




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the checkpointing capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

a__		    =    16
a__		    =    32





def 							A__   (snake_case      :		Accelerator		,  snake_case      :		int = 16							)    ->	int:
					__UpperCamelCase				:  Tuple								=			AutoTokenizer.from_pretrained("""bert-base-cased"""							)
					__UpperCamelCase				:  Optional[Any]								=			load_dataset("""glue"""		,  """mrpc"""							)

					def tokenize_function(snake_case      :		Optional[int]							):
										# max_length=None => use the model max length (it's actually the default)
										__UpperCamelCase				:  str								=			tokenizer(examples["""sentence1"""]		,  examples["""sentence2"""]		,  truncation=snake_case		,  max_length=snake_case							)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										__UpperCamelCase				:  Union[str, Any]								=			datasets.map(
										    snake_case		,  batched=snake_case		,  remove_columns=["""idx""", """sentence1""", """sentence2"""]		,  )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					__UpperCamelCase				:  int								=			tokenized_datasets.rename_column("""label"""		,  """labels"""							)

					def collate_fn(snake_case      :		List[Any]							):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										__UpperCamelCase				:  Optional[Any]								=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__UpperCamelCase				:  List[str]								=			16
										elif accelerator.mixed_precision != "no":
															__UpperCamelCase				:  str								=			8
										else:
															__UpperCamelCase				:  str								=			None

										return tokenizer.pad(
										    snake_case		,  padding="""longest"""		,  max_length=snake_case		,  pad_to_multiple_of=snake_case		,  return_tensors="""pt"""		,  )

					# Instantiate dataloaders.
					__UpperCamelCase				:  Optional[Any]								=			DataLoader(
					    tokenized_datasets["""train"""]		,  shuffle=snake_case		,  collate_fn=snake_case		,  batch_size=snake_case							)
					__UpperCamelCase				:  Dict								=			DataLoader(
					    tokenized_datasets["""validation"""]		,  shuffle=snake_case		,  collate_fn=snake_case		,  batch_size=snake_case							)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	a__		    =    mocked_dataloaders  # noqa: F811





def 							A__   (snake_case      :		Optional[Any]		,  snake_case      :		Optional[int]							)    ->	Any:
					# For testing only
					if os.environ.get("""TESTING_MOCKED_DATALOADERS"""		,  snake_case							) == "1":
										__UpperCamelCase				:  Dict								=			2
					# Initialize accelerator
					__UpperCamelCase				:  Optional[int]								=			Accelerator(cpu=args.cpu		,  mixed_precision=args.mixed_precision							)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__UpperCamelCase				:  Any								=			config["""lr"""]
					__UpperCamelCase				:  Optional[int]								=			int(config["""num_epochs"""]							)
					__UpperCamelCase				:  List[Any]								=			int(config["""seed"""]							)
					__UpperCamelCase				:  Optional[Any]								=			int(config["""batch_size"""]							)

					# New Code #
					# Parse out whether we are saving every epoch or after a certain number of batches
					if hasattr(args.checkpointing_steps		,  """isdigit"""							):
										if args.checkpointing_steps == "epoch":
															__UpperCamelCase				:  Dict								=			args.checkpointing_steps
										elif args.checkpointing_steps.isdigit():
															__UpperCamelCase				:  str								=			int(args.checkpointing_steps							)
										else:
															raise ValueError(
															    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''							)
					else:
										__UpperCamelCase				:  Any								=			None

					set_seed(snake_case							)

					__UpperCamelCase			, __UpperCamelCase				:  Union[str, Any]								=			get_dataloaders(snake_case		,  snake_case							)
					__UpperCamelCase				:  Tuple								=			evaluate.load("""glue"""		,  """mrpc"""							)

					# If the batch size is too big we use gradient accumulation
					__UpperCamelCase				:  Optional[int]								=			1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										__UpperCamelCase				:  List[Any]								=			batch_size // MAX_GPU_BATCH_SIZE
										__UpperCamelCase				:  int								=			MAX_GPU_BATCH_SIZE

