
'''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


UpperCamelCase__ :						Optional[Any]    							=					get_logger(__name__)
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_ = None      , )       ->       Dict:




    """simple docstring"""







    for i, state in enumerate(_UpperCAmelCase  ):
        _SCREAMING_SNAKE_CASE       	=			F"{MODEL_NAME}.bin" if i == 0 else F"{MODEL_NAME}_{i}.bin"
        _SCREAMING_SNAKE_CASE       	=			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  ):
        _SCREAMING_SNAKE_CASE       	=			opt.state_dict()
        _SCREAMING_SNAKE_CASE       	=			F"{OPTIMIZER_NAME}.bin" if i == 0 else F"{OPTIMIZER_NAME}_{i}.bin"
        _SCREAMING_SNAKE_CASE       	=			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  ):
        _SCREAMING_SNAKE_CASE       	=			scheduler.state_dict()
        _SCREAMING_SNAKE_CASE       	=			F"{SCHEDULER_NAME}.bin" if i == 0 else F"{SCHEDULER_NAME}_{i}.bin"
        _SCREAMING_SNAKE_CASE       	=			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:
        _SCREAMING_SNAKE_CASE       	=			scaler.state_dict()
        _SCREAMING_SNAKE_CASE       	=			os.path.join(_UpperCAmelCase      , _UpperCAmelCase  )
        torch.save(_UpperCAmelCase      , _UpperCAmelCase  )
        logger.info(F"Gradient scaler state saved in {output_scaler_file}"  )
    # Random number generator states
    _SCREAMING_SNAKE_CASE       	=			{}
    _SCREAMING_SNAKE_CASE       	=			F"{RNG_STATE_NAME}_{process_index}.pkl"
    _SCREAMING_SNAKE_CASE       	=			random.getstate()
    _SCREAMING_SNAKE_CASE       	=			np.random.get_state()
    _SCREAMING_SNAKE_CASE       	=			torch.get_rng_state()
    if is_xpu_available():
        _SCREAMING_SNAKE_CASE       	=			torch.xpu.get_rng_state_all()
    else:
        _SCREAMING_SNAKE_CASE       	=			torch.cuda.get_rng_state_all()
    if is_tpu_available():
        _SCREAMING_SNAKE_CASE       	=			xm.get_rng_state()
    _SCREAMING_SNAKE_CASE       	=			os.path.join(_UpperCAmelCase      , _UpperCAmelCase  )
    torch.save(_UpperCAmelCase      , _UpperCAmelCase  )
    logger.info(F"Random states saved in {output_states_file}"  )
    return output_dir
def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_=None      , SCREAMING_SNAKE_CASE_=None      , **SCREAMING_SNAKE_CASE_      , )       ->       Tuple:




    """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:
        _SCREAMING_SNAKE_CASE       	=			"cpu"
    elif map_location == "on_device":
        _SCREAMING_SNAKE_CASE       	=			PartialState().device
    # Model states
    for i, model in enumerate(_UpperCAmelCase  ):
        _SCREAMING_SNAKE_CASE       	=			F"{MODEL_NAME}.bin" if i == 0 else F"{MODEL_NAME}_{i}.bin"
        _SCREAMING_SNAKE_CASE       	=			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  ):
        _SCREAMING_SNAKE_CASE       	=			F"{OPTIMIZER_NAME}.bin" if i == 0 else F"{OPTIMIZER_NAME}_{i}.bin"
        _SCREAMING_SNAKE_CASE       	=			os.path.join(_UpperCAmelCase      , _UpperCAmelCase  )
        _SCREAMING_SNAKE_CASE       	=			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  ):
        _SCREAMING_SNAKE_CASE       	=			F"{SCHEDULER_NAME}.bin" if i == 0 else F"{SCHEDULER_NAME}_{i}.bin"
        _SCREAMING_SNAKE_CASE       	=			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:
        _SCREAMING_SNAKE_CASE       	=			os.path.join(_UpperCAmelCase      , _UpperCAmelCase  )
        scaler.load_state_dict(torch.load(_UpperCAmelCase  )  )
        logger.info("""GradScaler state loaded successfully"""  )

    # Random states
    try:
        _SCREAMING_SNAKE_CASE       	=			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_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_ = 0  )       ->       int:




    """simple docstring"""
    _SCREAMING_SNAKE_CASE       	=			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_		(			SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_ = 0  )       ->       Tuple:




    """simple docstring"""
    _SCREAMING_SNAKE_CASE       	=			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'''



#
# Sort large text files in a minimum amount of memory
#
import argparse
import os

class 				_a			:





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					'block_{0}.dat'






       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			filename
          _SCREAMING_SNAKE_CASE       	=			[]






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.BLOCK_FILENAME_FORMAT.format(A__ )
          with open(A__    ,							"""w""" ) as file:
             file.write(A__ )
          self.block_filenames.append(A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          return self.block_filenames






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			0
          with open(self.filename ) as file:
             while True:
                _SCREAMING_SNAKE_CASE       	=			file.readlines(A__ )

                if lines == []:
                   break

                if sort_key is None:
                   lines.sort()
                else:
                   lines.sort(key=A__ )

                self.write_block("""""".join(A__ )    ,							A__ )
                i += 1






       def     UpperCamelCase			(							self )      ->     List[Any]:
          map(os.remove    ,							self.block_filenames )






class 				_a			:





       """simple docstring"""




       def     UpperCamelCase			(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			-1
          _SCREAMING_SNAKE_CASE       	=			None

          for i in range(len(A__ ) ):
             if min_str is None or choices[i] < min_str:
                _SCREAMING_SNAKE_CASE       	=			i

          return min_index






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Dict:
          _SCREAMING_SNAKE_CASE       	=			files
          _SCREAMING_SNAKE_CASE       	=			set()
          _SCREAMING_SNAKE_CASE       	=			len(A__ )
          _SCREAMING_SNAKE_CASE       	=			{i: None for i in range(self.num_buffers )}






       def     UpperCamelCase			(							self )      ->     Dict:
          return {
              i: self.buffers[i] for i in range(self.num_buffers ) if i not in self.empty
          }






       def     UpperCamelCase			(							self )      ->     Tuple:
          for i in range(self.num_buffers ):
             if self.buffers[i] is None and i not in self.empty:
                _SCREAMING_SNAKE_CASE       	=			self.files[i].readline()

                if self.buffers[i] == "":
                   self.empty.add(A__ )
                   self.files[i].close()

          if len(self.empty ) == self.num_buffers:
             return False

          return True






       def     UpperCamelCase			(							self    ,							A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.buffers[index]
          _SCREAMING_SNAKE_CASE       	=			None

          return value






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			merge_strategy






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			FilesArray(self.get_file_handles(A__    ,							A__ ) )
          with open(A__    ,							"""w"""    ,							A__ ) as outfile:
             while buffers.refresh():
                _SCREAMING_SNAKE_CASE       	=			self.merge_strategy.select(buffers.get_dict() )
                outfile.write(buffers.unshift(A__ ) )






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			{}

          for i in range(len(A__ ) ):
             _SCREAMING_SNAKE_CASE       	=			open(filenames[i]    ,							"""r"""    ,							A__ )  # noqa: UP015

          return files






class 				_a			:





       """simple docstring"""




       def __init__(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			block_size






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.get_number_blocks(A__    ,							self.block_size )
          _SCREAMING_SNAKE_CASE       	=			FileSplitter(A__ )
          splitter.split(self.block_size    ,							A__ )

          _SCREAMING_SNAKE_CASE       	=			FileMerger(NWayMerge() )
          _SCREAMING_SNAKE_CASE       	=			self.block_size / (num_blocks + 1)
          merger.merge(splitter.get_block_filenames()    ,							filename + """.out"""    ,							A__ )

          splitter.cleanup()






       def     UpperCamelCase			(							self    ,							A__    ,							A__ )      ->     Any:
          return (os.stat(A__ ).st_size / block_size) + 1






def 				lowerCAmelCase_		(			SCREAMING_SNAKE_CASE_  )       ->       str:




   """simple docstring"""
   if string[-1].lower() == "k":
      return int(string[:-1]  ) * 10_24
   elif string[-1].lower() == "m":
      return int(string[:-1]  ) * 10_24 * 10_24
   elif string[-1].lower() == "g":
      return int(string[:-1]  ) * 10_24 * 10_24 * 10_24
   else:
      return int(SCREAMING_SNAKE_CASE_  )
def 				lowerCAmelCase_		(			)       ->       List[str]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE       	=			argparse.ArgumentParser()
   parser.add_argument(
       """-m"""      , """--mem"""      , help="""amount of memory to use for sorting"""      , default="""100M"""  )
   parser.add_argument(
       """filename"""      , metavar="""<filename>"""      , nargs=1      , help="""name of file to sort"""  )
   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   _SCREAMING_SNAKE_CASE       	=			ExternalSort(parse_memory(args.mem  )  )
   sorter.sort(args.filename[0]  )


if __name__ == "__main__":
     main()






