'''simple docstring'''

import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")

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





@dataclass
class 							lowercase						:
   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=UpperCamelCase_ ,				metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={
           'help': (
               'Whether to pad all samples to `max_seq_length`. '
               'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of training examples to this '
               'value if set.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
               'value if set.'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={
           'help': (
               'For debugging purposes or quicker training, truncate the number of prediction examples to this '
               'value if set.'
           )
       } ,				)





@dataclass
class 							lowercase						:
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Train language if it is different from the evaluation language.'} )
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} ,				)
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} ,				)
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} ,				)
   lowerCamelCase_    					=field(
       default='main' ,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} ,				)
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={
           'help': (
               'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
               'with private models).'
           )
       } ,				)
   lowerCamelCase_    					=field(
       default=UpperCamelCase_ ,				metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} ,				)




def __a (	)	->							Union[str, Any]:
      '''simple docstring'''


      lowercase_     =      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
      lowercase_     =      parser.parse_args_into_dataclasses()

      # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
      # information sent is the one passed as arguments along with your Python/PyTorch versions.
      send_example_telemetry("run_xnli" ,		__lowerCAmelCase				)

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,		datefmt="%m/%d/%Y %H:%M:%S" ,		handlers=[logging.StreamHandler(sys.stdout				)] ,		)

      if training_args.should_log:
            # The default of training_args.log_level is passive, so we set log level at info here to have that default.
            transformers.utils.logging.set_verbosity_info()

      lowercase_     =      training_args.get_process_log_level()
      logger.setLevel(__lowerCAmelCase				)
      datasets.utils.logging.set_verbosity(__lowerCAmelCase				)
      transformers.utils.logging.set_verbosity(__lowerCAmelCase				)
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()

      # Log on each process the small summary:
      logger.warning(
          f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
          + f'distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'				)
      logger.info(f'Training/evaluation parameters {training_args}'				)

      # Detecting last checkpoint.
      lowercase_     =      None
      if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
            lowercase_     =      get_last_checkpoint(training_args.output_dir				)
            if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
                  raise ValueError(
                      f'Output directory ({training_args.output_dir}) already exists and is not empty. '
                      "Use --overwrite_output_dir to overcome."				)
            elif last_checkpoint is not None:
                  logger.info(
                      f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                      "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."				)

    # Set seed before initializing model.
      set_seed(training_args.seed				)

      # In distributed training, the load_dataset function guarantees that only one local process can concurrently
      # download the dataset.
      # Downloading and loading xnli dataset from the hub.
      if training_args.do_train:
            if model_args.train_language is None:
                  lowercase_     =      load_dataset(
                      "xnli" ,		model_args.language ,		split="train" ,		cache_dir=model_args.cache_dir ,		use_auth_token=True if model_args.use_auth_token else None ,		)
            else:
                  lowercase_     =      load_dataset(
                      "xnli" ,		model_args.train_language ,		split="train" ,		cache_dir=model_args.cache_dir ,		use_auth_token=True if model_args.use_auth_token else None ,		)
            lowercase_     =      train_dataset.features["""label"""].names

      if training_args.do_eval:
            lowercase_     =      load_dataset(
                "xnli" ,		model_args.language ,		split="validation" ,		cache_dir=model_args.cache_dir ,		use_auth_token=True if model_args.use_auth_token else None ,		)
            lowercase_     =      eval_dataset.features["""label"""].names

      if training_args.do_predict:
            lowercase_     =      load_dataset(
                "xnli" ,		model_args.language ,		split="test" ,		cache_dir=model_args.cache_dir ,		use_auth_token=True if model_args.use_auth_token else None ,		)
            lowercase_     =      predict_dataset.features["""label"""].names