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					__UpperCamelCase				:  List[Any]								=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""		,  return_dict=snake_case							)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					__UpperCamelCase				:  Optional[Any]								=			model.to(accelerator.device							)

					# Instantiate optimizer
					__UpperCamelCase				:  List[Any]								=			AdamW(params=model.parameters()		,  lr=snake_case							)

					# Instantiate scheduler
					__UpperCamelCase				:  Dict								=			get_linear_schedule_with_warmup(
					    optimizer=snake_case		,  num_warmup_steps=1_00		,  num_training_steps=(len(snake_case							) * num_epochs) // gradient_accumulation_steps		,  )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					__UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase			, __UpperCamelCase				:  Any								=			accelerator.prepare(
					    snake_case		,  snake_case		,  snake_case		,  snake_case		,  snake_case							)

					# New Code #
					# We need to keep track of how many total steps we have iterated over
					__UpperCamelCase				:  List[str]								=			0
					# We also need to keep track of the stating epoch so files are named properly
					__UpperCamelCase				:  Dict								=			0

					# We need to load the checkpoint back in before training here with `load_state`
					# The total number of epochs is adjusted based on where the state is being loaded from,
					# as we assume continuation of the same training script
					if args.resume_from_checkpoint:
										if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
															accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''							)
															accelerator.load_state(args.resume_from_checkpoint							)
															__UpperCamelCase				:  Tuple								=			os.path.basename(args.resume_from_checkpoint							)
										else:
															# Get the most recent checkpoint
															__UpperCamelCase				:  int								=			[f.name for f in os.scandir(os.getcwd()							) if f.is_dir()]
															dirs.sort(key=os.path.getctime							)
															__UpperCamelCase				:  List[Any]								=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
										# Extract `epoch_{i}` or `step_{i}`
										__UpperCamelCase				:  Union[str, Any]								=			os.path.splitext(snake_case							)[0]

										if "epoch" in training_difference:
															__UpperCamelCase				:  List[Any]								=			int(training_difference.replace("""epoch_"""		,  """"""							)							) + 1
															__UpperCamelCase				:  Any								=			None
										else:
															__UpperCamelCase				:  Union[str, Any]								=			int(training_difference.replace("""step_"""		,  """"""							)							)
															__UpperCamelCase				:  List[str]								=			resume_step // len(snake_case							)
															resume_step -= starting_epoch * len(snake_case							)

    # Now we train the model
					for epoch in range(snake_case		,  snake_case							):
										model.train()
										# New Code #
										if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
															# We need to skip steps until we reach the resumed step
															__UpperCamelCase				:  Dict								=			accelerator.skip_first_batches(snake_case		,  snake_case							)
															overall_step += resume_step
										else:
															# After the first iteration though, we need to go back to the original dataloader
															__UpperCamelCase				:  Tuple								=			train_dataloader
										for step, batch in enumerate(snake_case							):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device							)
															__UpperCamelCase				:  str								=			model(**snake_case							)
															__UpperCamelCase				:  Optional[Any]								=			outputs.loss
															__UpperCamelCase				:  List[str]								=			loss / gradient_accumulation_steps
															accelerator.backward(snake_case							)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
															# New Code #
															overall_step += 1

															# New Code #
															# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
															# These are saved to folders named `step_{overall_step}`
															# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
															# If mixed precision was used, will also save a "scalar.bin" file
															if isinstance(snake_case		,  snake_case							):
																				__UpperCamelCase				:  Tuple								=			F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																									if args.output_dir is not None:
																														__UpperCamelCase				:  List[Any]								=			os.path.join(args.output_dir		,  snake_case							)
																									accelerator.save_state(snake_case							)

										model.eval()
										for step, batch in enumerate(snake_case							):
															# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
															batch.to(accelerator.device							)
															with torch.no_grad():
																				__UpperCamelCase				:  Optional[int]								=			model(**snake_case							)
															__UpperCamelCase				:  Optional[Any]								=			outputs.logits.argmax(dim=-1							)
															__UpperCamelCase			, __UpperCamelCase				:  Dict								=			accelerator.gather_for_metrics((predictions, batch["""labels"""])							)
															metric.add_batch(
															    predictions=snake_case		,  references=snake_case		,  )

