
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 							_lowerCamelCase			(					lowerCamelCase_:  List[Any]				,	lowerCamelCase_:  Union[str, Any]				,	lowerCamelCase_:  Dict				,	lowerCamelCase_:  Optional[Any]				,	lowerCamelCase_:  Optional[int]				,	lowerCamelCase_:  str = None				,	):



				'''simple docstring'''







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





def 							_lowerCamelCase			(					lowerCamelCase_:  List[Any]				,	lowerCamelCase_:  List[str]				,	lowerCamelCase_:  Optional[int]				,	lowerCamelCase_:  List[str]				,	lowerCamelCase_:  Optional[Any]				,	lowerCamelCase_:  Optional[int]=None				,	lowerCamelCase_:  str=None				,	**lowerCamelCase_:  List[Any]				,	):



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

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

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

				# GradScaler state
				if scaler is not None:
								A     :  List[str]   =				os.path.join(__a				,	__a		)
								scaler.load_state_dict(torch.load(__a		)		)
								logger.info('''GradScaler state loaded successfully'''		)

				# Random states
				try:
								A     :  List[Any]   =				torch.load(os.path.join(__a				,	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			(					lowerCamelCase_:  List[str]				,	lowerCamelCase_:  Union[str, Any]				,	lowerCamelCase_:  Optional[Any] = 0		):



				'''simple docstring'''







				A     :  str   =				Path(__a		) / f"""custom_checkpoint_{index}.pkl"""
				logger.info(f"""Saving the state of {get_pretty_name(__a		)} to {save_location}"""		)
				torch.save(obj.state_dict()				,	__a		)






def 							_lowerCamelCase			(					lowerCamelCase_:  List[Any]				,	lowerCamelCase_:  Union[str, Any]				,	lowerCamelCase_:  Dict = 0		):



				'''simple docstring'''







				A     :  Optional[Any]   =				f"""{path}/custom_checkpoint_{index}.pkl"""
				logger.info(f"""Loading the state of {get_pretty_name(__a		)} from {load_location}"""		)
				obj.load_state_dict(torch.load(__a				,	map_location='''cpu'''		)		)

from __future__ import annotations





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  dict[str, str]		):



				'''simple docstring'''







				A ,       A ,       A     :  str   =				'''''', '''''', ''''''
				A     :  Union[str, Any]   =				[]

				for character in message_part:
								tmp.append(character_to_number[character]		)

				for each in tmp:
								one += each[0]
								two += each[1]
								three += each[2]

				return one + two + three





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  dict[str, str]		):



				'''simple docstring'''







				A ,       A     :  Tuple   =				'''''', ''''''
				A     :  str   =				[]

				for character in message_part:
								this_part += character_to_number[character]

				for digit in this_part:
								tmp += digit
								if len(lowerCamelCase_		) == len(lowerCamelCase_		):
												result.append(lowerCamelCase_		)
												A     :  Dict   =				''''''

				return result[0], result[1], result[2]





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str		):



				'''simple docstring'''







				A     :  Dict   =				alphabet.replace(''' '''				,	''''''		).upper()
				A     :  List[str]   =				message.replace(''' '''				,	''''''		).upper()

				# Check length and characters
				if len(lowerCamelCase_		) != 27:
								raise KeyError('''Length of alphabet has to be 27.'''		)
				for each in message:
								if each not in alphabet:
												raise ValueError('''Each message character has to be included in alphabet!'''		)

    # Generate dictionares
				A     :  str   =				(
				    '''111''',
				    '''112''',
				    '''113''',
				    '''121''',
				    '''122''',
				    '''123''',
				    '''131''',
				    '''132''',
				    '''133''',
				    '''211''',
				    '''212''',
				    '''213''',
				    '''221''',
				    '''222''',
				    '''223''',
				    '''231''',
				    '''232''',
				    '''233''',
				    '''311''',
				    '''312''',
				    '''313''',
				    '''321''',
				    '''322''',
				    '''323''',
				    '''331''',
				    '''332''',
				    '''333''',
				)
				A     :  Union[str, Any]   =				{}
				A     :  int   =				{}
				for letter, number in zip(lowerCamelCase_				,	lowerCamelCase_		):
								A     :  Optional[Any]   =				number
								A     :  Union[str, Any]   =				letter

				return message, alphabet, character_to_number, number_to_character





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,	lowerCamelCase_:  int = 5		):



				'''simple docstring'''







				A ,       A ,       A ,       A     :  Optional[int]   =				__prepare(
				    lowerCamelCase_				,	lowerCamelCase_		)
				A ,       A     :  int   =				'''''', ''''''

				for i in range(0				,	len(lowerCamelCase_		) + 1				,	lowerCamelCase_		):
								encrypted_numeric += __encrypt_part(
								    message[i : i + period]				,	lowerCamelCase_		)

				for i in range(0				,	len(lowerCamelCase_		)				,	3		):
								encrypted += number_to_character[encrypted_numeric[i : i + 3]]

				return encrypted





def 							_lowerCamelCase			(					lowerCamelCase_:  str				,	lowerCamelCase_:  str = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."				,	lowerCamelCase_:  int = 5		):



				'''simple docstring'''







				A ,       A ,       A ,       A     :  Optional[int]   =				__prepare(
				    lowerCamelCase_				,	lowerCamelCase_		)
				A     :  Tuple   =				[]
				A     :  Optional[Any]   =				''''''

				for i in range(0				,	len(lowerCamelCase_		) + 1				,	lowerCamelCase_		):
								A ,       A ,       A     :  List[Any]   =				__decrypt_part(message[i : i + period]				,	lowerCamelCase_		)

								for j in range(0				,	len(lowerCamelCase_		)		):
												decrypted_numeric.append(a[j] + b[j] + c[j]		)

				for each in decrypted_numeric:
								decrypted += number_to_character[each]

				return decrypted


if __name__ == "__main__":
					UpperCamelCase_  =	"DEFEND THE EAST WALL OF THE CASTLE."
					UpperCamelCase_  =	encrypt_message(msg, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
					UpperCamelCase_  =	decrypt_message(encrypted, "EPSDUCVWYM.ZLKXNBTFGORIJHAQ")
					print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')