# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
UpperCamelCase__       							=      1e-10
UpperCamelCase__       							=      'This is incredible! I love it, this is the best chicken I have ever had.'
UpperCamelCase__       							=      1_0_0





class   A							(       nn.Module  ):


   def __init__(self  :		List[str] ,						__UpperCAmelCase  :		Union[str, Any] ,						__UpperCAmelCase  :		Dict="gpt2-medium" ,						__UpperCAmelCase  :		str=False ,						__UpperCAmelCase  :		str="cpu"	)		->       Union[str, Any]:
         """simple docstring"""



         super().__init__()
         UpperCAmelCase__			=						GPTaTokenizer.from_pretrained(__UpperCAmelCase	)
         UpperCAmelCase__			=						GPTaLMHeadModel.from_pretrained(__UpperCAmelCase	)
         UpperCAmelCase__			=						self.encoder.transformer.config.hidden_size
         UpperCAmelCase__			=						ClassificationHead(class_size=__UpperCAmelCase ,						embed_size=self.embed_size	)
         UpperCAmelCase__			=						cached_mode
         UpperCAmelCase__			=						device


   def     lowercase_   (self  :		Dict	)		->       Any:
         """simple docstring"""



         return self.classifier_head


   def     lowercase_   (self  :		Union[str, Any]	)		->       Dict:
         """simple docstring"""



         for param in self.encoder.parameters():
               UpperCAmelCase__			=						False
         self.classifier_head.train()


   def     lowercase_   (self  :		Tuple ,						__UpperCAmelCase  :		Union[str, Any]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__			=						x.ne(0	).unsqueeze(2	).repeat(1 ,						1 ,						self.embed_size	).float().to(self.device	).detach()
         UpperCAmelCase__			=						self.encoder.transformer(__UpperCAmelCase	)["last_hidden_state"]
         UpperCAmelCase__			=						hidden * mask
         UpperCAmelCase__			=						torch.sum(__UpperCAmelCase ,						dim=1	) / (torch.sum(__UpperCAmelCase ,						dim=1	).detach() + EPSILON)
         return avg_hidden





   def     lowercase_   (self  :		Union[str, Any] ,						__UpperCAmelCase  :		Tuple	)		->       Union[str, Any]:
         """simple docstring"""



         if self.cached_mode:
               UpperCAmelCase__			=						x.to(self.device	)
         else:
               UpperCAmelCase__			=						self.avg_representation(x.to(self.device	)	)

         UpperCAmelCase__			=						self.classifier_head(__UpperCAmelCase	)
         UpperCAmelCase__			=						nn.functional.log_softmax(__UpperCAmelCase ,						dim=-1	)

         return probs





class   A							(       data.Dataset  ):


   def __init__(self  :		Optional[Any] ,						__UpperCAmelCase  :		Union[str, Any] ,						__UpperCAmelCase  :		List[Any]	)		->       Optional[int]:
         """simple docstring"""



         UpperCAmelCase__			=						X
         UpperCAmelCase__			=						y


   def __len__(self  :		Union[str, Any]	)		->       Any:
         """simple docstring"""



         return len(self.X	)





   def __getitem__(self  :		Union[str, Any] ,						__UpperCAmelCase  :		List[Any]	)		->       Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						{}
         UpperCAmelCase__			=						self.X[index]
         UpperCAmelCase__			=						self.y[index]
         return data




def      lowerCAmelCase_				(  __A							)  ->       Dict:







      '''simple docstring'''


      def pad_sequences(__A							):
            UpperCAmelCase__			=						[len(__A							) for seq in sequences]

            UpperCAmelCase__			=						torch.zeros(len(__A							),						max(__A							)							).long()  # padding value = 0

            for i, seq in enumerate(__A							):
                  UpperCAmelCase__			=						lengths[i]
                  UpperCAmelCase__			=						seq[:end]

            return padded_sequences, lengths

      UpperCAmelCase__			=						{}
      for key in data[0].keys():
            UpperCAmelCase__			=						[d[key] for d in data]

