






'''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       snake_case__	:




					def __init__(   self   :						Optional[Any]							,      _A   :						Dict							,      _A   :						Union[str, Any]=13							,      _A   :						List[Any]=7							,      _A   :						Dict=True							,      _A   :						str=True							,      _A   :						str=True							,      _A   :						Tuple=True							,      _A   :						str=99							,      _A   :						Dict=32							,      _A   :						List[str]=2							,      _A   :						Tuple=4							,      _A   :						Union[str, Any]=37							,      _A   :						Any="gelu"							,      _A   :						Union[str, Any]=0.1							,      _A   :						int=0.1							,      _A   :						Optional[Any]=5_12							,      _A   :						str=16							,      _A   :						Union[str, Any]=2							,      _A   :						str=0.02							,      _A   :						List[Any]=3							,      _A   :						Tuple=4							,      _A   :						int=None							,      _A   :						List[str]=10_00							,      )  ->				List[str]:
								UpperCAmelCase_	:    Union[str, Any]	  =    parent
								UpperCAmelCase_	:    List[Any]	  =    batch_size
								UpperCAmelCase_	:    List[Any]	  =    seq_length
								UpperCAmelCase_	:    Union[str, Any]	  =    is_training
								UpperCAmelCase_	:    Union[str, Any]	  =    use_input_mask
								UpperCAmelCase_	:    str	  =    use_token_type_ids
								UpperCAmelCase_	:    Optional[int]	  =    use_labels
								UpperCAmelCase_	:    int	  =    vocab_size
								UpperCAmelCase_	:    List[str]	  =    hidden_size
								UpperCAmelCase_	:    Tuple	  =    num_hidden_layers
								UpperCAmelCase_	:    str	  =    num_attention_heads
								UpperCAmelCase_	:    str	  =    intermediate_size
								UpperCAmelCase_	:    str	  =    hidden_act
								UpperCAmelCase_	:    List[str]	  =    hidden_dropout_prob
								UpperCAmelCase_	:    List[Any]	  =    attention_probs_dropout_prob
								UpperCAmelCase_	:    List[Any]	  =    max_position_embeddings
								UpperCAmelCase_	:    int	  =    type_vocab_size
								UpperCAmelCase_	:    Dict	  =    type_sequence_label_size
								UpperCAmelCase_	:    Optional[int]	  =    initializer_range
								UpperCAmelCase_	:    str	  =    num_labels
								UpperCAmelCase_	:    Dict	  =    num_choices
								UpperCAmelCase_	:    List[str]	  =    scope
								UpperCAmelCase_	:    Optional[int]	  =    range_bbox




					def 						A	(   self   :						Any	)  ->				Any:
								UpperCAmelCase_	:    Optional[Any]	  =    ids_tensor([self.batch_size, self.seq_length]							,      self.vocab_size	)

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

								UpperCAmelCase_	:    int	  =    None
								if self.use_input_mask:
											UpperCAmelCase_	:    List[str]	  =    random_attention_mask([self.batch_size, self.seq_length]	)

								UpperCAmelCase_	:    Optional[int]	  =    None
								if self.use_token_type_ids:
											UpperCAmelCase_	:    List[str]	  =    ids_tensor([self.batch_size, self.seq_length]							,      self.type_vocab_size	)

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

								UpperCAmelCase_	:    str	  =    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 						A	(   self   :						Optional[int]							,      _A   :						str							,      _A   :						str							,      _A   :						Tuple							,      _A   :						List[Any]							,      _A   :						Optional[int]							,      _A   :						Tuple							,      _A   :						str							,      _A   :						List[Any]	)  ->				Tuple:
								UpperCAmelCase_	:    int	  =    TFLayoutLMModel(config=_A	)

