



'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import MPNetConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.mpnet.modeling_tf_mpnet import (
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetModel,
  )







class 						UpperCAmelCase						:


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_=1_3				,	lowerCAmelCase_=7				,	lowerCAmelCase_=True				,	lowerCAmelCase_=True				,	lowerCAmelCase_=False				,	lowerCAmelCase_=True				,	lowerCAmelCase_=9_9				,	lowerCAmelCase_=6_4				,	lowerCAmelCase_=2				,	lowerCAmelCase_=4				,	lowerCAmelCase_=6_4				,	lowerCAmelCase_="gelu"				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=5_1_2				,	lowerCAmelCase_=1_6				,	lowerCAmelCase_=2				,	lowerCAmelCase_=0.0_2				,	lowerCAmelCase_=3				,	lowerCAmelCase_=4				,	lowerCAmelCase_=None				,	)			->   Tuple:



      """simple docstring"""







      a_									=parent
      a_									=batch_size
      a_									=seq_length
      a_									=is_training
      a_									=use_input_mask
      a_									=use_token_type_ids
      a_									=use_labels
      a_									=vocab_size
      a_									=hidden_size
      a_									=num_hidden_layers
      a_									=num_attention_heads
      a_									=intermediate_size
      a_									=hidden_act
      a_									=hidden_dropout_prob
      a_									=attention_probs_dropout_prob
      a_									=max_position_embeddings
      a_									=type_vocab_size
      a_									=type_sequence_label_size
      a_									=initializer_range
      a_									=num_labels
      a_									=num_choices
      a_									=scope




 def    lowercase_	( self)			->   Any:



      """simple docstring"""







      a_									=ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)

      a_									=None
      if self.use_input_mask:
           a_									=random_attention_mask([self.batch_size, self.seq_length])

      a_									=None
      a_									=None
      a_									=None
      if self.use_labels:
           a_									=ids_tensor([self.batch_size]				,	self.type_sequence_label_size)
           a_									=ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels)
           a_									=ids_tensor([self.batch_size]				,	self.num_choices)

      a_									=MPNetConfig(
          vocab_size=self.vocab_size				,	hidden_size=self.hidden_size				,	num_hidden_layers=self.num_hidden_layers				,	num_attention_heads=self.num_attention_heads				,	intermediate_size=self.intermediate_size				,	hidden_act=self.hidden_act				,	hidden_dropout_prob=self.hidden_dropout_prob				,	attention_probs_dropout_prob=self.attention_probs_dropout_prob				,	max_position_embeddings=self.max_position_embeddings				,	initializer_range=self.initializer_range				,	)
      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Optional[Any]:



      """simple docstring"""







      a_									=TFMPNetModel(config=__SCREAMING_SNAKE_CASE)
      a_									={"input_ids": input_ids, "attention_mask": input_mask}
      a_									=model(__SCREAMING_SNAKE_CASE)
      a_									=[input_ids, input_mask]
      a_									=model(__SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Dict:



      """simple docstring"""







      a_									=TFMPNetForMaskedLM(config=__SCREAMING_SNAKE_CASE)
      a_									={"input_ids": input_ids, "attention_mask": input_mask}
      a_									=model(__SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size))




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Dict:



      """simple docstring"""







      a_									=TFMPNetForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
      a_									={
          "input_ids": input_ids,
          "attention_mask": input_mask,
      }
      a_									=model(__SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.start_logits.shape				,	(self.batch_size, self.seq_length))
      self.parent.assertEqual(result.end_logits.shape				,	(self.batch_size, self.seq_length))




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   str:



      """simple docstring"""







      a_									=self.num_labels
      a_									=TFMPNetForSequenceClassification(__SCREAMING_SNAKE_CASE)
      a_									={"input_ids": input_ids, "attention_mask": input_mask}
      a_									=model(__SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_labels))




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Any:



      """simple docstring"""







      a_									=self.num_choices
      a_									=TFMPNetForMultipleChoice(__SCREAMING_SNAKE_CASE)
      a_									=tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE				,	1)				,	(1, self.num_choices, 1))
      a_									=tf.tile(tf.expand_dims(__SCREAMING_SNAKE_CASE				,	1)				,	(1, self.num_choices, 1))
      a_									={
          "input_ids": multiple_choice_inputs_ids,
          "attention_mask": multiple_choice_input_mask,
      }
      a_									=model(__SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_choices))




 def    lowercase_	( self				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Optional[int]:



      """simple docstring"""







      a_									=self.num_labels
      a_									=TFMPNetForTokenClassification(__SCREAMING_SNAKE_CASE)
      a_									={"input_ids": input_ids, "attention_mask": input_mask}
      a_									=model(__SCREAMING_SNAKE_CASE)
      self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.num_labels))




 def    lowercase_	( self)			->   Optional[Any]:



      """simple docstring"""







      a_									=self.prepare_config_and_inputs()
      (a_)									=config_and_inputs
      a_									={"input_ids": input_ids, "attention_mask": input_mask}
      return config, inputs_dict







@require_tf
class 						UpperCAmelCase						(		snake_case__       ,						snake_case__       ,						unittest.TestCase):


 '''simple docstring'''




 __magic_name__       :			List[Any]	=   (
     (
         TFMPNetForMaskedLM,
         TFMPNetForMultipleChoice,
         TFMPNetForQuestionAnswering,
         TFMPNetForSequenceClassification,
         TFMPNetForTokenClassification,
         TFMPNetModel,
     )
     if is_tf_available()
     else ()
 )
 __magic_name__       :			Dict	=   (
     {
         "feature-extraction": TFMPNetModel,
         "fill-mask": TFMPNetForMaskedLM,
         "question-answering": TFMPNetForQuestionAnswering,
         "text-classification": TFMPNetForSequenceClassification,
         "token-classification": TFMPNetForTokenClassification,
         "zero-shot": TFMPNetForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __magic_name__       :			List[Any]	=   False
 __magic_name__       :			str	=   False




 def    lowercase_	( self)			->   Any:



      """simple docstring"""







      a_									=TFMPNetModelTester(self)
      a_									=ConfigTester(self				,	config_class=__SCREAMING_SNAKE_CASE				,	hidden_size=3_7)




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



      """simple docstring"""







      self.config_tester.run_common_tests()




 def    lowercase_	( self)			->   List[str]:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*__SCREAMING_SNAKE_CASE)




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_masked_lm(*__SCREAMING_SNAKE_CASE)




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*__SCREAMING_SNAKE_CASE)




 def    lowercase_	( self)			->   Optional[int]:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*__SCREAMING_SNAKE_CASE)




 def    lowercase_	( self)			->   str:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*__SCREAMING_SNAKE_CASE)




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*__SCREAMING_SNAKE_CASE)




 @slow
 def    lowercase_	( self)			->   Optional[int]:



      """simple docstring"""







      for model_name in ["microsoft/mpnet-base"]:
           a_									=TFMPNetModel.from_pretrained(__SCREAMING_SNAKE_CASE)
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE)







@require_tf
class 						UpperCAmelCase						(		unittest.TestCase):


 '''simple docstring'''




 @slow
 def    lowercase_	( self)			->   List[Any]:



      """simple docstring"""







      a_									=TFMPNetModel.from_pretrained("microsoft/mpnet-base")
      a_									=tf.constant([[0, 1, 2, 3, 4, 5]])
      a_									=model(__SCREAMING_SNAKE_CASE)[0]

      a_									=[1, 6, 7_6_8]
      self.assertEqual(output.shape				,	__SCREAMING_SNAKE_CASE)

      a_									=tf.constant(
          [
              [
                  [-0.1_0_6_7_1_7_2, 0.0_8_2_1_6_4_7_3, 0.0_0_2_4_5_4_3],
                  [-0.0_3_4_6_5_8_7_9, 0.8_3_5_4_1_1_8, -0.0_3_2_5_2_2_8_8],
                  [-0.0_6_5_6_9_4_7_6, -0.1_2_4_2_4_1_1_1, -0.0_4_9_4_4_3_6],
              ]
          ])
      tf.debugging.assert_near(output[:, :3, :3]				,	__SCREAMING_SNAKE_CASE				,	atol=1e-4)






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


__UpperCAmelCase      			=  """
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.
"""

__UpperCAmelCase      			=  logging.getLogger(__name__)



@dataclass
class 							lowercase__:






      '''simple docstring'''







      snake_case__       =					field(
          default='''roberta-large-mnli'''			,							metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}			)
      snake_case__       =					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.'''
              )
          }			,							)
      snake_case__       =					field(
          default=3_2			,							metadata={'''help''': '''Batch size for generating teacher predictions.'''}			)
      snake_case__       =					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).'''
              )
          }			,							)
      snake_case__       =					field(
          default=1.0			,							metadata={'''help''': '''Temperature applied to teacher softmax for distillation.'''}			)




@dataclass
class 							lowercase__:






      '''simple docstring'''







      snake_case__       =					field(
          default='''distilbert-base-uncased'''			,							metadata={'''help''': '''The NLI/zero-shot teacher model to be distilled.'''}			)




@dataclass
class 							lowercase__:






      '''simple docstring'''







      snake_case__       =					field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}			)
      snake_case__       =					field(metadata={'''help''': '''Text file with one class name per line.'''}			)
      snake_case__       =					field(
          default=snake_case__			,							metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not.'''}			,							)




@dataclass
class 							lowercase__(						snake_case__			):






      '''simple docstring'''







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






      '''simple docstring'''







      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=False)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			str 						=inputs["labels"]
         UpperCamelCase__ :			List[Any] 						=model(inputs["input_ids"]   ,			attention_mask=inputs["attention_mask"])
         UpperCamelCase__ :			List[str] 						=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 	_lowerCamelCase  (		A_  :	List[Any]      )			->			List[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			int 						=[]
   with open(A_							,  "r"      ) as f:
      for line in f:
         UpperCamelCase__ :			Any 						=line.strip()
         if len(A_      ) > 0:
            lines.append(A_      )
   return lines
def 	_lowerCamelCase  (		A_  :	Tuple							,  A_  :	Dict							,  A_  :	Tuple      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[int] 						=[]
   UpperCamelCase__ :			List[str] 						=[]
   for example in examples:
      for name in class_names:
         premises.append(A_      )
         hypotheses.append(hypothesis_template.format(A_      )      )
   return premises, hypotheses
def 	_lowerCamelCase  (		A_  :	int      )			->			Optional[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 	_lowerCamelCase  (		A_  :	str							,  A_  :	List[str]							,  A_  :	List[str]							,  A_  :	str							,  A_  :	int							,  A_  :	float							,  A_  :	bool							,  A_  :	bool							,  A_  :	bool							,  A_  :	bool							,  )			->			Tuple:

   '''simple docstring'''






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

   UpperCamelCase__						,	UpperCamelCase__ :			Any 						=get_premise_hypothesis_pairs(A_							,  A_							,  A_      )
   UpperCamelCase__ :			Union[str, Any] 						=[]

   for i in tqdm(range(0							,  len(A_      )							,  A_      )      ):
      UpperCamelCase__ :			Any 						=premises[i : i + batch_size]
      UpperCamelCase__ :			Optional[int] 						=hypotheses[i : i + batch_size]

      UpperCamelCase__ :			Tuple 						=tokenizer(
          A_							,  A_							,  padding=A_							,  truncation="only_first"							,  return_tensors="pt"							,  )

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

   UpperCamelCase__ :			Any 						=get_entailment_id(A_      )
   UpperCamelCase__ :			Union[str, Any] 						=-1 if entail_id == 0 else 0
   UpperCamelCase__ :			Optional[int] 						=torch.cat(A_							,  dim=0      )  # N*K x 3
   UpperCamelCase__ :			List[Any] 						=logits.reshape(len(A_      )							,  len(A_      )							,  -1      )[..., [contr_id, entail_id]]  # N x K x 2

   if multi_label:
      # softmax over (contr, entail) logits for each class independently
      UpperCamelCase__ :			Optional[Any] 						=(nli_logits / temperature).softmax(-1      )
   else:
      # softmax over entail logits across classes s.t. class probabilities sum to 1.
      UpperCamelCase__ :			int 						=(nli_logits / temperature).softmax(1      )

   return nli_prob[..., 1]  # N x K
def 	_lowerCamelCase  (		)			->			Any:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=HfArgumentParser(
       (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)							,  description=A_							,  )

   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__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ :			Union[str, Any] 						=parser.parse_json_file(
          json_file=os.path.abspath(sys.argv[1]      )      )
   else:
      UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__ :			Optional[Any] 						=parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   UpperCamelCase__ :			str 						=None
   if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
      UpperCamelCase__ :			str 						=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__ :			Dict 						=read_lines(data_args.data_file      )
   UpperCamelCase__ :			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"      )
   UpperCamelCase__ :			Union[str, Any] 						=get_teacher_predictions(
       teacher_args.teacher_name_or_path							,  A_							,  A_							,  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__ :			Any 						=Dataset.from_dict(
       {
           "text": examples,
           "labels": teacher_soft_preds,
       }      )

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

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

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

   UpperCamelCase__ :			str 						=DistillationTrainer(
       model=A_							,  tokenizer=A_							,  args=A_							,  train_dataset=A_							,  compute_metrics=A_							,  )

   if training_args.do_train:
      logger.info("Training student model on teacher predictions"      )
      trainer.train()

   if training_args.do_eval:
      UpperCamelCase__ :			Any 						=trainer.evaluate(eval_dataset=A_      )["eval_agreement"]
      logger.info(f'''Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%'''      )

   trainer.save_model()


if __name__ == "__main__":
     main()