      # Labels
      lowercase_     =      len(__lowerCAmelCase				)

      # Load pretrained model and tokenizer
      # In 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=__lowerCAmelCase ,		idalabel={str(__lowerCAmelCase				): label for i, label in enumerate(__lowerCAmelCase				)} ,		labelaid={label: i for i, label in enumerate(__lowerCAmelCase				)} ,		finetuning_task="xnli" ,		cache_dir=model_args.cache_dir ,		revision=model_args.model_revision ,		use_auth_token=True if model_args.use_auth_token else None ,		)
      lowercase_     =      AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,		do_lower_case=model_args.do_lower_case ,		cache_dir=model_args.cache_dir ,		use_fast=model_args.use_fast_tokenizer ,		revision=model_args.model_revision ,		use_auth_token=True if model_args.use_auth_token else None ,		)
      lowercase_     =      AutoModelForSequenceClassification.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 ,		revision=model_args.model_revision ,		use_auth_token=True if model_args.use_auth_token else None ,		ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,		)

      # Preprocessing the datasets
      # Padding strategy
      if data_args.pad_to_max_length:
            lowercase_     =      """max_length"""
      else:
            # We will pad later, dynamically at batch creation, to the max sequence length in each batch
            lowercase_     =      False

      def preprocess_function(__lowerCamelCase			:						List[str]				):
            # Tokenize the texts
            return tokenizer(
                examples["premise"] ,		examples["hypothesis"] ,		padding=__lowerCAmelCase ,		max_length=data_args.max_seq_length ,		truncation=__lowerCAmelCase ,		)

      if training_args.do_train:
            if data_args.max_train_samples is not None:
                  lowercase_     =      min(len(__lowerCAmelCase				) ,		data_args.max_train_samples				)
                  lowercase_     =      train_dataset.select(range(__lowerCAmelCase				)				)
            with training_args.main_process_first(desc="train dataset map pre-processing"				):
                  lowercase_     =      train_dataset.map(
                      __lowerCAmelCase ,		batched=__lowerCAmelCase ,		load_from_cache_file=not data_args.overwrite_cache ,		desc="Running tokenizer on train dataset" ,		)
            # Log a few random samples from the training set:
            for index in random.sample(range(len(__lowerCAmelCase				)				) ,		3				):
                  logger.info(f'Sample {index} of the training set: {train_dataset[index]}.'				)

      if training_args.do_eval:
            if data_args.max_eval_samples is not None:
                  lowercase_     =      min(len(__lowerCAmelCase				) ,		data_args.max_eval_samples				)
                  lowercase_     =      eval_dataset.select(range(__lowerCAmelCase				)				)
            with training_args.main_process_first(desc="validation dataset map pre-processing"				):
                  lowercase_     =      eval_dataset.map(
                      __lowerCAmelCase ,		batched=__lowerCAmelCase ,		load_from_cache_file=not data_args.overwrite_cache ,		desc="Running tokenizer on validation dataset" ,		)

      if training_args.do_predict:
            if data_args.max_predict_samples is not None:
                  lowercase_     =      min(len(__lowerCAmelCase				) ,		data_args.max_predict_samples				)
                  lowercase_     =      predict_dataset.select(range(__lowerCAmelCase				)				)
            with training_args.main_process_first(desc="prediction dataset map pre-processing"				):
                  lowercase_     =      predict_dataset.map(
                      __lowerCAmelCase ,		batched=__lowerCAmelCase ,		load_from_cache_file=not data_args.overwrite_cache ,		desc="Running tokenizer on prediction dataset" ,		)

    # Get the metric function
      lowercase_     =      evaluate.load("xnli"				)

      # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
      # predictions and label_ids field) and has to return a dictionary string to float.
      def compute_metrics(__lowerCamelCase			:						Optional[int]				):
            lowercase_     =      p.predictions[0] if isinstance(p.predictions ,		__lowerCAmelCase				) else p.predictions
            lowercase_     =      np.argmax(__lowerCAmelCase ,		axis=1				)
            return metric.compute(predictions=__lowerCAmelCase ,		references=p.label_ids				)

      # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
      if data_args.pad_to_max_length:
            lowercase_     =      default_data_collator
      elif training_args.fpaa:
            lowercase_     =      DataCollatorWithPadding(__lowerCAmelCase ,		pad_to_multiple_of=8				)
      else:
            lowercase_     =      None

