

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
       from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DPTImageProcessor


class    SCREAMING_SNAKE_CASE__      :





    def __init__(   self		,     a		,     a=2		,     a=32		,     a=16		,     a=3		,     a=True		,     a=True		,     a=32		,     a=4		,     a=[0, 1, 2, 3]		,     a=4		,     a=37		,     a="gelu"		,     a=0.1		,     a=0.1		,     a=0.02		,     a=3		,     a=False		,     a=None		,     ):
         lowercase__					:     List[str]   = parent
         lowercase__					:     str   = batch_size
         lowercase__					:     Optional[Any]   = image_size
         lowercase__					:     Any   = patch_size
         lowercase__					:     Optional[Any]   = num_channels
         lowercase__					:     str   = is_training
         lowercase__					:     int   = use_labels
         lowercase__					:     Dict   = hidden_size
         lowercase__					:     Union[str, Any]   = num_hidden_layers
         lowercase__					:     Dict   = backbone_out_indices
         lowercase__					:     List[Any]   = num_attention_heads
         lowercase__					:     Any   = intermediate_size
         lowercase__					:     Dict   = hidden_act
         lowercase__					:     int   = hidden_dropout_prob
         lowercase__					:     List[str]   = attention_probs_dropout_prob
         lowercase__					:     Optional[int]   = initializer_range
         lowercase__					:     int   = num_labels
         lowercase__					:     Union[str, Any]   = scope
         lowercase__					:     Dict   = is_hybrid
         # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
         lowercase__					:     Any   = (image_size // patch_size) ** 2
         lowercase__					:     str   = num_patches + 1





    def    snake_case_       (   self):
         lowercase__					:     List[Any]   = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         lowercase__					:     str   = None
         if self.use_labels:
              lowercase__					:     List[Any]   = ids_tensor([self.batch_size, self.image_size, self.image_size]		,     self.num_labels)

         lowercase__					:     int   = self.get_config()

         return config, pixel_values, labels





    def    snake_case_       (   self):
         return DPTConfig(
             image_size=self.image_size		,     patch_size=self.patch_size		,     num_channels=self.num_channels		,     hidden_size=self.hidden_size		,     num_hidden_layers=self.num_hidden_layers		,     backbone_out_indices=self.backbone_out_indices		,     num_attention_heads=self.num_attention_heads		,     intermediate_size=self.intermediate_size		,     hidden_act=self.hidden_act		,     hidden_dropout_prob=self.hidden_dropout_prob		,     attention_probs_dropout_prob=self.attention_probs_dropout_prob		,     is_decoder=a		,     initializer_range=self.initializer_range		,     is_hybrid=self.is_hybrid		,     )





    def    snake_case_       (   self		,     a		,     a		,     a):
         lowercase__					:     int   = DPTModel(config=a)
         model.to(a)
         model.eval()
         lowercase__					:     str   = model(a)
         self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size))





    def    snake_case_       (   self		,     a		,     a		,     a):
         lowercase__					:     Tuple   = self.num_labels
         lowercase__					:     int   = DPTForDepthEstimation(a)
         model.to(a)
         model.eval()
         lowercase__					:     Dict   = model(a)
         self.parent.assertEqual(result.predicted_depth.shape		,     (self.batch_size, self.image_size, self.image_size))





    def    snake_case_       (   self		,     a		,     a		,     a):
         lowercase__					:     Union[str, Any]   = self.num_labels
         lowercase__					:     Dict   = DPTForSemanticSegmentation(a)
         model.to(a)
         model.eval()
         lowercase__					:     Optional[Any]   = model(a		,     labels=a)
         self.parent.assertEqual(
             result.logits.shape		,     (self.batch_size, self.num_labels, self.image_size, self.image_size))





    def    snake_case_       (   self):
         lowercase__					:     Dict   = self.prepare_config_and_inputs()
         lowercase__   ,				lowercase__   ,				lowercase__					:     Any   = config_and_inputs
         lowercase__					:     Optional[int]   = {'pixel_values': pixel_values}
         return config, inputs_dict



