


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase      :Dict			    =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :str			    =       ['BridgeTowerImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Union[str, Any]			    =       [
          'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'BridgeTowerForContrastiveLearning',
          'BridgeTowerForImageAndTextRetrieval',
          'BridgeTowerForMaskedLM',
          'BridgeTowerModel',
          'BridgeTowerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_bridgetower import (
          BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BridgeTowerConfig,
          BridgeTowerTextConfig,
          BridgeTowerVisionConfig,
      )
      from .processing_bridgetower import BridgeTowerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_bridgetower import BridgeTowerImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bridgetower import (
                BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
                BridgeTowerForContrastiveLearning,
                BridgeTowerForImageAndTextRetrieval,
                BridgeTowerForMaskedLM,
                BridgeTowerModel,
                BridgeTowerPreTrainedModel,
            )


else:
      import sys

      __UpperCAmelCase      :Optional[int]			    =       _LazyModule(__name__, globals()["__file__"], _import_structure)



'''simple docstring'''



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.layoutlm.modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMModel,
      )



class      a    :


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		str							,			snake_case					:		Dict=13							,			snake_case					:		Optional[Any]=7							,			snake_case					:		Tuple=True							,			snake_case					:		Optional[int]=True							,			snake_case					:		str=True							,			snake_case					:		int=True							,			snake_case					:		List[str]=99							,			snake_case					:		Any=32							,			snake_case					:		List[str]=2							,			snake_case					:		Tuple=4							,			snake_case					:		Union[str, Any]=37							,			snake_case					:		Dict="gelu"							,			snake_case					:		str=0.1							,			snake_case					:		List[Any]=0.1							,			snake_case					:		Any=512							,			snake_case					:		Optional[Any]=16							,			snake_case					:		Optional[int]=2							,			snake_case					:		Union[str, Any]=0.02							,			snake_case					:		List[Any]=3							,			snake_case					:		str=4							,			snake_case					:		int=None							,			snake_case					:		Union[str, Any]=1000							,			)						->							Tuple:
          __UpperCAmelCase     :			int  							=      parent
          __UpperCAmelCase     :			Optional[int]  							=      batch_size
          __UpperCAmelCase     :			Dict  							=      seq_length
          __UpperCAmelCase     :			List[Any]  							=      is_training
          __UpperCAmelCase     :			Optional[Any]  							=      use_input_mask
          __UpperCAmelCase     :			List[Any]  							=      use_token_type_ids
          __UpperCAmelCase     :			str  							=      use_labels
          __UpperCAmelCase     :			Any  							=      vocab_size
          __UpperCAmelCase     :			List[str]  							=      hidden_size
          __UpperCAmelCase     :			Dict  							=      num_hidden_layers
          __UpperCAmelCase     :			Any  							=      num_attention_heads
          __UpperCAmelCase     :			List[str]  							=      intermediate_size
          __UpperCAmelCase     :			str  							=      hidden_act
          __UpperCAmelCase     :			int  							=      hidden_dropout_prob
          __UpperCAmelCase     :			List[Any]  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			List[str]  							=      max_position_embeddings
          __UpperCAmelCase     :			str  							=      type_vocab_size
          __UpperCAmelCase     :			Dict  							=      type_sequence_label_size
          __UpperCAmelCase     :			Any  							=      initializer_range
          __UpperCAmelCase     :			Optional[int]  							=      num_labels
          __UpperCAmelCase     :			Optional[Any]  							=      num_choices
          __UpperCAmelCase     :			List[Any]  							=      scope
          __UpperCAmelCase     :			str  							=      range_bbox

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Union[str, Any]:
          __UpperCAmelCase     :			List[str]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

