

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    : List[Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Tuple=1_3				,				UpperCAmelCase__    : str=7				,				UpperCAmelCase__    : Union[str, Any]=True				,				UpperCAmelCase__    : int=True				,				UpperCAmelCase__    : Optional[Any]=True				,				UpperCAmelCase__    : Any=True				,				UpperCAmelCase__    : Tuple=9_9				,				UpperCAmelCase__    : str=3_2				,				UpperCAmelCase__    : Tuple=2				,				UpperCAmelCase__    : Any=4				,				UpperCAmelCase__    : List[str]=3_7				,				UpperCAmelCase__    : Optional[Any]="gelu"				,				UpperCAmelCase__    : List[Any]=0.1				,				UpperCAmelCase__    : Any=0.1				,				UpperCAmelCase__    : Tuple=5_1_2				,				UpperCAmelCase__    : Union[str, Any]=1_6				,				UpperCAmelCase__    : Optional[int]=2				,				UpperCAmelCase__    : int=0.02				,				UpperCAmelCase__    : List[str]=3				,				UpperCAmelCase__    : List[str]=4				,				UpperCAmelCase__    : List[Any]=None				,				UpperCAmelCase__    : List[str]=1_0_0_0				,				)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   parent
        SCREAMING_SNAKE_CASE :      Dict       =   batch_size
        SCREAMING_SNAKE_CASE :      Dict       =   seq_length
        SCREAMING_SNAKE_CASE :      int       =   is_training
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   use_input_mask
        SCREAMING_SNAKE_CASE :      Any       =   use_token_type_ids
        SCREAMING_SNAKE_CASE :      Optional[int]       =   use_labels
        SCREAMING_SNAKE_CASE :      Any       =   vocab_size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   hidden_size
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   num_hidden_layers
        SCREAMING_SNAKE_CASE :      Optional[int]       =   num_attention_heads
        SCREAMING_SNAKE_CASE :      Any       =   intermediate_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_act
        SCREAMING_SNAKE_CASE :      List[Any]       =   hidden_dropout_prob
        SCREAMING_SNAKE_CASE :      List[Any]       =   attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE :      str       =   max_position_embeddings
        SCREAMING_SNAKE_CASE :      List[Any]       =   type_vocab_size
        SCREAMING_SNAKE_CASE :      str       =   type_sequence_label_size
        SCREAMING_SNAKE_CASE :      List[Any]       =   initializer_range
        SCREAMING_SNAKE_CASE :      Any       =   num_labels
        SCREAMING_SNAKE_CASE :      List[str]       =   num_choices
        SCREAMING_SNAKE_CASE :      Dict       =   scope
        SCREAMING_SNAKE_CASE :      List[Any]       =   range_bbox


  def  _lowercase      (			self    : List[str]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   ids_tensor([self.batch_size, self.seq_length]				,				self.vocab_size		)

        # convert bbox to numpy since TF does not support item assignment
        SCREAMING_SNAKE_CASE :      Union[str, 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]:
                          SCREAMING_SNAKE_CASE :      Union[str, Any]       =   bbox[i, j, 3]
                          SCREAMING_SNAKE_CASE :      Union[str, Any]       =   bbox[i, j, 1]
                          SCREAMING_SNAKE_CASE :      int       =   t
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                          SCREAMING_SNAKE_CASE :      int       =   bbox[i, j, 2]
                          SCREAMING_SNAKE_CASE :      List[Any]       =   bbox[i, j, 0]
                          SCREAMING_SNAKE_CASE :      Dict       =   t
        SCREAMING_SNAKE_CASE :      List[str]       =   tf.convert_to_tensor(UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   None
        if self.use_input_mask:
              SCREAMING_SNAKE_CASE :      str       =   random_attention_mask([self.batch_size, self.seq_length]		)

        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   None
        if self.use_token_type_ids:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ids_tensor([self.batch_size, self.seq_length]				,				self.type_vocab_size		)

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

        SCREAMING_SNAKE_CASE :      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  _lowercase      (			self    : Union[str, Any]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Union[str, Any]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   TFLayoutLMModel(config=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   model(UpperCAmelCase__				,				UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      List[str]       =   model(UpperCAmelCase__				,				UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      Tuple       =   model(UpperCAmelCase__				,				UpperCAmelCase__		)
        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  _lowercase      (			self    : Any				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : Optional[int]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Tuple		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   TFLayoutLMForMaskedLM(config=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Any       =   model(UpperCAmelCase__				,				UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__				,				labels=UpperCAmelCase__		)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.vocab_size)		)


  def  _lowercase      (			self    : Any				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Optional[Any]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : Union[str, Any]		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.num_labels
        SCREAMING_SNAKE_CASE :      List[Any]       =   TFLayoutLMForSequenceClassification(config=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      str       =   model(UpperCAmelCase__				,				UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__		)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)		)


  def  _lowercase      (			self    : str				,				UpperCAmelCase__    : Dict				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Optional[Any]		)					->List[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self.num_labels
        SCREAMING_SNAKE_CASE :      str       =   TFLayoutLMForTokenClassification(config=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Dict       =   model(UpperCAmelCase__				,				UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__				,				labels=UpperCAmelCase__		)
        self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.seq_length, self.num_labels)		)


