


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {
    '''ZinengTang/tvlt-base''': '''https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json''',
}


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  List[Any]				     =  """tvlt"""

       def __init__( self   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any]=2_2_4   ,				lowerCamelCase_   :  int=2_0_4_8   ,				lowerCamelCase_   :  List[str]=1_2_8   ,				lowerCamelCase_   :  int=[1_6, 1_6]   ,				lowerCamelCase_   :  Dict=[1_6, 1_6]   ,				lowerCamelCase_   :  str=3   ,				lowerCamelCase_   :  Tuple=1   ,				lowerCamelCase_   :  Optional[Any]=8   ,				lowerCamelCase_   :  Dict=7_6_8   ,				lowerCamelCase_   :  Any=1_2   ,				lowerCamelCase_   :  Union[str, Any]=1_2   ,				lowerCamelCase_   :  str=3_0_7_2   ,				lowerCamelCase_   :  str="gelu"   ,				lowerCamelCase_   :  int=0.0   ,				lowerCamelCase_   :  List[Any]=0.0   ,				lowerCamelCase_   :  Union[str, Any]=0.0_2   ,				lowerCamelCase_   :  Optional[Any]=1E-6   ,				lowerCamelCase_   :  str=True   ,				lowerCamelCase_   :  Optional[Any]=False   ,				lowerCamelCase_   :  Optional[Any]=1_6   ,				lowerCamelCase_   :  Union[str, Any]=5_1_2   ,				lowerCamelCase_   :  Any=8   ,				lowerCamelCase_   :  List[Any]=2_0_4_8   ,				lowerCamelCase_   :  List[str]=0.7_5   ,				lowerCamelCase_   :  List[str]=0.1_5   ,				lowerCamelCase_   :  Dict="frame-level"   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  str="classification"   ,				**lowerCamelCase_   :  Optional[Any]   ,				):
            super().__init__(**lowerCamelCase_     )

            if audio_mask_type not in ("frame-level", "patch_level"):
                 raise ValueError(
                     """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
                     F'''got {audio_mask_type}'''     )

            a_     :  List[str]				      =     image_size
            a_     :  List[str]				      =     spectrogram_length
            a_     :  int				      =     frequency_length
            a_     :  List[str]				      =     image_patch_size
            a_     :  Tuple				      =     audio_patch_size
            a_     :  List[str]				      =     num_image_channels
            a_     :  Tuple				      =     num_audio_channels
            a_     :  Dict				      =     num_frames

            a_     :  Any				      =     hidden_size
            a_     :  str				      =     num_hidden_layers
            a_     :  int				      =     num_attention_heads
            a_     :  Optional[int]				      =     intermediate_size
            a_     :  Dict				      =     hidden_act
            a_     :  List[Any]				      =     hidden_dropout_prob
            a_     :  Any				      =     attention_probs_dropout_prob
            a_     :  Union[str, Any]				      =     initializer_range
            a_     :  Optional[int]				      =     layer_norm_eps
            a_     :  Any				      =     qkv_bias
            a_     :  Any				      =     use_mean_pooling

            a_     :  str				      =     decoder_num_attention_heads
            a_     :  List[Any]				      =     decoder_hidden_size
            a_     :  Any				      =     decoder_num_hidden_layers
            a_     :  Optional[Any]				      =     decoder_intermediate_size
            a_     :  List[str]				      =     pixel_mask_ratio
            a_     :  Union[str, Any]				      =     audio_mask_ratio
            a_     :  int				      =     audio_mask_type

            a_     :  Optional[int]				      =     task_matching
            a_     :  Tuple				      =     task_mae
            a_     :  Union[str, Any]				      =     loss_type







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()



