code: 
  
  
  
  
  
  
  import getopt
  import json
  import os
  
  # import numpy as np
  import sys
  from collections import OrderedDict
  
  import datasets
  import numpy as np
  import torch
  from modeling_frcnn import GeneralizedRCNN
  from processing_image import Preprocess
  
  from utils import Config
  __A  							=  False
  __A  							=  Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
  __A  							=  datasets.Features(
      OrderedDict(
          {
              "attr_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "attr_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "boxes": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="float32"),
              "img_id": datasets.Value("int32"),
              "obj_ids": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "obj_probs": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("float32")),
              "roi_features": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 20_48), dtype="float32"),
              "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
              "preds_per_image": datasets.Value(dtype="int32"),
          }
      )
  )
  
  
  
  class 							_A :
      """simple docstring"""
  
      def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int=sys.argv[1:]			)			->       Tuple:
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=1
             __UpperCAmelCase							,   __UpperCAmelCase    							=getopt.getopt(__SCREAMING_SNAKE_CASE   ,    """i:o:b:s"""   ,    ["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]			)
             for opt, arg in opts:
                    if opt in ("-i", "--inputdir"):
                           __UpperCAmelCase    							=arg
                    elif opt in ("-o", "--outfile"):
                           __UpperCAmelCase    							=arg
                    elif opt in ("-b", "--batch_size"):
                           __UpperCAmelCase    							=int(__SCREAMING_SNAKE_CASE			)
                    elif opt in ("-s", "--subset_list"):
                           __UpperCAmelCase    							=arg
  
             assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
             assert outputfile is not None and not os.path.isfile(__SCREAMING_SNAKE_CASE			), f'''{outputfile}'''
             if subset_list is not None:
                    with open(os.path.realpath(__SCREAMING_SNAKE_CASE			)			) as f:
                           __UpperCAmelCase    							={self._vqa_file_split()[0] for x in tryload(__SCREAMING_SNAKE_CASE			)}
             else:
                    __UpperCAmelCase    							=None
  
             __UpperCAmelCase    							=CONFIG
             if torch.cuda.is_available():
                    __UpperCAmelCase    							="""cuda"""
             __UpperCAmelCase    							=os.path.realpath(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=os.path.realpath(__SCREAMING_SNAKE_CASE			)
             __UpperCAmelCase    							=Preprocess(self.config			)
             __UpperCAmelCase    							=GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""   ,    config=self.config			)
             __UpperCAmelCase    							=batch_size if batch_size != 0 else 1
             __UpperCAmelCase    							=DEFAULT_SCHEMA
  
  
  
      def _a       (							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]			)			->       Any:
             __UpperCAmelCase    							=int(file.split("""."""			)[0].split("""_"""			)[-1]			)
             __UpperCAmelCase    							=os.path.join(self.inputdir   ,    __SCREAMING_SNAKE_CASE			)
             return (img_id, filepath)
  
  
  
      @property
      def _a       (							self			:    Tuple			)			->       Dict:
             __UpperCAmelCase    							=[]
             for i, file in enumerate(os.listdir(self.inputdir			)			):
                    if self.subset_list is not None and i not in self.subset_list:
                           continue
                    batch.append(self._vqa_file_split(__SCREAMING_SNAKE_CASE			)			)
                    if len(__SCREAMING_SNAKE_CASE			) == self.batch:
                           __UpperCAmelCase    							=batch
                           __UpperCAmelCase    							=[]
                           yield list(map(__SCREAMING_SNAKE_CASE   ,    zip(*__SCREAMING_SNAKE_CASE			)			)			)
  
             for i in range(1			):
                    yield list(map(__SCREAMING_SNAKE_CASE   ,    zip(*__SCREAMING_SNAKE_CASE			)			)			)
  
