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							:						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							:						Union[str, Any]       							=					parser.parse_json_file(
  								    json_file=os.path.abspath(sys.argv[1]							)							)
  				else:
  								_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"""
  import os
  from typing import List, Union
  
  import tensorflow as tf
  from tensorflow_text import BertTokenizer as BertTokenizerLayer
  from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs
  
  from .tokenization_bert import BertTokenizer
  class 							_UpperCAmelCase							(				tf.keras.layers.Layer   ):
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = None ,			A = None ,			A = None ,			A = "longest" ,			A = True ,			A = 5_1_2 ,			A = None ,			A = True ,			A = True ,			A = True ,			**A ,			)							->      Tuple:
           super().__init__()
           if use_fast_bert_tokenizer:
               _UpperCAmelCase							:						Union[str, Any]       							=					FastBertTokenizer(
                   A ,			token_out_type=tf.intaa ,			lower_case_nfd_strip_accents=A ,			**A	)
           else:
               _UpperCAmelCase							:						List[Any]       							=					tf.lookup.StaticVocabularyTable(
                   tf.lookup.KeyValueTensorInitializer(
                       keys=A ,			key_dtype=tf.string ,			values=tf.range(tf.size(A ,			out_type=tf.intaa	) ,			dtype=tf.intaa	) ,			value_dtype=tf.intaa ,			) ,			num_oov_buckets=1 ,			)
               _UpperCAmelCase							:						str       							=					BertTokenizerLayer(
                   A ,			token_out_type=tf.intaa ,			lower_case=A ,			**A	)
  
           _UpperCAmelCase							:						int       							=					vocab_list
           _UpperCAmelCase							:						Dict       							=					do_lower_case
           _UpperCAmelCase							:						Optional[int]       							=					cls_token_id or vocab_list.index('''[CLS]'''	)
           _UpperCAmelCase							:						Any       							=					sep_token_id or vocab_list.index('''[SEP]'''	)
           _UpperCAmelCase							:						List[Any]       							=					pad_token_id or vocab_list.index('''[PAD]'''	)
           _UpperCAmelCase							:						List[str]       							=					ShrinkLongestTrimmer(max_length - 3 ,			axis=1	)  # Allow room for special tokens
           _UpperCAmelCase							:						Tuple       							=					max_length
           _UpperCAmelCase							:						List[Any]       							=					padding
           _UpperCAmelCase							:						List[Any]       							=					truncation
           _UpperCAmelCase							:						Union[str, Any]       							=					pad_to_multiple_of
           _UpperCAmelCase							:						Union[str, Any]       							=					return_token_type_ids
           _UpperCAmelCase							:						Union[str, Any]       							=					return_attention_mask
  
  
  
  
  
       @classmethod
       def 				__lowerCAmelCase			(      cls ,			A ,			**A	)							->      Dict:  # noqa: F821
           _UpperCAmelCase							:						Optional[int]       							=					kwargs.pop('''do_lower_case''' ,			A	)
           _UpperCAmelCase							:						int       							=					tokenizer.do_lower_case if do_lower_case is None else do_lower_case
           _UpperCAmelCase							:						Optional[Any]       							=					kwargs.pop('''cls_token_id''' ,			A	)
           _UpperCAmelCase							:						str       							=					tokenizer.cls_token_id if cls_token_id is None else cls_token_id
           _UpperCAmelCase							:						str       							=					kwargs.pop('''sep_token_id''' ,			A	)
           _UpperCAmelCase							:						int       							=					tokenizer.sep_token_id if sep_token_id is None else sep_token_id
           _UpperCAmelCase							:						List[Any]       							=					kwargs.pop('''pad_token_id''' ,			A	)
           _UpperCAmelCase							:						Optional[Any]       							=					tokenizer.pad_token_id if pad_token_id is None else pad_token_id
  
           _UpperCAmelCase							:						int       							=					tokenizer.get_vocab()
           _UpperCAmelCase							:						List[str]       							=					sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()] ,			key=lambda A	: x[1]	)
           _UpperCAmelCase							:						Dict       							=					[entry[0] for entry in vocab]
           return cls(
               vocab_list=A ,			do_lower_case=A ,			cls_token_id=A ,			sep_token_id=A ,			pad_token_id=A ,			**A ,			)
  
  
  
  
  
       @classmethod
       def 				__lowerCAmelCase			(      cls ,			A ,			*A ,			**A	)							->      int:
           try:
               _UpperCAmelCase							:						int       							=					BertTokenizer.from_pretrained(A ,			*A ,			**A	)
           except:  # noqa: E722
               from .tokenization_bert_fast import BertTokenizerFast
  
