
"""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


__lowerCAmelCase							:  Optional[Any] 				=		'''
Distills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class
names. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but
when only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled
dataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided
class names.
'''

__lowerCAmelCase							:  Tuple 				=		logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







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

@dataclass
class SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







	snake_case__							:		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'''







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

@dataclass
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







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






	'''simple docstring'''







	def    _UpperCamelCase							(  self :str       ,    __magic_name__ :int       ,    __magic_name__ :str       ,    __magic_name__ :List[str]=False			)			->   Tuple:

			'''simple docstring'''






			a__   		=     inputs['''labels''']
			a__   		=     model(inputs['''input_ids''']       ,    attention_mask=inputs['''attention_mask''']			)
			a__   		=     outputs[0]

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

			if return_outputs:
					return loss, outputs

			return loss

def __snake_case (      UpperCamelCase    )       ->    Dict:


		"""simple docstring"""





		a__   		=     []
		with open(UpperCamelCase						,					'''r'''    ) as f:
				for line in f:
						a__   		=     line.strip()
						if len(UpperCamelCase    ) > 0:
								lines.append(UpperCamelCase    )
		return lines

def __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )       ->    Union[str, Any]:


		"""simple docstring"""





		a__   		=     []
		a__   		=     []
		for example in examples:
				for name in class_names:
						premises.append(UpperCamelCase    )
						hypotheses.append(hypothesis_template.format(UpperCamelCase    )    )
		return premises, hypotheses

def __snake_case (      UpperCamelCase    )       ->    Any:


		"""simple docstring"""





		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 (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase						,					)       ->    Any:


		"""simple docstring"""





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

		a__		,	a__   		=     get_premise_hypothesis_pairs(UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase    )
		a__   		=     []

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

				a__   		=     tokenizer(
				    UpperCamelCase						,					UpperCamelCase						,					padding=UpperCamelCase						,					truncation='''only_first'''						,					return_tensors='''pt'''						,					)

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

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

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

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

def __snake_case (      )       ->    Union[str, Any]:


		"""simple docstring"""





		a__   		=     HfArgumentParser(
		    (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)						,					description=UpperCamelCase						,					)

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

		# Detecting last checkpoint.
		a__   		=     None
		if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
				a__   		=     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__   		=     read_lines(data_args.data_file    )
		a__   		=     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__   		=     get_teacher_predictions(
		    teacher_args.teacher_name_or_path						,					UpperCamelCase						,					UpperCamelCase						,					teacher_args.hypothesis_template						,					teacher_args.teacher_batch_size						,					teacher_args.temperature						,					teacher_args.multi_label						,					data_args.use_fast_tokenizer						,					training_args.no_cuda						,					training_args.fpaa						,					)
		a__   		=     Dataset.from_dict(
		    {
		        '''text''': examples,
		        '''labels''': teacher_soft_preds,
		    }    )

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

		# 4. train student on teacher predictions
		a__   		=     dataset.map(UpperCamelCase						,					input_columns='''text'''    )
		dataset.set_format('''torch'''    )

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

		a__   		=     DistillationTrainer(
		    model=UpperCamelCase						,					tokenizer=UpperCamelCase						,					args=UpperCamelCase						,					train_dataset=UpperCamelCase						,					compute_metrics=UpperCamelCase						,					)

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

		if training_args.do_eval:
				a__   		=     trainer.evaluate(eval_dataset=UpperCamelCase    )['''eval_agreement''']
				logger.info(f"Agreement of student and teacher predictions: {agreement * 100:0.2f}%"    )

		trainer.save_model()


if __name__ == "__main__":
					main()






"""simple docstring"""




import random
import shutil
import tempfile
import unittest

import numpy as np

from transformers import TaTokenizer, TaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torch
from transformers.utils.import_utils import is_speech_available, is_torch_available


if is_torch_available():
					pass

if is_speech_available():
					from transformers import EncodecFeatureExtractor, MusicgenProcessor


__lowerCAmelCase							:  Union[str, Any] 				=		random.Random()

def __snake_case (      UpperCamelCase						,					UpperCamelCase=1.0						,					UpperCamelCase=None						,					UpperCamelCase=None    )       ->    Tuple:


		"""simple docstring"""





		if rng is None:
				a__   		=     global_rng

		a__   		=     []
		for batch_idx in range(shape[0]    ):
				values.append([]    )
				for _ in range(shape[1]    ):
						values[-1].append(rng.random() * scale    )

		return values




@require_torch
@require_sentencepiece
class SCREAMING_SNAKE_CASE       (      unittest.TestCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Optional[int]			)			->   List[str]:

			'''simple docstring'''






			a__   		=     '''facebook/musicgen-small'''
			a__   		=     tempfile.mkdtemp()


	def    _UpperCamelCase							(  self :Union[str, Any]       ,    **__magic_name__ :Union[str, Any]			)			->   str:

			'''simple docstring'''






			return TaTokenizer.from_pretrained(self.checkpoint       ,    **__magic_name__			)


	def    _UpperCamelCase							(  self :Optional[int]       ,    **__magic_name__ :Any			)			->   Optional[int]:

			'''simple docstring'''






			return EncodecFeatureExtractor.from_pretrained(self.checkpoint       ,    **__magic_name__			)


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

			'''simple docstring'''






			shutil.rmtree(self.tmpdirname			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Any:

			'''simple docstring'''






			a__   		=     self.get_tokenizer()
			a__   		=     self.get_feature_extractor()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			processor.save_pretrained(self.tmpdirname			)
			a__   		=     MusicgenProcessor.from_pretrained(self.tmpdirname			)

			self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()			)
			self.assertIsInstance(processor.tokenizer       ,    __magic_name__			)

			self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor.to_json_string()			)
			self.assertIsInstance(processor.feature_extractor       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Dict			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     MusicgenProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()			)
			processor.save_pretrained(self.tmpdirname			)

			a__   		=     self.get_tokenizer(bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''			)
			a__   		=     self.get_feature_extractor(do_normalize=__magic_name__       ,    padding_value=1.0			)

			a__   		=     MusicgenProcessor.from_pretrained(
			    self.tmpdirname       ,    bos_token='''(BOS)'''       ,    eos_token='''(EOS)'''       ,    do_normalize=__magic_name__       ,    padding_value=1.0			)

			self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()			)
			self.assertIsInstance(processor.tokenizer       ,    __magic_name__			)

			self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor_add_kwargs.to_json_string()			)
			self.assertIsInstance(processor.feature_extractor       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Any			)			->   Tuple:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     floats_list((3, 1000)			)

			a__   		=     feature_extractor(__magic_name__       ,    return_tensors='''np'''			)
			a__   		=     processor(__magic_name__       ,    return_tensors='''np'''			)

			for key in input_feat_extract.keys():
					self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     '''This is a test string'''

			a__   		=     processor(text=__magic_name__			)

			a__   		=     tokenizer(__magic_name__			)

			for key in encoded_tok.keys():
					self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]			)


	def    _UpperCamelCase							(  self :Tuple			)			->   Optional[Any]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			a__   		=     processor.batch_decode(sequences=__magic_name__			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			self.assertListEqual(__magic_name__       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   int:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor()
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			self.assertListEqual(
			    processor.model_input_names       ,    feature_extractor.model_input_names       ,    msg='''`processor` and `feature_extractor` model input names do not match'''       ,    )






	def    _UpperCamelCase							(  self :Any			)			->   List[str]:

			'''simple docstring'''






			a__   		=     self.get_feature_extractor(padding_side='''left'''			)
			a__   		=     self.get_tokenizer()

			a__   		=     MusicgenProcessor(tokenizer=__magic_name__       ,    feature_extractor=__magic_name__			)

			a__   		=     [floats_list((1, x)			)[0] for x in range(5       ,    20       ,    5			)]
			a__   		=     processor(__magic_name__			).padding_mask

			a__   		=     np.asarray(floats_list((3, 20)			)			)[:, None, :]
			a__   		=     processor.batch_decode(__magic_name__       ,    padding_mask=__magic_name__			)

			self.assertIsInstance(__magic_name__       ,    __magic_name__			)

			for audio in decoded_audios:
					self.assertIsInstance(__magic_name__       ,    np.ndarray			)

			self.assertTrue(decoded_audios[0].shape == (1, 10)			)
			self.assertTrue(decoded_audios[1].shape == (1, 15)			)
			self.assertTrue(decoded_audios[2].shape == (1, 20)			)




