




from typing import Optional, Tuple

import flax.linen as nn
import jax
import jax.numpy as jnp
from configuration_hybrid_clip import HybridCLIPConfig
from flax.core.frozen_dict import FrozenDict

from transformers import FLAX_MODEL_MAPPING, FlaxCLIPVisionModel
from transformers.modeling_flax_utils import FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPOutput
from transformers.utils import logging


lowerCamelCase_  						=       logging.get_logger(__name__)
class 				__a (			nn.Module					):


     """simple docstring"""


     _A    :   HybridCLIPConfig
     _A    :   jnp.dtype     =  jnp.floataa




     def   __A    (     self      :			Dict						)		->       Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =self.config.text_config
         SCREAMING_SNAKE_CASE__       =self.config.vision_config

         SCREAMING_SNAKE_CASE__       =self.config.projection_dim
         SCREAMING_SNAKE_CASE__       =text_config.hidden_size
         SCREAMING_SNAKE_CASE__       =vision_config.hidden_size

         SCREAMING_SNAKE_CASE__       =FLAX_MODEL_MAPPING[self.config.text_config.__class__].module_class
         SCREAMING_SNAKE_CASE__       =FLAX_MODEL_MAPPING.get(self.config.vision_config.__class__    ,_UpperCamelCase						).module_class

         SCREAMING_SNAKE_CASE__       =text_module(_UpperCamelCase    ,dtype=self.dtype						)
         SCREAMING_SNAKE_CASE__       =vision_module(_UpperCamelCase    ,dtype=self.dtype						)

         SCREAMING_SNAKE_CASE__       =nn.Dense(
             self.projection_dim    ,dtype=self.dtype    ,kernel_init=jax.nn.initializers.normal(0.02						)    ,use_bias=_UpperCamelCase    ,)
         SCREAMING_SNAKE_CASE__       =nn.Dense(
             self.projection_dim    ,dtype=self.dtype    ,kernel_init=jax.nn.initializers.normal(0.02						)    ,use_bias=_UpperCamelCase    ,)
         SCREAMING_SNAKE_CASE__       =self.param("""logit_scale"""    ,jax.nn.initializers.ones    ,[]						)




     def __call__(     self      :			Optional[int]    ,_UpperCamelCase      :			int=None    ,_UpperCamelCase      :			int=None    ,_UpperCamelCase      :			Optional[int]=None    ,_UpperCamelCase      :			Optional[Any]=None    ,_UpperCamelCase      :			Optional[int]=None    ,_UpperCamelCase      :			bool = True    ,_UpperCamelCase      :			str=None    ,_UpperCamelCase      :			Any=None    ,_UpperCamelCase      :			str=None    ,)		->       str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =return_dict if return_dict is not None else self.config.return_dict

         SCREAMING_SNAKE_CASE__       =self.vision_model(
             pixel_values=_UpperCamelCase    ,deterministic=_UpperCamelCase    ,output_attentions=_UpperCamelCase    ,output_hidden_states=_UpperCamelCase    ,return_dict=_UpperCamelCase    ,)

         SCREAMING_SNAKE_CASE__       =self.text_model(
             input_ids=_UpperCamelCase    ,attention_mask=_UpperCamelCase    ,token_type_ids=_UpperCamelCase    ,position_ids=_UpperCamelCase    ,deterministic=_UpperCamelCase    ,output_attentions=_UpperCamelCase    ,output_hidden_states=_UpperCamelCase    ,return_dict=_UpperCamelCase    ,)

         SCREAMING_SNAKE_CASE__       =vision_outputs[1]
         SCREAMING_SNAKE_CASE__       =self.visual_projection(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =text_outputs[1]
         SCREAMING_SNAKE_CASE__       =self.text_projection(_UpperCamelCase						)

         # normalized features
         SCREAMING_SNAKE_CASE__       =image_embeds / jnp.linalg.norm(_UpperCamelCase    ,axis=-1    ,keepdims=_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =text_embeds / jnp.linalg.norm(_UpperCamelCase    ,axis=-1    ,keepdims=_UpperCamelCase						)