  def  _lowercase      (			self    : Any				,				UpperCAmelCase__    : List[Any]				,				UpperCAmelCase__    : List[str]				,				UpperCAmelCase__    : Tuple				,				UpperCAmelCase__    : int				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Union[str, Any]				,				UpperCAmelCase__    : Optional[int]		)					->int:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   TFLayoutLMForQuestionAnswering(config=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[str]       =   model(UpperCAmelCase__				,				UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__		)
        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  _lowercase      (			self    : Union[str, Any]		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self.prepare_config_and_inputs()
        (
            (
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	(
            SCREAMING_SNAKE_CASE
        )     ,	
        ) :      int       =   config_and_inputs
        SCREAMING_SNAKE_CASE :      Optional[int]       =   {
            """input_ids""": input_ids,
            """bbox""": bbox,
            """token_type_ids""": token_type_ids,
            """attention_mask""": input_mask,
        }
        return config, inputs_dict







@require_tf
class        a__       (   UpperCAmelCase	,			UpperCAmelCase	,			unittest.TestCase       ):







  """simple docstring"""




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


  def  _lowercase      (			self    : Any		)					->str:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   TFLayoutLMModelTester(self		)
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   ConfigTester(self				,				config_class=UpperCAmelCase__				,				hidden_size=3_7		)


  def  _lowercase      (			self    : str		)					->Dict:
        """simple docstring"""







        self.config_tester.run_common_tests()


  def  _lowercase      (			self    : Optional[int]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__		)


  def  _lowercase      (			self    : List[Any]		)					->Dict:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[Any]       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__		)


  def  _lowercase      (			self    : Union[str, Any]		)					->Any:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      int       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__		)


  def  _lowercase      (			self    : Any		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__		)


  def  _lowercase      (			self    : int		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__		)


  @slow
  def  _lowercase      (			self    : int		)					->Any:
        """simple docstring"""







        for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE :      int       =   TFLayoutLMModel.from_pretrained(UpperCAmelCase__		)
              self.assertIsNotNone(UpperCAmelCase__		)




  @unittest.skip("""Onnx compliancy broke with TF 2.10"""		)
  def  _lowercase      (			self    : Tuple		)					->Union[str, Any]:
        """simple docstring"""







        pass






def 					__lowercase      (     )     ->						str:
      # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
      # fmt: off
      SCREAMING_SNAKE_CASE :      int       =   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
      SCREAMING_SNAKE_CASE :      List[str]       =   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
      SCREAMING_SNAKE_CASE :      Tuple       =   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
      SCREAMING_SNAKE_CASE :      Any       =   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)
      SCREAMING_SNAKE_CASE :      List[str]       =   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  _lowercase      (			self    : int		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Any       =   TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased"""		)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Union[str, Any]       =   prepare_layoutlm_batch_inputs()

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

        # test the sequence output on [0, :3, :3]
        SCREAMING_SNAKE_CASE :      str       =   tf.convert_to_tensor(
            [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]				,				)

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

        # test the pooled output on [1, :3]
        SCREAMING_SNAKE_CASE :      Optional[int]       =   tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]		)

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


  @slow
  def  _lowercase      (			self    : str		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""				,				num_labels=2		)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      Union[str, Any]       =   prepare_layoutlm_batch_inputs()

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

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

        # test the shape of the logits
        SCREAMING_SNAKE_CASE :      Tuple       =   outputs.logits
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   (2, 2)
        self.assertEqual(logits.shape				,				UpperCAmelCase__		)


  @slow
  def  _lowercase      (			self    : List[str]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[int]       =   TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""				,				num_labels=1_3		)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      List[Any]       =   prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE :      int       =   model(
            input_ids=UpperCAmelCase__				,				bbox=UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__				,				labels=UpperCAmelCase__		)

        # test the shape of the logits
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   outputs.logits
        SCREAMING_SNAKE_CASE :      str       =   tf.convert_to_tensor((2, 2_5, 1_3)		)
        self.assertEqual(logits.shape				,				UpperCAmelCase__		)




  @slow
  def  _lowercase      (			self    : List[str]		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      str       =   TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased"""		)

        SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE     ,	SCREAMING_SNAKE_CASE :      str       =   prepare_layoutlm_batch_inputs()

        # forward pass
        SCREAMING_SNAKE_CASE :      List[str]       =   model(input_ids=UpperCAmelCase__				,				bbox=UpperCAmelCase__				,				attention_mask=UpperCAmelCase__				,				token_type_ids=UpperCAmelCase__		)

        # test the shape of the logits
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   tf.convert_to_tensor((2, 2_5)		)
        self.assertEqual(outputs.start_logits.shape				,				UpperCAmelCase__		)
        self.assertEqual(outputs.end_logits.shape				,				UpperCAmelCase__		)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase__ :     str				    =    {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Any				    =    ["""LayoutXLMTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     List[str]				    =    ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
  from .processing_layoutxlm import LayoutXLMProcessor

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm import LayoutXLMTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
  import sys

  UpperCAmelCase__ :     List[str]				    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




