



import math
import sys






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Tuple		):

											'''simple docstring'''
											__a					       =						0
											__a					       =						[]
											__a					       =						{}  # To store the pos of node in array





							def   UpperCamelCase_   (				self						:						List[str]		):

											'''simple docstring'''
											return self.cur_size == 0





							def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						int		):

											'''simple docstring'''
											__a					       =						self.left(__lowercase		)
											__a					       =						self.right(__lowercase		)
											if lc < self.cur_size and self.array(__lowercase		)[0] < self.array(__lowercase		)[0]:
															__a					       =						lc
											else:
															__a					       =						idx
											if rc < self.cur_size and self.array(__lowercase		)[0] < self.array(__lowercase		)[0]:
															__a					       =						rc
											if smallest != idx:
															self.swap(__lowercase ,			__lowercase		)
															self.min_heapify(__lowercase		)





							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											# Inserts a node into the Priority Queue
											__a					       =						self.cur_size
											self.cur_size += 1
											self.array.append((sys.maxsize, tup[1])		)
											self.decrease_key((sys.maxsize, tup[1]) ,			tup[0]		)





							def   UpperCamelCase_   (				self						:						List[Any]		):

											'''simple docstring'''
											# Removes and returns the min element at top of priority queue
											__a					       =						self.array[0][1]
											__a					       =						self.array[self.cur_size - 1]
											self.cur_size -= 1
											self.min_heapify(1		)
											del self.pos[min_node]
											return min_node





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Dict		):

											'''simple docstring'''
											# returns the index of left child
											return 2 * i + 1





							def   UpperCamelCase_   (				self						:						Any ,			__lowercase						:						Dict		):

											'''simple docstring'''
											# returns the index of right child
											return 2 * i + 2





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											# returns the index of parent
											return math.floor(i / 2		)





							def   UpperCamelCase_   (				self						:						List[str] ,			__lowercase						:						List[str] ,			__lowercase						:						Dict		):

											'''simple docstring'''
											# swaps array elements at indices i and j
											# update the pos{}
											__a					       =						j
											__a					       =						i
											__a					       =						self.array[i]
											__a					       =						self.array[j]
											__a					       =						temp







							def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						Any ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											__a					       =						self.pos[tup[1]]
											# assuming the new_d is atmost old_d
											__a					       =						(new_d, tup[1])
											while idx > 0 and self.array[self.par(__lowercase		)][0] > self.array[idx][0]:
															self.swap(__lowercase ,			self.par(__lowercase		)		)
															__a					       =						self.par(__lowercase		)






class 				SCREAMING_SNAKE_CASE     :





							def __init__(				self						:						Tuple ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											__a					       =						{}  # To store graph: u -> (v,w)
											__a					       =						num  # Number of nodes in graph
											# To store the distance from source vertex
											__a					       =						[0] * self.num_nodes
											__a					       =						[-1] * self.num_nodes  # To store the path





							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						int ,			__lowercase						:						List[Any] ,			__lowercase						:						Optional[int]		):

											'''simple docstring'''
											#  Edge going from node u to v and v to u with weight w
											# u (w)-> v, v (w) -> u
											# Check if u already in graph
											if u in self.adjList:
															self.adjList[u].append((v, w)		)
											else:
															__a					       =						[(v, w)]

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





							def   UpperCamelCase_   (				self						:						Optional[Any]		):

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





							def   UpperCamelCase_   (				self						:						Tuple ,			__lowercase						:						Optional[Any]		):

											'''simple docstring'''
											# Flush old junk values in par[]
											__a					       =						[-1] * self.num_nodes
											# src is the source node
											__a					       =						0
											__a					       =						PriorityQueue()
											q.insert((0, src)		)  # (dist from src, node)
											for u in self.adjList:
															if u != src:
																			__a					       =						sys.maxsize  # Infinity
																			__a					       =						-1

											while not q.is_empty():
															__a					       =						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					       =						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) ,			__lowercase		)
																							__a					       =						new_dist
																							__a					       =						u

