      )
						logger.info('''All random states loaded successfully'''      )
			except Exception:
						logger.info('''Could not load random states'''      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       int = 0      ):
			"""simple docstring"""
			lowerCamelCase__						:						str						      =Path(__lowerCamelCase      ) / f'''custom_checkpoint_{index}.pkl'''
			logger.info(f'''Saving the state of {get_pretty_name(__lowerCamelCase      )} to {save_location}'''      )
			torch.save(obj.state_dict()				,						__lowerCamelCase      )






def 	snake_case__  (						__lowerCamelCase							:       Any				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       int = 0      ):
			"""simple docstring"""
			lowerCamelCase__						:						int						      =f'''{path}/custom_checkpoint_{index}.pkl'''
			logger.info(f'''Loading the state of {get_pretty_name(__lowerCamelCase      )} from {load_location}'''      )
			obj.load_state_dict(torch.load(__lowerCamelCase				,						map_location='''cpu'''      )      )







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCamelCase   					=  2 << 2_0  # 2mb
lowerCamelCase   					=  get_logger(__name__)





class      snake_case_      (					_a				):


							"""simple docstring"""





							def 							A__	(			self				):
												__lowerCAmelCase       =				self._options.get_all_options().get('runner'				)
												return runner in [None, "DirectRunner", "PortableRunner"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if fs.exists(UpperCAmelCase__   ):
										if force_upload:
															logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""   )
										else:
															logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""   )
															return
					with fs.create(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'rb'   ) as local_file:
															__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )
															while chunk:
																				remote_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				local_file.read(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				FileSystems
					if os.path.exists(UpperCAmelCase__   ):
										if force_download:
															logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""   )
										else:
															logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""   )
															return
					with fs.open(UpperCAmelCase__   ) as remote_file:
										with open(UpperCAmelCase__		,       'wb'   ) as local_file:
															__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )
															while chunk:
																				local_file.write(UpperCAmelCase__   )
																				__lowerCAmelCase       =				remote_file.read(UpperCAmelCase__   )






