


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__ :			int         ='\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'

lowerCAmelCase__ :			Any         =logging.getLogger(__name__)







@dataclass
class   __lowercase						:





							"""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=__SCREAMING_SNAKE_CASE  ,					metadata={
							        """help""": (
							            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
							            """multi-class multi-label classification)."""
							        )
							    }  ,					)
							_UpperCAmelCase						     =	field(
							    default=1.0  ,					metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}     )







@dataclass
class   __lowercase						:





							"""simple docstring"""







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







@dataclass
class   __lowercase						:





							"""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=__SCREAMING_SNAKE_CASE  ,					metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}  ,					)







@dataclass
class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	field(
							    default=__SCREAMING_SNAKE_CASE  ,					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=__SCREAMING_SNAKE_CASE  ,					metadata={"""help""": """Whether to run training of student model."""}     )
							_UpperCAmelCase						     =	field(
							    default=__SCREAMING_SNAKE_CASE  ,					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   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__=False					):





										"""simple docstring"""




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

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

										if return_outputs:
													return loss, outputs

										return loss







def 	a__ (							A__							):
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				[]
			with open(A__,			'r'							) as f:
						for line in f:
									SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				line.strip()
									if len(A__							) > 0:
												lines.append(A__							)
			return lines






def 	a__ (							A__,			A__,			A__							):
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				[]
			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				[]
			for example in examples:
						for name in class_names:
									premises.append(A__							)
									hypotheses.append(hypothesis_template.format(A__							)							)
			return premises, hypotheses






def 	a__ (							A__							):
			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__ (							A__,			A__,			A__,			A__,			A__,			A__,			A__,			A__,			A__,			A__,			):
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				AutoModelForSequenceClassification.from_pretrained(A__							)
			SCREAMING_SNAKE_CASE_		:    str 		=				model.config
			if not no_cuda and torch.cuda.is_available():
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				nn.DataParallel(model.cuda()							)
						batch_size *= len(model.device_ids							)
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				AutoTokenizer.from_pretrained(A__,			use_fast=A__							)

			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    int 		=				get_premise_hypothesis_pairs(A__,			A__,			A__							)
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				[]

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

						SCREAMING_SNAKE_CASE_		:    Dict 		=				tokenizer(
						    A__,			A__,			padding=A__,			truncation='only_first',			return_tensors='pt',			)

						with torch.cuda.amp.autocast(enabled=A__							):
									with torch.no_grad():
												SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				model(**A__							)
						logits.append(outputs.logits.detach().cpu().float()							)

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

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

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






def 	a__ (							):
			SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				HfArgumentParser(
			    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments),			description=A__,			)

			if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				parser.parse_json_file(
						    json_file=os.path.abspath(sys.argv[1]							)							)
			else:
						SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				parser.parse_args_into_dataclasses()

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

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

			# 4. train student on teacher predictions
			SCREAMING_SNAKE_CASE_		:    List[Any] 		=				dataset.map(A__,			input_columns='text'							)
			dataset.set_format('torch'							)

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

			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				DistillationTrainer(
			    model=A__,			tokenizer=A__,			args=A__,			train_dataset=A__,			compute_metrics=A__,			)

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

			if training_args.do_eval:
						SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				trainer.evaluate(eval_dataset=A__							)['eval_agreement']
						logger.info(F'''Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%'''							)

			trainer.save_model()


if __name__ == "__main__":
				main()









import numpy as np







class   __lowercase						:





							"""simple docstring"""







							def __init__(			self ,	lowerCAmelCase__=5 ,	lowerCAmelCase__=5					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    List[str] 		=				depth
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				0
										SCREAMING_SNAKE_CASE_		:    int 		=				None
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				None
										SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				min_leaf_size
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				None






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if labels.ndim != 1:
													print('Error: Input labels must be one dimensional'					)

										return np.mean((labels - prediction) ** 2					)






