code: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  UpperCamelCase__				       =			{}
  
  try:
     if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			['BarthezTokenizer']
  
  try:
     if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
     pass
  else:
     UpperCamelCase__				       =			['BarthezTokenizerFast']
  
  
  if TYPE_CHECKING:
     try:
        if not is_sentencepiece_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_barthez import BarthezTokenizer
  
     try:
        if not is_tokenizers_available():
           raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
        pass
     else:
        from .tokenization_barthez_fast import BarthezTokenizerFast
  
  else:
     import sys
  
     UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
style_context: 
  
  
  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
  
  
  UpperCamelCase__				       =			'\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'
  
  UpperCamelCase__				       =			logging.getLogger(__name__)
  
  @dataclass
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  field(
           default='''roberta-large-mnli'''      ,						metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}		)
       UpperCAmelCase_         =  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.'''
               )
           }      ,						)
       UpperCAmelCase_         =  field(
           default=3_2      ,						metadata={'''help''': '''Batch size for generating teacher predictions.'''}		)
       UpperCAmelCase_         =  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).'''
               )
           }      ,						)
       UpperCAmelCase_         =  field(
           default=1.0      ,						metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''}		)
  
  
  
  
  @dataclass
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  field(
           default='''distilbert-base-uncased'''      ,						metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}		)
  
  
  
  
  @dataclass
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}		)
       UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one class name per line.'''}		)
       UpperCAmelCase_         =  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'''
       UpperCAmelCase_         =  field(
           default=A_      ,						metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}      ,						)
       UpperCAmelCase_         =  field(
           default=3_2      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''}		)
       UpperCAmelCase_         =  field(
           default=1_2_8      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''}		)
       UpperCAmelCase_         =  field(default=1.0      ,						metadata={'''help''': '''Total number of training epochs to perform.'''}		)
       UpperCAmelCase_         =  field(default=A_      ,						metadata={'''help''': '''Whether to run training of student model.'''}		)
       UpperCAmelCase_         =  field(
           default=A_      ,						metadata={
               '''help''': (
                   '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
                   '''after training.'''
               )
           }      ,						)
       UpperCAmelCase_         =  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       :       Dict	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       Dict=False		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[Any]           =			inputs['''labels''']
              _lowercase					:				List[Any]           =			model(inputs['''input_ids''']	,      attention_mask=inputs['''attention_mask''']		)
              _lowercase					:				int           =			outputs[0]
  
              _lowercase					:				List[str]           =			-torch.sum(target_p * logits.log_softmax(dim=-1		)	,      axis=-1		).mean()
  
              if return_outputs:
                     return loss, outputs
  
              return loss
  
  
  
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Tuple:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Tuple           =			[]
         with open(UpperCAmelCase_    ,     '''r'''			) as f:
                for line in f:
                       _lowercase					:				Optional[int]           =			line.strip()
                       if len(UpperCAmelCase_			) > 0:
                              lines.append(UpperCAmelCase_			)
         return lines
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Any           =			[]
         _lowercase					:				int           =			[]
         for example in examples:
                for name in class_names:
                       premises.append(UpperCAmelCase_			)
                       hypotheses.append(hypothesis_template.format(UpperCAmelCase_			)			)
         return premises, hypotheses
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							List[Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         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   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     )						->							int:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[Any]           =			AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase_			)
         _lowercase					:				Any           =			model.config
         if not no_cuda and torch.cuda.is_available():
                _lowercase					:				int           =			nn.DataParallel(model.cuda()			)
                batch_size *= len(model.device_ids			)
         _lowercase					:				Optional[int]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_    ,     use_fast=UpperCAmelCase_			)
  
         _lowercase       ,	_lowercase					:				Any           =			get_premise_hypothesis_pairs(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
         _lowercase					:				List[str]           =			[]
  
         for i in tqdm(range(0    ,     len(UpperCAmelCase_			)    ,     UpperCAmelCase_			)			):
                _lowercase					:				Tuple           =			premises[i : i + batch_size]
                _lowercase					:				Optional[int]           =			hypotheses[i : i + batch_size]
  
                _lowercase					:				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():
                              _lowercase					:				Dict           =			model(**UpperCAmelCase_			)
                logits.append(outputs.logits.detach().cpu().float()			)
  
         _lowercase					:				Any           =			get_entailment_id(UpperCAmelCase_			)
         _lowercase					:				Tuple           =			-1 if entail_id == 0 else 0
         _lowercase					:				Any           =			torch.cat(UpperCAmelCase_    ,     dim=0			)  # N*K x 3
         _lowercase					:				List[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
                _lowercase					:				Optional[int]           =			(nli_logits / temperature).softmax(-1			)
         else:
                # softmax over entail logits across classes s.t. class probabilities sum to 1.
                _lowercase					:				Union[str, Any]           =			(nli_logits / temperature).softmax(1			)
  
         return nli_prob[..., 1]  # N x K
  
  def   UpperCamelCase__   (    )						->							Union[str, Any]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				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.
                _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Union[str, Any]           =			parser.parse_json_file(
                    json_file=os.path.abspath(sys.argv[1]			)			)
         else:
                _lowercase       ,	_lowercase       ,	_lowercase       ,	_lowercase					:				Tuple           =			parser.parse_args_into_dataclasses()
  
         # Detecting last checkpoint.
         _lowercase					:				Tuple           =			None
         if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
                _lowercase					:				Optional[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
         _lowercase					:				str           =			read_lines(data_args.data_file			)
         _lowercase					:				List[str]           =			read_lines(data_args.class_names_file			)
  
         # 2. get teacher predictions and load into dataset
         logger.info('''Generating predictions from zero-shot teacher model'''			)
         _lowercase					:				Optional[int]           =			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    ,     )
         _lowercase					:				Optional[Any]           =			Dataset.from_dict(
             {
                 '''text''': examples,
                 '''labels''': teacher_soft_preds,
             }			)
  
         # 3. create student
         logger.info('''Initializing student model'''			)
         _lowercase					:				List[Any]           =			AutoModelForSequenceClassification.from_pretrained(
             student_args.student_name_or_path    ,     num_labels=len(UpperCAmelCase_			)			)
         _lowercase					:				Optional[Any]           =			AutoTokenizer.from_pretrained(student_args.student_name_or_path    ,     use_fast=data_args.use_fast_tokenizer			)
         _lowercase					:				Optional[int]           =			dict(enumerate(UpperCAmelCase_			)			)
         _lowercase					:				Union[str, Any]           =			{label: i for i, label in enumerate(UpperCAmelCase_			)}
  
         # 4. train student on teacher predictions
         _lowercase					:				List[str]           =			dataset.map(UpperCAmelCase_    ,     input_columns='''text'''			)
         dataset.set_format('''torch'''			)
  
         def compute_metrics(UpperCAmelCase_    ,     UpperCAmelCase_=False			):
                _lowercase					:				Optional[Any]           =			p.predictions.argmax(-1			)
                _lowercase					:				List[Any]           =			p.label_ids.argmax(-1			)  # "label_ids" are actually distributions
                return {"agreement": (preds == proxy_labels).mean().item()}
  
         _lowercase					:				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:
                _lowercase					:				Tuple           =			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()