      UpperCAmelCase__	,   UpperCAmelCase__			=						pad_sequences(item_info["X"]							)
      UpperCAmelCase__			=						torch.tensor(item_info["y"],						dtype=torch.long							)

      return x_batch, y_batch




def      lowerCAmelCase_				(  __A							)  ->       Union[str, Any]:







      '''simple docstring'''


      UpperCAmelCase__			=						{}
      for key in data[0].keys():
            UpperCAmelCase__			=						[d[key] for d in data]

      UpperCAmelCase__			=						torch.cat(item_info["X"],						0							)
      UpperCAmelCase__			=						torch.tensor(item_info["y"],						dtype=torch.long							)

      return x_batch, y_batch




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A=0,						__A=10,						__A="cpu"							)  ->       int:







      '''simple docstring'''


      UpperCAmelCase__			=						0
      discriminator.train_custom()
      for batch_idx, (input_t, target_t) in enumerate(__A							):
            UpperCAmelCase__	,   UpperCAmelCase__			=						input_t.to(__A							), target_t.to(__A							)

            optimizer.zero_grad()

            UpperCAmelCase__			=						discriminator(__A							)
            UpperCAmelCase__			=						nn.functional.nll_loss(__A,						__A							)
            loss.backward(retain_graph=__A							)
            optimizer.step()

            samples_so_far += len(__A							)

            if batch_idx % log_interval == 0:
                  print(
                      "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                          epoch + 1,						__A,						len(data_loader.dataset							),						100 * samples_so_far / len(data_loader.dataset							),						loss.item(),						)							)




def      lowerCAmelCase_				(  __A,						__A,						__A="cpu"							)  ->       Dict:







      '''simple docstring'''


      discriminator.eval()
      UpperCAmelCase__			=						0
      UpperCAmelCase__			=						0
      with torch.no_grad():
            for input_t, target_t in data_loader:
                  UpperCAmelCase__	,   UpperCAmelCase__			=						input_t.to(__A							), target_t.to(__A							)
                  UpperCAmelCase__			=						discriminator(__A							)
                  # sum up batch loss
                  test_loss += nn.functional.nll_loss(__A,						__A,						reduction="sum"							).item()
                  # get the index of the max log-probability
                  UpperCAmelCase__			=						output_t.argmax(dim=1,						keepdim=__A							)
                  correct += pred_t.eq(target_t.view_as(__A							)							).sum().item()

      test_loss /= len(data_loader.dataset							)

      print(
          "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
              __A,						__A,						len(data_loader.dataset							),						100.0 * correct / len(data_loader.dataset							)							)							)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A=False,						__A="cpu"							)  ->       Optional[int]:







      '''simple docstring'''


      UpperCAmelCase__			=						model.tokenizer.encode(__A							)
      UpperCAmelCase__			=						torch.tensor([input_t],						dtype=torch.long,						device=__A							)
      if cached:
            UpperCAmelCase__			=						model.avg_representation(__A							)

      UpperCAmelCase__			=						model(__A							).data.cpu().numpy().flatten().tolist()
      print("Input sentence:",						__A							)
      print(
          "Predictions:",						", ".join("{}: {:.4f}".format(__A,						math.exp(__A							)							) for c, log_prob in zip(__A,						__A							)							),						)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A=False,						__A="cpu"							)  ->       Any:







      '''simple docstring'''


      UpperCAmelCase__			=						torch.utils.data.DataLoader(dataset=__A,						batch_size=__A,						collate_fn=__A							)

      UpperCAmelCase__			=						[]
      UpperCAmelCase__			=						[]
      for batch_idx, (x, y) in enumerate(tqdm(__A,						ascii=__A							)							):
            with torch.no_grad():
                  UpperCAmelCase__			=						x.to(__A							)
                  UpperCAmelCase__			=						discriminator.avg_representation(__A							).cpu().detach()
                  UpperCAmelCase__			=						torch.unbind(avg_rep.unsqueeze(1							)							)
                  xs += avg_rep_list
                  ys += y.cpu().numpy().tolist()

