'''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


__SCREAMING_SNAKE_CASE :  Optional[Any]  =						"""
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.
"""

__SCREAMING_SNAKE_CASE :  Dict  =						logging.getLogger(__name__)

@dataclass
class 			lowerCamelCase_			:
   '''simple docstring'''

   __UpperCamelCase:						Optional[str]      			=		field(
       default="roberta-large-mnli"							,				metadata={"help": "The NLI/zero-shot teacher model to be distilled."}					)
   __UpperCamelCase:						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."
           )
       }							,				)
   __UpperCamelCase:						Optional[int]      			=		field(
       default=3_2							,				metadata={"help": "Batch size for generating teacher predictions."}					)
   __UpperCamelCase:						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)."
           )
       }							,				)
   __UpperCamelCase:						Optional[float]      			=		field(
       default=1.0							,				metadata={"help": "Temperature applied to teacher softmax for distillation."}					)

@dataclass
class 			lowerCamelCase_			:
   '''simple docstring'''

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

@dataclass
class 			lowerCamelCase_			:
   '''simple docstring'''

   __UpperCamelCase:						str      			=		field(metadata={"help": "Text file with one unlabeled instance per line."}					)
   __UpperCamelCase:						str      			=		field(metadata={"help": "Text file with one class name per line."}					)
   __UpperCamelCase:						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 			lowerCamelCase_			(snake_case__					):
   '''simple docstring'''

   __UpperCamelCase:						Optional[str]      			=		field(
       default=snake_case__							,				metadata={"help": "The output directory where the model predictions and checkpoints will be written."}							,				)
   __UpperCamelCase:						int      			=		field(
       default=3_2							,				metadata={"help": "Batch size per GPU/TPU core/CPU for training."}					)
   __UpperCamelCase:						int      			=		field(
       default=1_2_8							,				metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}					)
   __UpperCamelCase:						float      			=		field(default=1.0							,				metadata={"help": "Total number of training epochs to perform."}					)
   __UpperCamelCase:						bool      			=		field(default=snake_case__							,				metadata={"help": "Whether to run training of student model."}					)
   __UpperCamelCase:						bool      			=		field(
       default=snake_case__							,				metadata={
           "help": (
               "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
               "after training."
           )
       }							,				)
   __UpperCamelCase:						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 			lowerCamelCase_			(snake_case__					):
   '''simple docstring'''

   def   _A			( self		:       List[Any]		,      A		:       Dict		,      A		:       Optional[Any]		,      A		:       Dict=False ):
          _UpperCAmelCase  :						Any							   =					inputs["labels"]
          _UpperCAmelCase  :						Optional[int]							   =					model(inputs["input_ids"]		,      attention_mask=inputs["attention_mask"] )
          _UpperCAmelCase  :						Optional[int]							   =					outputs[0]

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

          if return_outputs:
                 return loss, outputs

          return loss

def 			UpperCamelCase_							(      _UpperCAmelCase						:   List[str]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						List[str]							   =					[]
       with open(_UpperCAmelCase    ,			"r"					) as f:
              for line in f:
                     _UpperCAmelCase  :						Dict							   =					line.strip()
                     if len(_UpperCAmelCase					) > 0:
                            lines.append(_UpperCAmelCase					)
       return lines

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[Any]    ,			_UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   Union[str, Any]					)					->					int:


       """simple docstring"""







       _UpperCAmelCase  :						Tuple							   =					[]
       _UpperCAmelCase  :						Any							   =					[]
       for example in examples:
              for name in class_names:
                     premises.append(_UpperCAmelCase					)
                     hypotheses.append(hypothesis_template.format(_UpperCAmelCase					)					)
       return premises, hypotheses

def 			UpperCamelCase_							(      _UpperCAmelCase						:   Optional[int]					)					->					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						:   str    ,			_UpperCAmelCase						:   List[str]    ,			_UpperCAmelCase						:   List[str]    ,			_UpperCAmelCase						:   str    ,			_UpperCAmelCase						:   int    ,			_UpperCAmelCase						:   float    ,			_UpperCAmelCase						:   bool    ,			_UpperCAmelCase						:   bool    ,			_UpperCAmelCase						:   bool    ,			_UpperCAmelCase						:   bool    ,			)					->					Optional[int]:


       """simple docstring"""







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

       _UpperCAmelCase  :						Optional[int]							   =					get_premise_hypothesis_pairs(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase					)
       _UpperCAmelCase  :						int							   =					[]

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

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

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

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

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

def 			UpperCamelCase_							(      )					->					Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase  :						Dict							   =					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  :						Dict							   =					parser.parse_json_file(
                  json_file=os.path.abspath(sys.argv[1]					)					)
       else:
              _UpperCAmelCase  :						Optional[Any]							   =					parser.parse_args_into_dataclasses()

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

       # 3. create student
       logger.info("Initializing student model"					)
       _UpperCAmelCase  :						Dict							   =					AutoModelForSequenceClassification.from_pretrained(
           student_args.student_name_or_path    ,			num_labels=len(_UpperCAmelCase					)					)
       _UpperCAmelCase  :						str							   =					AutoTokenizer.from_pretrained(student_args.student_name_or_path    ,			use_fast=data_args.use_fast_tokenizer					)
       _UpperCAmelCase  :						str							   =					dict(enumerate(_UpperCAmelCase					)					)
       _UpperCAmelCase  :						int							   =					{label: i for i, label in enumerate(_UpperCAmelCase					)}

