


'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a_   :      Tuple				    =      logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")

a_   :      Any				    =      list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
a_   :      Optional[Any]				    =      tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






def 		_A			(lowerCAmelCase__						)				->					List[Any]:




     '''simple docstring'''



     with open(snake_case_			,  'rb'						) as f:
          _a      =							Image.open(snake_case_						)
          return im.convert('RGB'						)




@dataclass
class 	a       :
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={
         """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
     }					,			)
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}    )
 _lowerCAmelCase	   =		field(default=_lowerCAmelCase					,			metadata={"""help""": """A folder containing the training data."""}    )
 _lowerCAmelCase	   =		field(default=_lowerCAmelCase					,			metadata={"""help""": """A folder containing the validation data."""}    )
 _lowerCAmelCase	   =		field(
     default=0.15					,			metadata={"""help""": """Percent to split off of train for validation."""}    )
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of training examples to this """
             """value if set."""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={
         """help""": (
             """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
             """value if set."""
         )
     }					,			)
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
           raise ValueError(
               'You must specify either a dataset name from the hub or a train and/or validation directory.'  )





@dataclass
class 	a       :
 _lowerCAmelCase	   =		field(
     default="""google/vit-base-patch16-224-in21k"""					,			metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}					,			)
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_lowerCAmelCase    )}					,			)
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}    )
 _lowerCAmelCase	   =		field(
     default="""main"""					,			metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}					,			)
 _lowerCAmelCase	   =		field(default=_lowerCAmelCase					,			metadata={"""help""": """Name or path of preprocessor config."""}    )
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={
         """help""": (
             """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
             """with private models)."""
         )
     }					,			)
 _lowerCAmelCase	   =		field(
     default=_lowerCAmelCase					,			metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}					,			)






def 		_A			(lowerCAmelCase__						)				->					List[str]:




     '''simple docstring'''



     _a      =							torch.stack([example['pixel_values'] for example in examples]						)
     _a      =							torch.tensor([example['labels'] for example in examples]						)
     return {"pixel_values": pixel_values, "labels": labels}






def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     _a      =							HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)						)
     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      =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
     else:
          _a ,				_a ,				_a      =							parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('run_image_classification'			,  snake_case_			,  snake_case_						)

     # 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						)]			,  )

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     _a      =							training_args.get_process_log_level()
     logger.setLevel(snake_case_						)
     transformers.utils.logging.set_verbosity(snake_case_						)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # 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}'						)
     logger.info(f'Training/evaluation parameters {training_args}'						)

     # Detecting last checkpoint.
     _a      =							None
     if os.path.isdir(training_args.output_dir						) and training_args.do_train and not training_args.overwrite_output_dir:
          _a      =							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 and training_args.resume_from_checkpoint is 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.'						)

    # Set seed before initializing model.
     set_seed(training_args.seed						)

     # Initialize our dataset and prepare it for the 'image-classification' task.
     if data_args.dataset_name is not None:
          _a      =							load_dataset(
              data_args.dataset_name			,  data_args.dataset_config_name			,  cache_dir=model_args.cache_dir			,  task='image-classification'			,  use_auth_token=True if model_args.use_auth_token else None			,  )
     else:
          _a      =							{}
          if data_args.train_dir is not None:
               _a      =							os.path.join(data_args.train_dir			,  '**'						)
          if data_args.validation_dir is not None:
               _a      =							os.path.join(data_args.validation_dir			,  '**'						)
          _a      =							load_dataset(
              'imagefolder'			,  data_files=snake_case_			,  cache_dir=model_args.cache_dir			,  task='image-classification'			,  )

     # If we don't have a validation split, split off a percentage of train as validation.
     _a      =							None if 'validation' in dataset.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split			,  snake_case_						) and data_args.train_val_split > 0.0:
          _a      =							dataset['train'].train_test_split(data_args.train_val_split						)
          _a      =							split['train']
          _a      =							split['test']

     # Prepare label mappings.
     # We'll include these in the model's config to get human readable labels in the Inference API.
     _a      =							dataset['train'].features['labels'].names
     _a ,				_a      =							{}, {}
     for i, label in enumerate(snake_case_						):
          _a      =							str(snake_case_						)
          _a      =							label

     # Load the accuracy metric from the datasets package
     _a      =							evaluate.load('accuracy'						)

     # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
     # predictions and label_ids field) and has to return a dictionary string to float.
     def compute_metrics(lowerCAmelCase__						):
          return metric.compute(predictions=np.argmax(p.predictions			,  axis=1						)			,  references=p.label_ids						)