      UpperCAmelCase__			=						torch.utils.data.DataLoader(
          dataset=Dataset(__A,						__A							),						batch_size=__A,						shuffle=__A,						collate_fn=__A							)

      return data_loader




def      lowerCAmelCase_				(  __A,						__A=None,						__A="gpt2-medium",						__A=10,						__A=64,						__A=10,						__A=False,						__A=False,						__A=False,						)  ->       str:







      '''simple docstring'''


      UpperCAmelCase__			=						"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

      print("Preprocessing {} dataset...".format(__A							)							)
      UpperCAmelCase__			=						time.time()

      if dataset == "SST":
            UpperCAmelCase__			=						["positive", "negative", "very positive", "very negative", "neutral"]
            UpperCAmelCase__			=						{c: i for i, c in enumerate(__A							)}

            UpperCAmelCase__			=						Discriminator(
                class_size=len(__A							),						pretrained_model=__A,						cached_mode=__A,						device=__A							).to(__A							)

            UpperCAmelCase__			=						torchtext_data.Field()
            UpperCAmelCase__			=						torchtext_data.Field(sequential=__A							)
            UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						datasets.SST.splits(
                __A,						__A,						fine_grained=__A,						train_subtrees=__A,						)

            UpperCAmelCase__			=						[]
            UpperCAmelCase__			=						[]
            for i in trange(len(__A							),						ascii=__A							):
                  UpperCAmelCase__			=						TreebankWordDetokenizer().detokenize(vars(train_data[i]							)["text"]							)
                  UpperCAmelCase__			=						discriminator.tokenizer.encode(__A							)
                  UpperCAmelCase__			=						torch.tensor([50_256] + seq,						device=__A,						dtype=torch.long							)
                  x.append(__A							)
                  y.append(classaidx[vars(train_data[i]							)["label"]]							)
            UpperCAmelCase__			=						Dataset(__A,						__A							)

            UpperCAmelCase__			=						[]
            UpperCAmelCase__			=						[]
            for i in trange(len(__A							),						ascii=__A							):
                  UpperCAmelCase__			=						TreebankWordDetokenizer().detokenize(vars(test_data[i]							)["text"]							)
                  UpperCAmelCase__			=						discriminator.tokenizer.encode(__A							)
                  UpperCAmelCase__			=						torch.tensor([50_256] + seq,						device=__A,						dtype=torch.long							)
                  test_x.append(__A							)
                  test_y.append(classaidx[vars(test_data[i]							)["label"]]							)
            UpperCAmelCase__			=						Dataset(__A,						__A							)

            UpperCAmelCase__			=						{
                "class_size": len(__A							),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 2,
            }

      elif dataset == "clickbait":
            UpperCAmelCase__			=						["non_clickbait", "clickbait"]
            UpperCAmelCase__			=						{c: i for i, c in enumerate(__A							)}

            UpperCAmelCase__			=						Discriminator(
                class_size=len(__A							),						pretrained_model=__A,						cached_mode=__A,						device=__A							).to(__A							)

            with open("datasets/clickbait/clickbait_train_prefix.txt"							) as f:
                  UpperCAmelCase__			=						[]
                  for i, line in enumerate(__A							):
                        try:
                              data.append(eval(__A							)							)
                        except Exception:
                              print("Error evaluating line {}: {}".format(__A,						__A							)							)
                              continue
            UpperCAmelCase__			=						[]
            UpperCAmelCase__			=						[]
            with open("datasets/clickbait/clickbait_train_prefix.txt"							) as f:
                  for i, line in enumerate(tqdm(__A,						ascii=__A							)							):
                        try:
                              UpperCAmelCase__			=						eval(__A							)
                              UpperCAmelCase__			=						discriminator.tokenizer.encode(d["text"]							)

