import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_lowerCamelCase    :      Optional[int]	 =						threading.Lock()
_lowerCamelCase    :      Optional[logging.Handler]	 =						None

_lowerCamelCase    :      Optional[int]	 =						{
    '''debug''': logging.DEBUG,
    '''info''': logging.INFO,
    '''warning''': logging.WARNING,
    '''error''': logging.ERROR,
    '''critical''': logging.CRITICAL,
}

_lowerCamelCase    :      List[Any]	 =						logging.WARNING

_lowerCamelCase    :      Optional[int]	 =						True





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				os.getenv("DIFFUSERS_VERBOSITY"					,       UpperCAmelCase__     )
					if env_level_str:
										if env_level_str in log_levels:
															return log_levels[env_level_str]
										else:
															logging.getLogger().warning(
															    F"Unknown option DIFFUSERS_VERBOSITY={env_level_str}, "
															    F"has to be one of: { ', '.join(log_levels.keys()     ) }"     )
					return _default_log_level





def 		__lowerCamelCase							():
					return __name__.split("."     )[0]





def 		__lowerCamelCase							():
					return logging.getLogger(_get_library_name()     )





def 		__lowerCamelCase							():
					global _default_handler

					with _lock:
										if _default_handler:
															# This library has already configured the library root logger.
															return
										SCREAMING_SNAKE_CASE				=				logging.StreamHandler()  # Set sys.stderr as stream.
										SCREAMING_SNAKE_CASE				=				sys.stderr.flush

										# Apply our default configuration to the library root logger.
										SCREAMING_SNAKE_CASE				=				_get_library_root_logger()
										library_root_logger.addHandler(_default_handler     )
										library_root_logger.setLevel(_get_default_logging_level()     )
										SCREAMING_SNAKE_CASE				=				False





def 		__lowerCamelCase							():
					global _default_handler

					with _lock:
										if not _default_handler:
															return

										SCREAMING_SNAKE_CASE				=				_get_library_root_logger()
										library_root_logger.removeHandler(_default_handler     )
										library_root_logger.setLevel(logging.NOTSET     )
										SCREAMING_SNAKE_CASE				=				None





def 		__lowerCamelCase							():
					return log_levels





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[str] = None     ):

					if name is None:
										SCREAMING_SNAKE_CASE				=				_get_library_name()

					_configure_library_root_logger()
					return logging.getLogger(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					_configure_library_root_logger()
					return _get_library_root_logger().getEffectiveLevel()





def 		__lowerCamelCase							(UpperCAmelCase__   :							int     ):
					_configure_library_root_logger()
					_get_library_root_logger().setLevel(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					return set_verbosity(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().removeHandler(_default_handler     )





def 		__lowerCamelCase							():
					_configure_library_root_logger()

					assert _default_handler is not None
					_get_library_root_logger().addHandler(_default_handler     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							logging.Handler     ):
					_configure_library_root_logger()

					assert handler is not None
					_get_library_root_logger().addHandler(UpperCAmelCase__     )





def 		__lowerCamelCase							(UpperCAmelCase__   :							logging.Handler     ):
					_configure_library_root_logger()

					assert handler is not None and handler not in _get_library_root_logger().handlers
					_get_library_root_logger().removeHandler(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					_configure_library_root_logger()
					SCREAMING_SNAKE_CASE				=				False





def 		__lowerCamelCase							():
					_configure_library_root_logger()
					SCREAMING_SNAKE_CASE				=				True





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				_get_library_root_logger().handlers

					for handler in handlers:
										SCREAMING_SNAKE_CASE				=				logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"     )
										handler.setFormatter(UpperCAmelCase__     )





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				_get_library_root_logger().handlers

					for handler in handlers:
										handler.setFormatter(UpperCAmelCase__     )





def 		__lowerCamelCase							(self   :							Optional[int]					,       *UpperCAmelCase__   :							Optional[Any]					,       **UpperCAmelCase__   :							Any     ):
					SCREAMING_SNAKE_CASE				=				os.getenv("DIFFUSERS_NO_ADVISORY_WARNINGS"					,       UpperCAmelCase__     )
					if no_advisory_warnings:
										return
					self.warning(*UpperCAmelCase__					,       **UpperCAmelCase__     )


_lowerCamelCase    :      str	 =						warning_advice


class   lowercase			:




							def __init__(  self      :    List[Any]		,							*_UpperCamelCase      :    Dict		,							**_UpperCamelCase      :    Optional[int]					)    -> List[str]:  # pylint: disable=unused-argument



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				args[0] if args else None




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



												'''simple docstring'''

												return iter(self._iterator					)




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



												'''simple docstring'''

												def empty_fn(*_UpperCamelCase      :    Union[str, Any]		,							**_UpperCamelCase      :    Tuple					):  # pylint: disable=unused-argument
																	return

												return empty_fn




							def __enter__(  self      :    Dict					)    -> int:



												'''simple docstring'''

												return self





							def __exit__(  self      :    str		,							_UpperCamelCase      :    Tuple		,							_UpperCamelCase      :    List[Any]		,							_UpperCamelCase      :    int					)    -> Dict:



												'''simple docstring'''

												return



class   lowercase			:




							def __call__(  self      :    List[Any]		,							*_UpperCamelCase      :    Union[str, Any]		,							**_UpperCamelCase      :    str					)    -> Optional[int]:



