




'''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      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=13			,     __SCREAMING_SNAKE_CASE=7			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=99			,     __SCREAMING_SNAKE_CASE=32			,     __SCREAMING_SNAKE_CASE=5			,     __SCREAMING_SNAKE_CASE=4			,     __SCREAMING_SNAKE_CASE=37			,     __SCREAMING_SNAKE_CASE="gelu"			,     __SCREAMING_SNAKE_CASE=0.1			,     __SCREAMING_SNAKE_CASE=0.1			,     __SCREAMING_SNAKE_CASE=512			,     __SCREAMING_SNAKE_CASE=16			,     __SCREAMING_SNAKE_CASE=2			,     __SCREAMING_SNAKE_CASE=0.02			,     __SCREAMING_SNAKE_CASE=3			,     __SCREAMING_SNAKE_CASE=4			,     __SCREAMING_SNAKE_CASE=None			,     __SCREAMING_SNAKE_CASE=1000			,     )					->     Optional[int]:



            '''simple docstring'''




            __snake_case			    =     parent
            __snake_case			    =     batch_size
            __snake_case			    =     seq_length
            __snake_case			    =     is_training
            __snake_case			    =     use_input_mask
            __snake_case			    =     use_token_type_ids
            __snake_case			    =     use_labels
            __snake_case			    =     vocab_size
            __snake_case			    =     hidden_size
            __snake_case			    =     num_hidden_layers
            __snake_case			    =     num_attention_heads
            __snake_case			    =     intermediate_size
            __snake_case			    =     hidden_act
            __snake_case			    =     hidden_dropout_prob
            __snake_case			    =     attention_probs_dropout_prob
            __snake_case			    =     max_position_embeddings
            __snake_case			    =     type_vocab_size
            __snake_case			    =     type_sequence_label_size
            __snake_case			    =     initializer_range
            __snake_case			    =     num_labels
            __snake_case			    =     num_choices
            __snake_case			    =     scope
            __snake_case			    =     range_bbox


       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size     )

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

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

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

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

            __snake_case			    =     self.get_config()

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


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



            '''simple docstring'''




            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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     List[str]:



            '''simple docstring'''




            __snake_case			    =     LayoutLMModel(config=__SCREAMING_SNAKE_CASE     )
            model.to(__SCREAMING_SNAKE_CASE     )
            model.eval()
            __snake_case			    =     model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE     )
            __snake_case			    =     model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )
            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			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     int:



            '''simple docstring'''




            __snake_case			    =     LayoutLMForMaskedLM(config=__SCREAMING_SNAKE_CASE     )
            model.to(__SCREAMING_SNAKE_CASE     )
            model.eval()
            __snake_case			    =     model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE			,     labels=__SCREAMING_SNAKE_CASE     )
            self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     int:



            '''simple docstring'''




            __snake_case			    =     self.num_labels
            __snake_case			    =     LayoutLMForSequenceClassification(__SCREAMING_SNAKE_CASE     )
            model.to(__SCREAMING_SNAKE_CASE     )
            model.eval()
            __snake_case			    =     model(
                __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE			,     labels=__SCREAMING_SNAKE_CASE     )
            self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     self.num_labels
            __snake_case			    =     LayoutLMForTokenClassification(config=__SCREAMING_SNAKE_CASE     )
            model.to(__SCREAMING_SNAKE_CASE     )
            model.eval()
            __snake_case			    =     model(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE			,     labels=__SCREAMING_SNAKE_CASE     )
            self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)     )


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     LayoutLMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE     )
            model.to(__SCREAMING_SNAKE_CASE     )
            model.eval()
            __snake_case			    =     model(
                __SCREAMING_SNAKE_CASE			,     bbox=__SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE			,     start_positions=__SCREAMING_SNAKE_CASE			,     end_positions=__SCREAMING_SNAKE_CASE			,     )
            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     )					->     Optional[int]:



            '''simple docstring'''