								UpperCAmelCase_	:    Optional[int]	  =    model(_A							,      _A							,      attention_mask=_A							,      token_type_ids=_A	)
								UpperCAmelCase_	:    Tuple	  =    model(_A							,      _A							,      token_type_ids=_A	)
								UpperCAmelCase_	:    Any	  =    model(_A							,      _A	)
								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 						A	(   self   :						List[Any]							,      _A   :						str							,      _A   :						Any							,      _A   :						Optional[int]							,      _A   :						Any							,      _A   :						Tuple							,      _A   :						List[Any]							,      _A   :						int							,      _A   :						Tuple	)  ->				Dict:
								UpperCAmelCase_	:    Dict	  =    TFLayoutLMForMaskedLM(config=_A	)

								UpperCAmelCase_	:    Tuple	  =    model(_A							,      _A							,      attention_mask=_A							,      token_type_ids=_A							,      labels=_A	)
								self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.vocab_size)	)




					def 						A	(   self   :						Dict							,      _A   :						Optional[int]							,      _A   :						Tuple							,      _A   :						List[str]							,      _A   :						Dict							,      _A   :						Optional[Any]							,      _A   :						List[str]							,      _A   :						Any							,      _A   :						int	)  ->				Union[str, Any]:
								UpperCAmelCase_	:    Tuple	  =    self.num_labels
								UpperCAmelCase_	:    List[str]	  =    TFLayoutLMForSequenceClassification(config=_A	)

								UpperCAmelCase_	:    Dict	  =    model(_A							,      _A							,      attention_mask=_A							,      token_type_ids=_A	)
								self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)	)




					def 						A	(   self   :						Any							,      _A   :						List[str]							,      _A   :						Union[str, Any]							,      _A   :						Dict							,      _A   :						int							,      _A   :						Union[str, Any]							,      _A   :						Tuple							,      _A   :						Union[str, Any]							,      _A   :						Dict	)  ->				Any:
								UpperCAmelCase_	:    Any	  =    self.num_labels
								UpperCAmelCase_	:    Dict	  =    TFLayoutLMForTokenClassification(config=_A	)

								UpperCAmelCase_	:    Optional[Any]	  =    model(_A							,      _A							,      attention_mask=_A							,      token_type_ids=_A							,      labels=_A	)
								self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.seq_length, self.num_labels)	)




					def 						A	(   self   :						int							,      _A   :						Optional[Any]							,      _A   :						List[str]							,      _A   :						List[str]							,      _A   :						str							,      _A   :						int							,      _A   :						int							,      _A   :						Tuple							,      _A   :						List[Any]	)  ->				List[Any]:
								UpperCAmelCase_	:    List[str]	  =    TFLayoutLMForQuestionAnswering(config=_A	)

								UpperCAmelCase_	:    Union[str, Any]	  =    model(_A							,      _A							,      attention_mask=_A							,      token_type_ids=_A	)
								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   :						int	)  ->				Any:
								UpperCAmelCase_	:    Union[str, Any]	  =    self.prepare_config_and_inputs()
								(
								    UpperCAmelCase_    
								)	:    Any	  =    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       snake_case__	( __SCREAMING_SNAKE_CASE			,							__SCREAMING_SNAKE_CASE			,							unittest.TestCase):
					a_								= (
					    (
					        TFLayoutLMModel,
					        TFLayoutLMForMaskedLM,
					        TFLayoutLMForTokenClassification,
					        TFLayoutLMForSequenceClassification,
					        TFLayoutLMForQuestionAnswering,
					    )
					    if is_tf_available()
					    else ()
					)
					a_								= (
					    {
					        """feature-extraction""": TFLayoutLMModel,
					        """fill-mask""": TFLayoutLMForMaskedLM,
					        """text-classification""": TFLayoutLMForSequenceClassification,
					        """token-classification""": TFLayoutLMForTokenClassification,
					        """zero-shot""": TFLayoutLMForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)
					a_								= False
					a_								= True
					a_								= 10




					def 						A	(   self   :						Union[str, Any]	)  ->				Optional[int]:
								UpperCAmelCase_	:    Any	  =    TFLayoutLMModelTester(self	)
								UpperCAmelCase_	:    str	  =    ConfigTester(self							,      config_class=_A							,      hidden_size=37	)




					def 						A	(   self   :						int	)  ->				Tuple:
								self.config_tester.run_common_tests()




