





"""simple docstring"""




import math
import sys

class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self    ):

        """simple docstring"""

        A_    :			Tuple									=    0
        A_    :			List[str]									=    []
        A_    :			Any									=    {}  # To store the pos of node in array

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.cur_size == 0

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Optional[int]									=    self.left(snake_case_    )
        A_    :			Dict									=    self.right(snake_case_    )
        if lc < self.cur_size and self.array(snake_case_    )[0] < self.array(snake_case_    )[0]:
          A_    :			List[Any]									=    lc
        else:
          A_    :			List[Any]									=    idx
        if rc < self.cur_size and self.array(snake_case_    )[0] < self.array(snake_case_    )[0]:
          A_    :			int									=    rc
        if smallest != idx:
          self.swap(snake_case_   , snake_case_    )
          self.min_heapify(snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			str									=    self.cur_size
        self.cur_size += 1
        self.array.append((sys.maxsize, tup[1])    )
        self.decrease_key((sys.maxsize, tup[1])   , tup[0]    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[Any]									=    self.array[0][1]
        A_    :			List[str]									=    self.array[self.cur_size - 1]
        self.cur_size -= 1
        self.min_heapify(1    )
        del self.pos[min_node]
        return min_node

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return 2 * i + 1

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return 2 * i + 2

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        return math.floor(i / 2    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    j
        A_    :			Optional[Any]									=    i
        A_    :			Union[str, Any]									=    self.array[i]
        A_    :			int									=    self.array[j]
        A_    :			Optional[Any]									=    temp

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			str									=    self.pos[tup[1]]
        # assuming the new_d is atmost old_d
        A_    :			Tuple									=    (new_d, tup[1])
        while idx > 0 and self.array[self.par(snake_case_    )][0] > self.array[idx][0]:
          self.swap(snake_case_   , self.par(snake_case_    )    )
          A_    :			Optional[int]									=    self.par(snake_case_    )


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_    ):

        """simple docstring"""

        A_    :			List[Any]									=    {}  # To store graph: u -> (v,w)
        A_    :			Dict									=    num  # Number of nodes in graph
        # To store the distance from source vertex
        A_    :			int									=    [0] * self.num_nodes
        A_    :			List[Any]									=    [-1] * self.num_nodes  # To store the path

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        if u in self.adjList:
          self.adjList[u].append((v, w)    )
        else:
          A_    :			Dict									=    [(v, w)]

        # Assuming undirected graph
        if v in self.adjList:
          self.adjList[v].append((u, w)    )
        else:
          A_    :			Tuple									=    [(u, w)]

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        for u in self.adjList:
          print(snake_case_   , '->'   , ' -> '.join(str(F"""{v}({w})"""    ) for v, w in self.adjList[u]    )    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			Optional[Any]									=    [-1] * self.num_nodes
        # src is the source node
        A_    :			List[str]									=    0
        A_    :			Dict									=    PriorityQueue()
        q.insert((0, src)    )  # (dist from src, node)
        for u in self.adjList:
          if u != src:
            A_    :			Optional[Any]									=    sys.maxsize  # Infinity
            A_    :			Union[str, Any]									=    -1

        while not q.is_empty():
          A_    :			List[Any]									=    q.extract_min()  # Returns node with the min dist from source
          # Update the distance of all the neighbours of u and
          # if their prev dist was INFINITY then push them in Q
          for v, w in self.adjList[u]:
            A_    :			str									=    self.dist[u] + w
            if self.dist[v] > new_dist:
              if self.dist[v] == sys.maxsize:
                q.insert((new_dist, v)    )
              else:
                q.decrease_key((self.dist[v], v)   , snake_case_    )
              A_    :			str									=    new_dist
              A_    :			str									=    u

        # Show the shortest distances from src
        self.show_distances(snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        print(F"""Distance from node: {src}"""    )
        for u in range(self.num_nodes    ):
          print(F"""Node {u} has distance: {self.dist[u]}"""    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    []
        A_    :			List[Any]									=    0
        A_    :			List[str]									=    dest
        # Backtracking from dest to src
        while self.par[temp] != -1:
          path.append(snake_case_    )
          if temp != src:
            for v, w in self.adjList[temp]:
              if v == self.par[temp]:
                cost += w
                break
          A_    :			Any									=    self.par[temp]
        path.append(snake_case_    )
        path.reverse()

        print(F"""----Path to reach {dest} from {src}----"""    )
        for u in path:
          print(F"""{u}"""   , end=' '    )
          if u != dest:
            print('-> '   , end=''    )

        print('\nTotal cost of path: '   , snake_case_    )


if __name__ == "__main__":
   lowerCamelCase_	:		Union[str, Any]         =   Graph(9)
   graph.add_edge(0, 1, 4)
   graph.add_edge(0, 7, 8)
   graph.add_edge(1, 2, 8)
   graph.add_edge(1, 7, 11)
   graph.add_edge(2, 3, 7)
   graph.add_edge(2, 8, 2)
   graph.add_edge(2, 5, 4)
   graph.add_edge(3, 4, 9)
   graph.add_edge(3, 5, 14)
   graph.add_edge(4, 5, 10)
   graph.add_edge(5, 6, 2)
   graph.add_edge(6, 7, 1)
   graph.add_edge(6, 8, 6)
   graph.add_edge(7, 8, 7)
   graph.show_graph()
   graph.dijkstra(0)
   graph.show_path(0, 4)

