code: 
  
  
  
  """simple docstring"""
  import logging
  import os
  import sys
  from dataclasses import dataclass, field
  from typing import List, Optional
  
  import torch
  from datasets import Dataset
  from torch import nn
  from tqdm.auto import tqdm
  
  from transformers import (
      AutoModelForSequenceClassification,
      AutoTokenizer,
      HfArgumentParser,
      Trainer,
      TrainingArguments,
      set_seed,
      utils,
  )
  from transformers.trainer_utils import get_last_checkpoint, is_main_process
  
  
  _lowerCAmelCase					:Tuple  =      '\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'
  
  _lowerCAmelCase					:Tuple  =      logging.getLogger(__name__)
  @dataclass
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =field(
           default='''roberta-large-mnli'''	,metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}   )
       a__             =field(
           default='''This example is {}.'''	,metadata={
               '''help''': (
                   '''Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}'''
                   '''where class name is inserted.'''
               )
           }	,)
       a__             =field(
           default=3_2	,metadata={'''help''': '''Batch size for generating teacher predictions.'''}   )
       a__             =field(
           default=a	,metadata={
               '''help''': (
                   '''Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'''
                   '''multi-class multi-label classification).'''
               )
           }	,)
       a__             =field(
           default=1.0	,metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''}   )
  
  
  
  @dataclass
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =field(
           default='''distilbert-base-uncased'''	,metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}   )
  
  
  
  @dataclass
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}   )
       a__             =field(metadata={'''help''': '''Text file with one class name per line.'''}   )
       a__             =field(
           default=a	,metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}	,)
  
  
  
  @dataclass
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       a__             =field(
           default=a	,metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}	,)
       a__             =field(
           default=3_2	,metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''}   )
       a__             =field(
           default=1_2_8	,metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''}   )
       a__             =field(default=1.0	,metadata={'''help''': '''Total number of training epochs to perform.'''}   )
       a__             =field(default=a	,metadata={'''help''': '''Whether to run training of student model.'''}   )
       a__             =field(
           default=a	,metadata={
               '''help''': (
                   '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
                   '''after training.'''
               )
           }	,)
       a__             =field(
           default=0	,metadata={
               '''help''': (
                   '''Limit the total amount of checkpoints.'''
                   '''Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints).'''
               )
           }	,)
  
  
  
  class 							_UpperCAmelCase							(				a   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A ,			A=False	)							->      Dict:
           _UpperCAmelCase							:						Dict       							=					inputs['''labels''']
           _UpperCAmelCase							:						Dict       							=					model(inputs['''input_ids'''] ,			attention_mask=inputs['''attention_mask''']	)
           _UpperCAmelCase							:						int       							=					outputs[0]
  
           _UpperCAmelCase							:						Any       							=					-torch.sum(target_p * logits.log_softmax(dim=-1	) ,			axis=-1	).mean()
  
           if return_outputs:
               return loss, outputs
  
           return loss
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[Any]							):
      _UpperCAmelCase							:						Any       							=					[]
      with open(UpperCamelCase__  , '''r'''							) as f:
          for line in f:
              _UpperCAmelCase							:						Tuple       							=					line.strip()
              if len(UpperCamelCase__							) > 0:
                  lines.append(UpperCamelCase__							)
      return lines
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Union[str, Any]  , UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Union[str, Any]							):
      _UpperCAmelCase							:						Dict       							=					[]
      _UpperCAmelCase							:						List[str]       							=					[]
      for example in examples:
          for name in class_names:
              premises.append(UpperCamelCase__							)
              hypotheses.append(hypothesis_template.format(UpperCamelCase__							)							)
      return premises, hypotheses
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	Optional[int]							):
      for label, ind in config.labelaid.items():
          if label.lower().startswith('''entail'''							):
              return ind
      logger.warning('''Could not identify entailment dimension from teacher config label2id. Setting to -1.'''							)
      return -1
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	str  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	List[str]  , UpperCamelCase__	:	str  , UpperCamelCase__	:	int  , UpperCamelCase__	:	float  , UpperCamelCase__	:	bool  , UpperCamelCase__	:	bool  , UpperCamelCase__	:	bool  , UpperCamelCase__	:	bool  , ):
      _UpperCAmelCase							:						List[Any]       							=					AutoModelForSequenceClassification.from_pretrained(UpperCamelCase__							)
      _UpperCAmelCase							:						List[str]       							=					model.config
      if not no_cuda and torch.cuda.is_available():
          _UpperCAmelCase							:						Any       							=					nn.DataParallel(model.cuda()							)
          batch_size *= len(model.device_ids							)
      _UpperCAmelCase							:						Union[str, Any]       							=					AutoTokenizer.from_pretrained(UpperCamelCase__  , use_fast=UpperCamelCase__							)
  
