
'''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


__SCREAMING_SNAKE_CASE		:  str        =      '\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'

__SCREAMING_SNAKE_CASE		:  Union[str, Any]        =      logging.getLogger(__name__)
@dataclass
class  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							field(
	    default="roberta-large-mnli"			,					metadata={"help": "The NLI/zero-shot teacher model to be distilled."}   )
	lowercase__	    =							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."
	        )
	    }			,					)
	lowercase__	    =							field(
	    default=32			,					metadata={"help": "Batch size for generating teacher predictions."}   )
	lowercase__	    =							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)."
	        )
	    }			,					)
	lowercase__	    =							field(
	    default=1.0			,					metadata={"help": "Temperature applied to teacher softmax for distillation."}   )







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







@dataclass
class  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							field(metadata={"help": "Text file with one unlabeled instance per line."}   )
	lowercase__	    =							field(metadata={"help": "Text file with one class name per line."}   )
	lowercase__	    =							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   ):
	lowercase__	    =							field(
	    default=__UpperCamelCase			,					metadata={"help": "The output directory where the model predictions and checkpoints will be written."}			,					)
	lowercase__	    =							field(
	    default=32			,					metadata={"help": "Batch size per GPU/TPU core/CPU for training."}   )
	lowercase__	    =							field(
	    default=1_28			,					metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}   )
	lowercase__	    =							field(default=1.0			,					metadata={"help": "Total number of training epochs to perform."}   )
	lowercase__	    =							field(default=__UpperCamelCase			,					metadata={"help": "Whether to run training of student model."}   )
	lowercase__	    =							field(
	    default=__UpperCamelCase			,					metadata={
	        "help": (
	            "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
	            "after training."
	        )
	    }			,					)
	lowercase__	    =							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   ):
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase=False			):



					'''simple docstring'''






					__a       :     List[str]								=	inputs["""labels"""]
					__a       :     str								=	model(inputs["""input_ids"""]  ,    attention_mask=inputs["""attention_mask"""]			)
					__a       :     Dict								=	outputs[0]

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

					if return_outputs:
									return loss, outputs

					return loss







def 				_snake_case (		lowercase						)       ->       Optional[Any]:
				__a       :     Tuple								=	[]
				with open(lowercase			,    """r"""						) as f:
								for line in f:
												__a       :     int								=	line.strip()
												if len(lowercase						) > 0:
																lines.append(lowercase						)
				return lines

def 				_snake_case (		lowercase			,    lowercase			,    lowercase						)       ->       List[Any]:
				__a       :     Optional[int]								=	[]
				__a       :     Dict								=	[]
				for example in examples:
								for name in class_names:
												premises.append(lowercase						)
												hypotheses.append(hypothesis_template.format(lowercase						)						)
				return premises, hypotheses

def 				_snake_case (		lowercase						)       ->       Dict:
				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 (		lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase			,    )       ->       Dict:
				__a       :     Any								=	AutoModelForSequenceClassification.from_pretrained(lowercase						)
				__a       :     List[str]								=	model.config
				if not no_cuda and torch.cuda.is_available():
								__a       :     str								=	nn.DataParallel(model.cuda()						)
								batch_size *= len(model.device_ids						)
				__a       :     Optional[Any]								=	AutoTokenizer.from_pretrained(lowercase			,    use_fast=lowercase						)

				__a							,	__a       :     int								=	get_premise_hypothesis_pairs(lowercase			,    lowercase			,    lowercase						)
				__a       :     Union[str, Any]								=	[]

				for i in tqdm(range(0			,    len(lowercase						)			,    lowercase						)						):
								__a       :     Optional[Any]								=	premises[i : i + batch_size]
								__a       :     Any								=	hypotheses[i : i + batch_size]

								__a       :     Optional[Any]								=	tokenizer(
								    lowercase			,    lowercase			,    padding=lowercase			,    truncation="""only_first"""			,    return_tensors="""pt"""			,    )

								with torch.cuda.amp.autocast(enabled=lowercase						):
												with torch.no_grad():
																__a       :     List[Any]								=	model(**lowercase						)
								logits.append(outputs.logits.detach().cpu().float()						)

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

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

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

def 				_snake_case (		)       ->       Any:
				__a       :     Optional[Any]								=	HfArgumentParser(
				    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)			,    description=lowercase			,    )

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

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

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

				# 4. train student on teacher predictions
				__a       :     List[Any]								=	dataset.map(lowercase			,    input_columns="""text"""						)
				dataset.set_format("""torch"""						)

