

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							:			Optional[Any]      		= get_logger(__name__)


def 				_UpperCAmelCase							(	a__     ,  a__     ,  a__     ,  a__     ,  a__     ,  a__ = None     ,  ):

			'''simple docstring'''



			for i, state in enumerate(a__):
						a_	:	List[str]          =   f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
						a_	:	Union[str, Any]          =   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_	:	Tuple          =   opt.state_dict()
						a_	:	Dict          =   f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
						a_	:	List[str]          =   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_	:	List[Any]          =   scheduler.state_dict()
						a_	:	Optional[int]          =   f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
						a_	:	int          =   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_	:	List[Any]          =   scaler.state_dict()
						a_	:	Optional[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_	:	Dict          =   {}
			a_	:	Dict          =   f'''{RNG_STATE_NAME}_{process_index}.pkl'''
			a_	:	List[str]          =   random.getstate()
			a_	:	Union[str, Any]          =   np.random.get_state()
			a_	:	int          =   torch.get_rng_state()
			if is_xpu_available():
						a_	:	str          =   torch.xpu.get_rng_state_all()
			else:
						a_	:	Optional[int]          =   torch.cuda.get_rng_state_all()
			if is_tpu_available():
						a_	:	Any          =   xm.get_rng_state()
			a_	:	Optional[int]          =   os.path.join(a__     ,  a__)
			torch.save(a__     ,  a__)
			logger.info(f'''Random states saved in {output_states_file}''')
			return output_dir


def 				_UpperCAmelCase							(	a__     ,  a__     ,  a__     ,  a__     ,  a__     ,  a__=None     ,  a__=None     ,  **a__     ,  ):

			'''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_	:	Optional[Any]          =   """cpu"""
			elif map_location == "on_device":
						a_	:	int          =   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_	:	Optional[int]          =   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_	:	Tuple          =   f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
						a_	:	List[str]          =   os.path.join(a__     ,  a__)
						a_	:	Optional[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_	:	Optional[int]          =   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_	:	Union[str, Any]          =   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 				_UpperCAmelCase							(	a__     ,  a__     ,  a__ = 0):

			'''simple docstring'''



			a_	:	Dict          =   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 				_UpperCAmelCase							(	a__     ,  a__     ,  a__ = 0):

			'''simple docstring'''



			a_	:	Optional[int]          =   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 cva import destroyAllWindows, imread, imshow, waitKey


def 				_UpperCAmelCase							(	a__     ,  a__):

			'''simple docstring'''



			a_       ,				a_	:	Optional[int]          =   img.shape[0], img.shape[1]

			def to_grayscale(a__     ,  a__     ,  a__):
						return 0.2126 * red + 0.587 * green + 0.114 * blue

			def normalize(a__):
						return min(a__     ,  2_5_5)

			for i in range(a__):
						for j in range(a__):
									a_	:	Optional[Any]          =   int(to_grayscale(*img[i][j]))
									a_	:	Union[str, Any]          =   [
									    normalize(a__),
									    normalize(greyscale + factor),
									    normalize(greyscale + 2 * factor),
									]

			return img


if __name__ == "__main__":
	# read original image
	__snake_case							:			Dict      		= {
	    percentage: imread("""image_data/lena.jpg""", 1) for percentage in (10, 20, 30, 40)
	}

	for percentage, img in images.items():
		make_sepia(img, percentage)

	for percentage, img in images.items():
		imshow(F"""Original image with sepia (factor: {percentage})""", img)

	waitKey(0)
	destroyAllWindows()


