






"""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


A_     =				get_logger(__name__)




def   _lowerCAmelCase		(				UpperCAmelCase__							:       str,    UpperCAmelCase__							:       List[dict],    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       list,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       GradScaler = None,    )		->Any:
						# Model states
						for i, state in enumerate(UpperCAmelCase__   ):
												A__    :	Optional[int]			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	int			=	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__   ):
												A__    :	str			=	opt.state_dict()
												A__    :	Any			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Optional[int]			=	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__   ):
												A__    :	Union[str, Any]			=	scheduler.state_dict()
												A__    :	Union[str, Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Tuple			=	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:
												A__    :	List[str]			=	scaler.state_dict()
												A__    :	Optional[Any]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
												logger.info(f'Gradient scaler state saved in {output_scaler_file}'   )
						# Random number generator states
						A__    :	str			=	{}
						A__    :	str			=	f'{RNG_STATE_NAME}_{process_index}.pkl'
						A__    :	Optional[Any]			=	random.getstate()
						A__    :	str			=	np.random.get_state()
						A__    :	List[str]			=	torch.get_rng_state()
						if is_xpu_available():
												A__    :	Optional[Any]			=	torch.xpu.get_rng_state_all()
						else:
												A__    :	str			=	torch.cuda.get_rng_state_all()
						if is_tpu_available():
												A__    :	Tuple			=	xm.get_rng_state()
						A__    :	str			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
						torch.save(UpperCAmelCase__,    UpperCAmelCase__   )
						logger.info(f'Random states saved in {output_states_file}'   )
						return output_dir




def   _lowerCAmelCase		(				UpperCAmelCase__							:       Any,    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Union[str, Any],    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Optional[Any]=None,    UpperCAmelCase__							:       str=None,    **UpperCAmelCase__							:       Optional[int],    )		->List[str]:
						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__    :	Union[str, Any]			=	"""cpu"""
						elif map_location == "on_device":
												A__    :	Any			=	PartialState().device
						# Model states
						for i, model in enumerate(UpperCAmelCase__   ):
												A__    :	Any			=	f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
												A__    :	List[str]			=	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__   ):
												A__    :	Union[str, Any]			=	f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
												A__    :	Dict			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												A__    :	List[Any]			=	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__   ):
												A__    :	List[Any]			=	f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
												A__    :	Optional[Any]			=	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:
												A__    :	List[str]			=	os.path.join(UpperCAmelCase__,    UpperCAmelCase__   )
												scaler.load_state_dict(torch.load(UpperCAmelCase__   )   )
												logger.info("""GradScaler state loaded successfully"""   )

						# Random states
						try:
												A__    :	Any			=	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   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       Dict,    UpperCAmelCase__							:       int = 0   )		->Dict:
						A__    :	Dict			=	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   _lowerCAmelCase		(				UpperCAmelCase__							:       Tuple,    UpperCAmelCase__							:       str,    UpperCAmelCase__							:       int = 0   )		->str:
						A__    :	Optional[int]			=	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"""



def   _lowerCAmelCase		(				UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float,    UpperCAmelCase__							:       float   )		->list[int]:
						if hue < 0 or hue > 3_6_0:
												raise Exception("""hue should be between 0 and 360"""   )

						if saturation < 0 or saturation > 1:
												raise Exception("""saturation should be between 0 and 1"""   )

						if value < 0 or value > 1:
												raise Exception("""value should be between 0 and 1"""   )

						A__    :	int			=	value * saturation
						A__    :	Any			=	hue / 6_0
						A__    :	Any			=	chroma * (1 - abs(hue_section % 2 - 1   ))
						A__    :	Optional[Any]			=	value - chroma

						if hue_section >= 0 and hue_section <= 1:
												A__    :	Dict			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Any			=	round(2_5_5 * (match_value)   )
						elif hue_section > 1 and hue_section <= 2:
												A__    :	Optional[int]			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (match_value)   )
						elif hue_section > 2 and hue_section <= 3:
												A__    :	Tuple			=	round(2_5_5 * (match_value)   )
												A__    :	List[str]			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Union[str, Any]			=	round(2_5_5 * (second_largest_component + match_value)   )
						elif hue_section > 3 and hue_section <= 4:
												A__    :	Any			=	round(2_5_5 * (match_value)   )
												A__    :	Dict			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	List[Any]			=	round(2_5_5 * (chroma + match_value)   )
						elif hue_section > 4 and hue_section <= 5:
												A__    :	Any			=	round(2_5_5 * (second_largest_component + match_value)   )
												A__    :	Dict			=	round(2_5_5 * (match_value)   )
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
						else:
												A__    :	Tuple			=	round(2_5_5 * (chroma + match_value)   )
												A__    :	Optional[int]			=	round(2_5_5 * (match_value)   )
												A__    :	str			=	round(2_5_5 * (second_largest_component + match_value)   )

						return [red, green, blue]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int,    UpperCAmelCase__							:       int   )		->list[float]:
						if red < 0 or red > 2_5_5:
												raise Exception("""red should be between 0 and 255"""   )

						if green < 0 or green > 2_5_5:
												raise Exception("""green should be between 0 and 255"""   )

						if blue < 0 or blue > 2_5_5:
												raise Exception("""blue should be between 0 and 255"""   )

						A__    :	int			=	red / 2_5_5
						A__    :	List[str]			=	green / 2_5_5
						A__    :	Optional[int]			=	blue / 2_5_5
						A__    :	Optional[int]			=	max(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Tuple			=	value - min(UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__   )
						A__    :	Any			=	0 if value == 0 else chroma / value

						if chroma == 0:
												A__    :	Optional[Any]			=	0.0
						elif value == float_red:
												A__    :	Optional[Any]			=	6_0 * (0 + (float_green - float_blue) / chroma)
						elif value == float_green:
												A__    :	Tuple			=	6_0 * (2 + (float_blue - float_red) / chroma)
						else:
												A__    :	int			=	6_0 * (4 + (float_red - float_green) / chroma)

						A__    :	Optional[int]			=	(hue + 3_6_0) % 3_6_0

						return [hue, saturation, value]




def   _lowerCAmelCase		(				UpperCAmelCase__							:       list[float],    UpperCAmelCase__							:       list[float]   )		->bool:
						A__    :	Optional[Any]			=	abs(hsv_a[0] - hsv_a[0]   ) < 0.2
						A__    :	str			=	abs(hsv_a[1] - hsv_a[1]   ) < 0.002
						A__    :	Optional[int]			=	abs(hsv_a[2] - hsv_a[2]   ) < 0.002

						return check_hue and check_saturation and check_value