@require_torch
class    SCREAMING_SNAKE_CASE__      (__snake_case	, __snake_case	, unittest.TestCase  ):
    __lowerCamelCase		: int														=  (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    __lowerCamelCase		: int														=  (
        {
            """depth-estimation""": DPTForDepthEstimation,
            """feature-extraction""": DPTModel,
            """image-segmentation""": DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    __lowerCamelCase		: List[str]														=  False
    __lowerCamelCase		: Any														=  False
    __lowerCamelCase		: Any														=  False





    def    snake_case_       (   self):
         lowercase__					:     List[Any]   = DPTModelTester(self)
         lowercase__					:     Dict   = ConfigTester(self		,     config_class=a		,     has_text_modality=a		,     hidden_size=37)





    def    snake_case_       (   self):
         self.config_tester.run_common_tests()





    @unittest.skip(reason='DPT does not use inputs_embeds')
    def    snake_case_       (   self):
         pass





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     Any   = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__					:     List[Any]   = model_class(a)
              self.assertIsInstance(model.get_input_embeddings()		,     (nn.Module))
              lowercase__					:     Any   = model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(a		,     nn.Linear))





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     List[Any]   = self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              lowercase__					:     Tuple   = model_class(a)
              lowercase__					:     Optional[int]   = inspect.signature(model.forward)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowercase__					:     List[Any]   = [*signature.parameters.keys()]

              lowercase__					:     Tuple   = ['pixel_values']
              self.assertListEqual(arg_names[:1]		,     a)





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a)





    def    snake_case_       (   self):
         lowercase__					:     int   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_depth_estimation(*a)





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_semantic_segmentation(*a)





    def    snake_case_       (   self):
         for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                   continue

              lowercase__   ,				lowercase__					:     Tuple   = self.model_tester.prepare_config_and_inputs_for_common()
              lowercase__					:     Optional[Any]   = True

              if model_class in get_values(a):
                   continue

              lowercase__					:     Dict   = model_class(a)
              model.to(a)
              model.train()
              lowercase__					:     str   = self._prepare_for_class(a		,     a		,     return_labels=a)
              lowercase__					:     Tuple   = model(**a).loss
              loss.backward()





    def    snake_case_       (   self):
         for model_class in self.all_model_classes:
              if model_class.__name__ == "DPTForDepthEstimation":
                   continue

              lowercase__   ,				lowercase__					:     int   = self.model_tester.prepare_config_and_inputs_for_common()
              lowercase__					:     Union[str, Any]   = False
              lowercase__					:     List[Any]   = True

              if model_class in get_values(a) or not model_class.supports_gradient_checkpointing:
                   continue
              lowercase__					:     List[Any]   = model_class(a)
              model.to(a)
              model.gradient_checkpointing_enable()
              model.train()
              lowercase__					:     Optional[int]   = self._prepare_for_class(a		,     a		,     return_labels=a)
              lowercase__					:     int   = model(**a).loss
              loss.backward()





    def    snake_case_       (   self):
         lowercase__   ,				lowercase__					:     Tuple   = self.model_tester.prepare_config_and_inputs_for_common()

         lowercase__					:     Optional[int]   = _config_zero_init(a)
         for model_class in self.all_model_classes:
              lowercase__					:     Any   = model_class(config=a)
              # Skip the check for the backbone
              lowercase__					:     Optional[Any]   = []
              for name, module in model.named_modules():
                   if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                        lowercase__					:     str   = [f"""{name}.{key}""" for key in module.state_dict().keys()]
                        break

              for name, param in model.named_parameters():
                   if param.requires_grad:
                        if name in backbone_params:
                             continue
                        self.assertIn(
                            ((param.data.mean() * 1e9).round() / 1e9).item()		,     [0.0, 1.0]		,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,     )





    @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
    def    snake_case_       (   self):
         pass





    @slow
    def    snake_case_       (   self):
         for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              lowercase__					:     Dict   = DPTModel.from_pretrained(a)
              self.assertIsNotNone(a)



def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     Optional[int]   = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
     return image


@require_torch
@require_vision
@slow
class    SCREAMING_SNAKE_CASE__      (unittest.TestCase  ):