         # cosine similarity as logits
         SCREAMING_SNAKE_CASE__       =jnp.exp(self.logit_scale						)
         SCREAMING_SNAKE_CASE__       =jnp.matmul(_UpperCamelCase    ,image_embeds.T						) * logit_scale
         SCREAMING_SNAKE_CASE__       =logits_per_text.T

         if not return_dict:
             return (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)

         return FlaxCLIPOutput(
             logits_per_image=_UpperCamelCase    ,logits_per_text=_UpperCamelCase    ,text_embeds=_UpperCamelCase    ,image_embeds=_UpperCamelCase    ,text_model_output=_UpperCamelCase    ,vision_model_output=_UpperCamelCase    ,)






class 				__a (			__lowerCamelCase					):


     """simple docstring"""


     _A    :   Tuple     =  HybridCLIPConfig
     _A    :   Optional[int]     =  FlaxHybridCLIPModule




     def __init__(     self      :			str    ,_UpperCamelCase      :			HybridCLIPConfig    ,_UpperCamelCase      :			Optional[Tuple] = None    ,_UpperCamelCase      :			int = 0    ,_UpperCamelCase      :			jnp.dtype = jnp.floataa    ,**_UpperCamelCase      :			str    ,)		->       Dict:


         '''simple docstring'''





         if input_shape is None:
             SCREAMING_SNAKE_CASE__       =((1, 1), (1, config.vision_config.image_size, config.vision_config.image_size, 3))

         SCREAMING_SNAKE_CASE__       =self.module_class(config=_UpperCamelCase    ,dtype=_UpperCamelCase    ,**_UpperCamelCase						)
         super().__init__(_UpperCamelCase    ,_UpperCamelCase    ,input_shape=_UpperCamelCase    ,seed=_UpperCamelCase    ,dtype=_UpperCamelCase						)




     def   __A    (     self      :			str    ,_UpperCamelCase      :			jax.random.PRNGKey    ,_UpperCamelCase      :			Tuple    ,_UpperCamelCase      :			FrozenDict = None						)		->       FrozenDict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =jnp.zeros(input_shape[0]    ,dtype="""i4"""						)
         SCREAMING_SNAKE_CASE__       =jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCamelCase						).shape[-1]						)    ,input_shape[0]						)
         SCREAMING_SNAKE_CASE__       =jnp.ones_like(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       =jnp.ones_like(_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =jax.random.normal(_UpperCamelCase    ,input_shape[1]						)

         SCREAMING_SNAKE_CASE__      ,		SCREAMING_SNAKE_CASE__       =jax.random.split(_UpperCamelCase						)
         SCREAMING_SNAKE_CASE__       ={"""params""": params_rng, """dropout""": dropout_rng}

         return self.module.init(_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase						)["params"]




     def __call__(     self      :			Any    ,_UpperCamelCase      :			str    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			List[str]=None    ,_UpperCamelCase      :			Any=None    ,_UpperCamelCase      :			Union[str, Any]=None    ,_UpperCamelCase      :			dict = None    ,_UpperCamelCase      :			jax.random.PRNGKey = None    ,_UpperCamelCase      :			bool = False    ,_UpperCamelCase      :			Optional[bool] = None    ,_UpperCamelCase      :			Optional[bool] = None    ,_UpperCamelCase      :			Optional[bool] = None    ,)		->       List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       =output_attentions if output_attentions is not None else self.config.output_attentions
         SCREAMING_SNAKE_CASE__       =(
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         SCREAMING_SNAKE_CASE__       =return_dict if return_dict is not None else self.config.return_dict

         if position_ids is None:
             SCREAMING_SNAKE_CASE__       =jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCamelCase						).shape[-1]						)    ,input_ids.shape						)

         if token_type_ids is None:
             SCREAMING_SNAKE_CASE__       =jnp.zeros_like(_UpperCamelCase						)

         if attention_mask is None:
             SCREAMING_SNAKE_CASE__       =jnp.ones_like(_UpperCamelCase						)

         # Handle any PRNG if needed
         SCREAMING_SNAKE_CASE__       ={}
         if dropout_rng is not None:
             SCREAMING_SNAKE_CASE__       =dropout_rng

         return self.module.apply(
             {"""params""": params or self.params}    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,jnp.array(_UpperCamelCase    ,dtype=jnp.floataa						)    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,not train    ,_UpperCamelCase    ,_UpperCamelCase    ,_UpperCamelCase    ,rngs=_UpperCamelCase    ,)




     def   __A    (     self      :			Tuple    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			Any=None    ,_UpperCamelCase      :			Tuple=None    ,_UpperCamelCase      :			Tuple=None    ,_UpperCamelCase      :			dict = None    ,_UpperCamelCase      :			jax.random.PRNGKey = None    ,_UpperCamelCase      :			List[Any]=False    ,)		->       Union[str, Any]:


         '''simple docstring'''





         if position_ids is None:
             SCREAMING_SNAKE_CASE__       =jnp.broadcast_to(jnp.arange(jnp.atleast_ad(_UpperCamelCase						).shape[-1]						)    ,input_ids.shape						)

         if token_type_ids is None:
             SCREAMING_SNAKE_CASE__       =jnp.zeros_like(_UpperCamelCase						)

         if attention_mask is None:
             SCREAMING_SNAKE_CASE__       =jnp.ones_like(_UpperCamelCase						)

         # Handle any PRNG if needed
         SCREAMING_SNAKE_CASE__       ={}
         if dropout_rng is not None:
             SCREAMING_SNAKE_CASE__       =dropout_rng

         def _get_features(_UpperCamelCase      :			List[str]    ,_UpperCamelCase      :			List[Any]    ,_UpperCamelCase      :			Any    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			Optional[Any]    ,_UpperCamelCase      :			List[str]						):
             SCREAMING_SNAKE_CASE__       =module.text_model(
                 input_ids=_UpperCamelCase    ,attention_mask=_UpperCamelCase    ,position_ids=_UpperCamelCase    ,token_type_ids=_UpperCamelCase    ,deterministic=_UpperCamelCase    ,)
             SCREAMING_SNAKE_CASE__       =text_outputs[1]
             SCREAMING_SNAKE_CASE__       =module.text_projection(_UpperCamelCase						)
             return text_features

         return self.module.apply(
             {"""params""": params or self.params}    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,jnp.array(_UpperCamelCase    ,dtype="""i4"""						)    ,not train    ,method=_get_features    ,rngs=_UpperCamelCase    ,)




     def   __A    (     self      :			Union[str, Any]    ,_UpperCamelCase      :			List[Any]    ,_UpperCamelCase      :			dict = None    ,_UpperCamelCase      :			jax.random.PRNGKey = None    ,_UpperCamelCase      :			Union[str, Any]=False						)		->       List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ={}
         if dropout_rng is not None:
             SCREAMING_SNAKE_CASE__       =dropout_rng

         def _get_features(_UpperCamelCase      :			Optional[int]    ,_UpperCamelCase      :			Union[str, Any]    ,_UpperCamelCase      :			int						):
             SCREAMING_SNAKE_CASE__       =module.vision_model(pixel_values=_UpperCamelCase    ,deterministic=_UpperCamelCase						)
             SCREAMING_SNAKE_CASE__       =vision_outputs[1]  # pooled_output
             SCREAMING_SNAKE_CASE__       =module.visual_projection(_UpperCamelCase						)
             return image_features

         return self.module.apply(
             {"""params""": params or self.params}    ,jnp.array(_UpperCamelCase    ,dtype=jnp.floataa						)    ,not train    ,method=_get_features    ,rngs=_UpperCamelCase    ,)




     @classmethod
     def   __A    (     cls      :			List[str]    ,_UpperCamelCase      :			str = None    ,_UpperCamelCase      :			str = None    ,*_UpperCamelCase      :			str    ,**_UpperCamelCase      :			Optional[Any]    ,)		->       FlaxPreTrainedModel:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__       ={
             argument[len("""text_"""						) :]: value for argument, value in kwargs.items() if argument.startswith("""text_"""						)
         }

         SCREAMING_SNAKE_CASE__       ={
             argument[len("""vision_"""						) :]: value for argument, value in kwargs.items() if argument.startswith("""vision_"""						)
         }

         # remove text, vision kwargs from kwargs
         for key in kwargs_text.keys():
             del kwargs["text_" + key]
         for key in kwargs_vision.keys():
             del kwargs["vision_" + key]

