code: 
  
  from typing import Any
  
  import numpy as np
  
  
  class    SCREAMING_SNAKE_CASE__      :
  
  
  
  
  
      def __init__(   self		,     a		,     a):
  
  
  
           if np.any(tableau[:, -1]		,     where=tableau[:, -1] < 0):
                raise ValueError('RHS must be > 0')
  
           lowercase__					:     List[str]   = tableau
           lowercase__   ,				lowercase__					:     Optional[int]   = tableau.shape
  
           # Number of decision variables x1, x2, x3...
           lowercase__					:     Optional[Any]   = n_vars
  
           # Number of artificial variables to be minimised
           lowercase__					:     Optional[int]   = len(np.where(tableau[self.n_vars : -1] == -1)[0])
  
           # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
           lowercase__					:     Dict   = (self.n_art_vars > 0) + 1
  
           # Number of slack variables added to make inequalities into equalities
           lowercase__					:     Union[str, Any]   = self.n_rows - self.n_stages
  
           # Objectives for each stage
           lowercase__					:     List[Any]   = ['max']
  
           # In two stage simplex, first minimise then maximise
           if self.n_art_vars:
                self.objectives.append('min')
  
           lowercase__					:     Union[str, Any]   = ['']
  
           # Index of current pivot row and column
           lowercase__					:     List[Any]   = None
           lowercase__					:     Tuple   = None
  
           # Does objective row only contain (non)-negative values?
           lowercase__					:     Dict   = False
  
  
  
  
  
      @staticmethod
      def    snake_case_       (   *a):
  
  
  
           if len(a_) != 3:
                raise ValueError('Must provide n_vars, n_slack, and n_art_vars')
  
           if not all(x >= 0 and isinstance(a_		,     a_) for x in args):
                raise ValueError('All arguments must be non-negative integers')
  
           # decision | slack | artificial
           lowercase__					:     str   = ['x', 's', 'a']
           lowercase__					:     Optional[int]   = []
           for i in range(3):
                for j in range(args[i]):
                     titles.append(string_starts[i] + str(j + 1))
           titles.append('RHS')
           return titles
  
  
  
  
  
      def    snake_case_       (   self		,     a):
           lowercase__					:     List[str]   = self.objectives[-1]
  
           # Find entries of highest magnitude in objective rows
           lowercase__					:     List[Any]   = (objective == 'min') - (objective == 'max')
           lowercase__					:     Optional[Any]   = np.argmax(sign * tableau[0, : self.n_vars])
  
           # Choice is only valid if below 0 for maximise, and above for minimise
           if sign * self.tableau[0, col_idx] <= 0:
                lowercase__					:     Dict   = True
                return 0, 0
  
           # Pivot row is chosen as having the lowest quotient when elements of
           # the pivot column divide the right-hand side
  
           # Slice excluding the objective rows
           lowercase__					:     List[str]   = slice(self.n_stages		,     self.n_rows)
  
           # RHS
           lowercase__					:     str   = tableau[s, -1]
  
           # Elements of pivot column within slice
           lowercase__					:     Any   = tableau[s, col_idx]
  
           # Array filled with nans
           lowercase__					:     Optional[int]   = np.full(self.n_rows - self.n_stages		,     np.nan)
  
           # If element in pivot column is greater than zeron_stages, return
           # quotient or nan otherwise
           lowercase__					:     Union[str, Any]   = np.divide(a_		,     a_		,     out=a_		,     where=divisor > 0)
  
           # Arg of minimum quotient excluding the nan values. n_stages is added
           # to compensate for earlier exclusion of objective columns
           lowercase__					:     Optional[int]   = np.nanargmin(a_) + self.n_stages
           return row_idx, col_idx
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a):
           lowercase__					:     List[Any]   = tableau[row_idx].copy()
  
           lowercase__					:     Tuple   = piv_row[col_idx]
  
           # Entry becomes 1
           piv_row *= 1 / piv_val
  
           # Variable in pivot column becomes basic, ie the only non-zero entry
           for idx, coeff in enumerate(tableau[:, col_idx]):
                tableau[idx] += -coeff * piv_row
           lowercase__					:     List[Any]   = piv_row
           return tableau
  
  
  
  
  
      def    snake_case_       (   self		,     a):
           self.objectives.pop()
  
           if not self.objectives:
                return tableau
  
           # Slice containing ids for artificial columns
           lowercase__					:     Union[str, Any]   = slice(-self.n_art_vars - 1		,     -1)
  
           # Delete the artificial variable columns
           lowercase__					:     str   = np.delete(a_		,     a_		,     axis=1)
  
           # Delete the objective row of the first stage
           lowercase__					:     Optional[int]   = np.delete(a_		,     0		,     axis=0)
  
           lowercase__					:     List[str]   = 1
           self.n_rows -= 1
           lowercase__					:     Tuple   = 0
           lowercase__					:     Optional[int]   = False
           return tableau
  
  
  
  
  
      def    snake_case_       (   self):
  
