
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


_snake_case			:       str      =						get_logger(__name__)

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







		'''simple docstring'''


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

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







		'''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:
				lowercase						    =			"cpu"
		elif map_location == "on_device":
				lowercase						    =			PartialState().device
		# Model states
		for i, model in enumerate(__magic_name__	):
				lowercase						    =			F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
				lowercase						    =			os.path.join(__magic_name__		,						__magic_name__	)
				models[i].load_state_dict(torch.load(__magic_name__		,						map_location=__magic_name__	)		,						**__magic_name__	)
		logger.info("All model weights loaded successfully"	)

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

		# Scheduler states
		for i, scheduler in enumerate(__magic_name__	):
				lowercase						    =			F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
				lowercase						    =			os.path.join(__magic_name__		,						__magic_name__	)
				scheduler.load_state_dict(torch.load(__magic_name__	)	)
		logger.info("All scheduler states loaded successfully"	)

		# GradScaler state
		if scaler is not None:
				lowercase						    =			os.path.join(__magic_name__		,						__magic_name__	)
				scaler.load_state_dict(torch.load(__magic_name__	)	)
				logger.info("GradScaler state loaded successfully"	)

		# Random states
		try:
				lowercase						    =			torch.load(os.path.join(__magic_name__		,						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       (  __magic_name__		,						__magic_name__		,						__magic_name__ = 0	):







		'''simple docstring'''


		lowercase						    =			Path(__magic_name__	) / F'''custom_checkpoint_{index}.pkl'''
		logger.info(F'''Saving the state of {get_pretty_name(__magic_name__	)} to {save_location}'''	)
		torch.save(obj.state_dict()		,						__magic_name__	)




def    __snake_case       (  __magic_name__		,						__magic_name__		,						__magic_name__ = 0	):







		'''simple docstring'''


		lowercase						    =			F'''{path}/custom_checkpoint_{index}.pkl'''
		logger.info(F'''Loading the state of {get_pretty_name(__magic_name__	)} from {load_location}'''	)
		obj.load_state_dict(torch.load(__magic_name__		,						map_location="cpu"	)	)


_snake_case			:       List[Any]      =						{
    "A": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "B": ("ABCDEFGHIJKLM", "NOPQRSTUVWXYZ"),
    "C": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "D": ("ABCDEFGHIJKLM", "ZNOPQRSTUVWXY"),
    "E": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "F": ("ABCDEFGHIJKLM", "YZNOPQRSTUVWX"),
    "G": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "H": ("ABCDEFGHIJKLM", "XYZNOPQRSTUVW"),
    "I": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "J": ("ABCDEFGHIJKLM", "WXYZNOPQRSTUV"),
    "K": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "L": ("ABCDEFGHIJKLM", "VWXYZNOPQRSTU"),
    "M": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "N": ("ABCDEFGHIJKLM", "UVWXYZNOPQRST"),
    "O": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "P": ("ABCDEFGHIJKLM", "TUVWXYZNOPQRS"),
    "Q": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "R": ("ABCDEFGHIJKLM", "STUVWXYZNOPQR"),
    "S": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "T": ("ABCDEFGHIJKLM", "RSTUVWXYZNOPQ"),
    "U": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "V": ("ABCDEFGHIJKLM", "QRSTUVWXYZNOP"),
    "W": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "X": ("ABCDEFGHIJKLM", "PQRSTUVWXYZNO"),
    "Y": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
    "Z": ("ABCDEFGHIJKLM", "OPQRSTUVWXYZN"),
}

def    __snake_case       (  __magic_name__	):







				'''simple docstring'''


				return [alphabet[char] for char in key.upper()]

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			""
				lowercase						    =			0
				lowercase						    =			generate_table(__magic_name__	)
				for char in words.upper():
								cipher += get_opponent(table[count]		,						__magic_name__	)
								lowercase						    =			(count + 1) % len(__magic_name__	)
				return cipher

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				return encrypt(__magic_name__		,						__magic_name__	)

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase						    =			0 if char in table[0] else 1
				lowercase						    =			table[row].index(__magic_name__	)
				return row, col

def    __snake_case       (  __magic_name__		,						__magic_name__	):







				'''simple docstring'''


				lowercase    ,  lowercase						    =			get_position(__magic_name__		,						char.upper()	)
				if row == 1:
								return table[0][col]
				else:
								return table[1][col] if row == 0 else char


if __name__ == "__main__":
						import doctest

						doctest.testmod()  # Fist ensure that all our tests are passing...
						_snake_case			:       List[Any]      =						input("Enter key: ").strip()
						_snake_case			:       Optional[int]      =						input("Enter text to encrypt: ").strip()
						_snake_case			:       Optional[Any]      =						encrypt(key, text)

						print(F"Encrypted: {cipher_text}")
						print(F"Decrypted with key: {decrypt(key, cipher_text)}")