# OUTPUT
# 0 -> 1(4) -> 7(8)
# 1 -> 0(4) -> 2(8) -> 7(11)
# 7 -> 0(8) -> 1(11) -> 6(1) -> 8(7)
# 2 -> 1(8) -> 3(7) -> 8(2) -> 5(4)
# 3 -> 2(7) -> 4(9) -> 5(14)
# 8 -> 2(2) -> 6(6) -> 7(7)
# 5 -> 2(4) -> 3(14) -> 4(10) -> 6(2)
# 4 -> 3(9) -> 5(10)
# 6 -> 5(2) -> 7(1) -> 8(6)
# Distance from node: 0
# Node 0 has distance: 0
# Node 1 has distance: 4
# Node 2 has distance: 12
# Node 3 has distance: 19
# Node 4 has distance: 21
# Node 5 has distance: 11
# Node 6 has distance: 9
# Node 7 has distance: 8
# Node 8 has distance: 14
# ----Path to reach 4 from 0----
# 0 -> 7 -> 6 -> 5 -> 4
# Total cost of path:  21



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					:	int          =				get_logger(__name__)
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase = None      ,							):
   # Model states
   for i, state in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Model weights saved in {output_model_file}'''							)
   # Optimizer states
   for i, opt in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= opt.state_dict()
      UpperCAmelCase_				:   Any							= F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   List[Any]							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Optimizer state saved in {output_optimizer_file}'''							)
   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Tuple							= scheduler.state_dict()
      UpperCAmelCase_				:   Tuple							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   Tuple							= os.path.join(__lowercase      ,							__lowercase							)
      save(__lowercase      ,							__lowercase							)
      logger.info(F'''Scheduler state saved in {output_scheduler_file}'''							)
   # GradScaler state
   if scaler is not None:
      UpperCAmelCase_				:   Union[str, Any]							= scaler.state_dict()
      UpperCAmelCase_				:   Union[str, Any]							= os.path.join(__lowercase      ,							__lowercase							)
      torch.save(__lowercase      ,							__lowercase							)
      logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''							)
   # Random number generator states
   UpperCAmelCase_				:   Union[str, Any]							= {}
   UpperCAmelCase_				:   Tuple							= F'''{RNG_STATE_NAME}_{process_index}.pkl'''
   UpperCAmelCase_				:   List[Any]							= random.getstate()
   UpperCAmelCase_				:   Any							= np.random.get_state()
   UpperCAmelCase_				:   List[Any]							= torch.get_rng_state()
   if is_xpu_available():
      UpperCAmelCase_				:   Optional[int]							= torch.xpu.get_rng_state_all()
   else:
      UpperCAmelCase_				:   Any							= torch.cuda.get_rng_state_all()
   if is_tpu_available():
      UpperCAmelCase_				:   List[str]							= xm.get_rng_state()
   UpperCAmelCase_				:   Any							= os.path.join(__lowercase      ,							__lowercase							)
   torch.save(__lowercase      ,							__lowercase							)
   logger.info(F'''Random states saved in {output_states_file}'''							)
   return output_dir
def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase      ,							__lowercase=None      ,							__lowercase=None      ,							**__lowercase      ,							):
   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_				:   Optional[Any]							= '''cpu'''
   elif map_location == "on_device":
      UpperCAmelCase_				:   Optional[int]							= PartialState().device
   # Model states
   for i, model in enumerate(__lowercase							):
      UpperCAmelCase_				:   List[Any]							= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      models[i].load_state_dict(torch.load(__lowercase      ,							map_location=__lowercase							)      ,							**__lowercase							)
   logger.info('''All model weights loaded successfully'''							)

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

   # Scheduler states
   for i, scheduler in enumerate(__lowercase							):
      UpperCAmelCase_				:   Dict							= F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
      UpperCAmelCase_				:   int							= os.path.join(__lowercase      ,							__lowercase							)
      scheduler.load_state_dict(torch.load(__lowercase							)							)
   logger.info('''All scheduler states loaded successfully'''							)

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

   # Random states
   try:
      UpperCAmelCase_				:   Optional[Any]							= torch.load(os.path.join(__lowercase      ,							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   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Optional[int]							= Path(__lowercase							) / F'''custom_checkpoint_{index}.pkl'''
   logger.info(F'''Saving the state of {get_pretty_name(__lowercase							)} to {save_location}'''							)
   torch.save(obj.state_dict()      ,							__lowercase							)


def   snake_case_    (			__lowercase      ,							__lowercase      ,							__lowercase = 0							):
   UpperCAmelCase_				:   Dict							= F'''{path}/custom_checkpoint_{index}.pkl'''
   logger.info(F'''Loading the state of {get_pretty_name(__lowercase							)} from {load_location}'''							)
   obj.load_state_dict(torch.load(__lowercase      ,							map_location='''cpu'''							)							)