


import unittest

from transformers import ErnieMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         ErnieMForInformationExtraction,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
         ErnieMModel,
     )
     from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST



class 							lowercase__:






      '''simple docstring'''







      def __init__(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=13   ,			__SCREAMING_SNAKE_CASE=7   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=True   ,			__SCREAMING_SNAKE_CASE=99   ,			__SCREAMING_SNAKE_CASE=32   ,			__SCREAMING_SNAKE_CASE=5   ,			__SCREAMING_SNAKE_CASE=4   ,			__SCREAMING_SNAKE_CASE=37   ,			__SCREAMING_SNAKE_CASE="gelu"   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=0.1   ,			__SCREAMING_SNAKE_CASE=5_12   ,			__SCREAMING_SNAKE_CASE=16   ,			__SCREAMING_SNAKE_CASE=2   ,			__SCREAMING_SNAKE_CASE=0.02   ,			__SCREAMING_SNAKE_CASE=3   ,			__SCREAMING_SNAKE_CASE=4   ,			__SCREAMING_SNAKE_CASE=None   ,			)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=parent
         UpperCamelCase__ :			Tuple 						=batch_size
         UpperCamelCase__ :			Union[str, Any] 						=seq_length
         UpperCamelCase__ :			Union[str, Any] 						=is_training
         UpperCamelCase__ :			Tuple 						=use_input_mask
         UpperCamelCase__ :			Union[str, Any] 						=use_labels
         UpperCamelCase__ :			Union[str, Any] 						=vocab_size
         UpperCamelCase__ :			List[Any] 						=hidden_size
         UpperCamelCase__ :			Dict 						=num_hidden_layers
         UpperCamelCase__ :			Any 						=num_attention_heads
         UpperCamelCase__ :			List[str] 						=intermediate_size
         UpperCamelCase__ :			List[Any] 						=hidden_act
         UpperCamelCase__ :			Tuple 						=hidden_dropout_prob
         UpperCamelCase__ :			Any 						=attention_probs_dropout_prob
         UpperCamelCase__ :			Tuple 						=max_position_embeddings
         UpperCamelCase__ :			Dict 						=type_vocab_size
         UpperCamelCase__ :			Tuple 						=type_sequence_label_size
         UpperCamelCase__ :			Tuple 						=initializer_range
         UpperCamelCase__ :			List[str] 						=num_labels
         UpperCamelCase__ :			Any 						=num_choices
         UpperCamelCase__ :			Tuple 						=scope




      def    UpperCAmelCase							(		self)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size)

         UpperCamelCase__ :			Any 						=None
         if self.use_input_mask:
            UpperCamelCase__ :			int 						=random_attention_mask([self.batch_size, self.seq_length])

         UpperCamelCase__ :			List[str] 						=None
         UpperCamelCase__ :			int 						=None
         UpperCamelCase__ :			Optional[Any] 						=None
         if self.use_labels:
            UpperCamelCase__ :			Dict 						=ids_tensor([self.batch_size]   ,			self.type_sequence_label_size)
            UpperCamelCase__ :			Dict 						=ids_tensor([self.batch_size, self.seq_length]   ,			self.num_labels)
            UpperCamelCase__ :			Optional[Any] 						=ids_tensor([self.batch_size]   ,			self.num_choices)

         UpperCamelCase__ :			Tuple 						=self.get_config()

         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size)

         UpperCamelCase__ :			List[str] 						=None
         if self.use_input_mask:
            UpperCamelCase__ :			Union[str, Any] 						=random_attention_mask([self.batch_size, self.seq_length])
         UpperCamelCase__ :			Union[str, Any] 						=self.get_config()

         return config, input_ids, input_mask




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







         """simple docstring"""







         return ErnieMConfig(
             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   ,			type_vocab_size=self.type_vocab_size   ,			initializer_range=self.initializer_range   ,			)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						str:







         """simple docstring"""







         UpperCamelCase__ :			Tuple 						=ErnieMModel(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			int 						=model(__SCREAMING_SNAKE_CASE   ,			return_dict=__SCREAMING_SNAKE_CASE)
         self.parent.assertEqual(result.last_hidden_state.shape   ,			(self.batch_size, self.seq_length, self.hidden_size))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[Any]:







         """simple docstring"""







