"""simple docstring"""






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


lowerCAmelCase__						:						Any		  =			logging.getLogger(__name__)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				return (preds == labels).mean()
@dataclass
class        snake_case :




					"""simple docstring"""

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






@dataclass
class        snake_case :




					"""simple docstring"""

					snake_case__       					=				field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys()       )}       )
					snake_case__       					=				field(metadata={"help": "Should contain the data files for the task."}       )
					snake_case__       					=				field(
					    default=1_28   ,					metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }   ,					)
					snake_case__       					=				field(
					    default=_a   ,					metadata={"help": "Overwrite the cached training and evaluation sets"}       )






def 						a_		(      ):
				UpperCAmelCase__    =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							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'      ,    lowerCamelCase )

				# Set seed
				set_seed(training_args.seed )

				try:
								UpperCAmelCase__    =							processors[data_args.task_name]()
								UpperCAmelCase__    =							processor.get_labels()
								UpperCAmelCase__    =							len(lowerCamelCase )
				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__    =							AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,    num_labels=lowerCamelCase      ,    finetuning_task=data_args.task_name      ,    cache_dir=model_args.cache_dir      ,    )
				UpperCAmelCase__    =							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__    =							AutoModelForMultipleChoice.from_pretrained(
				    model_args.model_name_or_path      ,    from_tf=bool('.ckpt' in model_args.model_name_or_path )      ,    config=lowerCamelCase      ,    cache_dir=model_args.cache_dir      ,    )

				# Get datasets
				UpperCAmelCase__    =							(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir      ,    tokenizer=lowerCamelCase      ,    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
				)
				UpperCAmelCase__    =							(
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir      ,    tokenizer=lowerCamelCase      ,    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(lowerCamelCase ) -> Dict:
								UpperCAmelCase__    =							np.argmax(p.predictions      ,    axis=1 )
								return {"acc": simple_accuracy(lowerCamelCase      ,    p.label_ids )}

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

				# Initialize our Trainer
				UpperCAmelCase__    =							Trainer(
				    model=lowerCamelCase      ,    args=lowerCamelCase      ,    train_dataset=lowerCamelCase      ,    eval_dataset=lowerCamelCase      ,    compute_metrics=lowerCamelCase      ,    data_collator=lowerCamelCase      ,    )

				# 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
				UpperCAmelCase__    =							{}
				if training_args.do_eval:
								logger.info('*** Evaluate ***' )

								UpperCAmelCase__    =							trainer.evaluate()

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

																results.update(lowerCamelCase )

				return results



def 						a_		(      lowerCamelCase ):
				main()


if __name__ == "__main__":
						main()




"""simple docstring"""






from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 						a_		(      lowerCamelCase ):
				return "".join(sorted(lowerCamelCase ) )



def 						a_		(      lowerCamelCase ):
				return word_by_signature[signature(lowerCamelCase )]


lowerCAmelCase__						:						str		  =			Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
lowerCAmelCase__						:						str		  =			sorted({word.strip().lower() for word in data.splitlines()})

lowerCAmelCase__						:						Optional[Any]		  =			collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



