'''simple docstring'''




import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
  )

class      lowerCamelCase_     :
      '''simple docstring'''

      def __init__(	self						:    Any						, A						:    Dict						, A						:    Union[str, Any]=13						, A						:    Optional[int]=7						, A						:    Optional[int]=True						, A						:    List[str]=True						, A						:    Union[str, Any]=True						, A						:    int=True						, A						:    Any=99						, A						:    Optional[int]=32						, A						:    str=5						, A						:    Tuple=4						, A						:    int=37						, A						:    Any="gelu"						, A						:    Optional[int]=0.1						, A						:    List[str]=0.1						, A						:    Dict=512						, A						:    List[Any]=16						, A						:    int=2						, A						:    int=0.02						, A						:    str=3						, A						:    Optional[int]=4						, A						:    List[str]=None						, A						:    List[str]=1000						, ):
             _UpperCAmelCase					:  List[str]											=   parent
             _UpperCAmelCase					:  Tuple											=   batch_size
             _UpperCAmelCase					:  List[str]											=   seq_length
             _UpperCAmelCase					:  Tuple											=   is_training
             _UpperCAmelCase					:  Tuple											=   use_input_mask
             _UpperCAmelCase					:  List[Any]											=   use_token_type_ids
             _UpperCAmelCase					:  int											=   use_labels
             _UpperCAmelCase					:  Dict											=   vocab_size
             _UpperCAmelCase					:  Tuple											=   hidden_size
             _UpperCAmelCase					:  Union[str, Any]											=   num_hidden_layers
             _UpperCAmelCase					:  Dict											=   num_attention_heads
             _UpperCAmelCase					:  str											=   intermediate_size
             _UpperCAmelCase					:  Optional[Any]											=   hidden_act
             _UpperCAmelCase					:  Tuple											=   hidden_dropout_prob
             _UpperCAmelCase					:  Optional[int]											=   attention_probs_dropout_prob
             _UpperCAmelCase					:  Any											=   max_position_embeddings
             _UpperCAmelCase					:  List[str]											=   type_vocab_size
             _UpperCAmelCase					:  Optional[Any]											=   type_sequence_label_size
             _UpperCAmelCase					:  List[Any]											=   initializer_range
             _UpperCAmelCase					:  Tuple											=   num_labels
             _UpperCAmelCase					:  Optional[int]											=   num_choices
             _UpperCAmelCase					:  Optional[int]											=   scope
             _UpperCAmelCase					:  List[str]											=   range_bbox




      def 					_A     (	self						:    List[Any]	):
             _UpperCAmelCase					:  Optional[Any]											=   ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size	)

             _UpperCAmelCase					:  str											=   ids_tensor([self.batch_size, self.seq_length, 4]						, self.range_bbox	)
             # 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					:  Tuple											=   bbox[i, j, 1]
                                  _UpperCAmelCase					:  Any											=   t
                           if bbox[i, j, 2] < bbox[i, j, 0]:
                                  _UpperCAmelCase					:  int											=   bbox[i, j, 2]
                                  _UpperCAmelCase					:  int											=   bbox[i, j, 0]
                                  _UpperCAmelCase					:  Tuple											=   t

             _UpperCAmelCase					:  Union[str, Any]											=   None
             if self.use_input_mask:
                    _UpperCAmelCase					:  Dict											=   ids_tensor([self.batch_size, self.seq_length]						, vocab_size=2	)

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

             _UpperCAmelCase					:  int											=   None
             _UpperCAmelCase					:  Any											=   None
             _UpperCAmelCase					:  Optional[Any]											=   None
             if self.use_labels:
                    _UpperCAmelCase					:  Optional[int]											=   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					:  Tuple											=   self.get_config()

             return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




      def 					_A     (	self						:    List[Any]	):
             return 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						, )