										__UpperCamelCase				:  List[str]								=			metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(F'''epoch {epoch}:'''		,  snake_case							)

										# New Code #
										# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
										# These are saved to folders named `epoch_{epoch}`
										# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
										# If mixed precision was used, will also save a "scalar.bin" file
										if checkpointing_steps == "epoch":
															__UpperCamelCase				:  List[str]								=			F'''epoch_{epoch}'''
															if args.output_dir is not None:
																				__UpperCamelCase				:  List[str]								=			os.path.join(args.output_dir		,  snake_case							)
															accelerator.save_state(snake_case							)





def 							A__   ()    ->	Tuple:
					__UpperCamelCase				:  List[Any]								=			argparse.ArgumentParser(description="""Simple example of training script."""							)
					parser.add_argument(
					    """--mixed_precision"""		,  type=snake_case		,  default=snake_case		,  choices=["""no""", """fp16""", """bf16""", """fp8"""]		,  help="""Whether to use mixed precision. Choose"""
					    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
					    """and an Nvidia Ampere GPU."""		,  )
					parser.add_argument("""--cpu"""		,  action="""store_true"""		,  help="""If passed, will train on the CPU."""							)
					parser.add_argument(
					    """--checkpointing_steps"""		,  type=snake_case		,  default=snake_case		,  help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""		,  )
					parser.add_argument(
					    """--output_dir"""		,  type=snake_case		,  default="""."""		,  help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""		,  )
					parser.add_argument(
					    """--resume_from_checkpoint"""		,  type=snake_case		,  default=snake_case		,  help="""If the training should continue from a checkpoint folder."""		,  )
					__UpperCamelCase				:  List[Any]								=			parser.parse_args()
					__UpperCamelCase				:  Dict								=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
					training_function(snake_case		,  snake_case							)


if __name__ == "__main__":
	main()












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


a__		    =    '''
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.
'''

a__		    =    logging.getLogger(__name__)


@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	__magic_name__      :					Optional[str]			  =		field(
	    default='roberta-large-mnli'  ,     metadata={'help': 'The NLI/zero-shot teacher model to be distilled.'}      )
	__magic_name__      :					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.'
	        )
	    }  ,     )
	__magic_name__      :					Optional[int]			  =		field(
	    default=32  ,     metadata={'help': 'Batch size for generating teacher predictions.'}      )
	__magic_name__      :					Optional[bool]			  =		field(
	    default=_UpperCamelCase  ,     metadata={
	        'help': (
	            'Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called'
	            'multi-class multi-label classification).'
	        )
	    }  ,     )
	__magic_name__      :					Optional[float]			  =		field(
	    default=1.0  ,     metadata={'help': 'Temperature applied to teacher softmax for distillation.'}      )






@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


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






@dataclass
class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


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






@dataclass
class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


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



	"""simple docstring"""


	def 				lowerCamelCase__    (				self		:	Any				,    lowerCAmelCase		:	int				,    lowerCAmelCase		:	Union[str, Any]				,    lowerCAmelCase		:	Tuple=False  )  ->					Any:







						"""simple docstring"""







						__UpperCamelCase				:  List[Any]								=			inputs["""labels"""]
						__UpperCamelCase				:  List[Any]								=			model(inputs["""input_ids"""]				,    attention_mask=inputs["""attention_mask"""]  )
						__UpperCamelCase				:  Optional[Any]								=			outputs[0]