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

				__a       :     Optional[Any]								=	DistillationTrainer(
				    model=lowercase			,    tokenizer=lowercase			,    args=lowercase			,    train_dataset=lowercase			,    compute_metrics=lowercase			,    )

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

				if training_args.do_eval:
								__a       :     Dict								=	trainer.evaluate(eval_dataset=lowercase						)["""eval_agreement"""]
								logger.info(F"""Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%"""						)

				trainer.save_model()


if __name__ == "__main__":
					main()

'''simple docstring'''







import unittest

from cer import CER


__SCREAMING_SNAKE_CASE		:  List[str]        =      CER()
class  SCREAMING_SNAKE_CASE__       (  unittest.TestCase   ):
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[Any]								=	["""White House"""]
					__a       :     Tuple								=	["""white house"""]
					# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
					__a       :     Any								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8			) < 1E-6			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[int]								=	["""were wolf"""]
					__a       :     Tuple								=	["""werewolf"""]
					# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
					__a       :     Any								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1			) < 1E-6			)

					__a       :     str								=	["""werewolf"""]
					__a       :     List[Any]								=	["""weae     wolf"""]
					# S = 1, D = 1, I = 0, N = 8, CER = 0.25
					__a       :     Tuple								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.2_5			) < 1E-6			)

					# consecutive whitespaces case 1
					__a       :     int								=	["""were wolf"""]
					__a       :     Dict								=	["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					__a       :     str								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.0			) < 1E-6			)

					# consecutive whitespaces case 2
					__a       :     List[str]								=	["""were   wolf"""]
					__a       :     Optional[int]								=	["""were               wolf"""]
					# S = 0, D = 0, I = 0, N = 9, CER = 0
					__a       :     Optional[Any]								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.0			) < 1E-6			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Dict								=	["""werewolf"""]
					__a       :     str								=	["""weaewolf"""]
					# S = 1, D = 0, I = 0, N = 8, CER = 0.125
					__a       :     Optional[int]								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.1_2_5			) < 1E-6			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[str]								=	["""werewolf"""]
					__a       :     Tuple								=	["""wereawolf"""]
					# S = 0, D = 1, I = 0, N = 8, CER = 0.125
					__a       :     Optional[Any]								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.1_2_5			) < 1E-6			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[str]								=	["""werewolf"""]
					__a       :     Optional[Any]								=	["""wereolf"""]
					# S = 0, D = 0, I = 1, N = 8, CER = 0.125
					__a       :     int								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.1_2_5			) < 1E-6			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Dict								=	["""werewolf"""]
					__a       :     Tuple								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertEqual(__UpperCamelCase  ,    0.0			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[str]								=	["""werewolf""", """I am your father"""]
					__a       :     int								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertEqual(__UpperCamelCase  ,    0.0			)

					__a       :     Dict								=	["""werewolf""", """I am your father""", """doge"""]
					__a       :     Optional[Any]								=	["""werxwolf""", """I       am your father""", """doge"""]
					# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
					__a       :     Union[str, Any]								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8			) < 1E-6			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[str]								=	["""My hovercraft""", """is full of eels"""]
					__a       :     Union[str, Any]								=	["""My hovercraft is full""", """ of eels"""]
					# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
					# whitespace at the front of " of eels" will be strip during preporcessing
					# so need to insert 2 whitespaces
					__a       :     str								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase  ,    concatenate_texts=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8			) < 1E-6			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     int								=	["""我能吞下玻璃而不伤身体"""]
					__a       :     Optional[Any]								=	[""" 能吞虾玻璃而 不霜身体啦"""]
					# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
					__a       :     List[Any]								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5			) < 1E-6			)

					__a       :     Union[str, Any]								=	["""我能吞下玻璃""", """而不伤身体"""]
					__a       :     Optional[Any]								=	["""我    能 吞 下 玻 璃""", """而不伤身体"""]
					# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
					__a       :     List[str]								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5			) < 1E-6			)

					__a       :     Union[str, Any]								=	["""我能吞下玻璃而不伤身体"""]
					__a       :     Dict								=	cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)
					self.assertFalse(__UpperCamelCase  ,    0.0			)

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[Any]								=	[""""""]
					__a       :     Dict								=	["""Hypothesis"""]
					with self.assertRaises(__UpperCamelCase			):
									cer.compute(predictions=__UpperCamelCase  ,    references=__UpperCamelCase			)


if __name__ == "__main__":
					unittest.main()