


import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  import PIL

  from transformers import FlavaImageProcessor
  from transformers.image_utils import PILImageResampling
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )
else:
  UpperCamelCase__				       =			None

class 						UpperCAmelCase__   (      unittest.TestCase		):



     '''simple docstring'''




     def __init__( self       :       Dict	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       str=7	,      UpperCamelCase       :       Tuple=3	,      UpperCamelCase       :       Any=30	,      UpperCamelCase       :       Tuple=4_00	,      UpperCamelCase       :       List[Any]=True	,      UpperCamelCase       :       List[str]=None	,      UpperCamelCase       :       Any=True	,      UpperCamelCase       :       int=None	,      UpperCamelCase       :       Union[str, Any]=None	,      UpperCamelCase       :       Optional[Any]=True	,      UpperCamelCase       :       Optional[Any]=1 / 2_55	,      UpperCamelCase       :       Union[str, Any]=True	,      UpperCamelCase       :       Optional[int]=FLAVA_IMAGE_MEAN	,      UpperCamelCase       :       List[str]=FLAVA_IMAGE_STD	,      UpperCamelCase       :       int=14	,      UpperCamelCase       :       Tuple=75	,      UpperCamelCase       :       Optional[int]=None	,      UpperCamelCase       :       List[str]=16	,      UpperCamelCase       :       Union[str, Any]=0.3	,      UpperCamelCase       :       Dict=None	,      UpperCamelCase       :       str=True	,      UpperCamelCase       :       Dict=None	,      UpperCamelCase       :       Dict=None	,      UpperCamelCase       :       List[str]=True	,      UpperCamelCase       :       int=None	,      UpperCamelCase       :       Dict=True	,      UpperCamelCase       :       Optional[Any]=True	,      UpperCamelCase       :       Optional[Any]=FLAVA_CODEBOOK_MEAN	,      UpperCamelCase       :       Tuple=FLAVA_CODEBOOK_STD	,      ):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
            _lowercase					:				List[str]           =			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
            _lowercase					:				Union[str, Any]           =			codebook_size if codebook_size is not None else {'''height''': 1_12, '''width''': 1_12}
            _lowercase					:				Union[str, Any]           =			codebook_crop_size if codebook_crop_size is not None else {'''height''': 1_12, '''width''': 1_12}

            _lowercase					:				Tuple           =			parent
            _lowercase					:				Optional[Any]           =			batch_size
            _lowercase					:				Union[str, Any]           =			num_channels
            _lowercase					:				List[Any]           =			do_resize
            _lowercase					:				Any           =			do_rescale
            _lowercase					:				str           =			rescale_factor
            _lowercase					:				Dict           =			min_resolution
            _lowercase					:				Union[str, Any]           =			max_resolution
            _lowercase					:				Optional[Any]           =			size
            _lowercase					:				List[Any]           =			resample if resample is not None else PILImageResampling.BICUBIC
            _lowercase					:				int           =			do_normalize
            _lowercase					:				Union[str, Any]           =			image_mean
            _lowercase					:				Dict           =			image_std
            _lowercase					:				Dict           =			do_center_crop
            _lowercase					:				List[str]           =			crop_size

            _lowercase					:				Any           =			input_size_patches
            _lowercase					:				List[Any]           =			total_mask_patches
            _lowercase					:				Dict           =			mask_group_max_patches
            _lowercase					:				str           =			mask_group_min_patches
            _lowercase					:				Optional[Any]           =			mask_group_min_aspect_ratio
            _lowercase					:				Optional[int]           =			mask_group_max_aspect_ratio

            _lowercase					:				Any           =			codebook_do_resize
            _lowercase					:				Dict           =			codebook_size
            _lowercase					:				Optional[int]           =			codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
            _lowercase					:				Optional[int]           =			codebook_do_center_crop
            _lowercase					:				Optional[int]           =			codebook_crop_size
            _lowercase					:				Dict           =			codebook_do_map_pixels
            _lowercase					:				Optional[int]           =			codebook_do_normalize
            _lowercase					:				Optional[int]           =			codebook_image_mean
            _lowercase					:				Dict           =			codebook_image_std




     def   lowerCAmelCase_   ( self       :       int		):