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

						if return_outputs:
											return loss, outputs

						return loss






def 							A__   (snake_case      :		Optional[int]							)    ->	List[Any]:
					__UpperCamelCase				:  Union[str, Any]								=			[]
					with open(snake_case		,  """r"""							) as f:
										for line in f:
															__UpperCamelCase				:  Tuple								=			line.strip()
															if len(snake_case							) > 0:
																				lines.append(snake_case							)
					return lines





def 							A__   (snake_case      :		Optional[int]		,  snake_case      :		Optional[Any]		,  snake_case      :		str							)    ->	str:
					__UpperCamelCase				:  Union[str, Any]								=			[]
					__UpperCamelCase				:  List[str]								=			[]
					for example in examples:
										for name in class_names:
															premises.append(snake_case							)
															hypotheses.append(hypothesis_template.format(snake_case							)							)
					return premises, hypotheses





def 							A__   (snake_case      :		Optional[Any]							)    ->	int:
					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__   (snake_case      :		str		,  snake_case      :		List[str]		,  snake_case      :		List[str]		,  snake_case      :		str		,  snake_case      :		int		,  snake_case      :		float		,  snake_case      :		bool		,  snake_case      :		bool		,  snake_case      :		bool		,  snake_case      :		bool		,  )    ->	List[Any]:
					__UpperCamelCase				:  Any								=			AutoModelForSequenceClassification.from_pretrained(snake_case							)
					__UpperCamelCase				:  str								=			model.config
					if not no_cuda and torch.cuda.is_available():
										__UpperCamelCase				:  List[Any]								=			nn.DataParallel(model.cuda()							)
										batch_size *= len(model.device_ids							)
					__UpperCamelCase				:  Optional[int]								=			AutoTokenizer.from_pretrained(snake_case		,  use_fast=snake_case							)

					__UpperCamelCase			, __UpperCamelCase				:  int								=			get_premise_hypothesis_pairs(snake_case		,  snake_case		,  snake_case							)
					__UpperCamelCase				:  Any								=			[]

					for i in tqdm(range(0		,  len(snake_case							)		,  snake_case							)							):
										__UpperCamelCase				:  List[str]								=			premises[i : i + batch_size]
										__UpperCamelCase				:  Dict								=			hypotheses[i : i + batch_size]

										__UpperCamelCase				:  Any								=			tokenizer(
										    snake_case		,  snake_case		,  padding=snake_case		,  truncation="""only_first"""		,  return_tensors="""pt"""		,  )

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

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

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

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





def 							A__   ()    ->	Optional[Any]:
					__UpperCamelCase				:  Dict								=			HfArgumentParser(
					    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)		,  description=snake_case		,  )

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

					# Detecting last checkpoint.
					__UpperCamelCase				:  Union[str, Any]								=			None
					if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
										__UpperCamelCase				:  Union[str, 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
					__UpperCamelCase				:  Union[str, Any]								=			read_lines(data_args.data_file							)
					__UpperCamelCase				:  int								=			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				:  List[Any]								=			get_teacher_predictions(
					    teacher_args.teacher_name_or_path		,  snake_case		,  snake_case		,  teacher_args.hypothesis_template		,  teacher_args.teacher_batch_size		,  teacher_args.temperature		,  teacher_args.multi_label		,  data_args.use_fast_tokenizer		,  training_args.no_cuda		,  training_args.fpaa		,  )
					__UpperCamelCase				:  Any								=			Dataset.from_dict(
					    {
					        """text""": examples,
					        """labels""": teacher_soft_preds,
					    }							)

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

					# 4. train student on teacher predictions
					__UpperCamelCase				:  Tuple								=			dataset.map(snake_case		,  input_columns="""text"""							)
					dataset.set_format("""torch"""							)

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

					__UpperCamelCase				:  List[str]								=			DistillationTrainer(
					    model=snake_case		,  tokenizer=snake_case		,  args=snake_case		,  train_dataset=snake_case		,  compute_metrics=snake_case		,  )

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

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

					trainer.save_model()


if __name__ == "__main__":
	main()






