

'''simple docstring'''

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


UpperCAmelCase							=					logging.getLogger(__name__)


@dataclass
class 					lowerCAmelCase       :
							lowerCAmelCase_				=	field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}				)
							lowerCAmelCase_				=	field(
							    default=A				,     metadata={"help": "Pretrained config name or path if not the same as model_name"}				)
							lowerCAmelCase_				=	field(
							    default=A				,     metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}				)
							lowerCAmelCase_				=	field(
							    default=A				,     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 selected in the list: " + ", ".join(hans_processors.keys()				)}				)
							lowerCAmelCase_				=	field(
							    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}				)
							lowerCAmelCase_				=	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."
							        )
							    }				,     )
							lowerCAmelCase_				=	field(
							    default=A				,     metadata={"help": "Overwrite the cached training and evaluation sets"}				)






def 			__UpperCamelCase							(		lowercase__  :					List[InputFeatures]	):






					'''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__  :					Optional[int]	):






					'''simple docstring'''





					main()


if __name__ == "__main__":
							main()





'''simple docstring'''

from .number_theory.prime_numbers import next_prime


class 					lowerCAmelCase       :







       def __init__(							self     :	int  , __lowercase     :	int  , __lowercase     :	int | None = None  , __lowercase     :	float | None = None  , ):







            """simple docstring"""







            __lowercase     						=size_table
            __lowercase     						=[None] * self.size_table
            __lowercase     						=0.7_5 if lim_charge is None else lim_charge
            __lowercase     						=1 if charge_factor is None else charge_factor
            __lowercase     						=[]
            __lowercase     						={}







       def      snake_case				(							self     :	Dict ):







            """simple docstring"""







            return self._keys







       def      snake_case				(							self     :	Optional[Any] ):







            """simple docstring"""







            return sum(1 for slot in self.values if slot is not None ) / (
                self.size_table * self.charge_factor
            )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            return key % self.size_table







       def      snake_case				(							self     :	int  , __lowercase     :	Any ):







            """simple docstring"""







            print(f'''step {step_ord}''' )
            print(list(range(len(self.values ) ) ) )
            print(self.values )







       def      snake_case				(							self     :	int  , __lowercase     :	int ):







            """simple docstring"""







            __lowercase     						=1
            __lowercase     						=values
            for value in values:
                 self.insert_data(__lowercase )
                 self._step_by_step(__lowercase )
                 i += 1







       def      snake_case				(							self     :	List[str]  , __lowercase     :	Optional[int]  , __lowercase     :	List[str] ):







            """simple docstring"""







            __lowercase     						=data
            __lowercase     						=data







       def      snake_case				(							self     :	int  , __lowercase     :	Any  , __lowercase     :	Optional[Any]=None ):







            """simple docstring"""







            __lowercase     						=self.hash_function(key + 1 )

            while self.values[new_key] is not None and self.values[new_key] != key:
                 if self.values.count(__lowercase ) > 0:
                      __lowercase     						=self.hash_function(new_key + 1 )
                 else:
                      __lowercase     						=None
                      break

            return new_key







       def      snake_case				(							self     :	Any ):







            """simple docstring"""







            __lowercase     						=[value for value in self.values if value is not None]
            __lowercase     						=next_prime(self.size_table  , factor=2 )
            self._keys.clear()
            __lowercase     						=[None] * self.size_table  # hell's pointers D: don't DRY ;/
            for value in survivor_values:
                 self.insert_data(__lowercase )







       def      snake_case				(							self     :	Optional[Any]  , __lowercase     :	Any ):







            """simple docstring"""







            __lowercase     						=self.hash_function(__lowercase )

            if self.values[key] is None:
                 self._set_value(__lowercase  , __lowercase )

            elif self.values[key] == data:
                 pass

            else:
                 __lowercase     						=self._collision_resolution(__lowercase  , __lowercase )
                 if collision_resolution is not None:
                      self._set_value(__lowercase  , __lowercase )
                 else:
                      self.rehashing()
                      self.insert_data(__lowercase )


