


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   	=  '''
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.
'''

__lowerCamelCase   	=  logging.getLogger(__name__)


@dataclass
class 	a__						:
       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=lowerCAmelCase_    ,							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 	a__						:
       lowerCamelCase__:  Optional[str]				     =  field(
           default="""distilbert-base-uncased"""    ,							metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}     )


@dataclass
class 	a__						:
       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=lowerCAmelCase_    ,							metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}    ,							)


@dataclass
class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  Optional[str]				     =  field(
           default=lowerCAmelCase_    ,							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=lowerCAmelCase_    ,							metadata={"""help""": """Whether to run training of student model."""}     )
       lowerCamelCase__:  bool				     =  field(
           default=lowerCAmelCase_    ,							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 	a__						(    lowerCAmelCase_     ):

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Union[str, Any]=False     ):
            a_     :  Union[str, Any]				      =     inputs["""labels"""]
            a_     :  Union[str, Any]				      =     model(inputs["""input_ids"""]   ,				attention_mask=inputs["""attention_mask"""]     )
            a_     :  str				      =     outputs[0]

            a_     :  Union[str, Any]				      =     -torch.sum(target_p * logits.log_softmax(dim=-1     )   ,				axis=-1     ).mean()

            if return_outputs:
                 return loss, outputs

            return loss



def 			_a      (    __UpperCamelCase			):
     a_     :  Dict				      =     []
     with open(__UpperCamelCase	,	"""r"""			) as f:
          for line in f:
               a_     :  Dict				      =     line.strip()
               if len(__UpperCamelCase			) > 0:
                    lines.append(__UpperCamelCase			)
     return lines



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     []
     a_     :  Dict				      =     []
     for example in examples:
          for name in class_names:
               premises.append(__UpperCamelCase			)
               hypotheses.append(hypothesis_template.format(__UpperCamelCase			)			)
     return premises, hypotheses



def 			_a      (    __UpperCamelCase			):
     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 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	):
     a_     :  Optional[int]				      =     AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase			)
     a_     :  Optional[int]				      =     model.config
     if not no_cuda and torch.cuda.is_available():
          a_     :  Union[str, Any]				      =     nn.DataParallel(model.cuda()			)
          batch_size *= len(model.device_ids			)
     a_     :  Optional[Any]				      =     AutoTokenizer.from_pretrained(__UpperCamelCase	,	use_fast=__UpperCamelCase			)

     a_      ,      a_     :  Dict				      =     get_premise_hypothesis_pairs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
     a_     :  List[str]				      =     []

     for i in tqdm(range(0	,	len(__UpperCamelCase			)	,	__UpperCamelCase			)			):
          a_     :  int				      =     premises[i : i + batch_size]
          a_     :  List[Any]				      =     hypotheses[i : i + batch_size]

          a_     :  Dict				      =     tokenizer(
              __UpperCamelCase	,	__UpperCamelCase	,	padding=__UpperCamelCase	,	truncation="""only_first"""	,	return_tensors="""pt"""	,	)

          with torch.cuda.amp.autocast(enabled=__UpperCamelCase			):
               with torch.no_grad():
                    a_     :  Any				      =     model(**__UpperCamelCase			)
          logits.append(outputs.logits.detach().cpu().float()			)

     a_     :  Optional[Any]				      =     get_entailment_id(__UpperCamelCase			)
     a_     :  List[str]				      =     -1 if entail_id == 0 else 0
     a_     :  Dict				      =     torch.cat(__UpperCamelCase	,	dim=0			)  # N*K x 3
     a_     :  str				      =     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
          a_     :  Optional[int]				      =     (nli_logits / temperature).softmax(-1			)
     else:
          # softmax over entail logits across classes s.t. class probabilities sum to 1.
          a_     :  Any				      =     (nli_logits / temperature).softmax(1			)

     return nli_prob[..., 1]  # N x K



def 			_a      (    ):
     a_     :  str				      =     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.
          a_      ,      a_      ,      a_      ,      a_     :  Dict				      =     parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          a_      ,      a_      ,      a_      ,      a_     :  Any				      =     parser.parse_args_into_dataclasses()

     # Detecting last checkpoint.
     a_     :  Dict				      =     None
     if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
          a_     :  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
     a_     :  List[str]				      =     read_lines(data_args.data_file			)
     a_     :  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"""			)
     a_     :  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	,	)
     a_     :  List[str]				      =     Dataset.from_dict(
         {
             """text""": examples,
             """labels""": teacher_soft_preds,
         }			)

