"""simple docstring"""


def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""





  if p < 2:
    raise ValueError("""p should not be less than 2!"""						)
  elif p == 2:
    return True

  _UpperCAmelCase         =					4
  _UpperCAmelCase         =					(1 << p) - 1
  for _ in range(p - 2						):
    _UpperCAmelCase         =					((s * s) - 2) % m
  return s == 0


if __name__ == "__main__":
    print(lucas_lehmer_test(7))
    print(lucas_lehmer_test(1_1))




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




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size    )

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

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

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

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

       a						:List[Any] 							=       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Optional[int] 							=       TFLayoutLMModel(config=_lowerCamelCase    )

       a						:Dict 							=       model(_lowerCamelCase					,  _lowerCamelCase					,  attention_mask=_lowerCamelCase					,  token_type_ids=_lowerCamelCase    )
       a						:Union[str, Any] 							=       model(_lowerCamelCase					,  _lowerCamelCase					,  token_type_ids=_lowerCamelCase    )
       a						:Union[str, Any] 							=       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:List[str] 							=       TFLayoutLMForMaskedLM(config=_lowerCamelCase    )

       a						:int 							=       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Optional[int] 							=       self.num_labels
       a						:List[Any] 							=       TFLayoutLMForSequenceClassification(config=_lowerCamelCase    )

       a						:int 							=       model(_lowerCamelCase					,  _lowerCamelCase					,  attention_mask=_lowerCamelCase					,  token_type_ids=_lowerCamelCase    )
       self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels)    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:int 							=       self.num_labels
       a						:Optional[int] 							=       TFLayoutLMForTokenClassification(config=_lowerCamelCase    )

       a						:int 							=       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Optional[Any] 							=       TFLayoutLMForQuestionAnswering(config=_lowerCamelCase    )

       a						:Optional[int] 							=       model(_lowerCamelCase					,  _lowerCamelCase					,  attention_mask=_lowerCamelCase					,  token_type_ids=_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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       self.prepare_config_and_inputs()
       (
           (
           a
       )							,				(
           a
       )							,				(
           a
       )							,				(
           a
       )							,				(
           a
       )							,				(
           a
       )							,				(
           a
       )							,				(
           a
       )							,				
       )						:List[Any] 							=       config_and_inputs
       a						:Union[str, 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  (      _snake_case						,    _snake_case						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       (
     (
         TFLayoutLMModel,
         TFLayoutLMForMaskedLM,
         TFLayoutLMForTokenClassification,
         TFLayoutLMForSequenceClassification,
         TFLayoutLMForQuestionAnswering,
     )
     if is_tf_available()
     else ()
 )
 SCREAMING_SNAKE_CASE__      =       (
     {
         'feature-extraction': TFLayoutLMModel,
         'fill-mask': TFLayoutLMForMaskedLM,
         'text-classification': TFLayoutLMForSequenceClassification,
         'token-classification': TFLayoutLMForTokenClassification,
         'zero-shot': TFLayoutLMForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 SCREAMING_SNAKE_CASE__      =       False
 SCREAMING_SNAKE_CASE__      =       True
 SCREAMING_SNAKE_CASE__      =       10




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Dict 							=       TFLayoutLMModelTester(self    )
       a						:Dict 							=       ConfigTester(self					,  config_class=_lowerCamelCase					,  hidden_size=37    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       self.config_tester.run_common_tests()




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:int 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase    )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             a						:str 							=       TFLayoutLMModel.from_pretrained(_lowerCamelCase    )
             self.assertIsNotNone(_lowerCamelCase    )




 @unittest.skip('''Onnx compliancy broke with TF 2.10'''    )
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       pass







def 				__lowerCamelCase						( ):




      """simple docstring"""





      a						:Tuple 							=       tf.convert_to_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]]						)  # noqa: E231
      a						: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
      a						:List[str] 							=       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						:List[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						: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        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''    )

       a							,				a							,				a							,				a							,				a						:Optional[Any] 							=       prepare_layoutlm_batch_inputs()

       # forward pass
       a						:Tuple 							=       model(input_ids=_lowerCamelCase					,  bbox=_lowerCamelCase					,  attention_mask=_lowerCamelCase					,  token_type_ids=_lowerCamelCase    )

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

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

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




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # initialize model with randomly initialized sequence classification head
       a						:str 							=       TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''					,  num_labels=2    )

       a							,				a							,				a							,				a							,				a						:List[str] 							=       prepare_layoutlm_batch_inputs()

       # forward pass
       a						:List[Any] 							=       model(
           input_ids=_lowerCamelCase					,  bbox=_lowerCamelCase					,  attention_mask=_lowerCamelCase					,  token_type_ids=_lowerCamelCase					,  labels=tf.convert_to_tensor([1, 1]    )					,  )

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

       # test the shape of the logits
       a						:Any 							=       outputs.logits
       a						:Tuple 							=       (2, 2)
       self.assertEqual(logits.shape					,  _lowerCamelCase    )




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # initialize model with randomly initialized token classification head
       a						:Dict 							=       TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''					,  num_labels=13    )

       a							,				a							,				a							,				a							,				a						:Dict 							=       prepare_layoutlm_batch_inputs()

       # forward pass
       a						:List[Any] 							=       model(
           input_ids=_lowerCamelCase					,  bbox=_lowerCamelCase					,  attention_mask=_lowerCamelCase					,  token_type_ids=_lowerCamelCase					,  labels=_lowerCamelCase    )

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




 @slow
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # initialize model with randomly initialized token classification head
       a						:List[Any] 							=       TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''    )

       a							,				a							,				a							,				a							,				a						:Any 							=       prepare_layoutlm_batch_inputs()

       # forward pass
       a						:str 							=       model(input_ids=_lowerCamelCase					,  bbox=_lowerCamelCase					,  attention_mask=_lowerCamelCase					,  token_type_ids=_lowerCamelCase    )

       # test the shape of the logits
       a						:Optional[int] 							=       tf.convert_to_tensor((2, 25)    )
       self.assertEqual(outputs.start_logits.shape					,  _lowerCamelCase    )
       self.assertEqual(outputs.end_logits.shape					,  _lowerCamelCase    )





