




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

       UpperCAmelCase__								=  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__								=  bbox[i, j, 3]
          UpperCAmelCase__								=  bbox[i, j, 1]
          UpperCAmelCase__								=  t
         if bbox[i, j, 2] < bbox[i, j, 0]:
          UpperCAmelCase__								=  bbox[i, j, 2]
          UpperCAmelCase__								=  bbox[i, j, 0]
          UpperCAmelCase__								=  t

       UpperCAmelCase__								=  None
       if self.use_input_mask:
        UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]	, vocab_size=2 )

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

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

       UpperCAmelCase__								=  self.get_config()

       return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def 		UpperCAmelCase_  (     self				:Optional[Any] )       ->     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 		UpperCAmelCase_  (     self				:Optional[Any]	, lowerCamelCase				:Tuple	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:List[Any]	, lowerCamelCase				:str	, lowerCamelCase				:List[str]	, lowerCamelCase				:int	, lowerCamelCase				:Tuple	, lowerCamelCase				:str )       ->     Dict:
       UpperCAmelCase__								=  LayoutLMModel(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase )
       UpperCAmelCase__								=  model(lowerCamelCase	, lowerCamelCase	, token_type_ids=lowerCamelCase )
       UpperCAmelCase__								=  model(lowerCamelCase	, lowerCamelCase )
       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				:Any	, lowerCamelCase				:Tuple	, lowerCamelCase				:Dict	, lowerCamelCase				:str	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Any	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[str] )       ->     Union[str, Any]:
       UpperCAmelCase__								=  LayoutLMForMaskedLM(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.vocab_size) )
      def 		UpperCAmelCase_  (     self				:Optional[int]	, lowerCamelCase				:Tuple	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[str]	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:List[str]	, lowerCamelCase				:Dict	, lowerCamelCase				:str	, lowerCamelCase				:List[str] )       ->     Optional[int]:
       UpperCAmelCase__								=  self.num_labels
       UpperCAmelCase__								=  LayoutLMForSequenceClassification(lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(
           lowerCamelCase	, lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.num_labels) )
      def 		UpperCAmelCase_  (     self				:Tuple	, lowerCamelCase				:Any	, lowerCamelCase				:Optional[Any]	, lowerCamelCase				:Optional[int]	, lowerCamelCase				:List[str]	, lowerCamelCase				:Tuple	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Any	, lowerCamelCase				:Union[str, Any] )       ->     Any:
       UpperCAmelCase__								=  self.num_labels
       UpperCAmelCase__								=  LayoutLMForTokenClassification(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(lowerCamelCase	, lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )
       self.parent.assertEqual(result.logits.shape	, (self.batch_size, self.seq_length, self.num_labels) )
      def 		UpperCAmelCase_  (     self				:Any	, lowerCamelCase				:Any	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Union[str, Any]	, lowerCamelCase				:List[Any]	, lowerCamelCase				:Tuple	, lowerCamelCase				:Dict	, lowerCamelCase				:Any	, lowerCamelCase				:Tuple )       ->     List[Any]:
       UpperCAmelCase__								=  LayoutLMForQuestionAnswering(config=lowerCamelCase )
       model.to(lowerCamelCase )
       model.eval()
       UpperCAmelCase__								=  model(
           lowerCamelCase	, bbox=lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, start_positions=lowerCamelCase	, end_positions=lowerCamelCase	, )
       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				:List[str] )       ->     str:
       UpperCAmelCase__								=  self.prepare_config_and_inputs()
       (
           (
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,	(
           UpperCAmelCase__	
       )	,		
       )								=  config_and_inputs
       UpperCAmelCase__								=  {
           "input_ids": input_ids,
           "bbox": bbox,
           "token_type_ids": token_type_ids,
           "attention_mask": input_mask,
       }
       return config, inputs_dict



@require_torch
class      _UpperCamelCase (			lowerCAmelCase		,    lowerCAmelCase		,    unittest.TestCase						):
      UpperCAmelCase_            =						(
          (
              LayoutLMModel,
              LayoutLMForMaskedLM,
              LayoutLMForSequenceClassification,
              LayoutLMForTokenClassification,
              LayoutLMForQuestionAnswering,
          )
          if is_torch_available()
          else None
      )
      UpperCAmelCase_            =						(
          {
              """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_            =						True
      def 		UpperCAmelCase_  (     self				:str )       ->     str:
       UpperCAmelCase__								=  LayoutLMModelTester(self )
       UpperCAmelCase__								=  ConfigTester(self	, config_class=lowerCamelCase	, hidden_size=37 )
      def 		UpperCAmelCase_  (     self				:List[str] )       ->     Tuple:
       self.config_tester.run_common_tests()
      def 		UpperCAmelCase_  (     self				:List[str] )       ->     Tuple:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Optional[int] )       ->     Optional[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       for type in ["absolute", "relative_key", "relative_key_query"]:
        UpperCAmelCase__								=  type
        self.model_tester.create_and_check_model(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:Union[str, Any] )       ->     Optional[int]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:List[str] )       ->     Union[str, Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:str )       ->     Optional[int]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:str )       ->     List[Any]:
       UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )







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



def   lowerCAmelCase						(						):




 """simple docstring"""







 UpperCAmelCase__								=  torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]  ,							device=_lowerCAmelCase		)  # noqa: E231
 UpperCAmelCase__								=  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=_lowerCAmelCase		)  # noqa: E231
 UpperCAmelCase__								=  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],[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]]]  ,							device=_lowerCAmelCase		)  # noqa: E231
 UpperCAmelCase__								=  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=_lowerCAmelCase		)  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 UpperCAmelCase__								=  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=_lowerCAmelCase		)  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels


@require_torch
class      _UpperCamelCase (			unittest.TestCase						):
      @slow
      def 		UpperCAmelCase_  (     self				:Tuple )       ->     int:
       UpperCAmelCase__								=  LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased" ).to(lowerCamelCase )

       UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__								=  prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCAmelCase__								=  model(input_ids=lowerCamelCase	, bbox=lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase )

       # test the sequence output on [0, :3, :3]
       UpperCAmelCase__								=  torch.tensor(
           [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]	, device=lowerCamelCase	, )

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

       # test the pooled output on [1, :3]
       UpperCAmelCase__								=  torch.tensor([-0.65_80, -0.02_14, 0.85_52]	, device=lowerCamelCase )

       self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]	, lowerCamelCase	, atol=1e-3 ) )
      @slow
      def 		UpperCAmelCase_  (     self				:List[str] )       ->     Union[str, Any]:
       # initialize model with randomly initialized sequence classification head
       UpperCAmelCase__								=  LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"	, num_labels=2 ).to(
           lowerCamelCase )

       UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__								=  prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCAmelCase__								=  model(
           input_ids=lowerCamelCase	, bbox=lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=torch.tensor([1, 1]	, device=lowerCamelCase )	, )

       # test whether we get a loss as a scalar
       UpperCAmelCase__								=  outputs.loss
       UpperCAmelCase__								=  torch.Size([] )
       self.assertEqual(loss.shape	, lowerCamelCase )

       # test the shape of the logits
       UpperCAmelCase__								=  outputs.logits
       UpperCAmelCase__								=  torch.Size((2, 2) )
       self.assertEqual(logits.shape	, lowerCamelCase )
      @slow
      def 		UpperCAmelCase_  (     self				:str )       ->     Optional[int]:
       # initialize model with randomly initialized token classification head
       UpperCAmelCase__								=  LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"	, num_labels=13 ).to(
           lowerCamelCase )

       UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__								=  prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCAmelCase__								=  model(
           input_ids=lowerCamelCase	, bbox=lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase	, labels=lowerCamelCase )

       # 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__								=  outputs.logits
       UpperCAmelCase__								=  torch.Size((2, 25, 13) )
       self.assertEqual(logits.shape	, lowerCamelCase )







      @slow
      def 		UpperCAmelCase_  (     self				:int )       ->     int:
       # initialize model with randomly initialized token classification head
       UpperCAmelCase__								=  LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased" ).to(lowerCamelCase )

       UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__								=  prepare_layoutlm_batch_inputs()

       # forward pass
       UpperCAmelCase__								=  model(input_ids=lowerCamelCase	, bbox=lowerCamelCase	, attention_mask=lowerCamelCase	, token_type_ids=lowerCamelCase )

       # test the shape of the logits
       UpperCAmelCase__								=  torch.Size((2, 25) )
       self.assertEqual(outputs.start_logits.shape	, lowerCamelCase )
       self.assertEqual(outputs.end_logits.shape	, lowerCamelCase )











from collections import defaultdict



def   lowerCAmelCase						(						_lowerCAmelCase							:				int = 5		):




 """simple docstring"""







 UpperCAmelCase__								=  defaultdict(_lowerCAmelCase		)
 UpperCAmelCase__								=  0

 while True:
  UpperCAmelCase__								=  get_digits(_lowerCAmelCase		)
  freqs[digits].append(_lowerCAmelCase		)

  if len(freqs[digits]		) == max_base:
   UpperCAmelCase__								=  freqs[digits][0] ** 3
   return base

  num += 1



def   lowerCAmelCase						(						_lowerCAmelCase							:				int		):




 """simple docstring"""







 return "".join(sorted(str(num**3		)		)		)


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





