code: 
  
  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)
  
  
style_context: 
  
  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()
  
  