     # 3. create student
     logger.info("""Initializing student model"""			)
     a_     :  Union[str, Any]				      =     AutoModelForSequenceClassification.from_pretrained(
         student_args.student_name_or_path	,	num_labels=len(__UpperCamelCase			)			)
     a_     :  Any				      =     AutoTokenizer.from_pretrained(student_args.student_name_or_path	,	use_fast=data_args.use_fast_tokenizer			)
     a_     :  str				      =     dict(enumerate(__UpperCamelCase			)			)
     a_     :  Optional[Any]				      =     {label: i for i, label in enumerate(__UpperCamelCase			)}

     # 4. train student on teacher predictions
     a_     :  Optional[int]				      =     dataset.map(__UpperCamelCase	,	input_columns="""text"""			)
     dataset.set_format("""torch"""			)

     def compute_metrics(__UpperCamelCase	,	__UpperCamelCase=False			):
          a_     :  Optional[Any]				      =     p.predictions.argmax(-1			)
          a_     :  Dict				      =     p.label_ids.argmax(-1			)  # "label_ids" are actually distributions
          return {"agreement": (preds == proxy_labels).mean().item()}

     a_     :  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:
          a_     :  Tuple				      =     trainer.evaluate(eval_dataset=__UpperCamelCase			)["""eval_agreement"""]
          logger.info(F'''Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%'''			)

     trainer.save_model()


if __name__ == "__main__":
       main()





from __future__ import annotations

from collections.abc import Iterable





class 				UpperCamelCase__		:






       """simple docstring"""


       def __init__(							self ,     _A ,     _A	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	=      float(_A	), float(_A	)



       def __eq__(							self ,     _A	)				->				str:
          return self.x == other.x and self.y == other.y



       def __ne__(							self ,     _A	)				->				Any:
          return not self == other



       def __gt__(							self ,     _A	)				->				List[Any]:
          if self.x > other.x:
             return True
          elif self.x == other.x:
             return self.y > other.y
          return False



       def __lt__(							self ,     _A	)				->				Optional[int]:
          return not self > other



       def __ge__(							self ,     _A	)				->				int:
          if self.x > other.x:
             return True
          elif self.x == other.x:
             return self.y >= other.y
          return False



       def __le__(							self ,     _A	)				->				str:
          if self.x < other.x:
             return True
          elif self.x == other.x:
             return self.y <= other.y
          return False



       def __repr__(							self	)				->				str:
          return F'''({self.x}, {self.y})'''




       def __hash__(							self	)				->				List[str]:
          return hash(self.x	)







def  A__ (       __lowerCamelCase,  ):
   SCREAMING_SNAKE_CASE_	=      []
   if list_of_tuples:
      for p in list_of_tuples:
         if isinstance(__lowerCamelCase,  __lowerCamelCase						):
            points.append(__lowerCamelCase						)
         else:
            try:
               points.append(Point(p[0],  p[1]						)						)
            except (IndexError, TypeError):
               print(
                   F'''Ignoring deformed point {p}. All points'''
                   ''' must have at least 2 coordinates.'''						)
   return points


def  A__ (       __lowerCamelCase						):

   if not hasattr(__lowerCamelCase,  '''__iter__'''						):
      SCREAMING_SNAKE_CASE_	=      F'''Expecting an iterable object but got an non-iterable type {points}'''
      raise ValueError(__lowerCamelCase						)

   if not points:
      SCREAMING_SNAKE_CASE_	=      F'''Expecting a list of points but got {points}'''
      raise ValueError(__lowerCamelCase						)

   return _construct_points(__lowerCamelCase						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      (a.x * b.y + b.x * c.y + c.x * a.y) - (a.y * b.x + b.y * c.x + c.y * a.x)
   return det


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      sorted(_validate_input(__lowerCamelCase						)						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      set()

   for i in range(n - 1						):
      for j in range(i + 1,  __lowerCamelCase						):
         SCREAMING_SNAKE_CASE_	=      SCREAMING_SNAKE_CASE_	=      False
         SCREAMING_SNAKE_CASE_	=      True
         for k in range(__lowerCamelCase						):
            if k != i and k != j:
               SCREAMING_SNAKE_CASE_	=      _det(points[i],  points[j],  points[k]						)

               if det_k > 0:
                  SCREAMING_SNAKE_CASE_	=      True
               elif det_k < 0:
                  SCREAMING_SNAKE_CASE_	=      True
               else:
                  # point[i], point[j], point[k] all lie on a straight line
                  # if point[k] is to the left of point[i] or it's to the
                  # right of point[j], then point[i], point[j] cannot be
                  # part of the convex hull of A
                  if points[k] < points[i] or points[k] > points[j]:
                     SCREAMING_SNAKE_CASE_	=      False
                     break

            if points_left_of_ij and points_right_of_ij:
               SCREAMING_SNAKE_CASE_	=      False
               break

         if ij_part_of_convex_hull:
            convex_set.update([points[i], points[j]]						)

