

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        _A						:
  SCREAMING_SNAKE_CASE			:					Optional[Any]				=     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.'''
          )
      }      ,   )
  SCREAMING_SNAKE_CASE			:					Optional[Any]				=     field(
      default=a__      ,   metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''})
  SCREAMING_SNAKE_CASE			:					Optional[Any]				=     field(
      default=a__      ,   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.'''
          )
      }      ,   )
  SCREAMING_SNAKE_CASE			:					Union[str, Any]				=     field(
      default=a__      ,   metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of training examples to this '''
              '''value if set.'''
          )
      }      ,   )
  SCREAMING_SNAKE_CASE			:					Optional[int]				=     field(
      default=a__      ,   metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
              '''value if set.'''
          )
      }      ,   )
  SCREAMING_SNAKE_CASE			:					int				=     field(
      default=a__      ,   metadata={
          '''help''': (
              '''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
              '''value if set.'''
          )
      }      ,   )

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

def       A_     (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_ :     List[Any]							=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )
      SCREAMING_SNAKE_CASE_ :     Tuple							=   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'    ,      a     )

      # 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()

      SCREAMING_SNAKE_CASE_ :     List[Any]							=   training_args.get_process_log_level()
      logger.setLevel(a     )
      datasets.utils.logging.set_verbosity(a     )
      transformers.utils.logging.set_verbosity(a     )
      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.
      SCREAMING_SNAKE_CASE_ :     Any							=   None
      if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_SNAKE_CASE_ :     List[str]							=   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:
                  SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   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:
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   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    ,      )
            SCREAMING_SNAKE_CASE_ :     Any							=   train_dataset.features['''label'''].names

      if training_args.do_eval:
            SCREAMING_SNAKE_CASE_ :     str							=   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    ,      )
            SCREAMING_SNAKE_CASE_ :     Optional[int]							=   eval_dataset.features['''label'''].names

      if training_args.do_predict:
            SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   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    ,      )
            SCREAMING_SNAKE_CASE_ :     Optional[int]							=   predict_dataset.features['''label'''].names

      # Labels
      SCREAMING_SNAKE_CASE_ :     str							=   len(a     )

      # Load pretrained model and tokenizer
      # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.
      SCREAMING_SNAKE_CASE_ :     Any							=   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path    ,      num_labels=a    ,      idalabel={str(a     ): label for i, label in enumerate(a     )}    ,      labelaid={label: i for i, label in enumerate(a     )}    ,      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    ,      )
      SCREAMING_SNAKE_CASE_ :     int							=   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    ,      )
      SCREAMING_SNAKE_CASE_ :     List[Any]							=   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    ,      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:
            SCREAMING_SNAKE_CASE_ :     int							=   '''max_length'''
      else:
            # We will pad later, dynamically at batch creation, to the max sequence length in each batch
            SCREAMING_SNAKE_CASE_ :     Any							=   False

      def preprocess_function(a     ):
            # Tokenize the texts
            return tokenizer(
                examples['premise']    ,      examples['hypothesis']    ,      padding=a    ,      max_length=data_args.max_seq_length    ,      truncation=a    ,      )

      if training_args.do_train:
            if data_args.max_train_samples is not None:
                  SCREAMING_SNAKE_CASE_ :     str							=   min(len(a     )    ,      data_args.max_train_samples     )
                  SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   train_dataset.select(range(a     )     )
            with training_args.main_process_first(desc='train dataset map pre-processing'     ):
                  SCREAMING_SNAKE_CASE_ :     Optional[int]							=   train_dataset.map(
                      a    ,      batched=a    ,      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(a     )     )    ,      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:
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   min(len(a     )    ,      data_args.max_eval_samples     )
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   eval_dataset.select(range(a     )     )
            with training_args.main_process_first(desc='validation dataset map pre-processing'     ):
                  SCREAMING_SNAKE_CASE_ :     Optional[int]							=   eval_dataset.map(
                      a    ,      batched=a    ,      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:
                  SCREAMING_SNAKE_CASE_ :     str							=   min(len(a     )    ,      data_args.max_predict_samples     )
                  SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   predict_dataset.select(range(a     )     )
            with training_args.main_process_first(desc='prediction dataset map pre-processing'     ):
                  SCREAMING_SNAKE_CASE_ :     Tuple							=   predict_dataset.map(
                      a    ,      batched=a    ,      load_from_cache_file=not data_args.overwrite_cache    ,      desc='Running tokenizer on prediction dataset'    ,      )

