
'''simple docstring'''

import unittest

import numpy as np
from datasets import load_dataset

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():
   from PIL import Image

   from transformers import SegformerImageProcessor


class 		_a     (							unittest.TestCase  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=7    ,__SCREAMING_SNAKE_CASE=3    ,__SCREAMING_SNAKE_CASE=30    ,__SCREAMING_SNAKE_CASE=400    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,__SCREAMING_SNAKE_CASE=False    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		size if size is not None else {"""height""": 30, """width""": 30}
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		parent
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		batch_size
    SCREAMING_SNAKE_CASE  :  Tuple								=		num_channels
    SCREAMING_SNAKE_CASE  :  Tuple								=		min_resolution
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		max_resolution
    SCREAMING_SNAKE_CASE  :  Tuple								=		do_resize
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		size
    SCREAMING_SNAKE_CASE  :  Tuple								=		do_normalize
    SCREAMING_SNAKE_CASE  :  Dict								=		image_mean
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		image_std
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		do_reduce_labels






   def 					__a			(       self      ):
    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_reduce_labels": self.do_reduce_labels,
    }



def        SCREAMING_SNAKE_CASE_  (	)       ->	List[Any]:
 SCREAMING_SNAKE_CASE  :  Any								=		load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'							)

 SCREAMING_SNAKE_CASE  :  List[Any]								=		Image.open(dataset[0]['file']							)
 SCREAMING_SNAKE_CASE  :  List[Any]								=		Image.open(dataset[1]['file']							)

 return image, map



def        SCREAMING_SNAKE_CASE_  (	)       ->	Union[str, Any]:
 SCREAMING_SNAKE_CASE  :  str								=		load_dataset('hf-internal-testing/fixtures_ade20k'   ,     split='test'							)

 SCREAMING_SNAKE_CASE  :  str								=		Image.open(dataset[0]['file']							)
 SCREAMING_SNAKE_CASE  :  str								=		Image.open(dataset[1]['file']							)
 SCREAMING_SNAKE_CASE  :  List[Any]								=		Image.open(dataset[2]['file']							)
 SCREAMING_SNAKE_CASE  :  str								=		Image.open(dataset[3]['file']							)

 return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class 		_a     (							lowerCAmelCase_			,    unittest.TestCase  ):







   """simple docstring"""




   A		= SegformerImageProcessor if is_vision_available() else None






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		SegformerImageProcessingTester(self      )






   @property
   def 					__a			(       self      ):
    return self.image_processor_tester.prepare_image_processor_dict()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  str								=		self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(lowerCamelCase_    ,'do_resize'      )      )
    self.assertTrue(hasattr(lowerCamelCase_    ,'size'      )      )
    self.assertTrue(hasattr(lowerCamelCase_    ,'do_normalize'      )      )
    self.assertTrue(hasattr(lowerCamelCase_    ,'image_mean'      )      )
    self.assertTrue(hasattr(lowerCamelCase_    ,'image_std'      )      )
    self.assertTrue(hasattr(lowerCamelCase_    ,'do_reduce_labels'      )      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size    ,{'height': 30, 'width': 30}      )
    self.assertEqual(image_processor.do_reduce_labels    ,lowerCamelCase_      )

    SCREAMING_SNAKE_CASE  :  Any								=		self.image_processing_class.from_dict(self.image_processor_dict    ,size=42    ,reduce_labels=lowerCamelCase_      )
    self.assertEqual(image_processor.size    ,{'height': 42, 'width': 42}      )
    self.assertEqual(image_processor.do_reduce_labels    ,lowerCamelCase_      )






   def 					__a			(       self      ):
    pass






   def 					__a			(       self      ):
    # Initialize image_processing
    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    SCREAMING_SNAKE_CASE  :  List[str]								=		prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase_      )
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_    ,Image.Image      )

    # Test not batched input
    SCREAMING_SNAKE_CASE  :  Tuple								=		image_processing(image_inputs[0]    ,return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)

    # Test batched
    SCREAMING_SNAKE_CASE  :  int								=		image_processing(lowerCamelCase_    ,return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)






   def 					__a			(       self      ):
    # Initialize image_processing
    SCREAMING_SNAKE_CASE  :  Any								=		self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase_    ,numpify=lowerCamelCase_      )
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_    ,np.ndarray      )