      def 					_A     (	self						:    Union[str, Any]						, A						:    Dict						, A						:    str						, A						:    int						, A						:    Optional[int]						, A						:    List[Any]						, A						:    List[str]						, A						:    List[Any]						, A						:    int	):
             _UpperCAmelCase					:  Dict											=   LayoutLMModel(config=A	)
             model.to(A	)
             model.eval()
             _UpperCAmelCase					:  Dict											=   model(A						, A						, attention_mask=A						, token_type_ids=A	)
             _UpperCAmelCase					:  Optional[int]											=   model(A						, A						, token_type_ids=A	)
             _UpperCAmelCase					:  Dict											=   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						:    Tuple						, A						:    Optional[Any]						, A						:    Optional[Any]						, A						:    Dict						, A						:    Optional[int]						, A						:    Any						, A						:    List[str]						, A						:    Dict	):
             _UpperCAmelCase					:  Optional[int]											=   LayoutLMForMaskedLM(config=A	)
             model.to(A	)
             model.eval()
             _UpperCAmelCase					:  int											=   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						:    List[Any]						, A						:    int						, A						:    int						, A						:    Optional[Any]						, A						:    int						, A						:    Optional[int]						, A						:    List[str]						, A						:    List[str]						, A						:    Any	):
             _UpperCAmelCase					:  Any											=   self.num_labels
             _UpperCAmelCase					:  str											=   LayoutLMForSequenceClassification(A	)
             model.to(A	)
             model.eval()
             _UpperCAmelCase					:  Tuple											=   model(
                 A						, A						, attention_mask=A						, token_type_ids=A						, labels=A	)
             self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels)	)




      def 					_A     (	self						:    List[Any]						, A						:    Tuple						, A						:    Any						, A						:    Optional[Any]						, A						:    Union[str, Any]						, A						:    Union[str, Any]						, A						:    List[Any]						, A						:    int						, A						:    List[Any]	):
             _UpperCAmelCase					:  Dict											=   self.num_labels
             _UpperCAmelCase					:  List[str]											=   LayoutLMForTokenClassification(config=A	)
             model.to(A	)
             model.eval()
             _UpperCAmelCase					:  List[str]											=   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						:    Optional[int]						, A						:    int						, A						:    Union[str, Any]						, A						:    Optional[Any]						, A						:    str						, A						:    Tuple						, A						:    str						, A						:    Tuple						, A						:    Dict	):
             _UpperCAmelCase					:  str											=   LayoutLMForQuestionAnswering(config=A	)
             model.to(A	)
             model.eval()
             _UpperCAmelCase					:  int											=   model(
                 A						, bbox=A						, attention_mask=A						, token_type_ids=A						, start_positions=A						, end_positions=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						:    List[Any]	):
             _UpperCAmelCase					:  int											=   self.prepare_config_and_inputs()
             (
                 (
                 _UpperCAmelCase
             )				,  (
                 _UpperCAmelCase
             )				,  (
                 _UpperCAmelCase
             )				,  (
                 _UpperCAmelCase
             )				,  (
                 _UpperCAmelCase
             )				,  (
                 _UpperCAmelCase
             )				,  (
                 _UpperCAmelCase
             )				,  (
                 _UpperCAmelCase
             )				,  
             )					:  Union[str, Any]											=   config_and_inputs
             _UpperCAmelCase					:  Tuple											=   {
                 "input_ids": input_ids,
                 "bbox": bbox,
                 "token_type_ids": token_type_ids,
                 "attention_mask": input_mask,
             }
             return config, inputs_dict

@require_torch
class      lowerCamelCase_     (snake_case__							,			snake_case__							,			unittest.TestCase   ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]      =						(
          (
              LayoutLMModel,
              LayoutLMForMaskedLM,
              LayoutLMForSequenceClassification,
              LayoutLMForTokenClassification,
              LayoutLMForQuestionAnswering,
          )
          if is_torch_available()
          else None
      )
      __UpperCamelCase:  Union[str, Any]      =						(
          {
              "document-question-answering": LayoutLMForQuestionAnswering,
              "feature-extraction": LayoutLMModel,
              "fill-mask": LayoutLMForMaskedLM,
              "text-classification": LayoutLMForSequenceClassification,
              "token-classification": LayoutLMForTokenClassification,
              "zero-shot": LayoutLMForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      __UpperCamelCase:  Union[str, Any]      =						True




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Optional[int]											=   LayoutLMModelTester(self	)
             _UpperCAmelCase					:  Any											=   ConfigTester(self						, config_class=A						, hidden_size=37	)




      def 					_A     (	self						:    List[str]	):
             self.config_tester.run_common_tests()




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  Tuple											=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*A	)




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  Optional[int]											=   self.model_tester.prepare_config_and_inputs()
             for type in ["absolute", "relative_key", "relative_key_query"]:
                    _UpperCAmelCase					:  Any											=   type
                    self.model_tester.create_and_check_model(*A	)




      def 					_A     (	self						:    str	):
             _UpperCAmelCase					:  Optional[Any]											=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_lm(*A	)




      def 					_A     (	self						:    List[Any]	):
             _UpperCAmelCase					:  Dict											=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_sequence_classification(*A	)




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  List[Any]											=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_token_classification(*A	)




      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  Tuple											=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_question_answering(*A	)





      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
      def 					_A     (	self						:    List[Any]	):
             pass

def      UpperCamelCase_							( )   ->   List[Any]:


       """simple docstring"""







       _UpperCAmelCase					:  Dict											=   torch.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]]      ,     device=_UpperCAmelCase   )  # noqa: E231
       _UpperCAmelCase					:  str											=   torch.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],]      ,     device=_UpperCAmelCase   )  # noqa: E231
       _UpperCAmelCase					:  Dict											=   torch.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]]]      ,     device=_UpperCAmelCase   )  # noqa: E231
       _UpperCAmelCase					:  int											=   torch.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]]      ,     device=_UpperCAmelCase   )  # noqa: E231
       # these are sequence labels (i.e. at the token level)
       _UpperCAmelCase					:  Optional[int]											=   torch.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]]      ,     device=_UpperCAmelCase   )  # noqa: E231
       # fmt: on

       return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      @slow
      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[Any]											=   LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"	).to(A	)

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

             # forward pass
             _UpperCAmelCase					:  Dict											=   model(input_ids=A						, bbox=A						, attention_mask=A						, token_type_ids=A	)

             # test the sequence output on [0, :3, :3]
             _UpperCAmelCase					:  List[str]											=   torch.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]]						, device=A						, )

             self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]						, A						, atol=1E-3	)	)

             # test the pooled output on [1, :3]
             _UpperCAmelCase					:  int											=   torch.tensor([-0.6_580, -0.0_214, 0.8_552]						, device=A	)

             self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]						, A						, atol=1E-3	)	)




      @slow
      def 					_A     (	self						:    List[Any]	):
             # initialize model with randomly initialized sequence classification head
             _UpperCAmelCase					:  List[Any]											=   LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"						, num_labels=2	).to(
                 A	)

             _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  int											=   prepare_layoutlm_batch_inputs()

             # forward pass
             _UpperCAmelCase					:  Any											=   model(
                 input_ids=A						, bbox=A						, attention_mask=A						, token_type_ids=A						, labels=torch.tensor([1, 1]						, device=A	)						, )

             # test whether we get a loss as a scalar
             _UpperCAmelCase					:  Dict											=   outputs.loss
             _UpperCAmelCase					:  str											=   torch.Size([]	)
             self.assertEqual(loss.shape						, A	)

             # test the shape of the logits
             _UpperCAmelCase					:  int											=   outputs.logits
             _UpperCAmelCase					:  str											=   torch.Size((2, 2)	)
             self.assertEqual(logits.shape						, A	)




      @slow
      def 					_A     (	self						:    Tuple	):
             # initialize model with randomly initialized token classification head
             _UpperCAmelCase					:  Dict											=   LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"						, num_labels=13	).to(
                 A	)

             _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  Dict											=   prepare_layoutlm_batch_inputs()

             # forward pass
             _UpperCAmelCase					:  str											=   model(
                 input_ids=A						, bbox=A						, attention_mask=A						, token_type_ids=A						, labels=A	)

             # test the loss calculation to be around 2.65
             # expected_loss = torch.tensor(2.65, device=torch_device)

             # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
             # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

             # test the shape of the logits
             _UpperCAmelCase					:  int											=   outputs.logits
             _UpperCAmelCase					:  Union[str, Any]											=   torch.Size((2, 25, 13)	)
             self.assertEqual(logits.shape						, A	)





      @slow
      def 					_A     (	self						:    Optional[int]	):
             # initialize model with randomly initialized token classification head
             _UpperCAmelCase					:  str											=   LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"	).to(A	)

             _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase					:  Tuple											=   prepare_layoutlm_batch_inputs()

             # forward pass
             _UpperCAmelCase					:  Tuple											=   model(input_ids=A						, bbox=A						, attention_mask=A						, token_type_ids=A	)

             # test the shape of the logits
             _UpperCAmelCase					:  List[str]											=   torch.Size((2, 25)	)
             self.assertEqual(outputs.start_logits.shape						, A	)
             self.assertEqual(outputs.end_logits.shape						, A	)


'''simple docstring'''




def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   bool:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[int]											=   9

       while num > 0:
              if num % 10 != digit:
                     return False
              num //= 100
              digit -= 1

       return True

def      UpperCamelCase_							( )   ->   int:


       """simple docstring"""







       _UpperCAmelCase					:  Optional[Any]											=   138_902_663

       while not is_square_form(num * num   ):
              if num % 10 == 3:
                     num -= 6  # (3 - 6) % 10 = 7
              else:
                     num -= 4  # (7 - 4) % 10 = 3

       return num * 10


if __name__ == "__main__":
  print(F'{solution() = }')