          # convert bbox to numpy since TF does not support item assignment
          __UpperCAmelCase     :			List[Any]  							=      ids_tensor([self.batch_size, self.seq_length, 4]							,			self.range_bbox      ).numpy()
          # Ensure that bbox is legal
          for i in range(bbox.shape[0]      ):
               for j in range(bbox.shape[1]      ):
                    if bbox[i, j, 3] < bbox[i, j, 1]:
                         __UpperCAmelCase     :			Optional[int]  							=      bbox[i, j, 3]
                         __UpperCAmelCase     :			Any  							=      bbox[i, j, 1]
                         __UpperCAmelCase     :			List[Any]  							=      t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         __UpperCAmelCase     :			str  							=      bbox[i, j, 2]
                         __UpperCAmelCase     :			List[Any]  							=      bbox[i, j, 0]
                         __UpperCAmelCase     :			Dict  							=      t
          __UpperCAmelCase     :			Any  							=      tf.convert_to_tensor(snake_case      )

          __UpperCAmelCase     :			List[Any]  							=      None
          if self.use_input_mask:
               __UpperCAmelCase     :			Tuple  							=      random_attention_mask([self.batch_size, self.seq_length]      )

          __UpperCAmelCase     :			int  							=      None
          if self.use_token_type_ids:
               __UpperCAmelCase     :			Optional[Any]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.type_vocab_size      )

          __UpperCAmelCase     :			Optional[Any]  							=      None
          __UpperCAmelCase     :			Optional[int]  							=      None
          __UpperCAmelCase     :			str  							=      None
          if self.use_labels:
               __UpperCAmelCase     :			Optional[int]  							=      ids_tensor([self.batch_size]							,			self.type_sequence_label_size      )
               __UpperCAmelCase     :			List[str]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels      )
               __UpperCAmelCase     :			str  							=      ids_tensor([self.batch_size]							,			self.num_choices      )

          __UpperCAmelCase     :			Optional[int]  							=      LayoutLMConfig(
              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							,			)

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		int							,			snake_case					:		str							,			snake_case					:		Tuple							,			snake_case					:		List[str]							,			snake_case					:		Any							,			snake_case					:		Any							,			snake_case					:		List[Any]							,			snake_case					:		Any      )						->							Optional[Any]:
          __UpperCAmelCase     :			Tuple  							=      TFLayoutLMModel(config=snake_case      )

          __UpperCAmelCase     :			Optional[Any]  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )
          __UpperCAmelCase     :			Tuple  							=      model(snake_case							,			snake_case							,			token_type_ids=snake_case      )
          __UpperCAmelCase     :			List[Any]  							=      model(snake_case							,			snake_case      )
          self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.seq_length, self.hidden_size)      )
          self.parent.assertEqual(result.pooler_output.shape							,			(self.batch_size, self.hidden_size)      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		Optional[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		str      )						->							int:
          __UpperCAmelCase     :			Any  							=      TFLayoutLMForMaskedLM(config=snake_case      )

          __UpperCAmelCase     :			List[Any]  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)      )

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		Any							,			snake_case					:		Dict							,			snake_case					:		str							,			snake_case					:		Tuple							,			snake_case					:		str							,			snake_case					:		Optional[Any]							,			snake_case					:		str							,			snake_case					:		str      )						->							Any:
          __UpperCAmelCase     :			List[str]  							=      self.num_labels
          __UpperCAmelCase     :			Optional[int]  							=      TFLayoutLMForSequenceClassification(config=snake_case      )

          __UpperCAmelCase     :			Any  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[Any]							,			snake_case					:		Union[str, Any]							,			snake_case					:		Any							,			snake_case					:		Tuple							,			snake_case					:		List[str]      )						->							List[str]:
          __UpperCAmelCase     :			Tuple  							=      self.num_labels
          __UpperCAmelCase     :			Optional[int]  							=      TFLayoutLMForTokenClassification(config=snake_case      )

          __UpperCAmelCase     :			Any  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Dict							,			snake_case					:		int							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Tuple							,			snake_case					:		Union[str, Any]							,			snake_case					:		Dict							,			snake_case					:		Optional[int]      )						->							Dict:
          __UpperCAmelCase     :			int  							=      TFLayoutLMForQuestionAnswering(config=snake_case      )

          __UpperCAmelCase     :			Union[str, Any]  							=      model(snake_case							,			snake_case							,			attention_mask=snake_case							,			token_type_ids=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 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			str  							=      self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Tuple  							=      config_and_inputs
          __UpperCAmelCase     :			Any  							=      {
              '''input_ids''': input_ids,
              '''bbox''': bbox,
              '''token_type_ids''': token_type_ids,
              '''attention_mask''': input_mask,
          }
          return config, inputs_dict