            __snake_case			    =     self.prepare_config_and_inputs()
            (
                (
                __snake_case
            )    ,			(
                __snake_case
            )    ,			(
                __snake_case
            )    ,			(
                __snake_case
            )    ,			(
                __snake_case
            )    ,			(
                __snake_case
            )    ,			(
                __snake_case
            )    ,			(
                __snake_case
            )    ,			
            )			    =     config_and_inputs
            __snake_case			    =     {
                '''input_ids''': input_ids,
                '''bbox''': bbox,
                '''token_type_ids''': token_type_ids,
                '''attention_mask''': input_mask,
            }
            return config, inputs_dict





@require_torch
class 			lowerCAmelCase      (       __lowerCAmelCase ,      __lowerCAmelCase ,      unittest.TestCase):
       __lowercase     :					Optional[Any]       				=	(
           (
               LayoutLMModel,
               LayoutLMForMaskedLM,
               LayoutLMForSequenceClassification,
               LayoutLMForTokenClassification,
               LayoutLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __lowercase     :					List[str]       				=	(
           {
               '''document-question-answering''': LayoutLMForQuestionAnswering,
               '''feature-extraction''': LayoutLMModel,
               '''fill-mask''': LayoutLMForMaskedLM,
               '''text-classification''': LayoutLMForSequenceClassification,
               '''token-classification''': LayoutLMForTokenClassification,
               '''zero-shot''': LayoutLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __lowercase     :					List[Any]       				=	True


       def 					lowerCAmelCase					(      self     )					->     Dict:



            '''simple docstring'''




            __snake_case			    =     LayoutLMModelTester(self     )
            __snake_case			    =     ConfigTester(self			,     config_class=__SCREAMING_SNAKE_CASE			,     hidden_size=37     )


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



            '''simple docstring'''




            self.config_tester.run_common_tests()


       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )


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



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            for type in ["absolute", "relative_key", "relative_key_query"]:
                 __snake_case			    =     type
                 self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self     )					->     int:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self     )					->     Optional[Any]:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self     )					->     Tuple:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE     )


       def 					lowerCAmelCase					(      self     )					->     str:



            '''simple docstring'''




            __snake_case			    =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE     )




       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
       def 					lowerCAmelCase					(      self     )					->     List[Any]:



            '''simple docstring'''




            pass







def 	_UpperCamelCase       ()->      Optional[Any]:




     '''simple docstring'''





     __snake_case			    =     torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]      ,		device=_lowerCamelCase		)  # noqa: E231
     __snake_case			    =     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
     __snake_case			    =     torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]      ,		device=_lowerCamelCase		)  # noqa: E231
     __snake_case			    =     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)
     __snake_case			    =     torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]      ,		device=_lowerCamelCase		)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 			lowerCAmelCase      (       unittest.TestCase):


       @slow
       def 					lowerCAmelCase					(      self     )					->     List[Any]:



            '''simple docstring'''




            __snake_case			    =     LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''     ).to(__SCREAMING_SNAKE_CASE     )

            __snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case			    =     prepare_layoutlm_batch_inputs()

            # forward pass
            __snake_case			    =     model(input_ids=__SCREAMING_SNAKE_CASE			,     bbox=__SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE     )

            # test the sequence output on [0, :3, :3]
            __snake_case			    =     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=__SCREAMING_SNAKE_CASE			,     )

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

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

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


       @slow
       def 					lowerCAmelCase					(      self     )					->     List[Any]:



            '''simple docstring'''




            __snake_case			    =     LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''			,     num_labels=2     ).to(
                __SCREAMING_SNAKE_CASE     )

            __snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case			    =     prepare_layoutlm_batch_inputs()

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

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

            # test the shape of the logits
            __snake_case			    =     outputs.logits
            __snake_case			    =     torch.Size((2, 2)     )
            self.assertEqual(logits.shape			,     __SCREAMING_SNAKE_CASE     )