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = DPTImageProcessor.from_pretrained('Intel/dpt-large')
         lowercase__					:     Any   = DPTForDepthEstimation.from_pretrained('Intel/dpt-large').to(a)

         lowercase__					:     Optional[int]   = prepare_img()
         lowercase__					:     int   = image_processor(images=a		,     return_tensors='pt').to(a)

         # forward pass
         with torch.no_grad():
              lowercase__					:     Tuple   = model(**a)
              lowercase__					:     Union[str, Any]   = outputs.predicted_depth

         # verify the predicted depth
         lowercase__					:     int   = torch.Size((1, 384, 384))
         self.assertEqual(predicted_depth.shape		,     a)

         lowercase__					:     Dict   = torch.tensor(
             [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]).to(a)

         self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]		,     a		,     atol=1e-4))





    def    snake_case_       (   self):
         lowercase__					:     str   = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
         lowercase__					:     List[Any]   = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(a)

         lowercase__					:     Optional[int]   = prepare_img()
         lowercase__					:     Dict   = image_processor(images=a		,     return_tensors='pt').to(a)

         # forward pass
         with torch.no_grad():
              lowercase__					:     List[str]   = model(**a)

         # verify the logits
         lowercase__					:     Optional[Any]   = torch.Size((1, 150, 480, 480))
         self.assertEqual(outputs.logits.shape		,     a)

         lowercase__					:     Any   = torch.tensor(
             [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]).to(a)

         self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]		,     a		,     atol=1e-4))





    def    snake_case_       (   self):
         lowercase__					:     List[str]   = DPTImageProcessor.from_pretrained('Intel/dpt-large-ade')
         lowercase__					:     Tuple   = DPTForSemanticSegmentation.from_pretrained('Intel/dpt-large-ade').to(a)

         lowercase__					:     str   = prepare_img()
         lowercase__					:     Union[str, Any]   = image_processor(images=a		,     return_tensors='pt').to(a)

         # forward pass
         with torch.no_grad():
              lowercase__					:     List[str]   = model(**a)

         lowercase__					:     List[str]   = outputs.logits.detach().cpu()

         lowercase__					:     Any   = image_processor.post_process_semantic_segmentation(outputs=a		,     target_sizes=[(500, 300)])
         lowercase__					:     Optional[int]   = torch.Size((500, 300))
         self.assertEqual(segmentation[0].shape		,     a)

         lowercase__					:     str   = image_processor.post_process_semantic_segmentation(outputs=a)
         lowercase__					:     str   = torch.Size((480, 480))
         self.assertEqual(segmentation[0].shape		,     a)




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


snake_case_    						=				'''
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.
'''

snake_case_    						=				logging.getLogger(__name__)


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



@dataclass
class    SCREAMING_SNAKE_CASE__      :
    __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=__snake_case	, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."""}	, )



@dataclass
class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
    __lowerCamelCase		: Optional[str]														=  field(
        default=__snake_case	, 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=__snake_case	, metadata={"""help""": """Whether to run training of student model."""}  )
    __lowerCamelCase		: bool														=  field(
        default=__snake_case	, 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    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    def    snake_case_       (   self		,     a		,     a		,     a=False):
         lowercase__					:     Tuple   = inputs['labels']
         lowercase__					:     Any   = model(inputs['input_ids']		,     attention_mask=inputs['attention_mask'])
         lowercase__					:     Optional[int]   = outputs[0]

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

         if return_outputs:
              return loss, outputs

         return loss



def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):



     '''simple docstring'''





     lowercase__					:     Any   = []
     with open(SCREAMING_SNAKE_CASE_			,  'r'    ) as f:
          for line in f:
               lowercase__					:     Dict   = line.strip()
               if len(SCREAMING_SNAKE_CASE_    ) > 0:
                    lines.append(SCREAMING_SNAKE_CASE_    )
     return lines

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Optional[Any]			,  SCREAMING_SNAKE_CASE_	:					Optional[int]			,  SCREAMING_SNAKE_CASE_	:					int    ):



     '''simple docstring'''





     lowercase__					:     Optional[Any]   = []
     lowercase__					:     Tuple   = []
     for example in examples:
          for name in class_names:
               premises.append(SCREAMING_SNAKE_CASE_    )
               hypotheses.append(hypothesis_template.format(SCREAMING_SNAKE_CASE_    )    )
     return premises, hypotheses