                              if len(__A							) < max_length_seq:
                                    UpperCAmelCase__			=						torch.tensor([50_256] + seq,						device=__A,						dtype=torch.long							)
                              else:
                                    print("Line {} is longer than maximum length {}".format(__A,						__A							)							)
                                    continue
                              x.append(__A							)
                              y.append(d["label"]							)
                        except Exception:
                              print("Error evaluating / tokenizing line {}, skipping it".format(__A							)							)
                              pass

            UpperCAmelCase__			=						Dataset(__A,						__A							)
            UpperCAmelCase__			=						int(0.9 * len(__A							)							)
            UpperCAmelCase__			=						len(__A							) - train_size
            UpperCAmelCase__	,   UpperCAmelCase__			=						torch.utils.data.random_split(__A,						[train_size, test_size]							)

            UpperCAmelCase__			=						{
                "class_size": len(__A							),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 1,
            }

      elif dataset == "toxic":
            UpperCAmelCase__			=						["non_toxic", "toxic"]
            UpperCAmelCase__			=						{c: i for i, c in enumerate(__A							)}

            UpperCAmelCase__			=						Discriminator(
                class_size=len(__A							),						pretrained_model=__A,						cached_mode=__A,						device=__A							).to(__A							)

            UpperCAmelCase__			=						[]
            UpperCAmelCase__			=						[]
            with open("datasets/toxic/toxic_train.txt"							) as f:
                  for i, line in enumerate(tqdm(__A,						ascii=__A							)							):
                        try:
                              UpperCAmelCase__			=						eval(__A							)
                              UpperCAmelCase__			=						discriminator.tokenizer.encode(d["text"]							)

                              if len(__A							) < max_length_seq:
                                    UpperCAmelCase__			=						torch.tensor([50_256] + seq,						device=__A,						dtype=torch.long							)
                              else:
                                    print("Line {} is longer than maximum length {}".format(__A,						__A							)							)
                                    continue
                              x.append(__A							)
                              y.append(int(np.sum(d["label"]							) > 0							)							)
                        except Exception:
                              print("Error evaluating / tokenizing line {}, skipping it".format(__A							)							)
                              pass

            UpperCAmelCase__			=						Dataset(__A,						__A							)
            UpperCAmelCase__			=						int(0.9 * len(__A							)							)
            UpperCAmelCase__			=						len(__A							) - train_size
            UpperCAmelCase__	,   UpperCAmelCase__			=						torch.utils.data.random_split(__A,						[train_size, test_size]							)

            UpperCAmelCase__			=						{
                "class_size": len(__A							),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 0,
            }

      else:  # if dataset == "generic":
            # This assumes the input dataset is a TSV with the following structure:
            # class \t text

            if dataset_fp is None:
                  raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell."							)

            UpperCAmelCase__			=						set()
            with open(__A							) as f:
                  UpperCAmelCase__			=						csv.reader(__A,						delimiter="\t"							)
                  for row in tqdm(__A,						ascii=__A							):
                        if row:
                              classes.add(row[0]							)

            UpperCAmelCase__			=						sorted(__A							)
            UpperCAmelCase__			=						{c: i for i, c in enumerate(__A							)}

            UpperCAmelCase__			=						Discriminator(
                class_size=len(__A							),						pretrained_model=__A,						cached_mode=__A,						device=__A							).to(__A							)

            UpperCAmelCase__			=						[]
            UpperCAmelCase__			=						[]
            with open(__A							) as f:
                  UpperCAmelCase__			=						csv.reader(__A,						delimiter="\t"							)
                  for i, row in enumerate(tqdm(__A,						ascii=__A							)							):
                        if row:
                              UpperCAmelCase__			=						row[0]
                              UpperCAmelCase__			=						row[1]

                              try:
                                    UpperCAmelCase__			=						discriminator.tokenizer.encode(__A							)
                                    if len(__A							) < max_length_seq:
                                          UpperCAmelCase__			=						torch.tensor([50_256] + seq,						device=__A,						dtype=torch.long							)