  
  
           for _ in range(100):
                # Completion of each stage removes an objective. If both stages
                # are complete, then no objectives are left
                if not self.objectives:
                     lowercase__					:     List[str]   = self.generate_col_titles(
                         self.n_vars		,     self.n_slack		,     self.n_art_vars)
  
                     # Find the values of each variable at optimal solution
                     return self.interpret_tableau(self.tableau		,     self.col_titles)
  
                lowercase__   ,				lowercase__					:     Any   = self.find_pivot(self.tableau)
  
                # If there are no more negative values in objective row
                if self.stop_iter:
                     # Delete artificial variable columns and rows. Update attributes
                     lowercase__					:     List[Any]   = self.change_stage(self.tableau)
                else:
                     lowercase__					:     Optional[int]   = self.pivot(self.tableau		,     a_		,     a_)
           return {}
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a):
           lowercase__					:     Dict   = {'P': abs(tableau[0, -1])}
  
           for i in range(self.n_vars):
                # Gives ids of nonzero entries in the ith column
                lowercase__					:     Optional[Any]   = np.nonzero(tableau[:, i])
                lowercase__					:     List[Any]   = len(nonzero[0])
  
                # First entry in the nonzero ids
                lowercase__					:     Union[str, Any]   = nonzero[0][0]
                lowercase__					:     int   = tableau[nonzero_rowidx, i]
  
                # If there is only one nonzero value in column, which is one
                if n_nonzero == nonzero_val == 1:
                     lowercase__					:     Dict   = tableau[nonzero_rowidx, -1]
                     lowercase__					:     int   = rhs_val
  
                 # Check for basic variables
           for title in col_titles:
                # Don't add RHS or slack variables to output dict
                if title[0] not in "R-s-a":
                     output_dict.setdefault(a_		,     0)
           return output_dict
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  
  
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
  
  
  snake_case_    						=				'''
  Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
  names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
  when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
  dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
  class names.
  '''
  
  snake_case_    						=				logging.getLogger(__name__)
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      :
      __lowerCamelCase		: Optional[str]														=  field(
          default="""roberta-large-mnli"""	, metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}  )
      __lowerCamelCase		: Optional[str]														=  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."""
              )
          }	, )
      __lowerCamelCase		: Optional[int]														=  field(
          default=32	, metadata={"""help""": """Batch size for generating teacher predictions."""}  )
      __lowerCamelCase		: Optional[bool]														=  field(
          default=__snake_case	, metadata={
              """help""": (
                  """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
                  """multi-class multi-label classification)."""
              )
          }	, )
      __lowerCamelCase		: Optional[float]														=  field(
          default=1.0	, metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}  )
  
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      :
      __lowerCamelCase		: Optional[str]														=  field(
          default="""distilbert-base-uncased"""	, metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}  )
  
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      :
      __lowerCamelCase		: str														=  field(metadata={"""help""": """Text file with one unlabeled instance per line."""}  )
      __lowerCamelCase		: str														=  field(metadata={"""help""": """Text file with one class name per line."""}  )
      __lowerCamelCase		: bool														=  field(
          default=__snake_case	, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}	, )
  
  
  
  @dataclass
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
      __lowerCamelCase		: Optional[str]														=  field(
          default=__snake_case	, metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}	, )
      __lowerCamelCase		: int														=  field(
          default=32	, metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}  )
      __lowerCamelCase		: int														=  field(
          default=128	, metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}  )
      __lowerCamelCase		: float														=  field(default=1.0	, metadata={"""help""": """Total number of training epochs to perform."""}  )
      __lowerCamelCase		: bool														=  field(default=__snake_case	, metadata={"""help""": """Whether to run training of student model."""}  )
      __lowerCamelCase		: bool														=  field(
          default=__snake_case	, metadata={
              """help""": (
                  """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
                  """after training."""
              )
          }	, )
      __lowerCamelCase		: Optional[int]														=  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    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
  
  
  
  
      def    snake_case_       (   self		,     a		,     a		,     a=False):
           lowercase__					:     Tuple   = inputs['labels']
           lowercase__					:     Any   = model(inputs['input_ids']		,     attention_mask=inputs['attention_mask'])
           lowercase__					:     Optional[int]   = outputs[0]
  
           lowercase__					:     Any   = -torch.sum(target_p * logits.log_softmax(dim=-1)		,     axis=-1).mean()
  
           if return_outputs:
                return loss, outputs
  
           return loss
  
  
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Any   = []
       with open(SCREAMING_SNAKE_CASE_			,  'r'    ) as f:
            for line in f:
                 lowercase__					:     Dict   = line.strip()
                 if len(SCREAMING_SNAKE_CASE_    ) > 0:
                      lines.append(SCREAMING_SNAKE_CASE_    )
       return lines
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Optional[Any]   = []
       lowercase__					:     Tuple   = []
       for example in examples:
            for name in class_names:
                 premises.append(SCREAMING_SNAKE_CASE_    )
                 hypotheses.append(hypothesis_template.format(SCREAMING_SNAKE_CASE_    )    )
       return premises, hypotheses
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):
  
  
  