       # 4. train student on teacher predictions
       _UpperCAmelCase  :						str							   =					dataset.map(_UpperCAmelCase    ,			input_columns="text"					)
       dataset.set_format("torch"					)

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

       _UpperCAmelCase  :						List[Any]							   =					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  :						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()


'''simple docstring'''




from typing import Any

import numpy as np

class 			lowerCamelCase_			:
      '''simple docstring'''

      def __init__( self		:       Optional[int]		,      A		:       np.ndarray		,      A		:       int ):
             # Check if RHS is negative
             if np.any(tableau[:, -1]		,      where=tableau[:, -1] < 0 ):
                    raise ValueError("RHS must be > 0" )

             _UpperCAmelCase  :						Union[str, Any]							   =					tableau
             _UpperCAmelCase   ,						_UpperCAmelCase  :						str							   =					tableau.shape

             # Number of decision variables x1, x2, x3...
             _UpperCAmelCase  :						Any							   =					n_vars

             # Number of artificial variables to be minimised
             _UpperCAmelCase  :						List[str]							   =					len(np.where(tableau[self.n_vars : -1] == -1 )[0] )

             # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
             _UpperCAmelCase  :						List[Any]							   =					(self.n_art_vars > 0) + 1

             # Number of slack variables added to make inequalities into equalities
             _UpperCAmelCase  :						int							   =					self.n_rows - self.n_stages

             # Objectives for each stage
             _UpperCAmelCase  :						Optional[Any]							   =					["max"]

             # In two stage simplex, first minimise then maximise
             if self.n_art_vars:
                    self.objectives.append("min" )

             _UpperCAmelCase  :						Any							   =					[""]

             # Index of current pivot row and column
             _UpperCAmelCase  :						Optional[Any]							   =					None
             _UpperCAmelCase  :						Dict							   =					None

             # Does objective row only contain (non)-negative values?
             _UpperCAmelCase  :						List[str]							   =					False




      @staticmethod
      def   _A			( *A		:       int ):
             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
             _UpperCAmelCase  :						Optional[Any]							   =					["x", "s", "a"]
             _UpperCAmelCase  :						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   _A			( self		:       List[str]		,      A		:       np.ndarray ):
             _UpperCAmelCase  :						str							   =					self.objectives[-1]

             # Find entries of highest magnitude in objective rows
             _UpperCAmelCase  :						List[str]							   =					(objective == "min") - (objective == "max")
             _UpperCAmelCase  :						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:
                    _UpperCAmelCase  :						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
             _UpperCAmelCase  :						Any							   =					slice(self.n_stages		,      self.n_rows )

             # RHS
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, -1]

             # Elements of pivot column within slice
             _UpperCAmelCase  :						Optional[Any]							   =					tableau[s, col_idx]

             # Array filled with nans
             _UpperCAmelCase  :						List[Any]							   =					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
             _UpperCAmelCase  :						str							   =					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
             _UpperCAmelCase  :						Optional[Any]							   =					np.nanargmin(A ) + self.n_stages
             return row_idx, col_idx




      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       int		,      A		:       int ):
             _UpperCAmelCase  :						Union[str, Any]							   =					tableau[row_idx].copy()

             _UpperCAmelCase  :						Optional[Any]							   =					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
             _UpperCAmelCase  :						Optional[Any]							   =					piv_row
             return tableau




      def   _A			( self		:       Optional[Any]		,      A		:       np.ndarray ):
             self.objectives.pop()

             if not self.objectives:
                    return tableau

             # Slice containing ids for artificial columns
             _UpperCAmelCase  :						Union[str, Any]							   =					slice(-self.n_art_vars - 1		,      -1 )

             # Delete the artificial variable columns
             _UpperCAmelCase  :						List[Any]							   =					np.delete(A		,      A		,      axis=1 )

             # Delete the objective row of the first stage
             _UpperCAmelCase  :						Union[str, Any]							   =					np.delete(A		,      0		,      axis=0 )

             _UpperCAmelCase  :						Any							   =					1
             self.n_rows -= 1
             _UpperCAmelCase  :						Dict							   =					0
             _UpperCAmelCase  :						List[str]							   =					False
             return tableau




      def   _A			( self		:       Optional[Any] ):
             # Stop simplex algorithm from cycling.
             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:
                           _UpperCAmelCase  :						Any							   =					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 )

                    _UpperCAmelCase   ,						_UpperCAmelCase  :						List[str]							   =					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
                           _UpperCAmelCase  :						int							   =					self.change_stage(self.tableau )
                    else:
                           _UpperCAmelCase  :						Optional[Any]							   =					self.pivot(self.tableau		,      A		,      A )
             return {}





      def   _A			( self		:       List[Any]		,      A		:       np.ndarray		,      A		:       list[str] ):
             _UpperCAmelCase  :						str							   =					{"P": abs(tableau[0, -1] )}

             for i in range(self.n_vars ):
                    # Gives ids of nonzero entries in the ith column
                    _UpperCAmelCase  :						Dict							   =					np.nonzero(tableau[:, i] )
                    _UpperCAmelCase  :						Dict							   =					len(nonzero[0] )

                    # First entry in the nonzero ids
                    _UpperCAmelCase  :						str							   =					nonzero[0][0]
                    _UpperCAmelCase  :						str							   =					tableau[nonzero_rowidx, i]

                    # If there is only one nonzero value in column, which is one
                    if n_nonzero == nonzero_val == 1:
                           _UpperCAmelCase  :						Optional[Any]							   =					tableau[nonzero_rowidx, -1]
                           _UpperCAmelCase  :						Any							   =					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()

