


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		:					List[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'''

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



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



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



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



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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					Tuple     ,						SCREAMING_SNAKE_CASE_       :					str=False				):
        __lowerCamelCase:					str	 =						inputs["""labels"""]
        __lowerCamelCase:					Optional[int]	 =						model(inputs["""input_ids"""]     ,						attention_mask=inputs["""attention_mask"""]				)
        __lowerCamelCase:					Tuple	 =						outputs[0]

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

        if return_outputs:
           return loss, outputs

        return loss



def      __lowerCAmelCase    (				snake_case						:     int			)							->				int:
   __lowerCamelCase:					List[Any]	 =						[]
   with open(a_    ,   """r"""			) as f:
      for line in f:
         __lowerCamelCase:					int	 =						line.strip()
         if len(a_			) > 0:
            lines.append(a_			)
   return lines



def      __lowerCAmelCase    (				snake_case						:     List[str]    ,   snake_case						:     List[Any]    ,   snake_case						:     Tuple			)							->				Any:
   __lowerCamelCase:					List[str]	 =						[]
   __lowerCamelCase:					Optional[Any]	 =						[]
   for example in examples:
      for name in class_names:
         premises.append(a_			)
         hypotheses.append(hypothesis_template.format(a_			)			)
   return premises, hypotheses



def      __lowerCAmelCase    (				snake_case						:     Optional[int]			)							->				str:







   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      __lowerCAmelCase    (				snake_case						:     str    ,   snake_case						:     List[str]    ,   snake_case						:     List[str]    ,   snake_case						:     str    ,   snake_case						:     int    ,   snake_case						:     float    ,   snake_case						:     bool    ,   snake_case						:     bool    ,   snake_case						:     bool    ,   snake_case						:     bool    ,   )							->				Tuple:
   __lowerCamelCase:					Union[str, Any]	 =						AutoModelForSequenceClassification.from_pretrained(a_			)
   __lowerCamelCase:					List[str]	 =						model.config
   if not no_cuda and torch.cuda.is_available():
      __lowerCamelCase:					List[Any]	 =						nn.DataParallel(model.cuda()			)
      batch_size *= len(model.device_ids			)
   __lowerCamelCase:					str	 =						AutoTokenizer.from_pretrained(a_    ,   use_fast=a_			)

   __lowerCamelCase ,  __lowerCamelCase:					List[str]	 =						get_premise_hypothesis_pairs(a_    ,   a_    ,   a_			)
   __lowerCamelCase:					List[Any]	 =						[]

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

      __lowerCamelCase:					Union[str, Any]	 =						tokenizer(
          a_    ,   a_    ,   padding=a_    ,   truncation="""only_first"""    ,   return_tensors="""pt"""    ,   )

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

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

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



def      __lowerCAmelCase    (				)							->				Any:
   __lowerCamelCase:					Dict	 =						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.
      __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						parser.parse_json_file(
          json_file=os.path.abspath(sys.argv[1]			)			)
   else:
      __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						parser.parse_args_into_dataclasses()

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

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

   # 4. train student on teacher predictions
   __lowerCamelCase:					List[str]	 =						dataset.map(a_    ,   input_columns="""text"""			)
   dataset.set_format("""torch"""			)

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

   __lowerCamelCase:					Tuple	 =						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:
      __lowerCamelCase:					Optional[Any]	 =						trainer.evaluate(eval_dataset=a_			)["""eval_agreement"""]
      logger.info(f'Agreement of student and teacher predictions: {agreement * 100:0.2f}%'			)

   trainer.save_model()


if __name__ == "__main__":
   main()





import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


A__							=				get_logger(__name__)




def        _lowercase  (					a_					:       int					)					->						Optional[int]:




					'''simple docstring'''







					return TestCommand(
					    args.dataset       ,args.name       ,args.cache_dir       ,args.data_dir       ,args.all_configs       ,args.save_info or args.save_infos       ,args.ignore_verifications       ,args.force_redownload       ,args.clear_cache       ,)


class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: List[Any]												=	False  # to tell pytest it's not a test class







	@staticmethod
	def 						_SCREAMING_SNAKE_CASE				( __UpperCamelCase:			ArgumentParser   ):

						'''simple docstring'''





						__magic_name__				=     parser.add_parser('test'	,			help='Test dataset implementation.'   )
						test_parser.add_argument('--name'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Dataset processing name'   )
						test_parser.add_argument(
						    '--cache_dir'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Cache directory where the datasets are stored.'	,			)
						test_parser.add_argument(
						    '--data_dir'	,			type=__UpperCamelCase	,			default=__UpperCamelCase	,			help='Can be used to specify a manual directory to get the files from.'	,			)
						test_parser.add_argument('--all_configs'	,			action='store_true'	,			help='Test all dataset configurations'   )
						test_parser.add_argument(
						    '--save_info'	,			action='store_true'	,			help='Save the dataset infos in the dataset card (README.md)'   )
						test_parser.add_argument(
						    '--ignore_verifications'	,			action='store_true'	,			help='Run the test without checksums and splits checks.'	,			)
						test_parser.add_argument('--force_redownload'	,			action='store_true'	,			help='Force dataset redownload'   )
						test_parser.add_argument(
						    '--clear_cache'	,			action='store_true'	,			help='Remove downloaded files and cached datasets after each config test'	,			)
						# aliases
						test_parser.add_argument('--save_infos'	,			action='store_true'	,			help='alias to save_info'   )
						test_parser.add_argument('dataset'	,			type=__UpperCamelCase	,			help='Name of the dataset to download'   )
						test_parser.set_defaults(func=_test_command_factory   )







	def __init__( self:			List[str]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			str	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			__UpperCamelCase:			bool	,			):

