

import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


a_						     =    logging.get_logger(__name__)

a_						     =    list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a_						     =    tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =field(
			    default=lowercase__							,	metadata={'help': 'Model type selected in the list: ' + ', '.join(lowercase__   )}   )
			lowerCamelCase__           =field(
			    default=lowercase__							,	metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'}   )
			lowerCamelCase__           =field(
			    default=128							,	metadata={
			        'help': (
			            'The maximum total input sequence length after tokenization. Sequences longer '
			            'than this will be truncated, sequences shorter will be padded.'
			        )
			    }							,	)
			lowerCamelCase__           =field(
			    default=128							,	metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'}							,	)
			lowerCamelCase__           =field(
			    default=64							,	metadata={
			        'help': (
			            'The maximum number of tokens for the question. Questions longer than this will '
			            'be truncated to this length.'
			        )
			    }							,	)
			lowerCamelCase__           =field(
			    default=30							,	metadata={
			        'help': (
			            'The maximum length of an answer that can be generated. This is needed because the start '
			            'and end predictions are not conditioned on one another.'
			        )
			    }							,	)
			lowerCamelCase__           =field(
			    default=lowercase__							,	metadata={'help': 'Overwrite the cached training and evaluation sets'}   )
			lowerCamelCase__           =field(
			    default=lowercase__							,	metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'}   )
			lowerCamelCase__           =field(
			    default=0.0							,	metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
			lowerCamelCase__           =field(
			    default=20							,	metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'}   )
			lowerCamelCase__           =field(
			    default=0							,	metadata={
			        'help': (
			            'language id of input for language-specific xlm models (see'
			            ' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
			        )
			    }							,	)
			lowerCamelCase__           =field(default=1							,	metadata={'help': 'multiple threads for converting example to features'}   )






class _UpperCamelCase							(							lowercase__   ):
			'''simple docstring'''







			lowerCamelCase__           ='train'
			lowerCamelCase__           ='dev'






class _UpperCamelCase							(							lowercase__   ):
			'''simple docstring'''







			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42
			lowerCamelCase__           =42
			def __init__(			self							: Optional[int]      ,      a							: SquadDataTrainingArguments      ,      a							: PreTrainedTokenizer      ,      a							: Optional[int] = None      ,      a							: Union[str, Split] = Split.train      ,      a							: Optional[bool] = False      ,      a							: Optional[str] = None      ,      a							: Optional[str] = "pt"      ,      )							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       args
				SCREAMING_SNAKE_CASE   :      Tuple				     =       is_language_sensitive
				SCREAMING_SNAKE_CASE   :      int				     =       SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
				if isinstance(__lowerCamelCase      ,      __lowerCamelCase	):
					try:
						SCREAMING_SNAKE_CASE   :      List[str]				     =       Split[mode]
					except KeyError:
						raise KeyError("mode is not a valid split name"	)
				SCREAMING_SNAKE_CASE   :      str				     =       mode
				# Load data features from cache or dataset file
				SCREAMING_SNAKE_CASE   :      Any				     =       "v2" if args.version_2_with_negative else "v1"
				SCREAMING_SNAKE_CASE   :      List[str]				     =       os.path.join(
				    cache_dir if cache_dir is not None else args.data_dir      ,      F"cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"      ,      )

				# Make sure only the first process in distributed training processes the dataset,
				# and the others will use the cache.
				SCREAMING_SNAKE_CASE   :      List[str]				     =       cached_features_file + ".lock"
				with FileLock(__lowerCamelCase	):
					if os.path.exists(__lowerCamelCase	) and not args.overwrite_cache:
						SCREAMING_SNAKE_CASE   :      str				     =       time.time()
						SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.load(__lowerCamelCase	)

						# Legacy cache files have only features, while new cache files
						# will have dataset and examples also.
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.old_features["features"]
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.old_features.get("dataset"      ,      __lowerCamelCase	)
						SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       self.old_features.get("examples"      ,      __lowerCamelCase	)
						logger.info(
						    F"Loading features from cached file {cached_features_file} [took %.3f s]"      ,      time.time() - start	)

						if self.dataset is None or self.examples is None:
							logger.warning(
							    F"Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"
							    " future run"	)
					else:
						if mode == Split.dev:
							SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.processor.get_dev_examples(args.data_dir	)
						else:
							SCREAMING_SNAKE_CASE   :      str				     =       self.processor.get_train_examples(args.data_dir	)

						SCREAMING_SNAKE_CASE   :      Tuple				     =       squad_convert_examples_to_features(
						    examples=self.examples      ,      tokenizer=__lowerCamelCase      ,      max_seq_length=args.max_seq_length      ,      doc_stride=args.doc_stride      ,      max_query_length=args.max_query_length      ,      is_training=mode == Split.train      ,      threads=args.threads      ,      return_dataset=__lowerCamelCase      ,      )

