





'''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


A :			Union[str, 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'

A :			str							   =      logging.getLogger(__name__)







@dataclass
class       lowerCamelCase       :
					_SCREAMING_SNAKE_CASE				 =     field(
					    default="roberta-large-mnli"  ,				metadata={"help": "The NLI/zero-shot teacher model to be distilled."}	)
					_SCREAMING_SNAKE_CASE				 =     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."
					        )
					    }  ,				)
					_SCREAMING_SNAKE_CASE				 =     field(
					    default=32  ,				metadata={"help": "Batch size for generating teacher predictions."}	)
					_SCREAMING_SNAKE_CASE				 =     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)."
					        )
					    }  ,				)
					_SCREAMING_SNAKE_CASE				 =     field(
					    default=1.0  ,				metadata={"help": "Temperature applied to teacher softmax for distillation."}	)







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







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







@dataclass
class       lowerCamelCase       (    __UpperCAmelCase	):
					_SCREAMING_SNAKE_CASE				 =     field(
					    default=__UpperCAmelCase  ,				metadata={"help": "The output directory where the model predictions and checkpoints will be written."}  ,				)
					_SCREAMING_SNAKE_CASE				 =     field(
					    default=32  ,				metadata={"help": "Batch size per GPU/TPU core/CPU for training."}	)
					_SCREAMING_SNAKE_CASE				 =     field(
					    default=128  ,				metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}	)
					_SCREAMING_SNAKE_CASE				 =     field(default=1.0  ,				metadata={"help": "Total number of training epochs to perform."}	)
					_SCREAMING_SNAKE_CASE				 =     field(default=__UpperCAmelCase  ,				metadata={"help": "Whether to run training of student model."}	)
					_SCREAMING_SNAKE_CASE				 =     field(
					    default=__UpperCAmelCase  ,				metadata={
					        "help": (
					            "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
					            "after training."
					        )
					    }  ,				)
					_SCREAMING_SNAKE_CASE				 =     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       (    __UpperCAmelCase	):


					def  SCREAMING_SNAKE_CASE_		(       self						:	str							,  __snake_case						:	Optional[int]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	List[str]=False							):





											'''simple docstring'''





											_snake_case: int	       =		inputs['labels']
											_snake_case: int	       =		model(inputs['input_ids']							,  attention_mask=inputs['attention_mask']							)
											_snake_case: List[str]	       =		outputs[0]

											_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    lowercase_   (		lowercase__    ) ->str:
						_snake_case: Optional[Any]	       =		[]
						with open(snake_case__					,	'r'    ) as f:
												for line in f:
																		_snake_case: Union[str, Any]	       =		line.strip()
																		if len(snake_case__    ) > 0:
																								lines.append(snake_case__    )
						return lines





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

						_snake_case       ,    _snake_case: List[str]	       =		get_premise_hypothesis_pairs(snake_case__					,	snake_case__					,	snake_case__    )
						_snake_case: List[str]	       =		[]

						for i in tqdm(range(0					,	len(snake_case__    )					,	snake_case__    )    ):
												_snake_case: Dict	       =		premises[i : i + batch_size]
												_snake_case: Dict	       =		hypotheses[i : i + batch_size]

												_snake_case: Optional[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():
																								_snake_case: int	       =		model(**snake_case__    )
												logits.append(outputs.logits.detach().cpu().float()    )

						_snake_case: Any	       =		get_entailment_id(snake_case__    )
						_snake_case: Optional[int]	       =		-1 if entail_id == 0 else 0
						_snake_case: List[str]	       =		torch.cat(snake_case__					,	dim=0    )  # N*K x 3
						_snake_case: int	       =		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
												_snake_case: Optional[Any]	       =		(nli_logits / temperature).softmax(-1    )
						else:
												# softmax over entail logits across classes s.t. class probabilities sum to 1.
												_snake_case: Any	       =		(nli_logits / temperature).softmax(1    )