  
  
      def __call__(							self			:    str			)			->       Optional[int]:
             # make writer
             if not TEST:
                    __UpperCAmelCase    							=datasets.ArrowWriter(features=self.schema   ,    path=self.outputfile			)
             # do file generator
             for i, (img_ids, filepaths) in enumerate(self.file_generator			):
                    __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase    							=self.preprocess(__SCREAMING_SNAKE_CASE			)
                    __UpperCAmelCase    							=self.model(
                        __SCREAMING_SNAKE_CASE   ,    __SCREAMING_SNAKE_CASE   ,    scales_yx=__SCREAMING_SNAKE_CASE   ,    padding="""max_detections"""   ,    max_detections=self.config.MAX_DETECTIONS   ,    pad_value=0   ,    return_tensors="""np"""   ,    location="""cpu"""   ,    )
                    __UpperCAmelCase    							=output_dict.pop("""normalized_boxes"""			)
                    if not TEST:
                           __UpperCAmelCase    							=np.array(__SCREAMING_SNAKE_CASE			)
                           __UpperCAmelCase    							=self.schema.encode_batch(__SCREAMING_SNAKE_CASE			)
                           writer.write_batch(__SCREAMING_SNAKE_CASE			)
                    if TEST:
                           break
                    # finalizer the writer
             if not TEST:
                    __UpperCAmelCase							,   __UpperCAmelCase    							=writer.finalize()
                    print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''			)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Optional[int]		)  ->							List[str]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         try:
                __UpperCAmelCase    							=json.load(A_		)
                try:
                       __UpperCAmelCase    							=list(data.keys()		)
                except Exception:
                       __UpperCAmelCase    							=[d["""img_id"""] for d in data]
         except Exception:
                try:
                       __UpperCAmelCase    							=eval(stream.read()		)
                except Exception:
                       __UpperCAmelCase    							=stream.read().split("""\n"""		)
         return data
  
  
  if __name__ == "__main__":
     __A  							=  Extract(sys.argv[1:])
     extract()
     if not TEST:
        __A  							=  datasets.Dataset.from_file(extract.outputfile)
        # wala!
        # print(np.array(dataset[0:2]["roi_features"]).shape)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  import unittest
  
  from transformers import ErnieMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
     import torch
  
     from transformers import (
         ErnieMForInformationExtraction,
         ErnieMForMultipleChoice,
         ErnieMForQuestionAnswering,
         ErnieMForSequenceClassification,
         ErnieMForTokenClassification,
         ErnieMModel,
     )
     from transformers.models.ernie_m.modeling_ernie_m import ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  class 							_A :
      """simple docstring"""
  
      def __init__(							self			:    Any   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    List[str]=13   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]=7   ,    __SCREAMING_SNAKE_CASE			:    List[Any]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=True   ,    __SCREAMING_SNAKE_CASE			:    str=99   ,    __SCREAMING_SNAKE_CASE			:    int=32   ,    __SCREAMING_SNAKE_CASE			:    str=5   ,    __SCREAMING_SNAKE_CASE			:    int=4   ,    __SCREAMING_SNAKE_CASE			:    List[str]=37   ,    __SCREAMING_SNAKE_CASE			:    int="gelu"   ,    __SCREAMING_SNAKE_CASE			:    Tuple=0.1   ,    __SCREAMING_SNAKE_CASE			:    List[str]=0.1   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]=512   ,    __SCREAMING_SNAKE_CASE			:    int=16   ,    __SCREAMING_SNAKE_CASE			:    int=2   ,    __SCREAMING_SNAKE_CASE			:    Any=0.02   ,    __SCREAMING_SNAKE_CASE			:    Dict=3   ,    __SCREAMING_SNAKE_CASE			:    Dict=4   ,    __SCREAMING_SNAKE_CASE			:    str=None   ,    )			->       Optional[Any]:
             __UpperCAmelCase    							=parent
             __UpperCAmelCase    							=batch_size
             __UpperCAmelCase    							=seq_length
             __UpperCAmelCase    							=is_training
             __UpperCAmelCase    							=use_input_mask
             __UpperCAmelCase    							=use_labels
             __UpperCAmelCase    							=vocab_size
             __UpperCAmelCase    							=hidden_size
             __UpperCAmelCase    							=num_hidden_layers
             __UpperCAmelCase    							=num_attention_heads
             __UpperCAmelCase    							=intermediate_size
             __UpperCAmelCase    							=hidden_act
             __UpperCAmelCase    							=hidden_dropout_prob
             __UpperCAmelCase    							=attention_probs_dropout_prob
             __UpperCAmelCase    							=max_position_embeddings
             __UpperCAmelCase    							=type_vocab_size
             __UpperCAmelCase    							=type_sequence_label_size
             __UpperCAmelCase    							=initializer_range
             __UpperCAmelCase    							=num_labels
             __UpperCAmelCase    							=num_choices
             __UpperCAmelCase    							=scope
  