						SCREAMING_SNAKE_CASE   :      int				     =       time.time()
						torch.save(
						    {"features": self.features, "dataset": self.dataset, "examples": self.examples}      ,      __lowerCamelCase      ,      )
						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
						logger.info(
						    F"Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"	)
			def __len__(			self							: List[Any]	)							->       Any:

				"""simple docstring"""

				return len(self.features	)






			def __getitem__(			self							: List[str]      ,      a							: Union[str, Any]	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       self.features[i]

				SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.tensor(feature.input_ids      ,      dtype=torch.long	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.tensor(feature.attention_mask      ,      dtype=torch.long	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       torch.tensor(feature.token_type_ids      ,      dtype=torch.long	)
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor(feature.cls_index      ,      dtype=torch.long	)
				SCREAMING_SNAKE_CASE   :      Any				     =       torch.tensor(feature.p_mask      ,      dtype=torch.float	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       torch.tensor(feature.is_impossible      ,      dtype=torch.float	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       {
				    "input_ids": input_ids,
				    "attention_mask": attention_mask,
				    "token_type_ids": token_type_ids,
				}

				if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
					del inputs["token_type_ids"]

				if self.args.model_type in ["xlnet", "xlm"]:
					inputs.update({"cls_index": cls_index, "p_mask": p_mask}	)
					if self.args.version_2_with_negative:
						inputs.update({"is_impossible": is_impossible}	)
					if self.is_language_sensitive:
						inputs.update({"langs": (torch.ones(input_ids.shape      ,      dtype=torch.intaa	) * self.args.lang_id)}	)

				if self.mode == Split.train:
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.tensor(feature.start_position      ,      dtype=torch.long	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       torch.tensor(feature.end_position      ,      dtype=torch.long	)
					inputs.update({"start_positions": start_positions, "end_positions": end_positions}	)

				return inputs


from __future__ import annotations

import math
import random
from typing import Any






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: Union[str, Any]	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      list[Any]				     =       []
				SCREAMING_SNAKE_CASE   :      int				     =       0
				SCREAMING_SNAKE_CASE   :      int				     =       0
			def 							__UpperCamelCase (			self							: List[Any]	)							->       bool:

				"""simple docstring"""

				return self.head == self.tail
			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				self.data.append(a	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.tail + 1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       self.data[self.head]
				SCREAMING_SNAKE_CASE   :      List[str]				     =       self.head + 1
				return ret
			def 							__UpperCamelCase (			self							: List[str]	)							->       int:

				"""simple docstring"""

				return self.tail - self.head






			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       None:

				"""simple docstring"""

				print(self.data	)
				print("**************"	)
				print(self.data[self.head : self.tail]	)






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: List[str]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Dict				     =       data
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
				SCREAMING_SNAKE_CASE   :      int				     =       1
			def 							__UpperCamelCase (			self							: Tuple	)							->       Any:

				"""simple docstring"""

				return self.data
			def 							__UpperCamelCase (			self							: Optional[int]	)							->       MyNode | None:

				"""simple docstring"""

				return self.left
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       MyNode | None:

				"""simple docstring"""

				return self.right
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				return self.height
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       data
			def 							__UpperCamelCase (			self							: Any      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       node
			def 							__UpperCamelCase (			self							: List[str]      ,      a							: MyNode | None	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       node






			def 							__UpperCamelCase (			self							: Optional[int]      ,      a							: int	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       height

def  lowerCamelCase__ (					_a):
	if node is None:
		return 0
	return node.get_height()

def  lowerCamelCase__ (					_a							,			_a):
	if a > b:
		return a
	return b

def  lowerCamelCase__ (					_a):
	print("left rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      List[str]				     =       node.get_left()
	assert ret is not None
	node.set_left(ret.get_right())
	ret.set_right(_a)
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	print("right rotation node:"							,			node.get_data())
	SCREAMING_SNAKE_CASE   :      Dict				     =       node.get_right()
	assert ret is not None
	node.set_right(ret.get_left())
	ret.set_left(_a)
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	SCREAMING_SNAKE_CASE   :      Tuple				     =       my_max(get_height(ret.get_right())							,			get_height(ret.get_left())) + 1
	ret.set_height(_a)
	return ret

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       node.get_left()
	assert left_child is not None
	node.set_left(left_rotation(_a))
	return right_rotation(_a)

def  lowerCamelCase__ (					_a):
	SCREAMING_SNAKE_CASE   :      int				     =       node.get_right()
	assert right_child is not None
	node.set_right(right_rotation(_a))
	return left_rotation(_a)

def  lowerCamelCase__ (					_a							,			_a):
	if node is None:
		return MyNode(_a)
	if data < node.get_data():
		node.set_left(insert_node(node.get_left()							,			_a))
		if (
		    get_height(node.get_left()) - get_height(node.get_right()) == 2
		):  # an unbalance detected
			SCREAMING_SNAKE_CASE   :      List[Any]				     =       node.get_left()
			assert left_child is not None
			if (
			    data < left_child.get_data()
			):  # new node is the left child of the left child
				SCREAMING_SNAKE_CASE   :      Tuple				     =       right_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       lr_rotation(_a)
	else:
		node.set_right(insert_node(node.get_right()							,			_a))
		if get_height(node.get_right()) - get_height(node.get_left()) == 2:
			SCREAMING_SNAKE_CASE   :      Any				     =       node.get_right()
			assert right_child is not None
			if data < right_child.get_data():
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       rl_rotation(_a)
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       left_rotation(_a)
	SCREAMING_SNAKE_CASE   :      str				     =       my_max(get_height(node.get_right())							,			get_height(node.get_left())) + 1
	node.set_height(_a)
	return node

