



import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


A_		:  str										=  collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Any       ):






 '''simple docstring'''

 print(F"""Reading saved model from {model_file}"""       )
 a							=		torch.load(__UpperCamelCase			,       map_location=lambda UpperCAmelCase__			,       UpperCAmelCase__       : default_restore_location(__UpperCamelCase			,       "cpu"       )       )
 return CheckpointState(**__UpperCamelCase       )



class 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							Path				)							->					List[Any]:


    """simple docstring"""






    a							=		src_file

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


    """simple docstring"""






    raise NotImplementedError




   @staticmethod
   def 		A				(   __lowerCAmelCase      :							str				, *__lowerCAmelCase      :							Optional[Any]				, **__lowerCAmelCase      :							Any				)							->					Optional[int]:


    """simple docstring"""






    if comp_type.startswith("c"				):
     return DPRContextEncoderState(*lowerCamelCase_				, **lowerCamelCase_				)
    if comp_type.startswith("q"				):
     return DPRQuestionEncoderState(*lowerCamelCase_				, **lowerCamelCase_				)
    if comp_type.startswith("r"				):
     return DPRReaderState(*lowerCamelCase_				, **lowerCamelCase_				)
    else:
     raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."				)



class 		_lowercase		(							lowerCAmelCase_						):

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


    """simple docstring"""






    a							=		DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"				)[0]				)				)
    print(f"""Loading DPR biencoder from {self.src_file}"""				)
    a							=		load_states_from_checkpoint(self.src_file				)
    a							=		model.ctx_encoder, """ctx_model."""
    # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
    a							=		{"""bert_model.embeddings.position_ids""": model.ctx_encoder.bert_model.embeddings.position_ids}
    for key, value in saved_state.model_dict.items():
     if key.startswith(lowerCamelCase_				):
      a							=		key[len(lowerCamelCase_				) :]
      if not key.startswith("encode_proj."				):
       a							=		"""bert_model.""" + key
      a							=		value
    encoder.load_state_dict(lowerCamelCase_				)
    return model



class 		_lowercase		(							lowerCAmelCase_						):

   def 		A				(   self      :							int				)							->					List[Any]:


    """simple docstring"""






    a							=		DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"				)[0]				)				)
    print(f"""Loading DPR biencoder from {self.src_file}"""				)
    a							=		load_states_from_checkpoint(self.src_file				)
    a							=		model.question_encoder, """question_model."""
    # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
    a							=		{"""bert_model.embeddings.position_ids""": model.question_encoder.bert_model.embeddings.position_ids}
    for key, value in saved_state.model_dict.items():
     if key.startswith(lowerCamelCase_				):
      a							=		key[len(lowerCamelCase_				) :]
      if not key.startswith("encode_proj."				):
       a							=		"""bert_model.""" + key
      a							=		value
    encoder.load_state_dict(lowerCamelCase_				)
    return model



class 		_lowercase		(							lowerCAmelCase_						):

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


    """simple docstring"""






    a							=		DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"				)[0]				)				)
    print(f"""Loading DPR reader from {self.src_file}"""				)
    a							=		load_states_from_checkpoint(self.src_file				)
    # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
    a							=		{
        """encoder.bert_model.embeddings.position_ids""": model.span_predictor.encoder.bert_model.embeddings.position_ids
    }
    for key, value in saved_state.model_dict.items():
     if key.startswith("encoder."				) and not key.startswith("encoder.encode_proj"				):
      a							=		"""encoder.bert_model.""" + key[len("encoder."				) :]
     a							=		value
    model.span_predictor.load_state_dict(lowerCamelCase_				)
    return model


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:int			,       UpperCAmelCase__			:Optional[Any]       ):






 '''simple docstring'''

 a							=		Path(__UpperCamelCase       )
 dest_dir.mkdir(exist_ok=__UpperCamelCase       )

 a							=		DPRState.from_type(__UpperCamelCase			,       src_file=__UpperCamelCase       )
 a							=		dpr_state.load_dpr_model()
 model.save_pretrained(__UpperCamelCase       )
 model.from_pretrained(__UpperCamelCase       )  # sanity check


if __name__ == "__main__":
     A_		:  Dict										=  argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
     )
     parser.add_argument(
         '''--src''',
         type=str,
         help=(
             '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
             ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
             ''' \'retriever\' checkpoints.'''
         ),
     )
     parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
     A_		:  Optional[int]										=  parser.parse_args()

     A_		:  Optional[int]										=  Path(args.src)
     A_		:  int										=  F"""converted-{src_file.name}""" if args.dest is None else args.dest
     A_		:  str										=  Path(dest_dir)
     assert src_file.exists()
     assert (
         args.type is not None
     ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
     convert(args.type, src_file, dest_dir)





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()