												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm(*_UpperCamelCase		,							**_UpperCamelCase					)
												else:
																	return EmptyTqdm(*_UpperCamelCase		,							**_UpperCamelCase					)




							def 		__snake_case(  self      :    str		,							*_UpperCamelCase      :    Optional[int]		,							**_UpperCamelCase      :    Union[str, Any]					)    -> Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				None
												if _tqdm_active:
																	return tqdm_lib.tqdm.set_lock(*_UpperCamelCase		,							**_UpperCamelCase					)





							def 		__snake_case(  self      :    List[Any]					)    -> Any:



												'''simple docstring'''

												if _tqdm_active:
																	return tqdm_lib.tqdm.get_lock()



_lowerCamelCase    :      Tuple	 =						_tqdm_cls()





def 		__lowerCamelCase							():
					global _tqdm_active
					return bool(_tqdm_active     )





def 		__lowerCamelCase							():
					global _tqdm_active
					SCREAMING_SNAKE_CASE				=				True





def 		__lowerCamelCase							():
					global _tqdm_active
					SCREAMING_SNAKE_CASE				=				False





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    :      Tuple	 =						'''
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    :      Optional[Any]	 =						logging.getLogger(__name__)


@dataclass
class   lowercase			:
							lowercase__  :				Optional[str]									=							field(
							    default="""roberta-large-mnli""" ,				metadata={"""help""": """The NLI/zero-shot teacher model to be distilled."""}   )
							lowercase__  :				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."""
							        )
							    } ,				)
							lowercase__  :				Optional[int]									=							field(
							    default=32 ,				metadata={"""help""": """Batch size for generating teacher predictions."""}   )
							lowercase__  :				Optional[bool]									=							field(
							    default=a ,				metadata={
							        """help""": (
							            """Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"""
							            """multi-class multi-label classification)."""
							        )
							    } ,				)
							lowercase__  :				Optional[float]									=							field(
							    default=1.0 ,				metadata={"""help""": """Temperature applied to teacher softmax for distillation."""}   )



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



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



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




							def 		__snake_case(  self      :    Optional[Any]		,							_UpperCamelCase      :    str		,							_UpperCamelCase      :    Dict		,							_UpperCamelCase      :    List[str]=False					)    -> Union[str, Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				inputs["labels"]
												SCREAMING_SNAKE_CASE				=				model(inputs["input_ids"]		,							attention_mask=inputs["attention_mask"]					)
												SCREAMING_SNAKE_CASE				=				outputs[0]

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

												if return_outputs:
																	return loss, outputs

												return loss





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





def 		__lowerCamelCase							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				[]
					SCREAMING_SNAKE_CASE				=				[]
					for example in examples:
										for name in class_names:
															premises.append(UpperCAmelCase__     )
															hypotheses.append(hypothesis_template.format(UpperCAmelCase__     )     )
					return premises, hypotheses





def 		__lowerCamelCase							(UpperCAmelCase__   :							Optional[int]     ):
					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							(UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							List[str]					,       UpperCAmelCase__   :							str					,       UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							float					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       UpperCAmelCase__   :							bool					,       ):
					SCREAMING_SNAKE_CASE				=				AutoModelForSequenceClassification.from_pretrained(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				model.config
					if not no_cuda and torch.cuda.is_available():
										SCREAMING_SNAKE_CASE				=				nn.DataParallel(model.cuda()     )
										batch_size *= len(model.device_ids     )
					SCREAMING_SNAKE_CASE				=				AutoTokenizer.from_pretrained(UpperCAmelCase__					,       use_fast=UpperCAmelCase__     )

					SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				get_premise_hypothesis_pairs(UpperCAmelCase__					,       UpperCAmelCase__					,       UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				[]

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

										SCREAMING_SNAKE_CASE				=				tokenizer(
										    UpperCAmelCase__					,       UpperCAmelCase__					,       padding=UpperCAmelCase__					,       truncation="only_first"					,       return_tensors="pt"					,       )

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

					SCREAMING_SNAKE_CASE				=				get_entailment_id(UpperCAmelCase__     )
					SCREAMING_SNAKE_CASE				=				-1 if entail_id == 0 else 0
					SCREAMING_SNAKE_CASE				=				torch.cat(UpperCAmelCase__					,       dim=0     )  # N*K x 3
					SCREAMING_SNAKE_CASE				=				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
										SCREAMING_SNAKE_CASE				=				(nli_logits / temperature).softmax(-1     )
					else:
										# softmax over entail logits across classes s.t. class probabilities sum to 1.
										SCREAMING_SNAKE_CASE				=				(nli_logits / temperature).softmax(1     )

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





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				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.
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_json_file(
										    json_file=os.path.abspath(sys.argv[1]     )     )
					else:
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				parser.parse_args_into_dataclasses()

					# Detecting last checkpoint.
					SCREAMING_SNAKE_CASE				=				None
					if os.path.isdir(training_args.output_dir     ) and training_args.do_train and not training_args.overwrite_output_dir:
										SCREAMING_SNAKE_CASE				=				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
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.data_file     )
					SCREAMING_SNAKE_CASE				=				read_lines(data_args.class_names_file     )

					# 2. get teacher predictions and load into dataset
					logger.info("Generating predictions from zero-shot teacher model"     )
					SCREAMING_SNAKE_CASE				=				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					,       )
					SCREAMING_SNAKE_CASE				=				Dataset.from_dict(
					    {
					        "text": examples,
					        "labels": teacher_soft_preds,
					    }     )

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

					# 4. train student on teacher predictions
					SCREAMING_SNAKE_CASE				=				dataset.map(UpperCAmelCase__					,       input_columns="text"     )
					dataset.set_format("torch"     )

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

					SCREAMING_SNAKE_CASE				=				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:
										SCREAMING_SNAKE_CASE				=				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()




