



"""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


_lowerCAmelCase					:Dict  =      get_logger(__name__)





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





def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	int  , UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	Any=None  , UpperCamelCase__	:	Optional[int]=None  , **UpperCamelCase__	:	Tuple  , ):
    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:
        _UpperCAmelCase							:						Dict       							=					'''cpu'''
    elif map_location == "on_device":
        _UpperCAmelCase							:						List[Any]       							=					PartialState().device
    # Model states
    for i, model in enumerate(UpperCamelCase__							):
        _UpperCAmelCase							:						Dict       							=					F'{MODEL_NAME}.bin' if i == 0 else F'{MODEL_NAME}_{i}.bin'
        _UpperCAmelCase							:						Tuple       							=					os.path.join(UpperCamelCase__  , UpperCamelCase__							)
        models[i].load_state_dict(torch.load(UpperCamelCase__  , map_location=UpperCamelCase__							)  , **UpperCamelCase__							)
    logger.info('''All model weights loaded successfully'''							)

    # Optimizer states
    for i, opt in enumerate(UpperCamelCase__							):
        _UpperCAmelCase							:						List[Any]       							=					F'{OPTIMIZER_NAME}.bin' if i == 0 else F'{OPTIMIZER_NAME}_{i}.bin'
        _UpperCAmelCase							:						Tuple       							=					os.path.join(UpperCamelCase__  , UpperCamelCase__							)
        _UpperCAmelCase							:						str       							=					torch.load(UpperCamelCase__  , map_location=UpperCamelCase__							)
        optimizers[i].load_state_dict(UpperCamelCase__							)
    logger.info('''All optimizer states loaded successfully'''							)

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

    # GradScaler state
    if scaler is not None:
        _UpperCAmelCase							:						Optional[int]       							=					os.path.join(UpperCamelCase__  , UpperCamelCase__							)
        scaler.load_state_dict(torch.load(UpperCamelCase__							)							)
        logger.info('''GradScaler state loaded successfully'''							)

    # Random states
    try:
        _UpperCAmelCase							:						Optional[int]       							=					torch.load(os.path.join(UpperCamelCase__  , 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 				lowerCamelCase_		(UpperCamelCase__	:	Any  , UpperCamelCase__	:	Any  , UpperCamelCase__	:	int = 0							):
    _UpperCAmelCase							:						Optional[Any]       							=					Path(UpperCamelCase__							) / F'custom_checkpoint_{index}.pkl'
    logger.info(F'Saving the state of {get_pretty_name(UpperCamelCase__							)} to {save_location}'							)
    torch.save(obj.state_dict()  , UpperCamelCase__							)





def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	int = 0							):
    _UpperCAmelCase							:						Optional[int]       							=					F'{path}/custom_checkpoint_{index}.pkl'
    logger.info(F'Loading the state of {get_pretty_name(UpperCamelCase__							)} from {load_location}'							)
    obj.load_state_dict(torch.load(UpperCamelCase__  , map_location='''cpu'''							)							)







"""simple docstring"""
import os

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

from .logging import get_logger


_lowerCAmelCase					:Optional[int]  =      2 << 20  # 2mb
_lowerCAmelCase					:Union[str, Any]  =      get_logger(__name__)
class 							_UpperCAmelCase							(				a   ):





     '''simple docstring'''



     def 				__lowerCAmelCase			(      self	)							->      Any:
         _UpperCAmelCase							:						str       							=					self._options.get_all_options().get('''runner'''	)
         return runner in [None, "DirectRunner", "PortableRunner"]





def 				lowerCamelCase_		(UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	Dict=False							):
    _UpperCAmelCase							:						Union[str, Any]       							=					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:
            _UpperCAmelCase							:						Optional[Any]       							=					local_file.read(UpperCamelCase__							)
            while chunk:
                remote_file.write(UpperCamelCase__							)
                _UpperCAmelCase							:						List[Any]       							=					local_file.read(UpperCamelCase__							)





def 				lowerCamelCase_		(UpperCamelCase__	:	int  , UpperCamelCase__	:	Dict  , UpperCamelCase__	:	str=False							):
    _UpperCAmelCase							:						int       							=					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:
            _UpperCAmelCase							:						List[str]       							=					remote_file.read(UpperCamelCase__							)
            while chunk:
                local_file.write(UpperCamelCase__							)
                _UpperCAmelCase							:						List[Any]       							=					remote_file.read(UpperCamelCase__							)