  
  
      def _a       (							self			:    str			)			->       Optional[Any]:
             __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)
  
             __UpperCAmelCase    							=None
             if self.use_input_mask:
                    __UpperCAmelCase    							=random_attention_mask([self.batch_size, self.seq_length]			)
  
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
             if self.use_labels:
                    __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.type_sequence_label_size			)
                    __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.num_labels			)
                    __UpperCAmelCase    							=ids_tensor([self.batch_size]   ,    self.num_choices			)
  
             __UpperCAmelCase    							=self.get_config()
  
             return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
      def _a       (							self			:    Optional[Any]			)			->       Any:
             __UpperCAmelCase    							=ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size			)
  
             __UpperCAmelCase    							=None
             if self.use_input_mask:
                    __UpperCAmelCase    							=random_attention_mask([self.batch_size, self.seq_length]			)
             __UpperCAmelCase    							=self.get_config()
  
             return config, input_ids, input_mask
  
  
  
      def _a       (							self			:    List[str]			)			->       List[str]:
             return ErnieMConfig(
                 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   ,    )
  
  
  
      def _a       (							self			:    str   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    str   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Optional[int]:
             __UpperCAmelCase    							=ErnieMModel(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    return_dict=__SCREAMING_SNAKE_CASE			)
             self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
      def _a       (							self			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       int:
             __UpperCAmelCase    							=ErnieMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=model(
                 __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_SNAKE_CASE   ,    )
             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 _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       Any:
             __UpperCAmelCase    							=ErnieMForInformationExtraction(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=torch.ones_like(__SCREAMING_SNAKE_CASE   ,    dtype=torch.floataa			)
             __UpperCAmelCase    							=model(
                 __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    start_positions=__SCREAMING_SNAKE_CASE   ,    end_positions=__SCREAMING_SNAKE_CASE   ,    )
             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 _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Any   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       str:
             __UpperCAmelCase    							=self.num_labels
             __UpperCAmelCase    							=ErnieMForSequenceClassification(__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)			)
  
  
  
      def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Dict			)			->       Dict:
             __UpperCAmelCase    							=self.num_labels
             __UpperCAmelCase    							=ErnieMForTokenClassification(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             input_ids.to(__SCREAMING_SNAKE_CASE			)
             input_mask.to(__SCREAMING_SNAKE_CASE			)
             token_labels.to(__SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE			)
  
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.num_labels)			)
  
  
  
      def _a       (							self			:    Optional[Any]   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Optional[int]   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    Tuple   ,    __SCREAMING_SNAKE_CASE			:    Optional[Any]			)			->       List[Any]:
             __UpperCAmelCase    							=self.num_choices
             __UpperCAmelCase    							=ErnieMForMultipleChoice(config=__SCREAMING_SNAKE_CASE			)
             model.to(__SCREAMING_SNAKE_CASE			)
             model.eval()
             __UpperCAmelCase    							=input_ids.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
             __UpperCAmelCase    							=input_mask.unsqueeze(1			).expand(-1   ,    self.num_choices   ,    -1			).contiguous()
             __UpperCAmelCase    							=model(
                 __SCREAMING_SNAKE_CASE   ,    attention_mask=__SCREAMING_SNAKE_CASE   ,    labels=__SCREAMING_SNAKE_CASE   ,    )
             self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_choices)			)
  
