




"""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


SCREAMING_SNAKE_CASE_      =					get_logger(__name__)





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase = None ,):


    """simple docstring"""







    for i, state in enumerate(UpperCamelCase__						):
        UpperCamelCase										=  f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
        UpperCamelCase										=  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										=  opt.state_dict()
        UpperCamelCase										=  f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
        UpperCamelCase										=  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										=  scheduler.state_dict()
        UpperCamelCase										=  f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
        UpperCamelCase										=  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										=  scaler.state_dict()
        UpperCamelCase										=  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										=  {}
    UpperCamelCase										=  f'{RNG_STATE_NAME}_{process_index}.pkl'
    UpperCamelCase										=  random.getstate()
    UpperCamelCase										=  np.random.get_state()
    UpperCamelCase										=  torch.get_rng_state()
    if is_xpu_available():
        UpperCamelCase										=  torch.xpu.get_rng_state_all()
    else:
        UpperCamelCase										=  torch.cuda.get_rng_state_all()
    if is_tpu_available():
        UpperCamelCase										=  xm.get_rng_state()
    UpperCamelCase										=  os.path.join(UpperCamelCase__ ,UpperCamelCase__						)
    torch.save(UpperCamelCase__ ,UpperCamelCase__						)
    logger.info(f'Random states saved in {output_states_file}'						)
    return output_dir





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase=None ,_lowercase=None ,**_lowercase ,):


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

    # Random states
    try:
        UpperCamelCase										=  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     __snake_case   (							_lowercase ,_lowercase ,_lowercase = 0						):


    """simple docstring"""







    UpperCamelCase										=  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     __snake_case   (							_lowercase ,_lowercase ,_lowercase = 0						):


    """simple docstring"""







    UpperCamelCase										=  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 string import ascii_letters

SCREAMING_SNAKE_CASE_      =					ascii_letters + ' \t\n'





def     __snake_case   (							):


    """simple docstring"""







    UpperCamelCase										=  os.path.split(os.path.realpath(_lowercase						)						)
    UpperCamelCase										=  {}
    with open(path[0] + '''/dictionary.txt'''						) as dictionary_file:
        for word in dictionary_file.read().split('''\n'''						):
            UpperCamelCase										=  None
    return english_words


SCREAMING_SNAKE_CASE_      =					load_dictionary()





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  message.upper()
    UpperCamelCase										=  remove_non_letters(_lowercase						)
    UpperCamelCase										=  message.split()
    UpperCamelCase										=  len([word for word in possible_words if word in ENGLISH_WORDS]						)
    return float(_lowercase						) / len(_lowercase						)





def     __snake_case   (							_lowercase						):


    """simple docstring"""







    return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE						)





def     __snake_case   (							_lowercase ,_lowercase = 20 ,_lowercase = 85						):


    """simple docstring"""







    UpperCamelCase										=  get_english_count(_lowercase						) * 100 >= word_percentage
    UpperCamelCase										=  len(remove_non_letters(_lowercase						)						)
    UpperCamelCase										=  (float(_lowercase						) / len(_lowercase						)) * 100
    UpperCamelCase										=  message_letters_percentage >= letter_percentage
    return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()