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





def    lowercase_   (		) ->Tuple:
						_snake_case: Any	       =		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.
												_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: str	       =		parser.parse_json_file(
												    json_file=os.path.abspath(sys.argv[1]    )    )
						else:
												_snake_case       ,    _snake_case       ,    _snake_case       ,    _snake_case: List[str]	       =		parser.parse_args_into_dataclasses()

						# Detecting last checkpoint.
						_snake_case: Union[str, Any]	       =		None
						if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
												_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
						_snake_case: Optional[Any]	       =		read_lines(data_args.data_file    )
						_snake_case: Optional[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'    )
						_snake_case: List[str]	       =		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					,	)
						_snake_case: Tuple	       =		Dataset.from_dict(
						    {
						        'text': examples,
						        'labels': teacher_soft_preds,
						    }    )

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

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

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

						_snake_case: Optional[Any]	       =		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:
												_snake_case: List[str]	       =		trainer.evaluate(eval_dataset=snake_case__    )['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 logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


__A   :							List[str]     	=						logging.getLogger(__name__)






@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Pretrained config name or path if not the same as model_name'}    )
     _snake_case							   =	field(
         default='NER'			,    metadata={'help': 'Task type to fine tune in training (e.g. NER, POS, etc)'}    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}    )
     _snake_case							   =	field(default=lowerCamelCase			,    metadata={'help': 'Set this flag to use fast tokenization.'}    )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}			,    )







@dataclass
class        a__     :
     '''simple docstring'''







     _snake_case							   =	field(
         metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'}    )
     _snake_case							   =	field(
         metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'}    )
     _snake_case							   =	field(
         default=128			,    metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }			,    )
     _snake_case							   =	field(
         default=lowerCamelCase			,    metadata={'help': 'Overwrite the cached training and evaluation sets'}    )







def     _UpperCamelCase				(  ):
      # See all possible arguments in src/transformers/training_args.py
      # or by passing the --help flag to this script.
      # We now keep distinct sets of args, for a cleaner separation of concerns.
      __A		 =   HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)				)
      __A ,					__A ,					__A		 =   parser.parse_args_into_dataclasses()

      if (
          os.path.exists(training_args.output_dir				)
          and os.listdir(training_args.output_dir				)
          and training_args.do_train
          and not training_args.overwrite_output_dir
      ):
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                " --overwrite_output_dir to overcome."				)

      __A		 =   import_module("tasks"				)

      try:
            __A		 =   getattr(snake_case__				,      model_args.task_type				)
            __A		 =   token_classification_task_clazz()
      except AttributeError:
            raise ValueError(
                f"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
                f"Available tasks classes are: {TokenClassificationTask.__subclasses__()}"				)

      # Setup logging
      logging.basicConfig(
          format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"				,      datefmt="%m/%d/%Y %H:%M:%S"				,      level=logging.INFO				,      )
      logger.info(
          "n_replicas: %s, distributed training: %s, 16-bits training: %s"				,      training_args.n_replicas				,      bool(training_args.n_replicas > 1				)				,      training_args.fpaa				,      )
      logger.info("Training/evaluation parameters %s"				,      snake_case__				)

      # Prepare Token Classification task
      __A		 =   token_classification_task.get_labels(data_args.labels				)
      __A		 =   dict(enumerate(snake_case__				)				)
      __A		 =   len(snake_case__				)

      # Load pretrained model and tokenizer
      #
      # Distributed training:
      # The .from_pretrained methods guarantee that only one local process can concurrently
      # download model & vocab.