            """simple docstring"""



            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "resample": self.resample,
                "do_rescale": self.do_rescale,
                "rescale_factor": self.rescale_factor,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "input_size_patches": self.input_size_patches,
                "total_mask_patches": self.total_mask_patches,
                "mask_group_max_patches": self.mask_group_max_patches,
                "mask_group_min_patches": self.mask_group_min_patches,
                "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
                "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
                "codebook_do_resize": self.codebook_do_resize,
                "codebook_size": self.codebook_size,
                "codebook_resample": self.codebook_resample,
                "codebook_do_center_crop": self.codebook_do_center_crop,
                "codebook_crop_size": self.codebook_crop_size,
                "codebook_do_map_pixels": self.codebook_do_map_pixels,
                "codebook_do_normalize": self.codebook_do_normalize,
                "codebook_image_mean": self.codebook_image_mean,
                "codebook_image_std": self.codebook_image_std,
            }




     def   lowerCAmelCase_   ( self       :       Optional[Any]		):





            """simple docstring"""



            return (self.size["height"], self.size["width"])




     def   lowerCAmelCase_   ( self       :       List[Any]		):





            """simple docstring"""



            return (
                (self.input_size_patches, self.input_size_patches)
                if not isinstance(self.input_size_patches	,      UpperCamelCase		)
                else self.input_size_patches
            )




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class 						UpperCAmelCase__   (      UpperCAmelCase_      ,						unittest.TestCase		):



     '''simple docstring'''
     UpperCAmelCase_         =  FlavaImageProcessor if is_vision_available() else None
     UpperCAmelCase_         =  None




     def   lowerCAmelCase_   ( self       :       Dict		):





            """simple docstring"""



            _lowercase					:				int           =			FlavaImageProcessingTester(self		)




     @property
     def   lowerCAmelCase_   ( self       :       List[Any]		):





            """simple docstring"""



            return self.image_processor_tester.prepare_image_processor_dict()




     def   lowerCAmelCase_   ( self       :       str		):





            """simple docstring"""



            _lowercase					:				Union[str, Any]           =			self.image_processing_class(**self.image_processor_dict		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''image_mean'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''image_std'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''do_normalize'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''do_resize'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''resample'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''crop_size'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''do_center_crop'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''do_rescale'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''rescale_factor'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''masking_generator'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_do_resize'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_size'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_resample'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_do_center_crop'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_crop_size'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_do_map_pixels'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_do_normalize'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_image_mean'''		)		)
            self.assertTrue(hasattr(UpperCamelCase	,      '''codebook_image_std'''		)		)




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            _lowercase					:				Any           =			self.image_processing_class.from_dict(self.image_processor_dict		)
            self.assertEqual(image_processor.size	,      {'''height''': 2_24, '''width''': 2_24}		)
            self.assertEqual(image_processor.crop_size	,      {'''height''': 2_24, '''width''': 2_24}		)
            self.assertEqual(image_processor.codebook_size	,      {'''height''': 1_12, '''width''': 1_12}		)
            self.assertEqual(image_processor.codebook_crop_size	,      {'''height''': 1_12, '''width''': 1_12}		)

            _lowercase					:				Optional[Any]           =			self.image_processing_class.from_dict(
                self.image_processor_dict	,      size=42	,      crop_size=84	,      codebook_size=33	,      codebook_crop_size=66		)
            self.assertEqual(image_processor.size	,      {'''height''': 42, '''width''': 42}		)
            self.assertEqual(image_processor.crop_size	,      {'''height''': 84, '''width''': 84}		)
            self.assertEqual(image_processor.codebook_size	,      {'''height''': 33, '''width''': 33}		)
            self.assertEqual(image_processor.codebook_crop_size	,      {'''height''': 66, '''width''': 66}		)




     def   lowerCAmelCase_   ( self       :       List[str]		):





