






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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A_			:Union[str, Any]	    =		logging.getLogger(__name__)




@dataclass
class 							__A	:





				"""simple docstring"""







				UpperCamelCase__					: str     					=field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					)
				UpperCamelCase__					: Optional[str]     					=field(
				    default=a ,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}					)
				UpperCamelCase__					: Optional[str]     					=field(
				    default=a ,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}					)
				UpperCamelCase__					: Optional[str]     					=field(
				    default=a ,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} ,	)




@dataclass
class 							__A	:





				"""simple docstring"""







				UpperCamelCase__					: str     					=field(
				    metadata={"""help""": """The name of the task to train selected in the list: """ + """, """.join(hans_processors.keys()					)}					)
				UpperCamelCase__					: str     					=field(
				    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}					)
				UpperCamelCase__					: int     					=field(
				    default=1_2_8 ,	metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    } ,	)
				UpperCamelCase__					: bool     					=field(
				    default=a ,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}					)






def 				A	(			a_   )	->		Dict[str, torch.Tensor]:
					__UpperCamelCase :   Tuple									=default_data_collator(a_   )
					__UpperCamelCase :   Any									=batch.pop('pairID'				,a_   )
					return batch






def 				A	(			)	->		Optional[Any]:
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					__UpperCamelCase :   int									=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   Tuple									=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'				,a_   )

					# Set seed
					set_seed(training_args.seed   )

					try:
										__UpperCamelCase :   int									=hans_tasks_num_labels[data_args.task_name]
					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.

					__UpperCamelCase :   Tuple									=AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path				,num_labels=a_				,finetuning_task=data_args.task_name				,cache_dir=model_args.cache_dir				,)
					__UpperCamelCase :   str									=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				,)
					__UpperCamelCase :   Optional[int]									=AutoModelForSequenceClassification.from_pretrained(
					    model_args.model_name_or_path				,from_tf=bool('.ckpt' in model_args.model_name_or_path   )				,config=a_				,cache_dir=model_args.cache_dir				,)

					# Get datasets
					__UpperCamelCase :   int									=(
					    HansDataset(
					        data_dir=data_args.data_dir				,tokenizer=a_				,task=data_args.task_name				,max_seq_length=data_args.max_seq_length				,overwrite_cache=data_args.overwrite_cache				,)
					    if training_args.do_train
					    else None
					)
					__UpperCamelCase :   Union[str, Any]									=(
					    HansDataset(
					        data_dir=data_args.data_dir				,tokenizer=a_				,task=data_args.task_name				,max_seq_length=data_args.max_seq_length				,overwrite_cache=data_args.overwrite_cache				,evaluate=a_				,)
					    if training_args.do_eval
					    else None
					)

					# Initialize our Trainer
					__UpperCamelCase :   int									=Trainer(
					    model=a_				,args=a_				,train_dataset=a_				,eval_dataset=a_				,data_collator=a_				,)

					# 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
					if training_args.do_eval:
										logger.info('*** Evaluate ***'   )

										__UpperCamelCase :   Optional[Any]									=trainer.predict(a_   )
										__UpperCamelCase :   List[str]									=output.predictions
										__UpperCamelCase :   List[Any]									=np.argmax(a_				,axis=1   )

										__UpperCamelCase :   List[str]									=[ex.pairID for ex in eval_dataset]
										__UpperCamelCase :   int									=os.path.join(training_args.output_dir				,'hans_predictions.txt'   )
										__UpperCamelCase :   str									=eval_dataset.get_labels()
										if trainer.is_world_master():
															with open(a_				,'w'   ) as writer:
																				writer.write('pairID,gold_label\n'   )
																				for pid, pred in zip(a_				,a_   ):
																									writer.write('ex' + str(a_   ) + ',' + label_list[int(a_   )] + '\n'   )

										trainer._log(output.metrics   )






def 				A	(			a_   )	->		Tuple:
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
							main()









from __future__ import annotations




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__ = 0       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=value
									__UpperCamelCase :   Node | None									=None
									__UpperCamelCase :   Node | None									=None






def 				A	(			a_				,a_   )	->		Node | None:
					if treea is None:
										return treea
					if treea is None:
										return treea

					__UpperCamelCase :   Dict									=treea.value + treea.value
					__UpperCamelCase :   Any									=merge_two_binary_trees(treea.left				,treea.left   )
					__UpperCamelCase :   Union[str, Any]									=merge_two_binary_trees(treea.right				,treea.right   )
					return treea






def 				A	(			a_   )	->		None:
					if root:
										print(root.value   )
										print_preorder(root.left   )
										print_preorder(root.right   )


if __name__ == "__main__":
							A_			:List[str]	    =		Node(1)
							A_			:Dict	    =		Node(2)
							A_			:str	    =		Node(3)
							A_			:Union[str, Any]	    =		Node(4)

							A_			:Any	    =		Node(2)
							A_			:Tuple	    =		Node(4)
							A_			:Optional[Any]	    =		Node(6)
							A_			:Optional[Any]	    =		Node(9)
							A_			:str	    =		Node(5)

							print('''Tree1 is: ''')
							print_preorder(treea)
							print('''Tree2 is: ''')
							print_preorder(treea)
							A_			:Optional[Any]	    =		merge_two_binary_trees(treea, treea)
							print('''Merged Tree is: ''')
							print_preorder(merged_tree)