      _UpperCAmelCase							,       _UpperCAmelCase							:						Union[str, Any]       							=					get_premise_hypothesis_pairs(UpperCamelCase__  , UpperCamelCase__  , UpperCamelCase__							)
      _UpperCAmelCase							:						Tuple       							=					[]
  
      for i in tqdm(range(0  , len(UpperCamelCase__							)  , UpperCamelCase__							)							):
          _UpperCAmelCase							:						str       							=					premises[i : i + batch_size]
          _UpperCAmelCase							:						Any       							=					hypotheses[i : i + batch_size]
  
          _UpperCAmelCase							:						Union[str, Any]       							=					tokenizer(
              UpperCamelCase__  , UpperCamelCase__  , padding=UpperCamelCase__  , truncation='''only_first'''  , return_tensors='''pt'''  , )
  
          with torch.cuda.amp.autocast(enabled=UpperCamelCase__							):
              with torch.no_grad():
                  _UpperCAmelCase							:						Dict       							=					model(**UpperCamelCase__							)
          logits.append(outputs.logits.detach().cpu().float()							)
  
      _UpperCAmelCase							:						Optional[int]       							=					get_entailment_id(UpperCamelCase__							)
      _UpperCAmelCase							:						Union[str, Any]       							=					-1 if entail_id == 0 else 0
      _UpperCAmelCase							:						List[Any]       							=					torch.cat(UpperCamelCase__  , dim=0							)  # N*K x 3
      _UpperCAmelCase							:						Any       							=					logits.reshape(len(UpperCamelCase__							)  , len(UpperCamelCase__							)  , -1							)[..., [contr_id, entail_id]]  # N x K x 2
  
      if multi_label:
          # softmax over (contr, entail) logits for each class independently
          _UpperCAmelCase							:						Tuple       							=					(nli_logits / temperature).softmax(-1							)
      else:
          # softmax over entail logits across classes s.t. class probabilities sum to 1.
          _UpperCAmelCase							:						Optional[int]       							=					(nli_logits / temperature).softmax(1							)
  
      return nli_prob[..., 1]  # N x K
  
  
  
  
  
  def 				lowerCamelCase_		():
      _UpperCAmelCase							:						Optional[Any]       							=					HfArgumentParser(
          (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)  , description=UpperCamelCase__  , )
  
      if len(sys.argv							) == 2 and sys.argv[1].endswith('''.json'''							):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						Union[str, Any]       							=					parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]							)							)
      else:
          _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							:						List[Any]       							=					parser.parse_args_into_dataclasses()
  
      # Detecting last checkpoint.
      _UpperCAmelCase							:						List[str]       							=					None
      if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
          _UpperCAmelCase							:						List[Any]       							=					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.'''							)
  
      # 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							)]  , )
      logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)
  
      # 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}'							)
      # Set the verbosity to info of the Transformers logger (on main process only):
      if is_main_process(training_args.local_rank							):
          utils.logging.set_verbosity_info()
          utils.logging.enable_default_handler()
          utils.logging.enable_explicit_format()
  
      if training_args.local_rank != -1:
          raise ValueError('''Distributed training is not currently supported.'''							)
      if training_args.tpu_num_cores is not None:
          raise ValueError('''TPU acceleration is not currently supported.'''							)
  
      logger.info(F'Training/evaluation parameters {training_args}'							)
  