            """simple docstring"""



            pass




     def   lowerCAmelCase_   ( self       :       List[str]		):





            """simple docstring"""



            _lowercase					:				List[Any]           =			self.image_processing_class(**self.image_processor_dict		)
            # create random PIL images
            _lowercase					:				Tuple           =			prepare_image_inputs(self.image_processor_tester	,      equal_resolution=UpperCamelCase		)
            for image in image_inputs:
                   self.assertIsInstance(UpperCamelCase	,      PIL.Image.Image		)

            # Test not batched input
            _lowercase					:				Dict           =			image_processing(image_inputs[0]	,      return_tensors='''pt'''		)

            # Test no bool masked pos
            self.assertFalse('''bool_masked_pos''' in encoded_images		)

            _lowercase       ,	_lowercase					:				Dict           =			self.image_processor_tester.get_expected_image_size()

            self.assertEqual(
                encoded_images.pixel_values.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

            # Test batched
            _lowercase					:				List[str]           =			image_processing(UpperCamelCase	,      return_tensors='''pt'''		)
            _lowercase       ,	_lowercase					:				Any           =			self.image_processor_tester.get_expected_image_size()

            # Test no bool masked pos
            self.assertFalse('''bool_masked_pos''' in encoded_images		)

            self.assertEqual(
                encoded_images.pixel_values.shape	,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,      )




     def   lowerCAmelCase_   ( self       :       Tuple	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       List[str]		):





            """simple docstring"""



            _lowercase					:				List[str]           =			self.image_processing_class(**self.image_processor_dict		)
            # create random tensors
            _lowercase					:				Optional[int]           =			prepare_image_inputs(self.image_processor_tester	,      equal_resolution=UpperCamelCase	,      **UpperCamelCase		)
            for image in image_inputs:
                   self.assertIsInstance(UpperCamelCase	,      UpperCamelCase		)

            # Test not batched input
            _lowercase					:				int           =			image_processing(image_inputs[0]	,      return_tensors='''pt'''		)

            _lowercase       ,	_lowercase					:				List[str]           =			self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.pixel_values.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

            _lowercase					:				str           =			image_processing(UpperCamelCase	,      return_image_mask=UpperCamelCase	,      return_tensors='''pt'''		)

            _lowercase       ,	_lowercase					:				List[str]           =			self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.pixel_values.shape	,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,      )

            _lowercase       ,	_lowercase					:				Union[str, Any]           =			self.image_processor_tester.get_expected_mask_size()
            self.assertEqual(
                encoded_images.bool_masked_pos.shape	,      (
                    self.image_processor_tester.batch_size,
                    expected_height,
                    expected_width,
                )	,      )

            # Test batched
            _lowercase					:				Optional[int]           =			image_processing(UpperCamelCase	,      return_tensors='''pt'''		).pixel_values

            _lowercase       ,	_lowercase					:				Dict           =			self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.shape	,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,      )

            # Test masking
            _lowercase					:				List[Any]           =			image_processing(UpperCamelCase	,      return_image_mask=UpperCamelCase	,      return_tensors='''pt'''		)

            _lowercase       ,	_lowercase					:				Any           =			self.image_processor_tester.get_expected_image_size()
            self.assertEqual(
                encoded_images.pixel_values.shape	,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,      )

            _lowercase       ,	_lowercase					:				Any           =			self.image_processor_tester.get_expected_mask_size()
            self.assertEqual(
                encoded_images.bool_masked_pos.shape	,      (
                    self.image_processor_tester.batch_size,
                    expected_height,
                    expected_width,
                )	,      )




     def   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            self._test_call_framework(np.ndarray	,      prepare_kwargs={'''numpify''': True}		)




     def   lowerCAmelCase_   ( self       :       Tuple		):