                   # Get the metric function
      SCREAMING_SNAKE_CASE_ :     Optional[int]							=   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(a     ):
            SCREAMING_SNAKE_CASE_ :     Dict							=   p.predictions[0] if isinstance(p.predictions    ,      a     ) else p.predictions
            SCREAMING_SNAKE_CASE_ :     int							=   np.argmax(a    ,      axis=1     )
            return metric.compute(predictions=a    ,      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:
            SCREAMING_SNAKE_CASE_ :     Any							=   default_data_collator
      elif training_args.fpaa:
            SCREAMING_SNAKE_CASE_ :     int							=   DataCollatorWithPadding(a    ,      pad_to_multiple_of=8     )
      else:
            SCREAMING_SNAKE_CASE_ :     List[Any]							=   None

      # Initialize our Trainer
      SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   Trainer(
          model=a    ,      args=a    ,      train_dataset=train_dataset if training_args.do_train else None    ,      eval_dataset=eval_dataset if training_args.do_eval else None    ,      compute_metrics=a    ,      tokenizer=a    ,      data_collator=a    ,      )

      # Training
      if training_args.do_train:
            SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_ :     Any							=   training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE_ :     List[str]							=   last_checkpoint
            SCREAMING_SNAKE_CASE_ :     Dict							=   trainer.train(resume_from_checkpoint=a     )
            SCREAMING_SNAKE_CASE_ :     int							=   train_result.metrics
            SCREAMING_SNAKE_CASE_ :     Tuple							=   (
                data_args.max_train_samples if data_args.max_train_samples is not None else len(a     )
            )
            SCREAMING_SNAKE_CASE_ :     Tuple							=   min(a    ,      len(a     )     )

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

            trainer.log_metrics('train'    ,      a     )
            trainer.save_metrics('train'    ,      a     )
            trainer.save_state()

      # Evaluation
      if training_args.do_eval:
            logger.info('*** Evaluate ***'     )
            SCREAMING_SNAKE_CASE_ :     str							=   trainer.evaluate(eval_dataset=a     )

            SCREAMING_SNAKE_CASE_ :     Optional[int]							=   data_args.max_eval_samples if data_args.max_eval_samples is not None else len(a     )
            SCREAMING_SNAKE_CASE_ :     Optional[int]							=   min(a    ,      len(a     )     )

            trainer.log_metrics('eval'    ,      a     )
            trainer.save_metrics('eval'    ,      a     )

      # Prediction
      if training_args.do_predict:
            logger.info('*** Predict ***'     )
            SCREAMING_SNAKE_CASE_ :     int							=   trainer.predict(a    ,      metric_key_prefix='predict'     )

            SCREAMING_SNAKE_CASE_ :     str							=   (
                data_args.max_predict_samples if data_args.max_predict_samples is not None else len(a     )
            )
            SCREAMING_SNAKE_CASE_ :     List[Any]							=   min(a    ,      len(a     )     )

            trainer.log_metrics('predict'    ,      a     )
            trainer.save_metrics('predict'    ,      a     )

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


if __name__ == "__main__":
    main()







'''simple docstring'''

import doctest
from collections import deque

import numpy as np



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self )  ->  None:
        lowercase__				:    str  =					[2, 1, 2, -1]
        lowercase__				:    str  =					[1, 2, 3, 4]


       def _lowerCAmelCase(						self )  ->  list[float]:
        lowercase__				:    Optional[Any]  =					len(self.first_signal )
        lowercase__				:    Union[str, Any]  =					len(self.second_signal )

        lowercase__				:    int  =					max(__lowerCAmelCase	,   __lowerCAmelCase )

        # create a zero matrix of max_length x max_length
        lowercase__				:    List[str]  =					[[0] * max_length for i in range(__lowerCAmelCase )]

        # fills the smaller signal with zeros to make both signals of same length
        if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
        elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
        for i in range(__lowerCAmelCase ):
         lowercase__				:    int  =					deque(self.second_signal )
         rotated_signal.rotate(__lowerCAmelCase )
         for j, item in enumerate(__lowerCAmelCase ):
          matrix[i][j] += item

        # multiply the matrix with the first signal
        lowercase__				:    Optional[int]  =					np.matmul(np.transpose(__lowerCAmelCase )	,   np.transpose(self.first_signal ) )

        # rounding-off to two decimal places
        return [round(__lowerCAmelCase	,   2 ) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()

