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  :					List[Any]          =  logging.getLogger(__name__)




@dataclass
class        _UpperCamelCase				:


			"""simple docstring"""






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




@dataclass
class        _UpperCamelCase				:


			"""simple docstring"""






			__a  :			str      			=						field(
			    metadata={'''help''': '''The name of the task to train selected in the list: ''' + ''', '''.join(hans_processors.keys()			)}			)
			__a  :			str      			=						field(
			    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}			)
			__a  :			int      			=						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.'''
			        )
			    }						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}			)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__lowercase			      =							default_data_collator(lowercase							)
				__lowercase			      =							batch.pop('''pairID'''							,      lowercase							)
				return batch





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
				__lowercase      ,					__lowercase      ,					__lowercase			      =							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'''							,      lowercase							)

				# Set seed
				set_seed(training_args.seed							)

				try:
								__lowercase			      =							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.

				__lowercase			      =							AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path							,      num_labels=lowercase							,      finetuning_task=data_args.task_name							,      cache_dir=model_args.cache_dir							,      )
				__lowercase			      =							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							,      )
				__lowercase			      =							AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path							,      from_tf=bool('''.ckpt''' in model_args.model_name_or_path							)							,      config=lowercase							,      cache_dir=model_args.cache_dir							,      )

				# Get datasets
				__lowercase			      =							(
				    HansDataset(
				        data_dir=data_args.data_dir							,      tokenizer=lowercase							,      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
				)
				__lowercase			      =							(
				    HansDataset(
				        data_dir=data_args.data_dir							,      tokenizer=lowercase							,      task=data_args.task_name							,      max_seq_length=data_args.max_seq_length							,      overwrite_cache=data_args.overwrite_cache							,      evaluate=lowercase							,      )
				    if training_args.do_eval
				    else None
				)

				# Initialize our Trainer
				__lowercase			      =							Trainer(
				    model=lowercase							,      args=lowercase							,      train_dataset=lowercase							,      eval_dataset=lowercase							,      data_collator=lowercase							,      )

				# 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 ***'''							)

								__lowercase			      =							trainer.predict(lowercase							)
								__lowercase			      =							output.predictions
								__lowercase			      =							np.argmax(lowercase							,      axis=1							)

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

								trainer._log(output.metrics							)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				main()


if __name__ == "__main__":
		main()
__a  :					Union[str, Any]          =  [[0, 1, 1], [0, 0, 1], [1, 0, 0]]




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							name
							__lowercase			      =							[]
							__lowercase			      =							[]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.inbound.append(lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							self.outbound.append(lowerCAmelCase__						)





			def __repr__(       self						)					-> Tuple:

							'''simple docstring'''


							return F"<node={self.name} inbound={self.inbound} outbound={self.outbound}>"





def     UpperCAmelCase						(   lowercase							,      lowercase=3							,      lowercase=0.85							):







				"""simple docstring"""



				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							1

				__lowercase			      =							{}
				for node in nodes:
								__lowercase			      =							len(node.outbound							)

				for i in range(lowercase							):
								print(F"======= Iteration {i + 1} ======="							)
								for _, node in enumerate(lowercase							):
												__lowercase			      =							(1 - d) + d * sum(
												    ranks[ib] / outbounds[ib] for ib in node.inbound							)
								print(lowercase							)





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							list(input('''Enter Names of the Nodes: '''							).split()							)

				__lowercase			      =							[Node(lowercase							) for name in names]

				for ri, row in enumerate(lowercase							):
								for ci, col in enumerate(lowercase							):
												if col == 1:
																nodes[ci].add_inbound(names[ri]							)
																nodes[ri].add_outbound(names[ci]							)

				print('''======= Nodes ======='''							)
				for node in nodes:
								print(lowercase							)

				page_rank(lowercase							)


if __name__ == "__main__":
		main()