"""simple docstring"""



from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

_UpperCamelCase         =		TypeVar("T")
_UpperCamelCase         =		TypeVar("U")
class 	__UpperCAmelCase						(Generic[T, U]    ):







			'''simple docstring'''





			def __init__( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= key
										A__					:    Any														= val
										A__					:    int														= 0
										A__					:    DoubleLinkedListNode[T, U] | None														= None
										A__					:    DoubleLinkedListNode[T, U] | None														= None





			def __repr__( self       ):







										'''simple docstring'''


										return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
										    self.key      ,			self.val      ,			self.freq      ,			self.next is not None      ,			self.prev is not None       )





class 	__UpperCAmelCase						(Generic[T, U]    ):







			'''simple docstring'''





			def __init__( self       ):







										'''simple docstring'''


										A__					:    DoubleLinkedListNode[T, U]														= DoubleLinkedListNode(snake_case_      ,			snake_case_       )
										A__					:    DoubleLinkedListNode[T, U]														= DoubleLinkedListNode(snake_case_      ,			snake_case_       )
										A__							,       A__					:    List[str]														= self.rear, self.head





			def __repr__( self       ):







										'''simple docstring'''


										A__					:    int														= ["""DoubleLinkedList"""]
										A__					:    Tuple														= self.head
										while node.next is not None:
																	rep.append(str(snake_case_       )       )
																	A__					:    Optional[Any]														= node.next
										rep.append(str(self.rear       )       )
										return ",\n    ".join(snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    Optional[Any]														= self.rear.prev

										# All nodes other than self.head are guaranteed to have non-None previous
										assert previous is not None

										A__					:    str														= node
										A__					:    str														= previous
										A__					:    Optional[Any]														= node
										A__					:    int														= self.rear
										node.freq += 1
										self._position_node(snake_case_       )





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										while node.prev is not None and node.prev.freq > node.freq:
																	# swap node with previous node
																	A__					:    List[Any]														= node.prev

																	A__					:    Dict														= previous_node.prev
																	A__					:    Optional[Any]														= node.prev
																	A__					:    int														= previous_node
																	A__					:    Optional[int]														= node





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										if node.prev is None or node.next is None:
																	return None

										A__					:    int														= node.next
										A__					:    Any														= node.prev
										A__					:    Optional[int]														= None
										A__					:    Union[str, Any]														= None
										return node





class 	__UpperCAmelCase						(Generic[T, U]    ):







			'''simple docstring'''
			_UpperCamelCase     :	dict[Callable[[T], U], LFUCache[T, U]]				    =							{}





			def __init__( self      ,			snake_case_       ):







										'''simple docstring'''


										A__					:    DoubleLinkedList[T, U]														= DoubleLinkedList()
										A__					:    int														= capacity
										A__					:    Dict														= 0
										A__					:    int														= 0
										A__					:    Union[str, Any]														= 0
										A__					:    dict[T, DoubleLinkedListNode[T, U]]														= {}





			def __repr__( self       ):







										'''simple docstring'''


										return (
										    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
										    F'''capacity={self.capacity}, current_size={self.num_keys})'''
										)





			def __contains__( self      ,			snake_case_       ):







										'''simple docstring'''


										return key in self.cache





			def 		lowerCamelCase				( self      ,			snake_case_       ):







										'''simple docstring'''


										if key in self.cache:
																	self.hits += 1
																	A__					:    DoubleLinkedListNode[T, U]														= self.cache[key]
																	A__					:    str														= self.list.remove(self.cache[key]       )
																	assert node == value_node

																	# node is guaranteed not None because it is in self.cache
																	assert node is not None
																	self.list.add(snake_case_       )
																	return node.val
										self.miss += 1
										return None





			def 		lowerCamelCase				( self      ,			snake_case_      ,			snake_case_       ):







										'''simple docstring'''


										if key not in self.cache:
																	if self.num_keys >= self.capacity:
																								# delete first node when over capacity
																								A__					:    Optional[Any]														= self.list.head.next

																								# guaranteed to have a non-None first node when num_keys > 0
																								# explain to type checker via assertions
																								assert first_node is not None
																								assert first_node.key is not None
																								assert self.list.remove(snake_case_       ) is not None
																								# first_node guaranteed to be in list

																								del self.cache[first_node.key]
																								self.num_keys -= 1
																	A__					:    str														= DoubleLinkedListNode(snake_case_      ,			snake_case_       )
																	self.list.add(self.cache[key]       )
																	self.num_keys += 1

										else:
																	A__					:    List[str]														= self.list.remove(self.cache[key]       )
																	assert node is not None  # node guaranteed to be in list
																	A__					:    Union[str, Any]														= value
																	self.list.add(snake_case_       )





			@classmethod
			def 		lowerCamelCase				( cls      ,			snake_case_ = 128       ):







										'''simple docstring'''


										def cache_decorator_inner(snake_case_       ) -> Callable[..., U]:
																	def cache_decorator_wrapper(*snake_case_       ) -> U:
																								if func not in cls.decorator_function_to_instance_map:
																															A__					:    Any														= LFUCache(snake_case_       )

																								A__					:    Optional[Any]														= cls.decorator_function_to_instance_map[func].get(args[0]       )
																								if result is None:
																															A__					:    str														= func(*snake_case_       )
																															cls.decorator_function_to_instance_map[func].put(args[0]      ,			snake_case_       )
																								return result

																	def cache_info() -> LFUCache[T, U]:
																								return cls.decorator_function_to_instance_map[func]

																	setattr(snake_case_      ,			"""cache_info"""      ,			snake_case_       )  # noqa: B010

																	return cache_decorator_wrapper

										return cache_decorator_inner


if __name__ == "__main__":
				import doctest

				doctest.testmod()






"""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


_UpperCamelCase         =		get_logger(__name__)

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

def    _A(		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:
														A__					:    Union[str, Any]														= """cpu"""
							elif map_location == "on_device":
														A__					:    str														= PartialState().device
							# Model states
							for i, model in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Optional[Any]														= 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       ):
														A__					:    str														= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
														A__					:    Union[str, Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														A__					:    List[str]														= 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       ):
														A__					:    Any														= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
														A__					:    Optional[int]														= 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:
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scaler.load_state_dict(torch.load(lowerCAmelCase       )       )
														logger.info("""GradScaler state loaded successfully"""       )

							# Random states
							try:
														A__					:    Union[str, Any]														= 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    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    int														= 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    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    List[str]														= 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"""       )       )