         # Load and initialize the text and vision model
         SCREAMING_SNAKE_CASE__       =kwargs_text.pop("""model"""    ,_UpperCamelCase						)
         if text_model is None:
             assert (
                 text_model_name_or_path is not None
             ), "If `model` is not defined as an argument, a `text_model_name_or_path` has to be defined"
             from transformers import FlaxAutoModel

             if "config" not in kwargs_text:
                 from transformers import AutoConfig

                 SCREAMING_SNAKE_CASE__       =AutoConfig.from_pretrained(_UpperCamelCase						)
                 SCREAMING_SNAKE_CASE__       =text_config

             SCREAMING_SNAKE_CASE__       =FlaxAutoModel.from_pretrained(_UpperCamelCase    ,*_UpperCamelCase    ,**_UpperCamelCase						)

         SCREAMING_SNAKE_CASE__       =kwargs_vision.pop("""model"""    ,_UpperCamelCase						)
         if vision_model is None:
             assert (
                 vision_model_name_or_path is not None
             ), "If `model` is not defined as an argument, a `vision_model_name_or_path` has to be defined"
             from transformers import FlaxAutoModel

             if "config" not in kwargs_vision:
                 from transformers import AutoConfig

                 SCREAMING_SNAKE_CASE__       =AutoConfig.from_pretrained(_UpperCamelCase						)
                 SCREAMING_SNAKE_CASE__       =vision_config

             SCREAMING_SNAKE_CASE__       =FlaxAutoModel.from_pretrained(_UpperCamelCase    ,*_UpperCamelCase    ,**_UpperCamelCase						)

         # instantiate config with corresponding kwargs
         SCREAMING_SNAKE_CASE__       =kwargs.pop("""dtype"""    ,jnp.floataa						)
         SCREAMING_SNAKE_CASE__       =HybridCLIPConfig.from_text_vision_configs(text_model.config    ,vision_model.config    ,**_UpperCamelCase						)

         # init model
         SCREAMING_SNAKE_CASE__       =cls(_UpperCamelCase    ,*_UpperCamelCase    ,dtype=_UpperCamelCase    ,**_UpperCamelCase						)

         if vision_config.model_type == "clip":
             SCREAMING_SNAKE_CASE__       =vision_model.params["""vision_model"""]
             SCREAMING_SNAKE_CASE__       =vision_model.params["""visual_projection"""]["""kernel"""]
         else:
             SCREAMING_SNAKE_CASE__       =vision_model.params

         SCREAMING_SNAKE_CASE__       =text_model.params

         return model












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_  						=       "\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"

lowerCamelCase_  						=       logging.getLogger(__name__)
@dataclass
class 				__a :


     """simple docstring"""


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






@dataclass
class 				__a :


     """simple docstring"""


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






@dataclass
class 				__a :


     """simple docstring"""


     _A    :   str     =  field(metadata={"help": "Text file with one unlabeled instance per line."}					)
     _A    :   str     =  field(metadata={"help": "Text file with one class name per line."}					)
     _A    :   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					):


     """simple docstring"""


     _A    :   Optional[str]     =  field(
         default=__lowerCamelCase							,					metadata={"help": "The output directory where the model predictions and checkpoints will be written."}							,					)
     _A    :   int     =  field(
         default=32							,					metadata={"help": "Batch size per GPU/TPU core/CPU for training."}					)
     _A    :   int     =  field(
         default=1_28							,					metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}					)
     _A    :   float     =  field(default=1.0							,					metadata={"help": "Total number of training epochs to perform."}					)
     _A    :   bool     =  field(default=__lowerCamelCase							,					metadata={"help": "Whether to run training of student model."}					)
     _A    :   bool     =  field(
         default=__lowerCamelCase							,					metadata={
             "help": (
                 "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
                 "after training."
             )
         }							,					)
     _A    :   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					):


     """simple docstring"""


     def   __A    (     self      :			int    ,_UpperCamelCase      :			Dict    ,_UpperCamelCase      :			Tuple    ,_UpperCamelCase      :			Optional[Any]=False						)		->       List[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      UpperCAmelCase_   (     __UpperCamelCase     ):
    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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase     ):
    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      UpperCAmelCase_   (     __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      UpperCAmelCase_   (     __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase,    ):
    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      UpperCAmelCase_   (     ):
    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,    __UpperCamelCase=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 * 100:0.2f}%"""     )

    trainer.save_model()


if __name__ == "__main__":
    main()






