






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)












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			)			)




