


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:
							SCREAMING_SNAKE_CASE__       			=				None







class     __lowerCamelCase     (  unittest.TestCase     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase=7						,       UpperCAmelCase=3						,       UpperCAmelCase=30						,       UpperCAmelCase=400						,       UpperCAmelCase=True						,       UpperCAmelCase=None						,       UpperCAmelCase=True						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase=True						,       UpperCAmelCase=1 / 255						,       UpperCAmelCase=True						,       UpperCAmelCase=FLAVA_IMAGE_MEAN						,       UpperCAmelCase=FLAVA_IMAGE_STD						,       UpperCAmelCase=14						,       UpperCAmelCase=75						,       UpperCAmelCase=None						,       UpperCAmelCase=16						,       UpperCAmelCase=0.3						,       UpperCAmelCase=None						,       UpperCAmelCase=True						,       UpperCAmelCase=None						,       UpperCAmelCase=None						,       UpperCAmelCase=True						,       UpperCAmelCase=None						,       UpperCAmelCase=True						,       UpperCAmelCase=True						,       UpperCAmelCase=FLAVA_CODEBOOK_MEAN						,       UpperCAmelCase=FLAVA_CODEBOOK_STD						,       )     ->       int:






								'''simple docstring'''






								lowercase_			 = size if size is not None else {"height": 224, "width": 224}
								lowercase_			 = crop_size if crop_size is not None else {"height": 224, "width": 224}
								lowercase_			 = codebook_size if codebook_size is not None else {"height": 112, "width": 112}
								lowercase_			 = codebook_crop_size if codebook_crop_size is not None else {"height": 112, "width": 112}

								lowercase_			 = parent
								lowercase_			 = batch_size
								lowercase_			 = num_channels
								lowercase_			 = do_resize
								lowercase_			 = do_rescale
								lowercase_			 = rescale_factor
								lowercase_			 = min_resolution
								lowercase_			 = max_resolution
								lowercase_			 = size
								lowercase_			 = resample if resample is not None else PILImageResampling.BICUBIC
								lowercase_			 = do_normalize
								lowercase_			 = image_mean
								lowercase_			 = image_std
								lowercase_			 = do_center_crop
								lowercase_			 = crop_size

								lowercase_			 = input_size_patches
								lowercase_			 = total_mask_patches
								lowercase_			 = mask_group_max_patches
								lowercase_			 = mask_group_min_patches
								lowercase_			 = mask_group_min_aspect_ratio
								lowercase_			 = mask_group_max_aspect_ratio

								lowercase_			 = codebook_do_resize
								lowercase_			 = codebook_size
								lowercase_			 = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
								lowercase_			 = codebook_do_center_crop
								lowercase_			 = codebook_crop_size
								lowercase_			 = codebook_do_map_pixels
								lowercase_			 = codebook_do_normalize
								lowercase_			 = codebook_image_mean
								lowercase_			 = codebook_image_std




							def 				A__      (  self  )     ->       Dict:






								'''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 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






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




							def 				A__      (  self  )     ->       Union[str, 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 				A__      (  self  )     ->       Optional[int]:






								'''simple docstring'''






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







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


							"""simple docstring"""

							lowerCAmelCase__          =				FlavaImageProcessor if is_vision_available() else None
							lowerCAmelCase__          =				None




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






								'''simple docstring'''






								lowercase_			 = FlavaImageProcessingTester(self  )




							@property
							def 				A__      (  self  )     ->       Dict:






								'''simple docstring'''






								return self.image_processor_tester.prepare_image_processor_dict()




							def 				A__      (  self  )     ->       Any:






								'''simple docstring'''






								lowercase_			 = 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 				A__      (  self  )     ->       str:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size						,       {"height": 224, "width": 224}  )
								self.assertEqual(image_processor.crop_size						,       {"height": 224, "width": 224}  )
								self.assertEqual(image_processor.codebook_size						,       {"height": 112, "width": 112}  )
								self.assertEqual(image_processor.codebook_crop_size						,       {"height": 112, "width": 112}  )

								lowercase_			 = 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 				A__      (  self  )     ->       Tuple:






								'''simple docstring'''






								pass




							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowercase_			 = 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_			 = image_processing(image_inputs[0]						,       return_tensors="pt"  )

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

								lowercase_ ,	lowercase_			 = 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_			 = image_processing(__UpperCamelCase						,       return_tensors="pt"  )
								lowercase_ ,	lowercase_			 = 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 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random tensors
								lowercase_			 = 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_			 = image_processing(image_inputs[0]						,       return_tensors="pt"  )

								lowercase_ ,	lowercase_			 = 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_			 = image_processing(__UpperCamelCase						,       return_image_mask=__UpperCamelCase						,       return_tensors="pt"  )

								lowercase_ ,	lowercase_			 = 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_			 = 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_			 = image_processing(__UpperCamelCase						,       return_tensors="pt"  ).pixel_values

								lowercase_ ,	lowercase_			 = 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_			 = image_processing(__UpperCamelCase						,       return_image_mask=__UpperCamelCase						,       return_tensors="pt"  )

								lowercase_ ,	lowercase_			 = 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_			 = 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 				A__      (  self  )     ->       Union[str, Any]:






								'''simple docstring'''






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




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






								'''simple docstring'''






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




							def 				A__      (  self  )     ->       Any:






								'''simple docstring'''






								random.seed(1234  )
								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								lowercase_			 = prepare_image_inputs(self.image_processor_tester						,       equal_resolution=__UpperCamelCase						,       torchify=__UpperCamelCase  )

								# Test not batched input
								lowercase_			 = image_processing(image_inputs[0]						,       return_image_mask=__UpperCamelCase						,       return_tensors="pt"  )
								self.assertEqual(encoded_images.bool_masked_pos.sum().item()						,       75  )




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






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowercase_			 = 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_			 = image_processing(image_inputs[0]						,       return_codebook_pixels=__UpperCamelCase						,       return_tensors="pt"  )
								lowercase_ ,	lowercase_			 = 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_			 = image_processing(__UpperCamelCase						,       return_codebook_pixels=__UpperCamelCase						,       return_tensors="pt"  )
								lowercase_ ,	lowercase_			 = 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,
								    )						,       )






'''simple docstring'''







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

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

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


lowercase__  :	List[Any]            =      '\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'

lowercase__  :	str            =      logging.getLogger(__name__)

@dataclass
class    lowerCamelCase_				:






   '''simple docstring'''

   A			:  Optional[str]					=						field(
       default="roberta-large-mnli"       ,     metadata={"help": "The NLI/zero-shot teacher model to be distilled."}	)
   A			:  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."
           )
       }       ,     )
   A			:  Optional[int]					=						field(
       default=32       ,     metadata={"help": "Batch size for generating teacher predictions."}	)
   A			:  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)."
           )
       }       ,     )
   A			:  Optional[float]					=						field(
       default=1.0       ,     metadata={"help": "Temperature applied to teacher softmax for distillation."}	)

@dataclass
class    lowerCamelCase_				:






   '''simple docstring'''

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

@dataclass
class    lowerCamelCase_				:






   '''simple docstring'''

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

@dataclass
class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  Optional[str]					=						field(
       default=lowerCAmelCase_       ,     metadata={"help": "The output directory where the model predictions and checkpoints will be written."}       ,     )
   A			:  int					=						field(
       default=32       ,     metadata={"help": "Batch size per GPU/TPU core/CPU for training."}	)
   A			:  int					=						field(
       default=128       ,     metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}	)
   A			:  float					=						field(default=1.0       ,     metadata={"help": "Total number of training epochs to perform."}	)
   A			:  bool					=						field(default=lowerCAmelCase_       ,     metadata={"help": "Whether to run training of student model."}	)
   A			:  bool					=						field(
       default=lowerCAmelCase_       ,     metadata={
           "help": (
               "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
               "after training."
           )
       }       ,     )
   A			:  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    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase=False			):
       UpperCamelCase__	     =      inputs["labels"]
       UpperCamelCase__	     =      model(inputs["input_ids"]  ,    attention_mask=inputs["attention_mask"]			)
       UpperCamelCase__	     =      outputs[0]