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					Tuple    ):



     '''simple docstring'''





     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 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					List[str]			,  SCREAMING_SNAKE_CASE_	:					str			,  SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					float			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  SCREAMING_SNAKE_CASE_	:					bool			,  ):



     '''simple docstring'''





     lowercase__					:     Union[str, Any]   = AutoModelForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Optional[Any]   = model.config
     if not no_cuda and torch.cuda.is_available():
          lowercase__					:     str   = nn.DataParallel(model.cuda()    )
          batch_size *= len(model.device_ids    )
     lowercase__					:     List[str]   = AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_			,  use_fast=SCREAMING_SNAKE_CASE_    )

     lowercase__   ,				lowercase__					:     List[str]   = get_premise_hypothesis_pairs(SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Any   = []

     for i in tqdm(range(0			,  len(SCREAMING_SNAKE_CASE_    )			,  SCREAMING_SNAKE_CASE_    )    ):
          lowercase__					:     Dict   = premises[i : i + batch_size]
          lowercase__					:     Union[str, Any]   = hypotheses[i : i + batch_size]

          lowercase__					:     List[str]   = tokenizer(
              SCREAMING_SNAKE_CASE_			,  SCREAMING_SNAKE_CASE_			,  padding=SCREAMING_SNAKE_CASE_			,  truncation='only_first'			,  return_tensors='pt'			,  )

          with torch.cuda.amp.autocast(enabled=SCREAMING_SNAKE_CASE_    ):
               with torch.no_grad():
                    lowercase__					:     Optional[Any]   = model(**SCREAMING_SNAKE_CASE_    )
          logits.append(outputs.logits.detach().cpu().float()    )

     lowercase__					:     str   = get_entailment_id(SCREAMING_SNAKE_CASE_    )
     lowercase__					:     Optional[Any]   = -1 if entail_id == 0 else 0
     lowercase__					:     int   = torch.cat(SCREAMING_SNAKE_CASE_			,  dim=0    )  # N*K x 3
     lowercase__					:     str   = logits.reshape(len(SCREAMING_SNAKE_CASE_    )			,  len(SCREAMING_SNAKE_CASE_    )			,  -1    )[..., [contr_id, entail_id]]  # N x K x 2

     if multi_label:
          # softmax over (contr, entail) logits for each class independently
          lowercase__					:     Optional[Any]   = (nli_logits / temperature).softmax(-1    )
     else:
          # softmax over entail logits across classes s.t. class probabilities sum to 1.
          lowercase__					:     Optional[Any]   = (nli_logits / temperature).softmax(1    )

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

def 			snake_case__    (					):



     '''simple docstring'''





     lowercase__					:     int   = HfArgumentParser(
         (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)			,  description=SCREAMING_SNAKE_CASE_			,  )

     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.
          lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     List[Any]   = parser.parse_json_file(
              json_file=os.path.abspath(sys.argv[1]    )    )
     else:
          lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:     List[Any]   = parser.parse_args_into_dataclasses()

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

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

     # 4. train student on teacher predictions
     lowercase__					:     Any   = dataset.map(SCREAMING_SNAKE_CASE_			,  input_columns='text'    )
     dataset.set_format('torch'    )

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

     lowercase__					:     int   = DistillationTrainer(
         model=SCREAMING_SNAKE_CASE_			,  tokenizer=SCREAMING_SNAKE_CASE_			,  args=SCREAMING_SNAKE_CASE_			,  train_dataset=SCREAMING_SNAKE_CASE_			,  compute_metrics=SCREAMING_SNAKE_CASE_			,  )

     if training_args.do_train:
          logger.info('Training student model on teacher predictions'    )
          trainer.train()

     if training_args.do_eval:
          lowercase__					:     str   = trainer.evaluate(eval_dataset=SCREAMING_SNAKE_CASE_    )['eval_agreement']
          logger.info(f"""Agreement of student and teacher predictions: {agreement * 100:0.2f}%"""    )

     trainer.save_model()


if __name__ == "__main__":
       main()

