


'''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        lowerCAmelCase_  :






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






       def   UpperCamelCase						( self   )->      str:
            _A				=							ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size   )

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

            _A				=							None
            if self.use_input_mask:
                 _A				=							random_attention_mask([self.batch_size, self.seq_length]   )

            _A				=							None
            if self.use_token_type_ids:
                 _A				=							ids_tensor([self.batch_size, self.seq_length]			,     self.type_vocab_size   )

            _A				=							None
            _A				=							None
            _A				=							None
            if self.use_labels:
                 _A				=							ids_tensor([self.batch_size]			,     self.type_sequence_label_size   )
                 _A				=							ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels   )
                 _A				=							ids_tensor([self.batch_size]			,     self.num_choices   )

            _A				=							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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Optional[int]:
            _A				=							TFLayoutLMModel(config=_UpperCamelCase   )

            _A				=							model(_UpperCamelCase			,     _UpperCamelCase			,     attention_mask=_UpperCamelCase			,     token_type_ids=_UpperCamelCase   )
            _A				=							model(_UpperCamelCase			,     _UpperCamelCase			,     token_type_ids=_UpperCamelCase   )
            _A				=							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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Any:
            _A				=							TFLayoutLMForMaskedLM(config=_UpperCamelCase   )

            _A				=							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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      int:
            _A				=							self.num_labels
            _A				=							TFLayoutLMForSequenceClassification(config=_UpperCamelCase   )

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






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Tuple:
            _A				=							self.num_labels
            _A				=							TFLayoutLMForTokenClassification(config=_UpperCamelCase   )

            _A				=							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   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Optional[int]:
            _A				=							TFLayoutLMForQuestionAnswering(config=_UpperCamelCase   )

            _A				=							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   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							self.prepare_config_and_inputs()
            (
                (
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				
            )				=							config_and_inputs
            _A				=							{
                'input_ids': input_ids,
                'bbox': bbox,
                'token_type_ids': token_type_ids,
                'attention_mask': input_mask,
            }
            return config, inputs_dict






@require_tf
class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=(
           (
               TFLayoutLMModel,
               TFLayoutLMForMaskedLM,
               TFLayoutLMForTokenClassification,
               TFLayoutLMForSequenceClassification,
               TFLayoutLMForQuestionAnswering,
           )
           if is_tf_available()
           else ()
       )
       __UpperCAmelCase					=(
           {
               'feature-extraction': TFLayoutLMModel,
               'fill-mask': TFLayoutLMForMaskedLM,
               'text-classification': TFLayoutLMForSequenceClassification,
               'token-classification': TFLayoutLMForTokenClassification,
               'zero-shot': TFLayoutLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       __UpperCAmelCase					=False
       __UpperCAmelCase					=True
       __UpperCAmelCase					=10






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							TFLayoutLMModelTester(self   )
            _A				=							ConfigTester(self			,     config_class=_UpperCamelCase			,     hidden_size=37   )






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            self.config_tester.run_common_tests()






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      int:
            _A				=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      str:
            _A				=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase   )






       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase   )






       @slow
       def   UpperCamelCase						( self   )->      Optional[Any]:
            for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A				=							TFLayoutLMModel.from_pretrained(_UpperCamelCase   )
                 self.assertIsNotNone(_UpperCamelCase   )






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






def 					lowerCamelCase_ ( )					->	Optional[int]:







     """simple docstring"""
     # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
     # fmt: off
     _A				=							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
     _A				=							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
     _A				=							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
     _A				=							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)
     _A				=							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        lowerCAmelCase_  (     unittest.TestCase	):






       @slow
       def   UpperCamelCase						( self   )->      List[Any]:
            _A				=							TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'   )

            _A		,				_A		,				_A		,				_A		,				_A				=							prepare_layoutlm_batch_inputs()

            # forward pass
            _A				=							model(input_ids=_UpperCamelCase			,     bbox=_UpperCamelCase			,     attention_mask=_UpperCamelCase			,     token_type_ids=_UpperCamelCase   )

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

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

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






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

            _A		,				_A		,				_A		,				_A		,				_A				=							prepare_layoutlm_batch_inputs()

            # forward pass
            _A				=							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
            _A				=							outputs.loss
            _A				=							(2,)
            self.assertEqual(loss.shape			,     _UpperCamelCase   )

            # test the shape of the logits
            _A				=							outputs.logits
            _A				=							(2, 2)
            self.assertEqual(logits.shape			,     _UpperCamelCase   )






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

            _A		,				_A		,				_A		,				_A		,				_A				=							prepare_layoutlm_batch_inputs()

            # forward pass
            _A				=							model(
                input_ids=_UpperCamelCase			,     bbox=_UpperCamelCase			,     attention_mask=_UpperCamelCase			,     token_type_ids=_UpperCamelCase			,     labels=_UpperCamelCase   )

            # test the shape of the logits
            _A				=							outputs.logits
            _A				=							tf.convert_to_tensor((2, 25, 13)   )
            self.assertEqual(logits.shape			,     _UpperCamelCase   )






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

            _A		,				_A		,				_A		,				_A		,				_A				=							prepare_layoutlm_batch_inputs()

            # forward pass
            _A				=							model(input_ids=_UpperCamelCase			,     bbox=_UpperCamelCase			,     attention_mask=_UpperCamelCase			,     token_type_ids=_UpperCamelCase   )

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









'''simple docstring'''
def 					lowerCamelCase_ ( __UpperCamelCase					:       int		)					->	bool:







     """simple docstring"""
     if num < 0:
          return False

     _A				=							num
     _A				=							0
     while num > 0:
          _A				=							rev_num * 1_0 + (num % 1_0)
          num //= 1_0

     return num_copy == rev_num


if __name__ == "__main__":
    import doctest

    doctest.testmod()