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
		if right_child is None:
			break
		SCREAMING_SNAKE_CASE   :      str				     =       right_child
	return root.get_data()

def  lowerCamelCase__ (					_a):
	while True:
		SCREAMING_SNAKE_CASE   :      Optional[int]				     =       root.get_left()
		if left_child is None:
			break
		SCREAMING_SNAKE_CASE   :      List[str]				     =       left_child
	return root.get_data()

def  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Any				     =       root.get_left()
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       root.get_right()
	if root.get_data() == data:
		if left_child is not None and right_child is not None:
			SCREAMING_SNAKE_CASE   :      Any				     =       get_left_most(_a)
			root.set_data(_a)
			root.set_right(del_node(_a							,			_a))
		elif left_child is not None:
			SCREAMING_SNAKE_CASE   :      Dict				     =       left_child
		elif right_child is not None:
			SCREAMING_SNAKE_CASE   :      str				     =       right_child
		else:
			return None
	elif root.get_data() > data:
		if left_child is None:
			print("No such data")
			return root
		else:
			root.set_left(del_node(_a							,			_a))
	else:  # root.get_data() < data
		if right_child is None:
			return root
		else:
			root.set_right(del_node(_a							,			_a))

	if get_height(_a) - get_height(_a) == 2:
		assert right_child is not None
		if get_height(right_child.get_right()) > get_height(right_child.get_left()):
			SCREAMING_SNAKE_CASE   :      List[str]				     =       left_rotation(_a)
		else:
			SCREAMING_SNAKE_CASE   :      int				     =       rl_rotation(_a)
	elif get_height(_a) - get_height(_a) == -2:
		assert left_child is not None
		if get_height(left_child.get_left()) > get_height(left_child.get_right()):
			SCREAMING_SNAKE_CASE   :      str				     =       right_rotation(_a)
		else:
			SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       lr_rotation(_a)
	SCREAMING_SNAKE_CASE   :      List[str]				     =       my_max(get_height(root.get_right())							,			get_height(root.get_left())) + 1
	root.set_height(_a)
	return root






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(			self							: str	)							->       None:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      MyNode | None				     =       None
			def 							__UpperCamelCase (			self							: Any	)							->       int:

				"""simple docstring"""

				return get_height(self.root	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("insert:" + str(a	)	)
				SCREAMING_SNAKE_CASE   :      Any				     =       insert_node(self.root      ,      a	)
			def 							__UpperCamelCase (			self							: List[Any]      ,      a							: Any	)							->       None:

				"""simple docstring"""

				print("delete:" + str(a	)	)
				if self.root is None:
					print("Tree is empty!"	)
					return
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       del_node(self.root      ,      a	)






			def __str__(			self							: Optional[int]      ,      )							->       str:  # a level traversale, gives a more intuitive look on the tree

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       ""
				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       MyQueue()
				q.push(self.root	)
				SCREAMING_SNAKE_CASE   :      Any				     =       self.get_height()
				if layer == 0:
					return output
				SCREAMING_SNAKE_CASE   :      Dict				     =       0
				while not q.is_empty():
					SCREAMING_SNAKE_CASE   :      Dict				     =       q.pop()
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       " " * int(math.pow(2      ,      layer - 1	)	)
					output += space
					if node is None:
						output += "*"
						q.push(a	)
						q.push(a	)
					else:
						output += str(node.get_data()	)
						q.push(node.get_left()	)
						q.push(node.get_right()	)
					output += space
					SCREAMING_SNAKE_CASE   :      List[str]				     =       cnt + 1
					for i in range(100	):
						if cnt == math.pow(2      ,      a	) - 1:
							SCREAMING_SNAKE_CASE   :      List[str]				     =       layer - 1
							if layer == 0:
								output += "\n*************************************"
								return output
							output += "\n"
							break
				output += "\n*************************************"
				return output

def  lowerCamelCase__ (					):
	import doctest

	doctest.testmod()


if __name__ == "__main__":
			_test()
			a_						     =    AVLtree()
			a_						     =    list(range(10))
			random.shuffle(lst)
			for i in lst:
						t.insert(i)
						print(str(t))
			random.shuffle(lst)
			for i in lst:
						t.del_node(i)
						print(str(t))