         UpperCamelCase__ :			str 						=ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			Dict 						=model(
             __SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			start_positions=__SCREAMING_SNAKE_CASE   ,			end_positions=__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    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			Any 						=ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			int 						=torch.ones_like(__SCREAMING_SNAKE_CASE   ,			dtype=torch.floataa)
         UpperCamelCase__ :			Union[str, Any] 						=model(
             __SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			start_positions=__SCREAMING_SNAKE_CASE   ,			end_positions=__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    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						List[str]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=self.num_labels
         UpperCamelCase__ :			List[Any] 						=ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			Union[str, Any] 						=model(__SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			labels=__SCREAMING_SNAKE_CASE)
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.num_labels))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=self.num_labels
         UpperCamelCase__ :			int 						=ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         input_ids.to(__SCREAMING_SNAKE_CASE)
         input_mask.to(__SCREAMING_SNAKE_CASE)
         token_labels.to(__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Tuple 						=model(__SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			labels=__SCREAMING_SNAKE_CASE)

         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.seq_length, self.num_labels))




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         UpperCamelCase__ :			List[Any] 						=self.num_choices
         UpperCamelCase__ :			List[str] 						=ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE)
         model.to(__SCREAMING_SNAKE_CASE)
         model.eval()
         UpperCamelCase__ :			Optional[Any] 						=input_ids.unsqueeze(1).expand(-1   ,			self.num_choices   ,			-1).contiguous()
         UpperCamelCase__ :			int 						=input_mask.unsqueeze(1).expand(-1   ,			self.num_choices   ,			-1).contiguous()
         UpperCamelCase__ :			Optional[Any] 						=model(
             __SCREAMING_SNAKE_CASE   ,			attention_mask=__SCREAMING_SNAKE_CASE   ,			labels=__SCREAMING_SNAKE_CASE   ,			)
         self.parent.assertEqual(result.logits.shape   ,			(self.batch_size, self.num_choices))




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







         """simple docstring"""







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




@require_torch
class 							lowercase__(						snake_case__			,							snake_case__			,							unittest.TestCase			):






      '''simple docstring'''







      snake_case__       =					(
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      snake_case__       =					()
      snake_case__       =					(
          {
              '''feature-extraction''': ErnieMModel,
              '''question-answering''': ErnieMForQuestionAnswering,
              '''text-classification''': ErnieMForSequenceClassification,
              '''token-classification''': ErnieMForTokenClassification,
              '''zero-shot''': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      snake_case__       =					False




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE)					->						Optional[Any]:







         """simple docstring"""







         if pipeline_test_casse_name == "QAPipelineTests":
            return True

         return False




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







         """simple docstring"""







         UpperCamelCase__ :			Dict 						=ErnieMModelTester(self)
         UpperCamelCase__ :			int 						=ConfigTester(self   ,			config_class=__SCREAMING_SNAKE_CASE   ,			hidden_size=37)




      def    UpperCAmelCase							(		self)					->						List[Any]:







         """simple docstring"""







         self.config_tester.run_common_tests()




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







         """simple docstring"""







         UpperCamelCase__ :			Any 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)




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







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCamelCase__ :			List[str] 						=type
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)




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







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE)




      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE)




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







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_information_extraction(*__SCREAMING_SNAKE_CASE)




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







         """simple docstring"""







         UpperCamelCase__ :			Optional[Any] 						=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE)




      @slow
      def    UpperCAmelCase							(		self)					->						Tuple:







         """simple docstring"""







         for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCamelCase__ :			Optional[Any] 						=ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE)
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE)




@require_torch
class 							lowercase__(						unittest.TestCase			):






      '''simple docstring'''







      @slow
      def    UpperCAmelCase							(		self)					->						Any:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=ErnieMModel.from_pretrained("susnato/ernie-m-base_pytorch")
         model.eval()
         UpperCamelCase__ :			int 						=torch.tensor([[0, 1, 2, 3, 4, 5]])
         UpperCamelCase__ :			List[Any] 						=model(__SCREAMING_SNAKE_CASE)[0]

         # TODO Replace vocab size
         UpperCamelCase__ :			List[Any] 						=7_68

         UpperCamelCase__ :			Dict 						=torch.Size((1, 6, hidden_size))
         self.assertEqual(output.shape   ,			__SCREAMING_SNAKE_CASE)

         UpperCamelCase__ :			Union[str, Any] 						=torch.tensor(
             [[[-0.00_12, 0.12_45, -0.02_14], [-0.07_42, 0.02_44, -0.07_71], [-0.03_33, 0.11_64, -0.15_54]]])

         self.assertTrue(torch.allclose(output[:, :3, :3]   ,			__SCREAMING_SNAKE_CASE   ,			atol=1E-3))





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()