      __A		 =   AutoConfig.from_pretrained(
          model_args.config_name if model_args.config_name else model_args.model_name_or_path				,      num_labels=snake_case__				,      idalabel=snake_case__				,      labelaid={label: i for i, label in enumerate(snake_case__				)}				,      cache_dir=model_args.cache_dir				,      )
      __A		 =   AutoTokenizer.from_pretrained(
          model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path				,      cache_dir=model_args.cache_dir				,      use_fast=model_args.use_fast				,      )

      with training_args.strategy.scope():
            __A		 =   TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path				,      from_pt=bool(".bin" in model_args.model_name_or_path				)				,      config=snake_case__				,      cache_dir=model_args.cache_dir				,      )

      # Get datasets
      __A		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.train				,      )
          if training_args.do_train
          else None
      )
      __A		 =   (
          TFTokenClassificationDataset(
              token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.dev				,      )
          if training_args.do_eval
          else None
      )

      def align_predictions(snake_case__				,      snake_case__				) -> Tuple[List[int], List[int]]:
            __A		 =   np.argmax(snake_case__				,      axis=2				)
            __A ,					__A		 =   preds.shape
            __A		 =   [[] for _ in range(snake_case__				)]
            __A		 =   [[] for _ in range(snake_case__				)]

            for i in range(snake_case__				):
                  for j in range(snake_case__				):
                        if label_ids[i, j] != -100:
                              out_label_list[i].append(label_map[label_ids[i][j]]				)
                              preds_list[i].append(label_map[preds[i][j]]				)

            return preds_list, out_label_list

      def compute_metrics(snake_case__				) -> Dict:
            __A ,					__A		 =   align_predictions(p.predictions				,      p.label_ids				)

            return {
                "precision": precision_score(snake_case__				,      snake_case__				),
                "recall": recall_score(snake_case__				,      snake_case__				),
                "f1": fa_score(snake_case__				,      snake_case__				),
            }

      # Initialize our Trainer
      __A		 =   TFTrainer(
          model=snake_case__				,      args=snake_case__				,      train_dataset=train_dataset.get_dataset() if train_dataset else None				,      eval_dataset=eval_dataset.get_dataset() if eval_dataset else None				,      compute_metrics=snake_case__				,      )

      # Training
      if training_args.do_train:
            trainer.train()
            trainer.save_model()
            tokenizer.save_pretrained(training_args.output_dir				)

      # Evaluation
      __A		 =   {}
      if training_args.do_eval:
            logger.info("*** Evaluate ***"				)

            __A		 =   trainer.evaluate()
            __A		 =   os.path.join(training_args.output_dir				,      "eval_results.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  logger.info("***** Eval results *****"				)

                  for key, value in result.items():
                        logger.info("  %s = %s"				,      snake_case__				,      snake_case__				)
                        writer.write("%s = %s\n" % (key, value)				)

                  results.update(snake_case__				)

    # Predict
      if training_args.do_predict:
            __A		 =   TFTokenClassificationDataset(
                token_classification_task=snake_case__				,      data_dir=data_args.data_dir				,      tokenizer=snake_case__				,      labels=snake_case__				,      model_type=config.model_type				,      max_seq_length=data_args.max_seq_length				,      overwrite_cache=data_args.overwrite_cache				,      mode=Split.test				,      )

            __A ,					__A ,					__A		 =   trainer.predict(test_dataset.get_dataset()				)
            __A ,					__A		 =   align_predictions(snake_case__				,      snake_case__				)
            __A		 =   classification_report(snake_case__				,      snake_case__				)

            logger.info("\n%s"				,      snake_case__				)

            __A		 =   os.path.join(training_args.output_dir				,      "test_results.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  writer.write("%s\n" % report				)

            # Save predictions
            __A		 =   os.path.join(training_args.output_dir				,      "test_predictions.txt"				)

            with open(snake_case__				,      "w"				) as writer:
                  with open(os.path.join(data_args.data_dir				,      "test.txt"				)				,      "r"				) as f:
                        __A		 =   0

                        for line in f:
                              if line.startswith("-DOCSTART-"				) or line == "" or line == "\n":
                                    writer.write(snake_case__				)

                                    if not preds_list[example_id]:
                                          example_id += 1
                              elif preds_list[example_id]:
                                    __A		 =   line.split()[0] + " " + preds_list[example_id].pop(0				) + "\n"

                                    writer.write(snake_case__				)
                              else:
                                    logger.warning("Maximum sequence length exceeded: No prediction for '%s'."				,      line.split()[0]				)

      return results


if __name__ == "__main__":
       main()