   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      sorted(_validate_input(__lowerCamelCase						)						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)

   # divide all the points into an upper hull and a lower hull
   # the left most point and the right most point are definitely
   # members of the convex hull by definition.
   # use these two anchors to divide all the points into two hulls,
   # an upper hull and a lower hull.

   # all points to the left (above) the line joining the extreme points belong to the
   # upper hull
   # all points to the right (below) the line joining the extreme points below to the
   # lower hull
   # ignore all points on the line joining the extreme points since they cannot be
   # part of the convex hull

   SCREAMING_SNAKE_CASE_	=      points[0]
   SCREAMING_SNAKE_CASE_	=      points[n - 1]

   SCREAMING_SNAKE_CASE_	=      {left_most_point, right_most_point}
   SCREAMING_SNAKE_CASE_	=      []
   SCREAMING_SNAKE_CASE_	=      []

   for i in range(1,  n - 1						):
      SCREAMING_SNAKE_CASE_	=      _det(__lowerCamelCase,  __lowerCamelCase,  points[i]						)

      if det > 0:
         upper_hull.append(points[i]						)
      elif det < 0:
         lower_hull.append(points[i]						)

   _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)
   _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)

   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						):
   if points:
      SCREAMING_SNAKE_CASE_	=      None
      SCREAMING_SNAKE_CASE_	=      float('''-inf'''						)
      SCREAMING_SNAKE_CASE_	=      []

      for p in points:
         SCREAMING_SNAKE_CASE_	=      _det(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)

         if det > 0:
            candidate_points.append(__lowerCamelCase						)

            if det > extreme_point_distance:
               SCREAMING_SNAKE_CASE_	=      det
               SCREAMING_SNAKE_CASE_	=      p

      if extreme_point:
         _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)
         convex_set.add(__lowerCamelCase						)
         _construct_hull(__lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase,  __lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      sorted(_validate_input(__lowerCamelCase						)						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      points[:2]
   for i in range(2,  __lowerCamelCase						):
      SCREAMING_SNAKE_CASE_	=      _det(convex_hull[1],  convex_hull[0],  points[i]						)
      if det > 0:
         convex_hull.insert(0,  points[i]						)
         break
      elif det < 0:
         convex_hull.append(points[i]						)
         break
      else:
         SCREAMING_SNAKE_CASE_	=      points[i]
   i += 1

   for j in range(__lowerCamelCase,  __lowerCamelCase						):
      if (
          _det(convex_hull[0],  convex_hull[-1],  points[j]						) > 0
          and _det(convex_hull[-1],  convex_hull[0],  points[1]						) < 0
      ):
         # The point lies within the convex hull
         continue

      convex_hull.insert(0,  points[j]						)
      convex_hull.append(points[j]						)
      while _det(convex_hull[0],  convex_hull[1],  convex_hull[2]						) >= 0:
         del convex_hull[1]
      while _det(convex_hull[-1],  convex_hull[-2],  convex_hull[-3]						) <= 0:
         del convex_hull[-2]

    # `convex_hull` is contains the convex hull in circular order
   return sorted(convex_hull[1:] if len(__lowerCamelCase						) > 3 else convex_hull						)


def  A__ (       ):
   SCREAMING_SNAKE_CASE_	=      [
       (0, 3),
       (2, 2),
       (1, 1),
       (2, 1),
       (3, 0),
       (0, 0),
       (3, 3),
       (2, -1),
       (2, -4),
       (1, -3),
   ]
   # the convex set of points is
   # [(0, 0), (0, 3), (1, -3), (2, -4), (3, 0), (3, 3)]
   SCREAMING_SNAKE_CASE_	=      convex_hull_bf(__lowerCamelCase						)

   SCREAMING_SNAKE_CASE_	=      convex_hull_recursive(__lowerCamelCase						)
   assert results_bf == results_recursive

   SCREAMING_SNAKE_CASE_	=      convex_hull_melkman(__lowerCamelCase						)
   assert results_bf == results_melkman

   print(__lowerCamelCase						)


if __name__ == "__main__":
   main()