     _a      =							AutoConfig.from_pretrained(
         model_args.config_name or model_args.model_name_or_path			,  num_labels=len(snake_case_						)			,  labelaid=snake_case_			,  idalabel=snake_case_			,  finetuning_task='image-classification'			,  cache_dir=model_args.cache_dir			,  revision=model_args.model_revision			,  use_auth_token=True if model_args.use_auth_token else None			,  )
     _a      =							AutoModelForImageClassification.from_pretrained(
         model_args.model_name_or_path			,  from_tf=bool('.ckpt' in model_args.model_name_or_path						)			,  config=snake_case_			,  cache_dir=model_args.cache_dir			,  revision=model_args.model_revision			,  use_auth_token=True if model_args.use_auth_token else None			,  ignore_mismatched_sizes=model_args.ignore_mismatched_sizes			,  )
     _a      =							AutoImageProcessor.from_pretrained(
         model_args.image_processor_name or model_args.model_name_or_path			,  cache_dir=model_args.cache_dir			,  revision=model_args.model_revision			,  use_auth_token=True if model_args.use_auth_token else None			,  )

     # Define torchvision transforms to be applied to each image.
     if "shortest_edge" in image_processor.size:
          _a      =							image_processor.size['shortest_edge']
     else:
          _a      =							(image_processor.size['height'], image_processor.size['width'])
     _a      =							Normalize(mean=image_processor.image_mean			,  std=image_processor.image_std						)
     _a      =							Compose(
         [
             RandomResizedCrop(snake_case_						),
             RandomHorizontalFlip(),
             ToTensor(),
             normalize,
         ]						)
     _a      =							Compose(
         [
             Resize(snake_case_						),
             CenterCrop(snake_case_						),
             ToTensor(),
             normalize,
         ]						)

     def train_transforms(lowerCAmelCase__						):
          _a      =							[
              _train_transforms(pil_img.convert('RGB'						)						) for pil_img in example_batch['image']
          ]
          return example_batch

     def val_transforms(lowerCAmelCase__						):
          _a      =							[_val_transforms(pil_img.convert('RGB'						)						) for pil_img in example_batch['image']]
          return example_batch

     if training_args.do_train:
          if "train" not in dataset:
               raise ValueError('--do_train requires a train dataset'						)
          if data_args.max_train_samples is not None:
               _a      =							(
                   dataset['train'].shuffle(seed=training_args.seed						).select(range(data_args.max_train_samples						)						)
               )
          # Set the training transforms
          dataset["train"].set_transform(snake_case_						)

     if training_args.do_eval:
          if "validation" not in dataset:
               raise ValueError('--do_eval requires a validation dataset'						)
          if data_args.max_eval_samples is not None:
               _a      =							(
                   dataset['validation'].shuffle(seed=training_args.seed						).select(range(data_args.max_eval_samples						)						)
               )
          # Set the validation transforms
          dataset["validation"].set_transform(snake_case_						)

     # Initalize our trainer
     _a      =							Trainer(
         model=snake_case_			,  args=snake_case_			,  train_dataset=dataset['train'] if training_args.do_train else None			,  eval_dataset=dataset['validation'] if training_args.do_eval else None			,  compute_metrics=snake_case_			,  tokenizer=snake_case_			,  data_collator=snake_case_			,  )

     # Training
     if training_args.do_train:
          _a      =							None
          if training_args.resume_from_checkpoint is not None:
               _a      =							training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               _a      =							last_checkpoint
          _a      =							trainer.train(resume_from_checkpoint=snake_case_						)
          trainer.save_model()
          trainer.log_metrics('train'			,  train_result.metrics						)
          trainer.save_metrics('train'			,  train_result.metrics						)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          _a      =							trainer.evaluate()
          trainer.log_metrics('eval'			,  snake_case_						)
          trainer.save_metrics('eval'			,  snake_case_						)

     # Write model card and (optionally) push to hub
     _a      =							{
         'finetuned_from': model_args.model_name_or_path,
         'tasks': 'image-classification',
         'dataset': data_args.dataset_name,
         'tags': ['image-classification', 'vision'],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**snake_case_						)
     else:
          trainer.create_model_card(**snake_case_						)


if __name__ == "__main__":
 main()





'''simple docstring'''






