"""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   ,      lowercase   ,      lowercase=13   ,      lowercase=7   ,      lowercase=True   ,      lowercase=True   ,      lowercase=True   ,      lowercase=True   ,      lowercase=99   ,      lowercase=32   ,      lowercase=2   ,      lowercase=4   ,      lowercase=37   ,      lowercase="gelu"   ,      lowercase=0.1   ,      lowercase=0.1   ,      lowercase=5_12   ,      lowercase=16   ,      lowercase=2   ,      lowercase=0.02   ,      lowercase=3   ,      lowercase=4   ,      lowercase=None   ,      lowercase=10_00   ,      )     ->      int:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size)

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

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

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

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

        a__:					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   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     TFLayoutLMModel(config=lowercase)

        a__:					Optional[Any]      							=     model(lowercase   ,      lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase)
        a__:					Tuple      							=     model(lowercase   ,      lowercase   ,      token_type_ids=lowercase)
        a__:					Any      							=     model(lowercase   ,      lowercase)
        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   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[str]:





        '''simple docstring'''





        a__:					int      							=     TFLayoutLMForMaskedLM(config=lowercase)

        a__:					Optional[int]      							=     model(lowercase   ,      lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase   ,      labels=lowercase)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.vocab_size))



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase)     ->      str:





        '''simple docstring'''





        a__:					int      							=     self.num_labels
        a__:					List[str]      							=     TFLayoutLMForSequenceClassification(config=lowercase)

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



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Optional[int]:





        '''simple docstring'''





        a__:					List[Any]      							=     self.num_labels
        a__:					Any      							=     TFLayoutLMForTokenClassification(config=lowercase)

        a__:					List[Any]      							=     model(lowercase   ,      lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase   ,      labels=lowercase)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.seq_length, self.num_labels))



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Dict:





        '''simple docstring'''





        a__:					Tuple      							=     TFLayoutLMForQuestionAnswering(config=lowercase)

        a__:					Union[str, Any]      							=     model(lowercase   ,      lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase)
        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)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[int]      							=     self.prepare_config_and_inputs()
        (
            (
            a__
        )							,   (
            a__
        )							,   (
            a__
        )							,   (
            a__
        )							,   (
            a__
        )							,   (
            a__
        )							,   (
            a__
        )							,   (
            a__
        )							,   
        ):					Optional[Any]      							=     config_and_inputs
        a__:					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        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase	,				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   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					int      							=     TFLayoutLMModelTester(self)
        a__:					Any      							=     ConfigTester(self   ,      config_class=lowercase   ,      hidden_size=37)



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





        '''simple docstring'''





        self.config_tester.run_common_tests()



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					int      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					Optional[int]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowercase)



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





        '''simple docstring'''





        a__:					Union[str, Any]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowercase)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					List[Any]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      Tuple:





        '''simple docstring'''





        for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					Optional[int]      							=     TFLayoutLMModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)



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





        '''simple docstring'''





        pass







def 						__a       (     )     ->List[Any]:
  # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
  # fmt: off
  a__:					List[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
  a__:					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
  a__:					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
  a__:					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)
  a__:					Union[str, Any]      							=     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        __snake_case	(					unittest.TestCase    ):



      @slow
      def   lowerCamelCase_       (						self)     ->      Union[str, Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased')

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

        # forward pass
        a__:					str      							=     model(input_ids=lowercase   ,      bbox=lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase)

        # test the sequence output on [0, :3, :3]
        a__:					Optional[int]      							=     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]   ,      lowercase   ,      atol=1e-3))

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

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



      @slow
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Dict      							=     TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'   ,      num_labels=2)

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

        # forward pass
        a__:					int      							=     model(
            input_ids=lowercase   ,      bbox=lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase   ,      labels=tf.convert_to_tensor([1, 1])   ,      )

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

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



      @slow
      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Any      							=     TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'   ,      num_labels=13)

        a__							,   a__							,   a__							,   a__							,   a__:					Dict      							=     prepare_layoutlm_batch_inputs()

        # forward pass
        a__:					int      							=     model(
            input_ids=lowercase   ,      bbox=lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase   ,      labels=lowercase)

        # test the shape of the logits
        a__:					Tuple      							=     outputs.logits
        a__:					Any      							=     tf.convert_to_tensor((2, 25, 13))
        self.assertEqual(logits.shape   ,      lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





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

        a__							,   a__							,   a__							,   a__							,   a__:					str      							=     prepare_layoutlm_batch_inputs()

        # forward pass
        a__:					Union[str, Any]      							=     model(input_ids=lowercase   ,      bbox=lowercase   ,      attention_mask=lowercase   ,      token_type_ids=lowercase)

        # test the shape of the logits
        a__:					Tuple      							=     tf.convert_to_tensor((2, 25))
        self.assertEqual(outputs.start_logits.shape   ,      lowercase)
        self.assertEqual(outputs.end_logits.shape   ,      lowercase)



"""simple docstring"""
from __future__ import annotations




class        __snake_case	:



      def __init__(						self   ,      lowercase=None)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     data
        a__:					str      							=     None



      def __repr__(						self)     ->      List[str]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        a__:					Union[str, Any]      							=     self
        while temp:
          string_rep.append(f'{temp.data}')
          a__:					Tuple      							=     temp.next
        return "->".join(lowercase)







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->str:
  if not elements_list:
    raise Exception('The Elements List is empty'       )

  a__:					Any      							=     Node(elements_list[0]       )
  for i in range(1		,					len(_SCREAMING_SNAKE_CASE       )       ):
    a__:					Optional[Any]      							=     Node(elements_list[i]       )
    a__:					Tuple      							=     current.next
  return head







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->None:
  if head_node is not None and isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
    print_reverse(head_node.next       )
    print(head_node.data       )







def 						__a       (     )     ->Optional[Any]:
  from doctest import testmod

  testmod()

  a__:					Tuple      							=     make_linked_list([14, 52, 14, 12, 43]       )
  print('Linked List:'       )
  print(_SCREAMING_SNAKE_CASE       )
  print('Elements in Reverse:'       )
  print_reverse(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 main()