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





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict		):

											'''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   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						Dict ,			__lowercase						:						Dict		):

											'''simple docstring'''
											# To show the shortest path from src to dest
											# WARNING: Use it *after* calling dijkstra
											__a					       =						[]
											__a					       =						0
											__a					       =						dest
											# Backtracking from dest to src
											while self.par[temp] != -1:
															path.append(__lowercase		)
															if temp != src:
																			for v, w in self.adjList[temp]:
																							if v == self.par[temp]:
																											cost += w
																											break
															__a					       =						self.par[temp]
											path.append(__lowercase		)
											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: """ ,			__lowercase		)


if __name__ == "__main__":
						lowerCamelCase__     =	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 logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import transformers
from transformers import (
    AutoConfig,
    AutoModelForQuestionAnswering,
    AutoTokenizer,
    DataCollatorWithPadding,
    HfArgumentParser,
    SquadDataset,
    Trainer,
    TrainingArguments,
)
from transformers import SquadDataTrainingArguments as DataTrainingArguments
from transformers.trainer_utils import is_main_process


lowerCamelCase__     =	logging.getLogger(__name__)






@dataclass
class 				SCREAMING_SNAKE_CASE     :
							__lowerCamelCase		:		str      =field(
							    metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}   )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}   )
							__lowerCamelCase		:		bool      =field(default=lowerCamelCase__     , metadata={'help': 'Set this flag to use fast tokenization.'}   )
							# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
							# or just modify its tokenizer_config.json.
							__lowerCamelCase		:		Optional[str]      =field(
							    default=lowerCamelCase__     , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}     , )



def 					lowerCAmelCase__  (							):
				"""simple docstring"""







				__a					       =						HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )

				if len(sys.argv      ) == 2 and sys.argv[1].endswith(""".json"""      ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								__a       ,   __a       ,   __a					       =						parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
				else:
								__a       ,   __a       ,   __a					       =						parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir      )
				    and os.listdir(training_args.output_dir      )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
								    """ --overwrite_output_dir to overcome."""      )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""			,       datefmt="""%m/%d/%Y %H:%M:%S"""			,       level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN			,       )
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""			,       training_args.local_rank			,       training_args.device			,       training_args.n_gpu			,       bool(training_args.local_rank != -1      )			,       training_args.fpaa			,       )
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank      ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""			,       _SCREAMING_SNAKE_CASE      )

				# Prepare Question-Answering task
				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				__a					       =						AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       )
				__a					       =						AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path			,       cache_dir=model_args.cache_dir			,       use_fast=_SCREAMING_SNAKE_CASE			,       )
				__a					       =						AutoModelForQuestionAnswering.from_pretrained(
				    model_args.model_name_or_path			,       from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )			,       config=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )

				# Get datasets
				__a					       =						hasattr(model.config			,       """lang2id"""      )
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir      )
				    if training_args.do_train
				    else None
				)
				__a					       =						(
				    SquadDataset(
				        _SCREAMING_SNAKE_CASE			,       tokenizer=_SCREAMING_SNAKE_CASE			,       mode="""dev"""			,       is_language_sensitive=_SCREAMING_SNAKE_CASE			,       cache_dir=model_args.cache_dir			,       )
				    if training_args.do_eval
				    else None
				)

				# Data collator
				__a					       =						DataCollatorWithPadding(_SCREAMING_SNAKE_CASE			,       pad_to_multiple_of=8      ) if training_args.fpaa else None

				# Initialize our Trainer
				__a					       =						Trainer(
				    model=_SCREAMING_SNAKE_CASE			,       args=_SCREAMING_SNAKE_CASE			,       train_dataset=_SCREAMING_SNAKE_CASE			,       eval_dataset=_SCREAMING_SNAKE_CASE			,       data_collator=_SCREAMING_SNAKE_CASE			,       )

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir      )

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







				main()


if __name__ == "__main__":
						main()