       @slow
       def 					lowerCAmelCase					(      self     )					->     str:



            '''simple docstring'''




            __snake_case			    =     LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''			,     num_labels=13     ).to(
                __SCREAMING_SNAKE_CASE     )

            __snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case			    =     prepare_layoutlm_batch_inputs()

            # forward pass
            __snake_case			    =     model(
                input_ids=__SCREAMING_SNAKE_CASE			,     bbox=__SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE			,     labels=__SCREAMING_SNAKE_CASE     )

            # 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
            __snake_case			    =     outputs.logits
            __snake_case			    =     torch.Size((2, 25, 13)     )
            self.assertEqual(logits.shape			,     __SCREAMING_SNAKE_CASE     )




       @slow
       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            __snake_case			    =     LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''     ).to(__SCREAMING_SNAKE_CASE     )

            __snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case    ,			__snake_case			    =     prepare_layoutlm_batch_inputs()

            # forward pass
            __snake_case			    =     model(input_ids=__SCREAMING_SNAKE_CASE			,     bbox=__SCREAMING_SNAKE_CASE			,     attention_mask=__SCREAMING_SNAKE_CASE			,     token_type_ids=__SCREAMING_SNAKE_CASE     )

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












'''simple docstring'''



def 	_UpperCamelCase       (_lowerCamelCase							:   list[list[int]]      ,		_lowerCamelCase							:   list[list[int]]		)->      list[list[int]]:




     '''simple docstring'''





     __snake_case			    =     []
     __snake_case			    =     len(_lowerCamelCase		)
     for i in range(_lowerCamelCase		):
          __snake_case			    =     []
          for j in range(_lowerCamelCase		):
               __snake_case			    =     0
               for k in range(_lowerCamelCase		):
                    __snake_case			    =     val + matrix_a[i][k] * matrix_b[k][j]
               list_a.append(_lowerCamelCase		)
          matrix_c.append(_lowerCamelCase		)
     return matrix_c







def 	_UpperCamelCase       (_lowerCamelCase							:   int		)->      list[list[int]]:




     '''simple docstring'''





     return [[int(row == column		) for column in range(_lowerCamelCase		)] for row in range(_lowerCamelCase		)]







def 	_UpperCamelCase       (_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if n <= 1:
          return n
     __snake_case			    =     identity(2		)
     __snake_case			    =     [[1, 1], [1, 0]]
     __snake_case			    =     n - 1
     while n > 0:
          if n % 2 == 1:
               __snake_case			    =     multiply(_lowerCamelCase      ,		_lowerCamelCase		)
          __snake_case			    =     multiply(_lowerCamelCase      ,		_lowerCamelCase		)
          __snake_case			    =     int(n / 2		)
     return res_matrix[0][0]







def 	_UpperCamelCase       (_lowerCamelCase							:   int		)->      int:




     '''simple docstring'''





     if n <= 1:
          return n
     __snake_case			    =     0
     __snake_case			    =     1
     for _ in range(2      ,		n + 1		):
          __snake_case    ,			__snake_case			    =     fiba, fiba + fiba
     return fiba







def 	_UpperCamelCase       ()->      None:




     '''simple docstring'''





     for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
          __snake_case			    =     int(''''''.join(c for c in ordinal if c in '''0123456789'''		)		)  # 1000th --> 1000
          print(
              f'''{ordinal} fibonacci number using matrix exponentiation is '''
              f'''{nth_fibonacci_matrix(_lowerCamelCase		)} and using bruteforce is '''
              f'''{nth_fibonacci_bruteforce(_lowerCamelCase		)}\n'''		)
     # from timeit import timeit
     # print(timeit("nth_fibonacci_matrix(1000000)",
     #              "from main import nth_fibonacci_matrix", number=5))
     # print(timeit("nth_fibonacci_bruteforce(1000000)",
     #              "from main import nth_fibonacci_bruteforce", number=5))
     # 2.3342058970001744
     # 57.256506615000035


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()