            """simple docstring"""



            self._test_call_framework(torch.Tensor	,      prepare_kwargs={'''torchify''': True}		)




     def   lowerCAmelCase_   ( self       :       List[str]		):





            """simple docstring"""



            random.seed(12_34		)
            _lowercase					:				Optional[int]           =			self.image_processing_class(**self.image_processor_dict		)
            _lowercase					:				Union[str, Any]           =			prepare_image_inputs(self.image_processor_tester	,      equal_resolution=UpperCamelCase	,      torchify=UpperCamelCase		)

            # Test not batched input
            _lowercase					:				Any           =			image_processing(image_inputs[0]	,      return_image_mask=UpperCamelCase	,      return_tensors='''pt'''		)
            self.assertEqual(encoded_images.bool_masked_pos.sum().item()	,      75		)




     def   lowerCAmelCase_   ( self       :       Optional[int]		):





            """simple docstring"""



            _lowercase					:				int           =			self.image_processing_class(**self.image_processor_dict		)
            # create random PIL images
            _lowercase					:				Optional[int]           =			prepare_image_inputs(self.image_processor_tester	,      equal_resolution=UpperCamelCase		)
            for image in image_inputs:
                   self.assertIsInstance(UpperCamelCase	,      PIL.Image.Image		)

            # Test not batched input
            _lowercase					:				List[str]           =			image_processing(image_inputs[0]	,      return_codebook_pixels=UpperCamelCase	,      return_tensors='''pt'''		)
            _lowercase       ,	_lowercase					:				Optional[Any]           =			self.image_processor_tester.get_expected_codebook_image_size()
            self.assertEqual(
                encoded_images.codebook_pixel_values.shape	,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)	,      )

            # Test batched
            _lowercase					:				Dict           =			image_processing(UpperCamelCase	,      return_codebook_pixels=UpperCamelCase	,      return_tensors='''pt'''		)
            _lowercase       ,	_lowercase					:				List[Any]           =			self.image_processor_tester.get_expected_codebook_image_size()
            self.assertEqual(
                encoded_images.codebook_pixel_values.shape	,      (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	,      )



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__				       =			'\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'

UpperCamelCase__				       =			logging.getLogger(__name__)

@dataclass
class 						UpperCAmelCase__   :



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




@dataclass
class 						UpperCAmelCase__   :



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




@dataclass
class 						UpperCAmelCase__   :



     '''simple docstring'''
     UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one unlabeled instance per line.'''}		)
     UpperCAmelCase_         =  field(metadata={'''help''': '''Text file with one class name per line.'''}		)
     UpperCAmelCase_         =  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'''
     UpperCAmelCase_         =  field(
         default=A_      ,						metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}      ,						)
     UpperCAmelCase_         =  field(
         default=3_2      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for training.'''}		)
     UpperCAmelCase_         =  field(
         default=1_2_8      ,						metadata={'''help''': '''Batch size per GPU/TPU core/CPU for evaluation.'''}		)
     UpperCAmelCase_         =  field(default=1.0      ,						metadata={'''help''': '''Total number of training epochs to perform.'''}		)
     UpperCAmelCase_         =  field(default=A_      ,						metadata={'''help''': '''Whether to run training of student model.'''}		)
     UpperCAmelCase_         =  field(
         default=A_      ,						metadata={
             '''help''': (
                 '''Whether to evaluate the agreement of the final student predictions and the teacher predictions'''
                 '''after training.'''
             )
         }      ,						)
     UpperCAmelCase_         =  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       :       Dict	,      UpperCamelCase       :       Optional[int]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       Dict=False		):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			inputs['''labels''']
            _lowercase					:				List[Any]           =			model(inputs['''input_ids''']	,      attention_mask=inputs['''attention_mask''']		)
            _lowercase					:				int           =			outputs[0]