    # Test not batched input
    SCREAMING_SNAKE_CASE  :  Dict								=		image_processing(image_inputs[0]    ,return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)

    # Test batched
    SCREAMING_SNAKE_CASE  :  Tuple								=		image_processing(lowerCamelCase_    ,return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)






   def 					__a			(       self      ):
    # Initialize image_processing
    SCREAMING_SNAKE_CASE  :  Dict								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    SCREAMING_SNAKE_CASE  :  List[Any]								=		prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase_    ,torchify=lowerCamelCase_      )
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_    ,torch.Tensor      )

    # Test not batched input
    SCREAMING_SNAKE_CASE  :  List[Any]								=		image_processing(image_inputs[0]    ,return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)

    # Test batched
    SCREAMING_SNAKE_CASE  :  Dict								=		image_processing(lowerCamelCase_    ,return_tensors='pt'      ).pixel_values
    self.assertEqual(
        encoded_images.shape    ,(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)






   def 					__a			(       self      ):
    # Initialize image_processing
    SCREAMING_SNAKE_CASE  :  Dict								=		self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		prepare_image_inputs(self.image_processor_tester    ,equal_resolution=lowerCamelCase_    ,torchify=lowerCamelCase_      )
    SCREAMING_SNAKE_CASE  :  str								=		[]
    for image in image_inputs:
     self.assertIsInstance(lowerCamelCase_    ,torch.Tensor      )
     maps.append(torch.zeros(image.shape[-2:]      ).long()      )

    # Test not batched input
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_processing(image_inputs[0]    ,maps[0]    ,return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape    ,(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(
        encoding['labels'].shape    ,(
            1,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(encoding['labels'].dtype    ,torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 255      )

    # Test batched
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_processing(lowerCamelCase_    ,lowerCamelCase_    ,return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape    ,(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(
        encoding['labels'].shape    ,(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(encoding['labels'].dtype    ,torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 255      )

    # Test not batched input (PIL images)
    SCREAMING_SNAKE_CASE  :  Tuple								=		prepare_semantic_single_inputs()

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		image_processing(lowerCamelCase_    ,lowerCamelCase_    ,return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape    ,(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(
        encoding['labels'].shape    ,(
            1,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(encoding['labels'].dtype    ,torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 255      )

    # Test batched input (PIL images)
    SCREAMING_SNAKE_CASE  :  int								=		prepare_semantic_batch_inputs()

    SCREAMING_SNAKE_CASE  :  str								=		image_processing(lowerCamelCase_    ,lowerCamelCase_    ,return_tensors='pt'      )
    self.assertEqual(
        encoding['pixel_values'].shape    ,(
            2,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(
        encoding['labels'].shape    ,(
            2,
            self.image_processor_tester.size['height'],
            self.image_processor_tester.size['width'],
        )    ,)
    self.assertEqual(encoding['labels'].dtype    ,torch.long      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 255      )






   def 					__a			(       self      ):
    # Initialize image_processing
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		self.image_processing_class(**self.image_processor_dict      )

    # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
    SCREAMING_SNAKE_CASE  :  List[Any]								=		prepare_semantic_single_inputs()
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		image_processing(lowerCamelCase_    ,lowerCamelCase_    ,return_tensors='pt'      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 150      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		True
    SCREAMING_SNAKE_CASE  :  Dict								=		image_processing(lowerCamelCase_    ,lowerCamelCase_    ,return_tensors='pt'      )
    self.assertTrue(encoding['labels'].min().item() >= 0      )
    self.assertTrue(encoding['labels'].max().item() <= 255      )






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   	=  '''
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.
'''