               _UpperCAmelCase							:						Dict       							=					BertTokenizerFast.from_pretrained(A ,			*A ,			**A	)
           return cls.from_tokenizer(A ,			**A	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      Any:
           if self.do_lower_case:
               _UpperCAmelCase							:						int       							=					case_fold_utfa(A	)
           _UpperCAmelCase							:						str       							=					self.tf_tokenizer.tokenize(A	)
           return tokens.merge_dims(1 ,			-1	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			A=None ,			)							->      Optional[Any]:
           if padding is None:
               _UpperCAmelCase							:						str       							=					self.padding
           if padding not in ("longest", "max_length"):
               raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''	)
           if max_length is not None and text_pair is not None:
               # Because we have to instantiate a Trimmer to do it properly
               raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''	)
           if max_length is None:
               _UpperCAmelCase							:						Union[str, Any]       							=					self.max_length
           if truncation is None:
               _UpperCAmelCase							:						List[Any]       							=					self.truncation
           if pad_to_multiple_of is None:
               _UpperCAmelCase							:						List[Any]       							=					self.pad_to_multiple_of
           if return_token_type_ids is None:
               _UpperCAmelCase							:						List[str]       							=					self.return_token_type_ids
           if return_attention_mask is None:
               _UpperCAmelCase							:						Any       							=					self.return_attention_mask
           if not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						Dict       							=					tf.convert_to_tensor(A	)
           if text_pair is not None and not isinstance(A ,			tf.Tensor	):
               _UpperCAmelCase							:						str       							=					tf.convert_to_tensor(A	)
           if text_pair is not None:
               if text.shape.rank > 1:
                   raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''	)
               if text_pair.shape.rank > 1:
                   raise ValueError('''text_pair should not be multidimensional!'''	)
           if text.shape.rank == 2:
               _UpperCAmelCase							,       _UpperCAmelCase							:						str       							=					text[:, 0], text[:, 1]
           _UpperCAmelCase							:						Optional[Any]       							=					self.unpaired_tokenize(A	)
           if text_pair is None:  # Unpaired text
               if truncation:
                   _UpperCAmelCase							:						Optional[int]       							=					text[:, : max_length - 2]  # Allow room for special tokens
               _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					combine_segments(
                   (text,) ,			start_of_sequence_id=self.cls_token_id ,			end_of_segment_id=self.sep_token_id	)
           else:  # Paired text
               _UpperCAmelCase							:						Tuple       							=					self.unpaired_tokenize(A	)
               if truncation:
                   _UpperCAmelCase							,       _UpperCAmelCase							:						Optional[int]       							=					self.paired_trimmer.trim([text, text_pair]	)
               _UpperCAmelCase							,       _UpperCAmelCase							:						Union[str, Any]       							=					combine_segments(
                   (text, text_pair) ,			start_of_sequence_id=self.cls_token_id ,			end_of_segment_id=self.sep_token_id	)
           if padding == "longest":
               _UpperCAmelCase							:						List[Any]       							=					input_ids.bounding_shape(axis=1	)
               if pad_to_multiple_of is not None:
                   # No ceiling division in tensorflow, so we negate floordiv instead
                   _UpperCAmelCase							:						Tuple       							=					pad_to_multiple_of * (-tf.math.floordiv(-pad_length ,			A	))
           else:
               _UpperCAmelCase							:						int       							=					max_length
  
           _UpperCAmelCase							,       _UpperCAmelCase							:						Any       							=					pad_model_inputs(A ,			max_seq_length=A ,			pad_value=self.pad_token_id	)
           _UpperCAmelCase							:						Dict       							=					{'''input_ids''': input_ids}
           if return_attention_mask:
               _UpperCAmelCase							:						Optional[int]       							=					attention_mask
           if return_token_type_ids:
               _UpperCAmelCase							,       _UpperCAmelCase							:						Tuple       							=					pad_model_inputs(
                   A ,			max_seq_length=A ,			pad_value=self.pad_token_id	)
               _UpperCAmelCase							:						int       							=					token_type_ids
           return output
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      List[str]:
           return {
               "vocab_list": self.vocab_list,
               "do_lower_case": self.do_lower_case,
               "cls_token_id": self.cls_token_id,
               "sep_token_id": self.sep_token_id,
               "pad_token_id": self.pad_token_id,
           }
  
  
  