       UpperCamelCase__	     =      -torch.sum(target_p * logits.log_softmax(dim=-1			)  ,    axis=-1			).mean()

       if return_outputs:
           return loss, outputs

       return loss





def     snake_case__			(lowerCAmelCase_						):
    UpperCamelCase__	     =      []
    with open(lowerCAmelCase_       ,	"r"						) as f:
        for line in f:
            UpperCamelCase__	     =      line.strip()
            if len(lowerCAmelCase_						) > 0:
                lines.append(lowerCAmelCase_						)
    return lines





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						):
    UpperCamelCase__	     =      []
    UpperCamelCase__	     =      []
    for example in examples:
        for name in class_names:
            premises.append(lowerCAmelCase_						)
            hypotheses.append(hypothesis_template.format(lowerCAmelCase_						)						)
    return premises, hypotheses





def     snake_case__			(lowerCAmelCase_						):
    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     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_       ,	):
    UpperCamelCase__	     =      AutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_						)
    UpperCamelCase__	     =      model.config
    if not no_cuda and torch.cuda.is_available():
        UpperCamelCase__	     =      nn.DataParallel(model.cuda()						)
        batch_size *= len(model.device_ids						)
    UpperCamelCase__	     =      AutoTokenizer.from_pretrained(lowerCAmelCase_       ,	use_fast=lowerCAmelCase_						)

    UpperCamelCase__     ,	UpperCamelCase__	     =      get_premise_hypothesis_pairs(lowerCAmelCase_       ,	lowerCAmelCase_       ,	lowerCAmelCase_						)
    UpperCamelCase__	     =      []

    for i in tqdm(range(0       ,	len(lowerCAmelCase_						)       ,	lowerCAmelCase_						)						):
        UpperCamelCase__	     =      premises[i : i + batch_size]
        UpperCamelCase__	     =      hypotheses[i : i + batch_size]

        UpperCamelCase__	     =      tokenizer(
            lowerCAmelCase_       ,	lowerCAmelCase_       ,	padding=lowerCAmelCase_       ,	truncation="only_first"       ,	return_tensors="pt"       ,	)

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

    UpperCamelCase__	     =      get_entailment_id(lowerCAmelCase_						)
    UpperCamelCase__	     =      -1 if entail_id == 0 else 0
    UpperCamelCase__	     =      torch.cat(lowerCAmelCase_       ,	dim=0						)  # N*K x 3
    UpperCamelCase__	     =      logits.reshape(len(lowerCAmelCase_						)       ,	len(lowerCAmelCase_						)       ,	-1						)[..., [contr_id, entail_id]]  # N x K x 2

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

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





def     snake_case__			():
    UpperCamelCase__	     =      HfArgumentParser(
        (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)       ,	description=lowerCAmelCase_       ,	)

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

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

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

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

    def compute_metrics(lowerCAmelCase_       ,	lowerCAmelCase_=False						):
        UpperCamelCase__	     =      p.predictions.argmax(-1						)
        UpperCamelCase__	     =      p.label_ids.argmax(-1						)  # "label_ids" are actually distributions
        return {"agreement": (preds == proxy_labels).mean().item()}

    UpperCamelCase__	     =      DistillationTrainer(
        model=lowerCAmelCase_       ,	tokenizer=lowerCAmelCase_       ,	args=lowerCAmelCase_       ,	train_dataset=lowerCAmelCase_       ,	compute_metrics=lowerCAmelCase_       ,	)

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

    if training_args.do_eval:
        UpperCamelCase__	     =      trainer.evaluate(eval_dataset=lowerCAmelCase_						)["eval_agreement"]
        logger.info(F'Agreement of student and teacher predictions: {agreement * 1_00:0.2f}%'						)

    trainer.save_model()


if __name__ == "__main__":
      main()




