



"""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]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[str]=13			,     _SCREAMING_SNAKE_CASE:						Any=7			,     _SCREAMING_SNAKE_CASE:						str=True			,     _SCREAMING_SNAKE_CASE:						int=True			,     _SCREAMING_SNAKE_CASE:						str=True			,     _SCREAMING_SNAKE_CASE:						Optional[int]=True			,     _SCREAMING_SNAKE_CASE:						Tuple=99			,     _SCREAMING_SNAKE_CASE:						str=32			,     _SCREAMING_SNAKE_CASE:						List[Any]=2			,     _SCREAMING_SNAKE_CASE:						Tuple=4			,     _SCREAMING_SNAKE_CASE:						Optional[int]=37			,     _SCREAMING_SNAKE_CASE:						Any="gelu"			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						Dict=0.1			,     _SCREAMING_SNAKE_CASE:						List[str]=512			,     _SCREAMING_SNAKE_CASE:						Optional[int]=16			,     _SCREAMING_SNAKE_CASE:						Optional[int]=2			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						List[str]=3			,     _SCREAMING_SNAKE_CASE:						int=4			,     _SCREAMING_SNAKE_CASE:						List[str]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=1000			,     )							->			Dict:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size)

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

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

				__lowerCAmelCase :    List[Any]				=			None
				if self.use_token_type_ids:
							__lowerCAmelCase :    Tuple				=			ids_tensor([self.batch_size, self.seq_length]			,     self.type_vocab_size)

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

				__lowerCAmelCase :    Union[str, Any]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			TFLayoutLMModel(config=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Any				=			model(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			model(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			model(_SCREAMING_SNAKE_CASE			,     _SCREAMING_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 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Union[str, Any])							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			TFLayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    int				=			model(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size))

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						List[str])							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.num_labels
				__lowerCAmelCase :    List[Any]				=			TFLayoutLMForSequenceClassification(config=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    int				=			model(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels))

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						Dict			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						int)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.num_labels
				__lowerCAmelCase :    Any				=			TFLayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Union[str, Any]				=			model(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels))

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Union[str, Any])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			TFLayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Tuple				=			model(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_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 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Optional[int]:

				"""simple docstring"""

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






@require_tf
class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      (
	    (
	        TFLayoutLMModel,
	        TFLayoutLMForMaskedLM,
	        TFLayoutLMForTokenClassification,
	        TFLayoutLMForSequenceClassification,
	        TFLayoutLMForQuestionAnswering,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE        =      (
	    {
	        'feature-extraction': TFLayoutLMModel,
	        'fill-mask': TFLayoutLMForMaskedLM,
	        'text-classification': TFLayoutLMForSequenceClassification,
	        'token-classification': TFLayoutLMForTokenClassification,
	        'zero-shot': TFLayoutLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      True
	SCREAMING_SNAKE_CASE        =      1_0

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			TFLayoutLMModelTester(self)
				__lowerCAmelCase :    Union[str, Any]				=			ConfigTester(self			,     config_class=_SCREAMING_SNAKE_CASE			,     hidden_size=37)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			List[Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE)

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			List[Any]:

				"""simple docstring"""

				for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase :    Union[str, Any]				=			TFLayoutLMModel.from_pretrained(_SCREAMING_SNAKE_CASE)
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)







	@unittest.skip("Onnx compliancy broke with TF 2.10")
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			int:

				"""simple docstring"""

				pass






def      _lowercase							(    )		->  int:
			# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
			# fmt: off
			__lowerCAmelCase :    Any				=			tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]      )  # noqa: E231
			__lowerCAmelCase :    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
			__lowerCAmelCase :    List[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],[1_000,1_000,1_000,1_000]],[[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],[1_000,1_000,1_000,1_000]]]      )  # noqa: E231
			__lowerCAmelCase :    List[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)
			__lowerCAmelCase :    Dict				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")

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

				# forward pass
				__lowerCAmelCase :    Optional[int]				=			model(input_ids=_SCREAMING_SNAKE_CASE			,     bbox=_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE)

				# test the sequence output on [0, :3, :3]
				__lowerCAmelCase :    List[str]				=			tf.convert_to_tensor(
				    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]			,     )

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

				# test the pooled output on [1, :3]
				__lowerCAmelCase :    str				=			tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])

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

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			List[str]:

				"""simple docstring"""

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

				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    str				=			prepare_layoutlm_batch_inputs()

				# forward pass
				__lowerCAmelCase :    List[str]				=			model(
				    input_ids=_SCREAMING_SNAKE_CASE			,     bbox=_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     labels=tf.convert_to_tensor([1, 1])			,     )

				# test whether we get a loss as a scalar
				__lowerCAmelCase :    Any				=			outputs.loss
				__lowerCAmelCase :    Tuple				=			(2,)
				self.assertEqual(loss.shape			,     _SCREAMING_SNAKE_CASE)

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

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,     num_labels=13)

				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    Optional[int]				=			prepare_layoutlm_batch_inputs()

				# forward pass
				__lowerCAmelCase :    str				=			model(
				    input_ids=_SCREAMING_SNAKE_CASE			,     bbox=_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE			,     labels=_SCREAMING_SNAKE_CASE)

				# test the shape of the logits
				__lowerCAmelCase :    List[str]				=			outputs.logits
				__lowerCAmelCase :    List[str]				=			tf.convert_to_tensor((2, 25, 13))
				self.assertEqual(logits.shape			,     _SCREAMING_SNAKE_CASE)







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased")

				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    int				=			prepare_layoutlm_batch_inputs()

				# forward pass
				__lowerCAmelCase :    Dict				=			model(input_ids=_SCREAMING_SNAKE_CASE			,     bbox=_SCREAMING_SNAKE_CASE			,     attention_mask=_SCREAMING_SNAKE_CASE			,     token_type_ids=_SCREAMING_SNAKE_CASE)

				# test the shape of the logits
				__lowerCAmelCase :    int				=			tf.convert_to_tensor((2, 25))
				self.assertEqual(outputs.start_logits.shape			,     _SCREAMING_SNAKE_CASE)
				self.assertEqual(outputs.end_logits.shape			,     _SCREAMING_SNAKE_CASE)




"""simple docstring"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 				_SCREAMING_SNAKE_CASE  (	_SCREAMING_SNAKE_CASE:						ArgumentParser)							->			Optional[int]:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Dict:

				"""simple docstring"""

				raise NotImplementedError()