      # Set seed before initializing model.
      set_seed(training_args.seed							)
  
      # 1. read in data
      _UpperCAmelCase							:						Any       							=					read_lines(data_args.data_file							)
      _UpperCAmelCase							:						Optional[int]       							=					read_lines(data_args.class_names_file							)
  
      # 2. get teacher predictions and load into dataset
      logger.info('''Generating predictions from zero-shot teacher model'''							)
      _UpperCAmelCase							:						Optional[Any]       							=					get_teacher_predictions(
          teacher_args.teacher_name_or_path  , UpperCamelCase__  , UpperCamelCase__  , teacher_args.hypothesis_template  , teacher_args.teacher_batch_size  , teacher_args.temperature  , teacher_args.multi_label  , data_args.use_fast_tokenizer  , training_args.no_cuda  , training_args.fpaa  , )
      _UpperCAmelCase							:						int       							=					Dataset.from_dict(
          {
              '''text''': examples,
              '''labels''': teacher_soft_preds,
          }							)
  
      # 3. create student
      logger.info('''Initializing student model'''							)
      _UpperCAmelCase							:						List[Any]       							=					AutoModelForSequenceClassification.from_pretrained(
          student_args.student_name_or_path  , num_labels=len(UpperCamelCase__							)							)
      _UpperCAmelCase							:						Any       							=					AutoTokenizer.from_pretrained(student_args.student_name_or_path  , use_fast=data_args.use_fast_tokenizer							)
      _UpperCAmelCase							:						List[Any]       							=					dict(enumerate(UpperCamelCase__							)							)
      _UpperCAmelCase							:						Optional[Any]       							=					{label: i for i, label in enumerate(UpperCamelCase__							)}
  
      # 4. train student on teacher predictions
      _UpperCAmelCase							:						List[str]       							=					dataset.map(UpperCamelCase__  , input_columns='''text'''							)
      dataset.set_format('''torch'''							)
  
      def compute_metrics(UpperCamelCase__	:	Dict  , UpperCamelCase__	:	Dict=False							):
          _UpperCAmelCase							:						Dict       							=					p.predictions.argmax(-1							)
          _UpperCAmelCase							:						Dict       							=					p.label_ids.argmax(-1							)  # "label_ids" are actually distributions
          return {"agreement": (preds == proxy_labels).mean().item()}
  
      _UpperCAmelCase							:						Optional[int]       							=					DistillationTrainer(
          model=UpperCamelCase__  , tokenizer=UpperCamelCase__  , args=UpperCamelCase__  , train_dataset=UpperCamelCase__  , compute_metrics=UpperCamelCase__  , )
  
      if training_args.do_train:
          logger.info('''Training student model on teacher predictions'''							)
          trainer.train()
  
      if training_args.do_eval:
          _UpperCAmelCase							:						Dict       							=					trainer.evaluate(eval_dataset=UpperCamelCase__							)['''eval_agreement''']
          logger.info(F'Agreement of student and teacher predictions: {agreement * 100:0.2f}%'							)
  
      trainer.save_model()
  
  
  if __name__ == "__main__":
    main()
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	int							):
      sequence.append(UpperCamelCase__							)
  
      _UpperCAmelCase							:						str       							=					0
      while sequence[index] != target:
          index += 1
  
      sequence.pop()
  
      if index == len(UpperCamelCase__							):
          return None
  
      return index
  
  
  if __name__ == "__main__":
    _lowerCAmelCase					:Dict  =      input('Enter numbers separated by comma:\n').strip()
    _lowerCAmelCase					:List[Any]  =      [int(item) for item in user_input.split(',')]
  
    _lowerCAmelCase					:int  =      input('Enter a single number to be found in the list:\n')
    _lowerCAmelCase					:List[Any]  =      int(target_input)
    _lowerCAmelCase					:Optional[int]  =      sentinel_linear_search(sequence, target)
    if result is not None:
      print(f"{target} found at positions: {result}")
    else:
      print('Not found')
  
  
  