					def 						A	(   self   :						int	)  ->				Tuple:
								UpperCAmelCase_	:    Tuple	  =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A	)




					def 						A	(   self   :						List[str]	)  ->				List[Any]:
								UpperCAmelCase_	:    Any	  =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_A	)




					def 						A	(   self   :						List[Any]	)  ->				List[Any]:
								UpperCAmelCase_	:    str	  =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*_A	)




					def 						A	(   self   :						Optional[int]	)  ->				List[Any]:
								UpperCAmelCase_	:    int	  =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*_A	)




					def 						A	(   self   :						Optional[Any]	)  ->				Any:
								UpperCAmelCase_	:    List[Any]	  =    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*_A	)




					@slow
					def 						A	(   self   :						List[Any]	)  ->				List[Any]:
								for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											UpperCAmelCase_	:    str	  =    TFLayoutLMModel.from_pretrained(_A	)
											self.assertIsNotNone(_A	)





					@unittest.skip('''Onnx compliancy broke with TF 2.10'''	)
					def 						A	(   self   :						List[str]	)  ->				str:
								pass






def __UpperCAmelCase					( )  ->					Optional[int]:
			# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
			# fmt: off
			UpperCAmelCase_	:    int	  =    tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]						)  # noqa: E231
			UpperCAmelCase_	:    Union[str, 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[int]	  =    tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]						)  # noqa: E231
			UpperCAmelCase_	:    Optional[int]	  =    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_	:    str	  =    tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]						)  # noqa: E231
			# fmt: on

			return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class       snake_case__	( unittest.TestCase):




					@slow
					def 						A	(   self   :						Union[str, Any]	)  ->				Union[str, Any]:
								UpperCAmelCase_	:    Dict	  =    TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''	)

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

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

								# test the sequence output on [0, :3, :3]
								UpperCAmelCase_	:    List[str]	  =    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]							,      _A							,      atol=1e-3	)	)

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

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




					@slow
					def 						A	(   self   :						Dict	)  ->				Dict:
								# initialize model with randomly initialized sequence classification head
								UpperCAmelCase_	:    Union[str, Any]	  =    TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,      num_labels=2	)

								UpperCAmelCase_	:    List[str]	  =    prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase_	:    Union[str, Any]	  =    model(
								    input_ids=_A							,      bbox=_A							,      attention_mask=_A							,      token_type_ids=_A							,      labels=tf.convert_to_tensor([1, 1]	)							,      )

								# test whether we get a loss as a scalar
								UpperCAmelCase_	:    List[str]	  =    outputs.loss
								UpperCAmelCase_	:    Dict	  =    (2,)
								self.assertEqual(loss.shape							,      _A	)

								# test the shape of the logits
								UpperCAmelCase_	:    int	  =    outputs.logits
								UpperCAmelCase_	:    str	  =    (2, 2)
								self.assertEqual(logits.shape							,      _A	)




					@slow
					def 						A	(   self   :						Any	)  ->				Optional[int]:
								# initialize model with randomly initialized token classification head
								UpperCAmelCase_	:    int	  =    TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''							,      num_labels=13	)

								UpperCAmelCase_	:    Tuple	  =    prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase_	:    int	  =    model(
								    input_ids=_A							,      bbox=_A							,      attention_mask=_A							,      token_type_ids=_A							,      labels=_A	)

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





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

								UpperCAmelCase_	:    Optional[int]	  =    prepare_layoutlm_batch_inputs()

								# forward pass
								UpperCAmelCase_	:    Dict	  =    model(input_ids=_A							,      bbox=_A							,      attention_mask=_A							,      token_type_ids=_A	)

								# test the shape of the logits
								UpperCAmelCase_	:    Dict	  =    tf.convert_to_tensor((2, 25)	)
								self.assertEqual(outputs.start_logits.shape							,      _A	)
								self.assertEqual(outputs.end_logits.shape							,      _A	)




def     _a					(						lowerCamelCase							):
 return " ".join(
     """""".join(word[::-1]							) if len(lowerCamelCase							) > 4 else word for word in sentence.split()							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(reverse_long_words("""Hey wollef sroirraw"""))