       '''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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					float			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     Union[str, Any]   = AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[Any]   = model.config
       if not no_cuda and torch.cuda.is_available():
            lowercase__					:     str   = nn.DataParallel(model.cuda()    )
            batch_size *= len(model.device_ids    )
       lowercase__					:     List[str]   = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			,  use_fast=SCREAMING_SNAKE_CASE_    )
  
       lowercase__   ,				lowercase__					:     List[str]   = get_premise_hypothesis_pairs(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Any   = []
  
       for i in tqdm(range(0			,  len(SCREAMING_SNAKE_CASE_    )			,  SCREAMING_SNAKE_CASE_    )    ):
            lowercase__					:     Dict   = premises[i : i + batch_size]
            lowercase__					:     Union[str, Any]   = hypotheses[i : i + batch_size]
  
            lowercase__					:     List[str]   = tokenizer(
                SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  padding=SCREAMING_SNAKE_CASE_			,  truncation='only_first'			,  return_tensors='pt'			,  )
  
            with torch.cuda.amp.autocast(enabled=SCREAMING_SNAKE_CASE_    ):
                 with torch.no_grad():
                      lowercase__					:     Optional[Any]   = model(**SCREAMING_SNAKE_CASE_    )
            logits.append(outputs.logits.detach().cpu().float()    )
  
       lowercase__					:     str   = get_entailment_id(SCREAMING_SNAKE_CASE_    )
       lowercase__					:     Optional[Any]   = -1 if entail_id == 0 else 0
       lowercase__					:     int   = torch.cat(SCREAMING_SNAKE_CASE_			,  dim=0    )  # N*K x 3
       lowercase__					:     str   = logits.reshape(len(SCREAMING_SNAKE_CASE_    )			,  len(SCREAMING_SNAKE_CASE_    )			,  -1    )[..., [contr_id, entail_id]]  # N x K x 2
  
       if multi_label:
            # softmax over (contr, entail) logits for each class independently
            lowercase__					:     Optional[Any]   = (nli_logits / temperature).softmax(-1    )
       else:
            # softmax over entail logits across classes s.t. class probabilities sum to 1.
            lowercase__					:     Optional[Any]   = (nli_logits / temperature).softmax(1    )
  
       return nli_prob[..., 1]  # N x K
  
  def 			snake_case__    (					):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__					:     int   = HfArgumentParser(
           (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)			,  description=SCREAMING_SNAKE_CASE_			,  )
  
       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__					:     List[Any]   = parser.parse_json_file(
                json_file=os.path.abspath(sys.argv[1]    )    )
       else:
            lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     List[Any]   = parser.parse_args_into_dataclasses()
  
       # Detecting last checkpoint.
       lowercase__					:     List[str]   = None
       if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
            lowercase__					:     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__					:     Optional[Any]   = read_lines(data_args.data_file    )
       lowercase__					:     List[Any]   = 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__					:     Tuple   = get_teacher_predictions(
           teacher_args.teacher_name_or_path			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  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__					:     int   = Dataset.from_dict(
           {
               'text': examples,
               'labels': teacher_soft_preds,
           }    )
  
       # 3. create student
       logger.info('Initializing student model'    )
       lowercase__					:     str   = AutoModelForSequenceClassification.from_pretrained(
           student_args.student_name_or_path			,  num_labels=len(SCREAMING_SNAKE_CASE_    )    )
       lowercase__					:     Optional[Any]   = AutoTokenizer.from_pretrained(student_args.student_name_or_path			,  use_fast=data_args.use_fast_tokenizer    )
       lowercase__					:     Optional[int]   = dict(enumerate(SCREAMING_SNAKE_CASE_    )    )
       lowercase__					:     int   = {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE_    )}
  
       # 4. train student on teacher predictions
       lowercase__					:     Any   = dataset.map(SCREAMING_SNAKE_CASE_			,  input_columns='text'    )
       dataset.set_format('torch'    )
  
       def compute_metrics(SCREAMING_SNAKE_CASE_	:					Any			,  SCREAMING_SNAKE_CASE_	:					List[str]=False    ):
            lowercase__					:     Any   = p.predictions.argmax(-1    )
            lowercase__					:     Dict   = p.label_ids.argmax(-1    )  # "label_ids" are actually distributions
            return {"agreement": (preds == proxy_labels).mean().item()}
  
       lowercase__					:     int   = DistillationTrainer(
           model=SCREAMING_SNAKE_CASE_			,  tokenizer=SCREAMING_SNAKE_CASE_			,  args=SCREAMING_SNAKE_CASE_			,  train_dataset=SCREAMING_SNAKE_CASE_			,  compute_metrics=SCREAMING_SNAKE_CASE_			,  )
  
       if training_args.do_train:
            logger.info('Training student model on teacher predictions'    )
            trainer.train()
  
       if training_args.do_eval:
            lowercase__					:     str   = trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE_    )['eval_agreement']
            logger.info(f"""Agreement of student and teacher predictions: {agreement * 100:0.2f}%"""    )
  
       trainer.save_model()
  
  
  if __name__ == "__main__":
         main()
  
  