  
  
      def _a       (							self			:    Any			)			->       List[str]:
             __UpperCAmelCase    							=self.prepare_config_and_inputs()
             (
                 (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   (
                 __UpperCAmelCase
             )							,   
             )    							=config_and_inputs
             __UpperCAmelCase    							={"""input_ids""": input_ids, """attention_mask""": input_mask}
             return config, inputs_dict
  
  
  
  
  @require_torch
  class 							_A (    UpperCamelCase				,				UpperCamelCase				,				unittest.TestCase							):
      """simple docstring"""
  
      lowerCamelCase  :       Union[str, Any]   =   (
          (
              ErnieMModel,
              ErnieMForMultipleChoice,
              ErnieMForQuestionAnswering,
              ErnieMForSequenceClassification,
              ErnieMForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      lowerCamelCase  :       Optional[Any]   =   ()
      lowerCamelCase  :       str   =   (
          {
              'feature-extraction': ErnieMModel,
              'question-answering': ErnieMForQuestionAnswering,
              'text-classification': ErnieMForSequenceClassification,
              'token-classification': ErnieMForTokenClassification,
              'zero-shot': ErnieMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      lowerCamelCase  :       Optional[int]   =   False
  
  
  
      def _a       (							self			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Dict   ,    __SCREAMING_SNAKE_CASE			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    List[Any]   ,    __SCREAMING_SNAKE_CASE			:    int   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       Optional[int]:
             if pipeline_test_casse_name == "QAPipelineTests":
                    return True
  
             return False
  
  
  
      def _a       (							self			:    Optional[int]			)			->       List[Any]:
             __UpperCAmelCase    							=ErnieMModelTester(self			)
             __UpperCAmelCase    							=ConfigTester(self   ,    config_class=__SCREAMING_SNAKE_CASE   ,    hidden_size=37			)
  
  
  
      def _a       (							self			:    int			)			->       List[str]:
             self.config_tester.run_common_tests()
  
  
  
      def _a       (							self			:    str			)			->       str:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       List[str]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    __UpperCAmelCase    							=type
                    self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Optional[int]			)			->       str:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[str]			)			->       Union[str, Any]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    Any			)			->       str:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    str			)			->       List[Any]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_information_extraction(*__SCREAMING_SNAKE_CASE			)
  
  
  
      def _a       (							self			:    List[Any]			)			->       List[str]:
             __UpperCAmelCase    							=self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE			)
  
  
  
      @slow
      def _a       (							self			:    List[str]			)			->       Optional[Any]:
             for model_name in ERNIE_M_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    __UpperCAmelCase    							=ErnieMModel.from_pretrained(__SCREAMING_SNAKE_CASE			)
                    self.assertIsNotNone(__SCREAMING_SNAKE_CASE			)
  
  
  
  
  @require_torch
  class 							_A (    unittest.TestCase							):
      """simple docstring"""
  
      @slow
      def _a       (							self			:    Tuple			)			->       Optional[Any]:
             __UpperCAmelCase    							=ErnieMModel.from_pretrained("""susnato/ernie-m-base_pytorch"""			)
             model.eval()
             __UpperCAmelCase    							=torch.tensor([[0, 1, 2, 3, 4, 5]]			)
             __UpperCAmelCase    							=model(__SCREAMING_SNAKE_CASE			)[0]
  
             # TODO Replace vocab size
             __UpperCAmelCase    							=768
  
             __UpperCAmelCase    							=torch.Size((1, 6, hidden_size)			)
             self.assertEqual(output.shape   ,    __SCREAMING_SNAKE_CASE			)
  
             __UpperCAmelCase    							=torch.tensor(
                 [[[-0.0_012, 0.1_245, -0.0_214], [-0.0_742, 0.0_244, -0.0_771], [-0.0_333, 0.1_164, -0.1_554]]]			)
  
             self.assertTrue(torch.allclose(output[:, :3, :3]   ,    __SCREAMING_SNAKE_CASE   ,    atol=1e-3			)			)
  
  
  
  
  