






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     				=			get_logger(__name__)






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = None			,							):







	for i, state in enumerate(_UpperCAmelCase							):
		lowercase					  =   F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
		lowercase					  =   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							):
		lowercase					  =   opt.state_dict()
		lowercase					  =   F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
		lowercase					  =   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							):
		lowercase					  =   scheduler.state_dict()
		lowercase					  =   F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
		lowercase					  =   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:
		lowercase					  =   scaler.state_dict()
		lowercase					  =   os.path.join(_UpperCAmelCase			,							_UpperCAmelCase							)
		torch.save(_UpperCAmelCase			,							_UpperCAmelCase							)
		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(_UpperCAmelCase			,							_UpperCAmelCase							)
	torch.save(_UpperCAmelCase			,							_UpperCAmelCase							)
	logger.info(F'''Random states saved in {output_states_file}'''							)
	return output_dir






def 							UpperCAmelCase_		(							__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			,							):







	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(_UpperCAmelCase							):
		lowercase					  =   F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
		lowercase					  =   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							):
		lowercase					  =   F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
		lowercase					  =   os.path.join(_UpperCAmelCase			,							_UpperCAmelCase							)
		lowercase					  =   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							):
		lowercase					  =   F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
		lowercase					  =   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:
		lowercase					  =   os.path.join(_UpperCAmelCase			,							_UpperCAmelCase							)
		scaler.load_state_dict(torch.load(_UpperCAmelCase							)							)
		logger.info('GradScaler state loaded successfully'							)

	# Random states
	try:
		lowercase					  =   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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = 0							):
	lowercase					  =   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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = 0							):
	lowercase					  =   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'							)							)



from string import ascii_uppercase






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE = False			,							__SCREAMING_SNAKE_CASE = ascii_uppercase							):
	lowercase					  =   keyword.upper()
	lowercase					  =   plaintext.upper()
	lowercase					  =   set(__SCREAMING_SNAKE_CASE							)

	# create a list of unique characters in the keyword - their order matters
	# it determines how we will map plaintext characters to the ciphertext
	lowercase					  =   []
	for char in keyword:
		if char in alphabet_set and char not in unique_chars:
			unique_chars.append(__SCREAMING_SNAKE_CASE							)
    # the number of those unique characters will determine the number of rows
	lowercase					  =   len(__SCREAMING_SNAKE_CASE							)

	# create a shifted version of the alphabet
	lowercase					  =   unique_chars + [
	    char for char in alphabet if char not in unique_chars
	]

	# create a modified alphabet by splitting the shifted alphabet into rows
	lowercase					  =   [
	    shifted_alphabet[k : k + num_unique_chars_in_keyword]
	    for k in range(0			,							26			,							__SCREAMING_SNAKE_CASE							)
	]

	# map the alphabet characters to the modified alphabet characters
	# going 'vertically' through the modified alphabet - consider columns first
	lowercase					  =   {}
	lowercase					  =   0
	for column in range(__SCREAMING_SNAKE_CASE							):
		for row in modified_alphabet:
			# if current row (the last one) is too short, break out of loop
			if len(__SCREAMING_SNAKE_CASE							) <= column:
				break

			# map current letter to letter in modified alphabet
			lowercase					  =   row[column]
			letter_index += 1

	if verbose:
		print(__SCREAMING_SNAKE_CASE							)
	# create the encrypted text by mapping the plaintext to the modified alphabet
	return "".join(mapping[char] if char in mapping else char for char in plaintext							)


if __name__ == "__main__":
	# example use
	print(mixed_keyword('''college''', '''UNIVERSITY'''))

