



import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__	      =       logging.getLogger(__name__)


def 		_UpperCAmelCase				(      snake_case	,						snake_case			):





					"""simple docstring"""

					return (preds == labels).mean()





@dataclass
class 					__lowerCAmelCase :
				__lowerCamelCase =				field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
				__lowerCamelCase =				field(
				    default=lowerCamelCase__       ,						metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}       )
				__lowerCamelCase =				field(
				    default=lowerCamelCase__       ,						metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}       )
				__lowerCamelCase =				field(
				    default=lowerCamelCase__       ,						metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}       ,						)





@dataclass
class 					__lowerCAmelCase :
				__lowerCamelCase =				field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(processors.keys()       )}       )
				__lowerCamelCase =				field(metadata={'''help''': '''Should contain the data files for the 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=lowerCamelCase__       ,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )


def 		_UpperCAmelCase				(      ):





					"""simple docstring"""

					_lowerCAmelCase						=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
					_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase						=    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"""	,						snake_case			)

					# Set seed
					set_seed(training_args.seed			)

					try:
										_lowerCAmelCase						=    processors[data_args.task_name]()
										_lowerCAmelCase						=    processor.get_labels()
										_lowerCAmelCase						=    len(snake_case			)
					except KeyError:
										raise ValueError("""Task not found: %s""" % (data_args.task_name)			)

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

					_lowerCAmelCase						=    AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,						num_labels=snake_case	,						finetuning_task=data_args.task_name	,						cache_dir=model_args.cache_dir	,						)
					_lowerCAmelCase						=    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	,						)
					_lowerCAmelCase						=    AutoModelForMultipleChoice.from_pretrained(
					    model_args.model_name_or_path	,						from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)	,						config=snake_case	,						cache_dir=model_args.cache_dir	,						)

					# Get datasets
					_lowerCAmelCase						=    (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir	,						tokenizer=snake_case	,						task=data_args.task_name	,						max_seq_length=data_args.max_seq_length	,						overwrite_cache=data_args.overwrite_cache	,						mode=Split.train	,						)
					    if training_args.do_train
					    else None
					)
					_lowerCAmelCase						=    (
					    MultipleChoiceDataset(
					        data_dir=data_args.data_dir	,						tokenizer=snake_case	,						task=data_args.task_name	,						max_seq_length=data_args.max_seq_length	,						overwrite_cache=data_args.overwrite_cache	,						mode=Split.dev	,						)
					    if training_args.do_eval
					    else None
					)

					def compute_metrics(snake_case			) -> Dict:
										_lowerCAmelCase						=    np.argmax(p.predictions	,						axis=1			)
										return {"acc": simple_accuracy(snake_case	,						p.label_ids			)}

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

					# Initialize our Trainer
					_lowerCAmelCase						=    Trainer(
					    model=snake_case	,						args=snake_case	,						train_dataset=snake_case	,						eval_dataset=snake_case	,						compute_metrics=snake_case	,						data_collator=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			)

    # Evaluation
					_lowerCAmelCase						=    {}
					if training_args.do_eval:
										logger.info("""*** Evaluate ***"""			)

										_lowerCAmelCase						=    trainer.evaluate()

										_lowerCAmelCase						=    os.path.join(training_args.output_dir	,						"""eval_results.txt"""			)
										if trainer.is_world_master():
															with open(snake_case	,						"""w"""			) as writer:
																				logger.info("""***** Eval results *****"""			)
																				for key, value in result.items():
																									logger.info("""  %s = %s"""	,						snake_case	,						snake_case			)
																									writer.write("""%s = %s\n""" % (key, value)			)

																				results.update(snake_case			)

					return results


def 		_UpperCAmelCase				(      snake_case			):





					"""simple docstring"""

					main()


if __name__ == "__main__":
	main()







from __future__ import annotations

import math






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_    )  ->       None:
         __UpperCamelCase    							=size
         # approximate the overall size of segment tree with given value
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         # create array to store lazy update
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]
         __UpperCamelCase    							=[0 for i in range(0					,   4 * size    )]  # flag for lazy update






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2






    def _a					(			self					,   A_    )  ->       int:
         return idx * 2 + 1






    def _a					(			self					,   A_					,   A_					,   A_					,   A_    )  ->       None:
         if left_element == right_element:
              __UpperCamelCase    							=a[left_element - 1]
         else:
              __UpperCamelCase    							=(left_element + right_element) // 2
              self.build(self.left(A_    )					,   A_					,   A_					,   A_    )
              self.build(self.right(A_    )					,   mid + 1					,   A_					,   A_    )
              __UpperCamelCase    							=max(
                  self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       bool:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True

         if right_element < a or left_element > b:
              return True
         if left_element >= a and right_element <= b:
              __UpperCamelCase    							=val
              if left_element != right_element:
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=val
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
              return True
         __UpperCamelCase    							=(left_element + right_element) // 2
         self.update(self.left(A_    )					,   A_					,   A_					,   A_					,   A_					,   A_    )
         self.update(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=max(
             self.segment_tree[self.left(A_    )]					,   self.segment_tree[self.right(A_    )]    )
         return True






    def _a					(			self					,   A_					,   A_					,   A_					,   A_					,   A_    )  ->       int | float:
         if self.flag[idx] is True:
              __UpperCamelCase    							=self.lazy[idx]
              __UpperCamelCase    							=False
              if left_element != right_element:
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=self.lazy[idx]
                   __UpperCamelCase    							=True
                   __UpperCamelCase    							=True
         if right_element < a or left_element > b:
              return -math.inf
         if left_element >= a and right_element <= b:
              return self.segment_tree[idx]
         __UpperCamelCase    							=(left_element + right_element) // 2
         __UpperCamelCase    							=self.query(self.left(A_    )					,   A_					,   A_					,   A_					,   A_    )
         __UpperCamelCase    							=self.query(self.right(A_    )					,   mid + 1					,   A_					,   A_					,   A_    )
         return max(A_					,   A_    )






    def __str__(			self    )  ->       str:
         return str([self.query(1					,   1					,   self.size					,   A_					,   A_    ) for i in range(1					,   self.size + 1    )]    )


if __name__ == "__main__":
   _A		       =						[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
   _A		       =						15
   _A		       =						SegmentTree(size)
   segt.build(1, 1, size, A)
   print(segt.query(1, 1, size, 4, 6))
   print(segt.query(1, 1, size, 7, 11))
   print(segt.query(1, 1, size, 7, 12))
   segt.update(1, 1, size, 1, 3, 111)
   print(segt.query(1, 1, size, 1, 15))
   segt.update(1, 1, size, 7, 8, 235)
   print(segt)





