



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 :				str							=get_logger(__name__)



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



def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase=None ,						_lowerCAmelCase=None ,						**_lowerCAmelCase ,						):
					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:
										UpperCamelCase_			= "cpu"
					elif map_location == "on_device":
										UpperCamelCase_			= PartialState().device
					# Model states
					for i, model in enumerate(_lowerCAmelCase):
										UpperCamelCase_			= f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
										UpperCamelCase_			= os.path.join(_lowerCAmelCase ,						_lowerCAmelCase)
										models[i].load_state_dict(torch.load(_lowerCAmelCase ,						map_location=_lowerCAmelCase) ,						**_lowerCAmelCase)
					logger.info("All model weights loaded successfully")

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

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

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

					# Random states
					try:
										UpperCamelCase_			= torch.load(os.path.join(_lowerCAmelCase ,						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 ,						_lowerCAmelCase ,						_lowerCAmelCase = 0):
					UpperCamelCase_			= Path(_lowerCAmelCase) / f"""custom_checkpoint_{index}.pkl"""
					logger.info(f"""Saving the state of {get_pretty_name(_lowerCAmelCase)} to {save_location}""")
					torch.save(obj.state_dict() ,						_lowerCAmelCase)






def  _lowerCAmelCase     (_lowerCAmelCase ,						_lowerCAmelCase ,						_lowerCAmelCase = 0):
					UpperCamelCase_			= f"""{path}/custom_checkpoint_{index}.pkl"""
					logger.info(f"""Loading the state of {get_pretty_name(_lowerCAmelCase)} from {load_location}""")
					obj.load_state_dict(torch.load(_lowerCAmelCase ,						map_location="cpu"))






"""simple docstring"""





_lowercase						   =     {0: [1, 2], 1: [0, 3], 2: [0], 3: [1], 4: [5, 6], 5: [4, 6], 6: [4, 5]}

_lowercase						   =     {0: [1, 2, 3], 1: [0, 3], 2: [0], 3: [0, 1], 4: [], 5: []}

def 		lowerCAmelCase				(			lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Any				      =       True
						snake_case__							:       int				      =       []

						for neighbour in graph[vert]:
												if not visited[neighbour]:
																		connected_verts += dfs(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		)

						return [vert, *connected_verts]

def 		lowerCAmelCase				(			lowerCamelCase		):
						'''simple docstring'''


						snake_case__							:       Optional[int]				      =       len(lowerCamelCase		)
						snake_case__							:       str				      =       graph_size * [False]
						snake_case__							:       List[str]				      =       []

						for i in range(lowerCamelCase		):
												if not visited[i]:
																		snake_case__							:       Dict				      =       dfs(lowerCamelCase   ,      lowerCamelCase   ,      lowerCamelCase		)
																		components_list.append(lowerCamelCase		)

						return components_list


if __name__ == "__main__":
							import doctest

							doctest.testmod()