__lowerCamelCase   	=  logging.getLogger(__name__)


@dataclass
class 	a__						:
       lowerCamelCase__:  Optional[str]				     =  field(
           default="""roberta-large-mnli"""    ,							metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}     )
       lowerCamelCase__:  Optional[str]				     =  field(
           default="""This example is {}."""    ,							metadata={
               """help""": (
                   """Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"""
                   """where class name is inserted."""
               )
           }    ,							)
       lowerCamelCase__:  Optional[int]				     =  field(
           default=32    ,							metadata={"""help""": """Batch size for generating teacher predictions."""}     )
       lowerCamelCase__:  Optional[bool]				     =  field(
           default=lowerCAmelCase_    ,							metadata={
               """help""": (
                   """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
                   """multi-class multi-label classification)."""
               )
           }    ,							)
       lowerCamelCase__:  Optional[float]				     =  field(
           default=1.0    ,							metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}     )


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


@dataclass
class 	a__						:
       lowerCamelCase__:  str				     =  field(metadata={"""help""": """Text file with one unlabeled instance per line."""}     )
       lowerCamelCase__:  str				     =  field(metadata={"""help""": """Text file with one class name per line."""}     )
       lowerCamelCase__:  bool				     =  field(
           default=lowerCAmelCase_    ,							metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}    ,							)


@dataclass
class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  Optional[str]				     =  field(
           default=lowerCAmelCase_    ,							metadata={"""help""": """The output directory where the model predictions and checkpoints will be written."""}    ,							)
       lowerCamelCase__:  int				     =  field(
           default=32    ,							metadata={"""help""": """Batch size per GPU/TPU core/CPU for training."""}     )
       lowerCamelCase__:  int				     =  field(
           default=128    ,							metadata={"""help""": """Batch size per GPU/TPU core/CPU for evaluation."""}     )
       lowerCamelCase__:  float				     =  field(default=1.0    ,							metadata={"""help""": """Total number of training epochs to perform."""}     )
       lowerCamelCase__:  bool				     =  field(default=lowerCAmelCase_    ,							metadata={"""help""": """Whether to run training of student model."""}     )
       lowerCamelCase__:  bool				     =  field(
           default=lowerCAmelCase_    ,							metadata={
               """help""": (
                   """Whether to evaluate the agreement of the final student predictions and the teacher predictions"""
                   """after training."""
               )
           }    ,							)
       lowerCamelCase__:  Optional[int]				     =  field(
           default=0    ,							metadata={
               """help""": (
                   """Limit the total amount of checkpoints."""
                   """Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."""
               )
           }    ,							)


class 	a__						(    lowerCAmelCase_     ):

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Union[str, Any]=False     ):
            a_     :  Union[str, Any]				      =     inputs["""labels"""]
            a_     :  Union[str, Any]				      =     model(inputs["""input_ids"""]   ,				attention_mask=inputs["""attention_mask"""]     )
            a_     :  str				      =     outputs[0]

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

            if return_outputs:
                 return loss, outputs

            return loss



def 			_a      (    __UpperCamelCase			):
     a_     :  Dict				      =     []
     with open(__UpperCamelCase	,	"""r"""			) as f:
          for line in f:
               a_     :  Dict				      =     line.strip()
               if len(__UpperCamelCase			) > 0:
                    lines.append(__UpperCamelCase			)
     return lines



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     []
     a_     :  Dict				      =     []
     for example in examples:
          for name in class_names:
               premises.append(__UpperCamelCase			)
               hypotheses.append(hypothesis_template.format(__UpperCamelCase			)			)
     return premises, hypotheses



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

     a_      ,      a_     :  Dict				      =     get_premise_hypothesis_pairs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
     a_     :  List[str]				      =     []

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

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

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

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

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



def 			_a      (    ):
     a_     :  str				      =     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.
          a_      ,      a_      ,      a_      ,      a_     :  Dict				      =     parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          a_      ,      a_      ,      a_      ,      a_     :  Any				      =     parser.parse_args_into_dataclasses()

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

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

     # 4. train student on teacher predictions
     a_     :  Optional[int]				      =     dataset.map(__UpperCamelCase	,	input_columns="""text"""			)
     dataset.set_format("""torch"""			)

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

     a_     :  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:
          a_     :  Tuple				      =     trainer.evaluate(eval_dataset=__UpperCamelCase			)["""eval_agreement"""]
          logger.info(F'''Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%'''			)

     trainer.save_model()


if __name__ == "__main__":
       main()