						'''simple docstring'''





						__magic_name__				=     dataset
						__magic_name__				=     name
						__magic_name__				=     cache_dir
						__magic_name__				=     data_dir
						__magic_name__				=     all_configs
						__magic_name__				=     save_infos
						__magic_name__				=     ignore_verifications
						__magic_name__				=     force_redownload
						__magic_name__				=     clear_cache
						if clear_cache and not cache_dir:
											print(
											    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
											    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
											    'Please provide a --cache_dir that will be used to test the dataset script.'   )
											exit(1   )
						if save_infos:
											__magic_name__				=     True







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						fl_logger().setLevel(__UpperCamelCase   )
						if self._name is not None and self._all_configs:
											print('Both parameters `config` and `all_configs` can\'t be used at once.'   )
											exit(1   )
						__magic_name__,						__magic_name__				=     self._dataset, self._name
						__magic_name__				=     dataset_module_factory(__UpperCamelCase   )
						__magic_name__				=     import_main_class(module.module_path   )
						__magic_name__				=     len(builder_cls.BUILDER_CONFIGS   ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

						def get_builders() -> Generator[DatasetBuilder, None, None]:
											if self._all_configs and builder_cls.BUILDER_CONFIGS:
																for i, config in enumerate(builder_cls.BUILDER_CONFIGS   ):
																					if "config_name" in module.builder_kwargs:
																										yield builder_cls(
																										    cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)
																					else:
																										yield builder_cls(
																										    config_name=config.name	,			cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)
											else:
																if "config_name" in module.builder_kwargs:
																					yield builder_cls(cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs   )
																else:
																					yield builder_cls(
																					    config_name=__UpperCamelCase	,			cache_dir=self._cache_dir	,			data_dir=self._data_dir	,			**module.builder_kwargs	,			)

						for j, builder in enumerate(get_builders()   ):
											print(F'Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'   )
											__magic_name__				=     os.path.exists(
											    os.path.join(builder.get_imported_module_dir()	,			datasets.config.DATASETDICT_INFOS_FILENAME   )   )  # record checksums only if we need to update a (deprecated) dataset_infos.json
											builder.download_and_prepare(
											    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
											    if not self._force_redownload
											    else DownloadMode.FORCE_REDOWNLOAD	,			verification_mode=VerificationMode.NO_CHECKS
											    if self._ignore_verifications
											    else VerificationMode.ALL_CHECKS	,			try_from_hf_gcs=__UpperCamelCase	,			)
											builder.as_dataset()
											if self._save_infos:
																builder._save_infos()

											# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
											# The dataset_infos are saved in the YAML part of the README.md

											# Let's move it to the original directory of the dataset script, to allow the user to
											# upload them on S3 at the same time afterwards.
											if self._save_infos:
																__magic_name__				=     os.path.join(builder_cls.get_imported_module_dir()	,			'README.md'   )
																__magic_name__				=     Path(__UpperCamelCase   ).name + '.py'
																__magic_name__				=     os.path.join(__UpperCamelCase	,			__UpperCamelCase   )
																if os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     os.path.dirname(__UpperCamelCase   )
																elif os.path.isfile(__UpperCamelCase   ):
																					__magic_name__				=     path
																elif os.path.isdir(__UpperCamelCase   ):  # for local directories containing only data files
																					__magic_name__				=     path
																else:  # in case of a remote dataset
																					__magic_name__				=     None
																					print(F'Dataset card saved at {dataset_readme_path}'   )

																# Move dataset_info back to the user
																if dataset_dir is not None:
																					__magic_name__				=     os.path.join(__UpperCamelCase	,			'README.md'   )
																					copyfile(__UpperCamelCase	,			__UpperCamelCase   )
																					print(F'Dataset card saved at {user_dataset_readme_path}'   )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
											if self._clear_cache:
																if os.path.isdir(builder._cache_dir   ):
																					logger.warning(F'Clearing cache at {builder._cache_dir}'   )
																					rmtree(builder._cache_dir   )
																__magic_name__				=     os.path.join(self._cache_dir	,			datasets.config.DOWNLOADED_DATASETS_DIR   )
																if os.path.isdir(__UpperCamelCase   ):
																					logger.warning(F'Clearing cache at {download_dir}'   )
																					rmtree(__UpperCamelCase   )

						print('Test successful.'   )