                                    else:
                                          print("Line {} is longer than maximum length {}".format(__A,						__A							)							)
                                          continue

                                    x.append(__A							)
                                    y.append(classaidx[label]							)

                              except Exception:
                                    print("Error tokenizing line {}, skipping it".format(__A							)							)
                                    pass

            UpperCAmelCase__			=						Dataset(__A,						__A							)
            UpperCAmelCase__			=						int(0.9 * len(__A							)							)
            UpperCAmelCase__			=						len(__A							) - train_size
            UpperCAmelCase__	,   UpperCAmelCase__			=						torch.utils.data.random_split(__A,						[train_size, test_size]							)

            UpperCAmelCase__			=						{
                "class_size": len(__A							),
                "embed_size": discriminator.embed_size,
                "pretrained_model": pretrained_model,
                "class_vocab": classaidx,
                "default_class": 0,
            }

      UpperCAmelCase__			=						time.time()
      print("Preprocessed {} data points".format(len(__A							) + len(__A							)							)							)
      print("Data preprocessing took: {:.3f}s".format(end - start							)							)

      if cached:
            print("Building representation cache..."							)

            UpperCAmelCase__			=						time.time()

            UpperCAmelCase__			=						get_cached_data_loader(__A,						__A,						__A,						shuffle=__A,						device=__A							)

            UpperCAmelCase__			=						get_cached_data_loader(__A,						__A,						__A,						device=__A							)

            UpperCAmelCase__			=						time.time()
            print("Building representation cache took: {:.3f}s".format(end - start							)							)

      else:
            UpperCAmelCase__			=						torch.utils.data.DataLoader(
                dataset=__A,						batch_size=__A,						shuffle=__A,						collate_fn=__A							)
            UpperCAmelCase__			=						torch.utils.data.DataLoader(dataset=__A,						batch_size=__A,						collate_fn=__A							)

      if save_model:
            with open("{}_classifier_head_meta.json".format(__A							),						"w"							) as meta_file:
                  json.dump(__A,						__A							)

      UpperCAmelCase__			=						optim.Adam(discriminator.parameters(),						lr=0.0001							)

      for epoch in range(__A							):
            UpperCAmelCase__			=						time.time()
            print("\nEpoch",						epoch + 1							)

            train_epoch(
                discriminator=__A,						data_loader=__A,						optimizer=__A,						epoch=__A,						log_interval=__A,						device=__A,						)
            evaluate_performance(data_loader=__A,						discriminator=__A,						device=__A							)

            UpperCAmelCase__			=						time.time()
            print("Epoch took: {:.3f}s".format(end - start							)							)

            print("\nExample prediction"							)
            predict(__A,						__A,						__A,						cached=__A,						device=__A							)

            if save_model:
                  # torch.save(discriminator.state_dict(),
                  #           "{}_discriminator_{}.pt".format(
                  #               args.dataset, epoch + 1
                  #               ))
                  torch.save(
                      discriminator.get_classifier().state_dict(),						"{}_classifier_head_epoch_{}.pt".format(__A,						epoch + 1							),						)


if __name__ == "__main__":
      UpperCamelCase__       							=      argparse.ArgumentParser(description='Train a discriminator on top of GPT-2 representations')
      parser.add_argument(
          '--dataset',
          type=str,
          default='SST',
          choices=('SST', 'clickbait', 'toxic', 'generic'),
          help=(
              'dataset to train the discriminator on.'
              'In case of generic, the dataset is expected'
              'to be a TSBV file with structure: class \\t text'
          ),
      )
      parser.add_argument(
          '--dataset_fp',
          type=str,
          default='',
          help='File path of the dataset to use. Needed only in case of generic datadset',
      )
      parser.add_argument(
          '--pretrained_model', type=str, default='gpt2-medium', help='Pretrained model to use as encoder'
      )
      parser.add_argument('--epochs', type=int, default=1_0, metavar='N', help='Number of training epochs')
      parser.add_argument(
          '--batch_size', type=int, default=6_4, metavar='N', help='input batch size for training (default: 64)'
      )
      parser.add_argument(
          '--log_interval',
          type=int,
          default=1_0,
          metavar='N',
          help='how many batches to wait before logging training status',
      )
      parser.add_argument('--save_model', action='store_true', help='whether to save the model')
      parser.add_argument('--cached', action='store_true', help='whether to cache the input representations')
      parser.add_argument('--no_cuda', action='store_true', help='use to turn off cuda')
      UpperCamelCase__       							=      parser.parse_args()