import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__=13 ,			__magic_name__=7 ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=True ,			__magic_name__=99 ,			__magic_name__=32 ,			__magic_name__=5 ,			__magic_name__=4 ,			__magic_name__=37 ,			__magic_name__="gelu" ,			__magic_name__=0.1 ,			__magic_name__=0.1 ,			__magic_name__=5_12 ,			__magic_name__=16 ,			__magic_name__=2 ,			__magic_name__=0.0_2 ,			__magic_name__=False ,			__magic_name__=True ,			__magic_name__="None" ,			__magic_name__=3 ,			__magic_name__=4 ,			__magic_name__=None ,			)   ->							Any:
      _a      =							parent
      _a      =							batch_size
      _a      =							seq_length
      _a      =							is_training
      _a      =							use_input_mask
      _a      =							use_token_type_ids
      _a      =							use_labels
      _a      =							vocab_size
      _a      =							hidden_size
      _a      =							num_hidden_layers
      _a      =							num_attention_heads
      _a      =							intermediate_size
      _a      =							hidden_act
      _a      =							hidden_dropout_prob
      _a      =							attention_probs_dropout_prob
      _a      =							max_position_embeddings
      _a      =							type_vocab_size
      _a      =							type_sequence_label_size
      _a      =							initializer_range
      _a      =							num_labels
      _a      =							num_choices
      _a      =							relative_attention
      _a      =							position_biased_input
      _a      =							pos_att_type
      _a      =							scope
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size  )

      _a      =							None
      if self.use_input_mask:
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			vocab_size=2  )

      _a      =							None
      if self.use_token_type_ids:
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size  )

      _a      =							None
      _a      =							None
      _a      =							None
      if self.use_labels:
           _a      =							ids_tensor([self.batch_size] ,			self.type_sequence_label_size  )
           _a      =							ids_tensor([self.batch_size, self.seq_length] ,			self.num_labels  )
           _a      =							ids_tensor([self.batch_size] ,			self.num_choices  )

      _a      =							self.get_config()

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return DebertaConfig(
          vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			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 ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			initializer_range=self.initializer_range ,			relative_attention=self.relative_attention ,			position_biased_input=self.position_biased_input ,			pos_att_type=self.pos_att_type ,			)
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      _a      =							self.get_config()
      _a      =							3_00
      return config
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Dict:
      self.parent.assertListEqual(list(result.loss.size()  ) ,			[]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaModel(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__ ,			token_type_ids=__magic_name__  )[0]
      _a      =							model(__magic_name__  )[0]

      self.parent.assertListEqual(list(sequence_output.size()  ) ,			[self.batch_size, self.seq_length, self.hidden_size]  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							DebertaForMaskedLM(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )

      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							self.num_labels
      _a      =							DebertaForSequenceClassification(__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertListEqual(list(result.logits.size()  ) ,			[self.batch_size, self.num_labels]  )
      self.check_loss_output(__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Dict:
      _a      =							self.num_labels
      _a      =							DebertaForTokenClassification(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(__magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			labels=__magic_name__  )
      self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Optional[int]:
      _a      =							DebertaForQuestionAnswering(config=__magic_name__  )
      model.to(__magic_name__  )
      model.eval()
      _a      =							model(
          __magic_name__ ,			attention_mask=__magic_name__ ,			token_type_ids=__magic_name__ ,			start_positions=__magic_name__ ,			end_positions=__magic_name__ ,			)
      self.parent.assertEqual(result.start_logits.shape ,			(self.batch_size, self.seq_length)  )
      self.parent.assertEqual(result.end_logits.shape ,			(self.batch_size, self.seq_length)  )




 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.prepare_config_and_inputs()
      (
          (
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				(
          _a
      ) ,				
      )      =							config_and_inputs
      _a      =							{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict





@require_torch
class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		(
     (
         DebertaModel,
         DebertaForMaskedLM,
         DebertaForSequenceClassification,
         DebertaForTokenClassification,
         DebertaForQuestionAnswering,
     )
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase	   =		(
     {
         """feature-extraction""": DebertaModel,
         """fill-mask""": DebertaForMaskedLM,
         """question-answering""": DebertaForQuestionAnswering,
         """text-classification""": DebertaForSequenceClassification,
         """token-classification""": DebertaForTokenClassification,
         """zero-shot""": DebertaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 _lowerCAmelCase	   =		False
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      _a      =							DebertaModelTester(self  )
      _a      =							ConfigTester(self ,			config_class=__magic_name__ ,			hidden_size=37  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_model(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_sequence_classification(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_masked_lm(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_question_answering(*__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      _a      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_deberta_for_token_classification(*__magic_name__  )




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							Optional[Any]:
      for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a      =							DebertaModel.from_pretrained(__magic_name__  )
           self.assertIsNotNone(__magic_name__  )





@require_torch
@require_sentencepiece
@require_tokenizers
class 	a       (       unittest.TestCase    ):
 @unittest.skip(reason='Model not available yet'  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      pass




 @slow
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							DebertaModel.from_pretrained('microsoft/deberta-base'  )

      _a      =							torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
      _a      =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
      with torch.no_grad():
           _a      =							model(__magic_name__ ,			attention_mask=__magic_name__  )[0]
      # compare the actual values for a slice.
      _a      =							torch.tensor(
          [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]  )
      self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,			__magic_name__ ,			atol=1e-4  ) ,			f'{output[:, 1:4, 1:4]}'  )

