


# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


__lowerCamelCase   	=  '''Update an existing config file with the latest defaults while maintaining the old configuration.'''



def 			_a      (    __UpperCamelCase			):
     a_     :  Optional[int]				      =     args.config_file
     if config_file is None and Path(__UpperCamelCase			).exists():
          a_     :  Optional[int]				      =     default_config_file
     elif not Path(__UpperCamelCase			).exists():
          raise ValueError(F'''The passed config file located at {config_file} doesn\'t exist.'''			)
     a_     :  List[str]				      =     load_config_from_file(__UpperCamelCase			)

     if config_file.endswith(""".json"""			):
          config.to_json_file(__UpperCamelCase			)
     else:
          config.to_yaml_file(__UpperCamelCase			)
     return config_file



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  List[Any]				      =     parser.add_parser("""update"""	,	parents=__UpperCamelCase	,	help=__UpperCamelCase	,	formatter_class=__UpperCamelCase			)
     parser.add_argument(
         """--config_file"""	,	default=__UpperCamelCase	,	help=(
             """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
             """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
             """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
             """with 'huggingface'."""
         )	,	)

     parser.set_defaults(func=__UpperCamelCase			)
     return parser



def 			_a      (    __UpperCamelCase			):
     a_     :  str				      =     update_config(__UpperCamelCase			)
     print(F'''Sucessfully updated the configuration file at {config_file}.'''			)







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   	=  '''
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   	=  logging.getLogger(__name__)


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


@dataclass
class 	a__						:
       lowerCamelCase__:  Optional[str]				     =  field(
           default="""distilbert-base-uncased"""    ,							metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}     )


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


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

       def     UpperCAmelCase( self   :  List[str]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Union[str, Any]=False     ):
            a_     :  Union[str, Any]				      =     inputs["""labels"""]
            a_     :  Union[str, Any]				      =     model(inputs["""input_ids"""]   ,				attention_mask=inputs["""attention_mask"""]     )
            a_     :  str				      =     outputs[0]

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

            if return_outputs:
                 return loss, outputs

            return loss



def 			_a      (    __UpperCamelCase			):
     a_     :  Dict				      =     []
     with open(__UpperCamelCase	,	"""r"""			) as f:
          for line in f:
               a_     :  Dict				      =     line.strip()
               if len(__UpperCamelCase			) > 0:
                    lines.append(__UpperCamelCase			)
     return lines



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Dict				      =     []
     a_     :  Dict				      =     []
     for example in examples:
          for name in class_names:
               premises.append(__UpperCamelCase			)
               hypotheses.append(hypothesis_template.format(__UpperCamelCase			)			)
     return premises, hypotheses



def 			_a      (    __UpperCamelCase			):
     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      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase	,	):
     a_     :  Optional[int]				      =     AutoModelForSequenceClassification.from_pretrained(__UpperCamelCase			)
     a_     :  Optional[int]				      =     model.config
     if not no_cuda and torch.cuda.is_available():
          a_     :  Union[str, Any]				      =     nn.DataParallel(model.cuda()			)
          batch_size *= len(model.device_ids			)
     a_     :  Optional[Any]				      =     AutoTokenizer.from_pretrained(__UpperCamelCase	,	use_fast=__UpperCamelCase			)

     a_      ,      a_     :  Dict				      =     get_premise_hypothesis_pairs(__UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			)
     a_     :  List[str]				      =     []

     for i in tqdm(range(0	,	len(__UpperCamelCase			)	,	__UpperCamelCase			)			):
          a_     :  int				      =     premises[i : i + batch_size]
          a_     :  List[Any]				      =     hypotheses[i : i + batch_size]

          a_     :  Dict				      =     tokenizer(
              __UpperCamelCase	,	__UpperCamelCase	,	padding=__UpperCamelCase	,	truncation="""only_first"""	,	return_tensors="""pt"""	,	)

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

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

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



def 			_a      (    ):
     a_     :  str				      =     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_     :  Dict				      =     parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]			)			)
     else:
          a_      ,      a_      ,      a_      ,      a_     :  Any				      =     parser.parse_args_into_dataclasses()

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

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

     # 4. train student on teacher predictions
     a_     :  Optional[int]				      =     dataset.map(__UpperCamelCase	,	input_columns="""text"""			)
     dataset.set_format("""torch"""			)

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

     a_     :  int				      =     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_     :  Tuple				      =     trainer.evaluate(eval_dataset=__UpperCamelCase			)["""eval_agreement"""]
          logger.info(F'''Agreement of student and teacher predictions: {agreement * 1_0_0:0.2f}%'''			)

     trainer.save_model()


if __name__ == "__main__":
       main()