      train_discriminator(**(vars(args)))




from __future__ import annotations

UpperCamelCase__       							=      [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]





class   A							:


   def __init__(self  :		Optional[int] ,						__UpperCAmelCase  :		int	)		->       None:
         """simple docstring"""



         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						-1
         UpperCAmelCase__			=						[-1] * length
         UpperCAmelCase__			=						None
         UpperCAmelCase__			=						None





   def __repr__(self  :		Union[str, Any]	)		->       str:
         """simple docstring"""



         return f"""Node(min_value={self.minn} max_value={self.maxx})"""




def      lowerCAmelCase_				(  __A							)  ->       Node | None:







      '''simple docstring'''


      UpperCAmelCase__			=						Node(len(__A							)							)
      UpperCAmelCase__	,   UpperCAmelCase__			=						min(__A							), max(__A							)
      # Leaf node case where the node contains only one unique value
      if root.minn == root.maxx:
            return root
      UpperCAmelCase__			=						(root.minn + root.maxx) // 2

      UpperCAmelCase__			=						[]
      UpperCAmelCase__			=						[]

      for index, num in enumerate(__A							):
            if num <= pivot:
                  left_arr.append(__A							)
            else:
                  right_arr.append(__A							)
            UpperCAmelCase__			=						len(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      UpperCAmelCase__			=						build_tree(__A							)
      return root




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index < 0 or node is None:
            return 0
      # Leaf node cases
      if node.minn == node.maxx:
            return index + 1 if node.minn == num else 0
      UpperCAmelCase__			=						(node.minn + node.maxx) // 2
      if num <= pivot:
            # go the left subtree and map index to the left subtree
            return rank_till_index(node.left,						__A,						node.map_left[index] - 1							)
      else:
            # go to the right subtree and map index to the right subtree
            return rank_till_index(node.right,						__A,						index - node.map_left[index]							)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if start > end:
            return 0
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						__A							)
      UpperCAmelCase__			=						rank_till_index(__A,						__A,						start - 1							)
      return rank_till_end - rank_before_start




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if index > (end - start) or start > end or node is None:
            return -1
      # Leaf node case
      if node.minn == node.maxx:
            return node.minn
      # Number of elements in the left subtree in interval [start, end]
      UpperCAmelCase__			=						node.map_left[end] - (
          node.map_left[start - 1] if start else 0
      )
      if num_elements_in_left_tree > index:
            return quantile(
                node.left,						__A,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						)
      else:
            return quantile(
                node.right,						index - num_elements_in_left_tree,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						)




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A,						__A							)  ->       int:







      '''simple docstring'''


      if (
          start > end
          or node is None
          or start_num > end_num
          or node.minn > end_num
          or node.maxx < start_num
      ):
            return 0
      if start_num <= node.minn and node.maxx <= end_num:
            return end - start + 1
      UpperCAmelCase__			=						range_counting(
          node.left,						(node.map_left[start - 1] if start else 0),						node.map_left[end] - 1,						__A,						__A,						)
      UpperCAmelCase__			=						range_counting(
          node.right,						start - (node.map_left[start - 1] if start else 0),						end - node.map_left[end],						__A,						__A,						)
      return left + right


if __name__ == "__main__":
      import doctest

      doctest.testmod()