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

            if return_outputs:
                   return loss, outputs

            return loss




def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Tuple:




       '''simple docstring'''






       _lowercase					:				Tuple           =			[]
       with open(UpperCAmelCase_    ,     '''r'''			) as f:
              for line in f:
                     _lowercase					:				Optional[int]           =			line.strip()
                     if len(UpperCAmelCase_			) > 0:
                            lines.append(UpperCAmelCase_			)
       return lines

def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							Dict:




       '''simple docstring'''






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

def   UpperCamelCase__   (    UpperCAmelCase_			)						->							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_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     )						->							int:




       '''simple docstring'''






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

       _lowercase       ,	_lowercase					:				Any           =			get_premise_hypothesis_pairs(UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)
       _lowercase					:				List[str]           =			[]

       for i in tqdm(range(0    ,     len(UpperCAmelCase_			)    ,     UpperCAmelCase_			)			):
              _lowercase					:				Tuple           =			premises[i : i + batch_size]
              _lowercase					:				Optional[int]           =			hypotheses[i : i + batch_size]

              _lowercase					:				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():
                            _lowercase					:				Dict           =			model(**UpperCAmelCase_			)
              logits.append(outputs.logits.detach().cpu().float()			)

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

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

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




       '''simple docstring'''






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

       # Detecting last checkpoint.
       _lowercase					:				Tuple           =			None
       if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
              _lowercase					:				Optional[Any]           =			get_last_checkpoint(training_args.output_dir			)
              if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
                     raise ValueError(
                         F'Output directory ({training_args.output_dir}) already exists and is not empty. '
                         '''Use --overwrite_output_dir to overcome.'''			)
              elif last_checkpoint is not None:
                     logger.info(
                         F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
                         '''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'''			)

    # Setup logging
       logging.basicConfig(
           format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''    ,     datefmt='''%m/%d/%Y %H:%M:%S'''    ,     handlers=[logging.StreamHandler(sys.stdout			)]    ,     )
       logger.setLevel(logging.INFO if is_main_process(training_args.local_rank			) else logging.WARN			)

       # Log on each process the small summary:
       logger.warning(
           F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
           + F'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank			):
              utils.logging.set_verbosity_info()
              utils.logging.enable_default_handler()
              utils.logging.enable_explicit_format()

       if training_args.local_rank != -1:
              raise ValueError('''Distributed training is not currently supported.'''			)
       if training_args.tpu_num_cores is not None:
              raise ValueError('''TPU acceleration is not currently supported.'''			)

       logger.info(F'Training/evaluation parameters {training_args}'			)

       # Set seed before initializing model.
       set_seed(training_args.seed			)

       # 1. read in data
       _lowercase					:				str           =			read_lines(data_args.data_file			)
       _lowercase					:				List[str]           =			read_lines(data_args.class_names_file			)

       # 2. get teacher predictions and load into dataset
       logger.info('''Generating predictions from zero-shot teacher model'''			)
       _lowercase					:				Optional[int]           =			get_teacher_predictions(
           teacher_args.teacher_name_or_path    ,     UpperCAmelCase_    ,     UpperCAmelCase_    ,     teacher_args.hypothesis_template    ,     teacher_args.teacher_batch_size    ,     teacher_args.temperature    ,     teacher_args.multi_label    ,     data_args.use_fast_tokenizer    ,     training_args.no_cuda    ,     training_args.fpaa    ,     )
       _lowercase					:				Optional[Any]           =			Dataset.from_dict(
           {
               '''text''': examples,
               '''labels''': teacher_soft_preds,
           }			)

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

       # 4. train student on teacher predictions
       _lowercase					:				List[str]           =			dataset.map(UpperCAmelCase_    ,     input_columns='''text'''			)
       dataset.set_format('''torch'''			)

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

       _lowercase					:				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:
              _lowercase					:				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()