@require_tf
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Dict							       =       (
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       (
         {
             "feature-extraction": TFLayoutLMModel,
             "fill-mask": TFLayoutLMForMaskedLM,
             "text-classification": TFLayoutLMForSequenceClassification,
             "token-classification": TFLayoutLMForTokenClassification,
             "zero-shot": TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     SCREAMING_SNAKE_CASE	:			Dict							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       True
     SCREAMING_SNAKE_CASE	:			List[str]							       =       1_0

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      TFLayoutLMModelTester(self      )
          __UpperCAmelCase     :			Dict  							=      ConfigTester(self							,			config_class=snake_case							,			hidden_size=37      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Dict:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Any:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*snake_case      )

     @slow
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Union[str, Any]:
          for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			int  							=      TFLayoutLMModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )






     @unittest.skip('''Onnx compliancy broke with TF 2.10'''      )
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Dict:
          pass




def      _a     (						):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]  )  # noqa: E231
     __UpperCAmelCase     :			Optional[Any]  							=      tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]  )  # noqa: E231
     __UpperCAmelCase     :			Optional[Any]  							=      tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]  )  # noqa: E231
     __UpperCAmelCase     :			str  							=      tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]  )  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     __UpperCAmelCase     :			Optional[int]  							=      tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]  )  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class      a    (		unittest.TestCase	):


     """simple docstring"""






     @slow
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[int]:
          __UpperCAmelCase     :			int  							=      TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''      )

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase     :			Dict  							=      model(input_ids=snake_case							,			bbox=snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )

          # test the sequence output on [0, :3, :3]
          __UpperCAmelCase     :			Union[str, Any]  							=      tf.convert_to_tensor(
              [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]							,			)

          self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]							,			snake_case							,			atol=1E-3      )      )

          # test the pooled output on [1, :3]
          __UpperCAmelCase     :			str  							=      tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552]      )

          self.assertTrue(np.allclose(outputs.pooler_output[1, :3]							,			snake_case							,			atol=1E-3      )      )

     @slow
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          # initialize model with randomly initialized sequence classification head
          __UpperCAmelCase     :			str  							=      TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,			num_labels=2      )

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase     :			Tuple  							=      model(
              input_ids=snake_case							,			bbox=snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=tf.convert_to_tensor([1, 1]      )							,			)

          # test whether we get a loss as a scalar
          __UpperCAmelCase     :			str  							=      outputs.loss
          __UpperCAmelCase     :			Optional[Any]  							=      (2,)
          self.assertEqual(loss.shape							,			snake_case      )

          # test the shape of the logits
          __UpperCAmelCase     :			List[str]  							=      outputs.logits
          __UpperCAmelCase     :			List[Any]  							=      (2, 2)
          self.assertEqual(logits.shape							,			snake_case      )

     @slow
     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							str:
          # initialize model with randomly initialized token classification head
          __UpperCAmelCase     :			Union[str, Any]  							=      TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,			num_labels=13      )

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase     :			Tuple  							=      model(
              input_ids=snake_case							,			bbox=snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )

          # test the shape of the logits
          __UpperCAmelCase     :			List[Any]  							=      outputs.logits
          __UpperCAmelCase     :			Optional[int]  							=      tf.convert_to_tensor((2, 25, 13)      )
          self.assertEqual(logits.shape							,			snake_case      )






     @slow
     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Union[str, Any]:
          # initialize model with randomly initialized token classification head
          __UpperCAmelCase     :			Dict  							=      TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''      )

          __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Tuple  							=      prepare_layoutlm_batch_inputs()

          # forward pass
          __UpperCAmelCase     :			Optional[Any]  							=      model(input_ids=snake_case							,			bbox=snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )

          # test the shape of the logits
          __UpperCAmelCase     :			Union[str, Any]  							=      tf.convert_to_tensor((2, 25)      )
          self.assertEqual(outputs.start_logits.shape							,			snake_case      )
          self.assertEqual(outputs.end_logits.shape							,			snake_case      )