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										if x.ndim != 1:
													print('Error: Input data set must be one dimensional'					)
													return
										if len(lowerCAmelCase__					) != len(lowerCAmelCase__					):
													print('Error: X and y have different lengths'					)
													return
										if y.ndim != 1:
													print('Error: Data set labels must be one dimensional'					)
													return

										if len(lowerCAmelCase__					) < 2 * self.min_leaf_size:
													SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				np.mean(lowerCAmelCase__					)
													return

										if self.depth == 1:
													SCREAMING_SNAKE_CASE_		:    Tuple 		=				np.mean(lowerCAmelCase__					)
													return

										SCREAMING_SNAKE_CASE_		:    int 		=				0
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				self.mean_squared_error(lowerCAmelCase__ ,	np.mean(lowerCAmelCase__					)					) * 2
										for i in range(len(lowerCAmelCase__					)					):
													if len(x[:i]					) < self.min_leaf_size:
																continue
													elif len(x[i:]					) < self.min_leaf_size:
																continue
													else:
																SCREAMING_SNAKE_CASE_		:    Dict 		=				self.mean_squared_error(x[:i] ,	np.mean(y[:i]					)					)
																SCREAMING_SNAKE_CASE_		:    int 		=				self.mean_squared_error(x[i:] ,	np.mean(y[i:]					)					)
																SCREAMING_SNAKE_CASE_		:    Any 		=				error_left + error_right
																if error < min_error:
																			SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				i
																			SCREAMING_SNAKE_CASE_		:    int 		=				error

										if best_split != 0:
													SCREAMING_SNAKE_CASE_		:    int 		=				x[:best_split]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				y[:best_split]
													SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				x[best_split:]
													SCREAMING_SNAKE_CASE_		:    str 		=				y[best_split:]

													SCREAMING_SNAKE_CASE_		:    str 		=				x[best_split]
													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				DecisionTree(
													    depth=self.depth - 1 ,	min_leaf_size=self.min_leaf_size					)
													SCREAMING_SNAKE_CASE_		:    int 		=				DecisionTree(
													    depth=self.depth - 1 ,	min_leaf_size=self.min_leaf_size					)
													self.left.train(lowerCAmelCase__ ,	lowerCAmelCase__					)
													self.right.train(lowerCAmelCase__ ,	lowerCAmelCase__					)
										else:
													SCREAMING_SNAKE_CASE_		:    List[Any] 		=				np.mean(lowerCAmelCase__					)

										return






							def 				UpperCamelCase__    (			self ,	lowerCAmelCase__					):





										"""simple docstring"""




										if self.prediction is not None:
													return self.prediction
										elif self.left or self.right is not None:
													if x >= self.decision_boundary:
																return self.right.predict(lowerCAmelCase__					)
													else:
																return self.left.predict(lowerCAmelCase__					)
										else:
													print('Error: Decision tree not yet trained'					)
													return None







class   __lowercase						:





							"""simple docstring"""







							@staticmethod
							def 				UpperCamelCase__    (			lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				float(0					)
										for label in labels:
													squared_error_sum += (label - prediction) ** 2

										return float(squared_error_sum / labels.size					)







def 	a__ (							):
			SCREAMING_SNAKE_CASE_		:    List[str] 		=				np.arange(-1.0,			1.0,			0.0_05							)
			SCREAMING_SNAKE_CASE_		:    Dict 		=				np.sin(A__							)

			SCREAMING_SNAKE_CASE_		:    str 		=				DecisionTree(depth=1_0,			min_leaf_size=1_0							)
			tree.train(A__,			A__							)

			SCREAMING_SNAKE_CASE_		:    Dict 		=				(np.random.rand(1_0							) * 2) - 1
			SCREAMING_SNAKE_CASE_		:    Any 		=				np.array([tree.predict(A__							) for x in test_cases]							)
			SCREAMING_SNAKE_CASE_		:    Optional[int] 		=				np.mean((predictions - test_cases) ** 2							)

			print('Test values: ' + str(A__							)							)
			print('Predictions: ' + str(A__							)							)
			print('Average error: ' + str(A__							)							)


if __name__ == "__main__":
				main()
				import doctest

				doctest.testmod(name='mean_squarred_error', verbose=True)