      # Initialize our Trainer
      lowercase_     =      Trainer(
          model=__lowerCAmelCase ,		args=__lowerCAmelCase ,		train_dataset=train_dataset if training_args.do_train else None ,		eval_dataset=eval_dataset if training_args.do_eval else None ,		compute_metrics=__lowerCAmelCase ,		tokenizer=__lowerCAmelCase ,		data_collator=__lowerCAmelCase ,		)

      # Training
      if training_args.do_train:
            lowercase_     =      None
            if training_args.resume_from_checkpoint is not None:
                  lowercase_     =      training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  lowercase_     =      last_checkpoint
            lowercase_     =      trainer.train(resume_from_checkpoint=__lowerCAmelCase				)
            lowercase_     =      train_result.metrics
            lowercase_     =      (
                data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCAmelCase				)
            )
            lowercase_     =      min(__lowerCAmelCase ,		len(__lowerCAmelCase				)				)

            trainer.save_model()  # Saves the tokenizer too for easy upload

            trainer.log_metrics("train" ,		__lowerCAmelCase				)
            trainer.save_metrics("train" ,		__lowerCAmelCase				)
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            logger.info("*** Evaluate ***"				)
            lowercase_     =      trainer.evaluate(eval_dataset=__lowerCAmelCase				)

            lowercase_     =      data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__lowerCAmelCase				)
            lowercase_     =      min(__lowerCAmelCase ,		len(__lowerCAmelCase				)				)

            trainer.log_metrics("eval" ,		__lowerCAmelCase				)
            trainer.save_metrics("eval" ,		__lowerCAmelCase				)

      # Prediction
      if training_args.do_predict:
            logger.info("*** Predict ***"				)
            lowercase_     =      trainer.predict(__lowerCAmelCase ,		metric_key_prefix="predict"				)

            lowercase_     =      (
                data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__lowerCAmelCase				)
            )
            lowercase_     =      min(__lowerCAmelCase ,		len(__lowerCAmelCase				)				)

            trainer.log_metrics("predict" ,		__lowerCAmelCase				)
            trainer.save_metrics("predict" ,		__lowerCAmelCase				)

            lowercase_     =      np.argmax(__lowerCAmelCase ,		axis=1				)
            lowercase_     =      os.path.join(training_args.output_dir ,		"predictions.txt"				)
            if trainer.is_world_process_zero():
                  with open(__lowerCAmelCase ,		"w"				) as writer:
                        writer.write("index\tprediction\n"				)
                        for index, item in enumerate(__lowerCAmelCase				):
                              lowercase_     =      label_list[item]
                              writer.write(f'{index}\t{item}\n'				)


if __name__ == "__main__":
       main()







'''simple docstring'''

import functools
from typing import Any


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						list[str]				)	->							bool:
      '''simple docstring'''


      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or len(__lowerCamelCase				) == 0:
            raise ValueError("the string should be not empty string"				)

      if not isinstance(__lowerCamelCase ,		__lowerCamelCase				) or not all(
          isinstance(__lowerCamelCase ,		__lowerCamelCase				) and len(__lowerCamelCase				) > 0 for item in words				):
            raise ValueError("the words should be a list of non-empty strings"				)

      # Build trie
      lowercase_     =      {}
      lowercase_     =      "WORD_KEEPER"

      for word in words:
            lowercase_     =      trie
            for c in word:
                  if c not in trie_node:
                        lowercase_     =      {}

                  lowercase_     =      trie_node[c]

            lowercase_     =      True

      lowercase_     =      len(__lowerCamelCase				)

      # Dynamic programming method
      @functools.cache
      def is_breakable(__lowerCamelCase			:						int				) -> bool:
            if index == len_string:
                  return True

            lowercase_     =      trie
            for i in range(__lowerCamelCase ,		__lowerCamelCase				):
                  lowercase_     =      trie_node.get(string[i] ,		__lowerCamelCase				)

                  if trie_node is None:
                        return False

                  if trie_node.get(__lowerCamelCase ,		__lowerCamelCase				) and is_breakable(i + 1				):
                        return True

            return False

      return is_breakable(0				)


if __name__ == "__main__":
       import doctest

       doctest.testmod()






