





'''simple docstring'''




import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     def __init__(   self							:			List[str]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			int=13   ,       __snake_case							:			Tuple=7   ,       __snake_case							:			Union[str, Any]=True   ,       __snake_case							:			List[str]=True   ,       __snake_case							:			List[str]=True   ,       __snake_case							:			Optional[int]=True   ,       __snake_case							:			Optional[Any]=99   ,       __snake_case							:			str=32   ,       __snake_case							:			Dict=5   ,       __snake_case							:			List[str]=4   ,       __snake_case							:			Dict=37   ,       __snake_case							:			Dict="gelu"   ,       __snake_case							:			Union[str, Any]=0.1   ,       __snake_case							:			Tuple=0.1   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			Optional[Any]=16   ,       __snake_case							:			List[str]=2   ,       __snake_case							:			Optional[Any]=0.02   ,       __snake_case							:			str=False   ,       __snake_case							:			Optional[Any]=True   ,       __snake_case							:			Optional[int]="None"   ,       __snake_case							:			Optional[Any]=3   ,       __snake_case							:			Dict=4   ,       __snake_case							:			str=None   ,       )->	List[Any]:
        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 						=   relative_attention
        snake_case 						=   position_biased_input
        snake_case 						=   pos_att_type
        snake_case 						=   scope



     def      lowerCAmelCase			(   self							:			Dict						)->	Tuple:
        snake_case 						=   ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size						)

        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, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



     def      lowerCAmelCase			(   self							:			Dict						)->	Tuple:
        return DebertaConfig(
            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   ,       relative_attention=self.relative_attention   ,       position_biased_input=self.position_biased_input   ,       pos_att_type=self.pos_att_type   ,       )



     def      lowerCAmelCase			(   self							:			Dict						)->	List[Any]:
        snake_case 						=   self.get_config()
        snake_case 						=   3_00
        return config



     def      lowerCAmelCase			(   self							:			int   ,       __snake_case							:			Optional[Any]						)->	str:
        self.parent.assertListEqual(list(result.loss.size()						)   ,       []						)



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			Dict   ,       __snake_case							:			Dict   ,       __snake_case							:			str   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			Dict   ,       __snake_case							:			List[Any]						)->	Union[str, Any]:
        snake_case 						=   DebertaModel(config=__snake_case						)
        model.to(__snake_case						)
        model.eval()
        snake_case 						=   model(__snake_case   ,       attention_mask=__snake_case   ,       token_type_ids=__snake_case						)[0]
        snake_case 						=   model(__snake_case   ,       token_type_ids=__snake_case						)[0]
        snake_case 						=   model(__snake_case						)[0]

        self.parent.assertListEqual(list(sequence_output.size()						)   ,       [self.batch_size, self.seq_length, self.hidden_size]						)



     def      lowerCAmelCase			(   self							:			List[Any]   ,       __snake_case							:			Any   ,       __snake_case							:			Tuple   ,       __snake_case							:			Tuple   ,       __snake_case							:			List[str]   ,       __snake_case							:			int   ,       __snake_case							:			Optional[Any]   ,       __snake_case							:			Dict						)->	str:
        snake_case 						=   DebertaForMaskedLM(config=__snake_case						)
        model.to(__snake_case						)
        model.eval()
        snake_case 						=   model(__snake_case   ,       attention_mask=__snake_case   ,       token_type_ids=__snake_case   ,       labels=__snake_case						)

        self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.vocab_size)						)



     def      lowerCAmelCase			(   self							:			List[str]   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			Tuple   ,       __snake_case							:			Any   ,       __snake_case							:			int   ,       __snake_case							:			str   ,       __snake_case							:			List[Any]						)->	Union[str, Any]:
        snake_case 						=   self.num_labels
        snake_case 						=   DebertaForSequenceClassification(__snake_case						)
        model.to(__snake_case						)
        model.eval()
        snake_case 						=   model(__snake_case   ,       attention_mask=__snake_case   ,       token_type_ids=__snake_case   ,       labels=__snake_case						)
        self.parent.assertListEqual(list(result.logits.size()						)   ,       [self.batch_size, self.num_labels]						)
        self.check_loss_output(__snake_case						)



     def      lowerCAmelCase			(   self							:			Tuple   ,       __snake_case							:			Dict   ,       __snake_case							:			Tuple   ,       __snake_case							:			str   ,       __snake_case							:			Union[str, Any]   ,       __snake_case							:			int   ,       __snake_case							:			Any   ,       __snake_case							:			Tuple						)->	Union[str, Any]:
        snake_case 						=   self.num_labels
        snake_case 						=   DebertaForTokenClassification(config=__snake_case						)
        model.to(__snake_case						)
        model.eval()
        snake_case 						=   model(__snake_case   ,       attention_mask=__snake_case   ,       token_type_ids=__snake_case   ,       labels=__snake_case						)
        self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.seq_length, self.num_labels)						)



     def      lowerCAmelCase			(   self							:			Optional[int]   ,       __snake_case							:			int   ,       __snake_case							:			Optional[int]   ,       __snake_case							:			List[str]   ,       __snake_case							:			List[Any]   ,       __snake_case							:			int   ,       __snake_case							:			str   ,       __snake_case							:			Optional[Any]						)->	Any:
        snake_case 						=   DebertaForQuestionAnswering(config=__snake_case						)
        model.to(__snake_case						)
        model.eval()
        snake_case 						=   model(
            __snake_case   ,       attention_mask=__snake_case   ,       token_type_ids=__snake_case   ,       start_positions=__snake_case   ,       end_positions=__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							:			List[str]						)->	Dict:
        snake_case 						=   self.prepare_config_and_inputs()
        (
            (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    (
            snake_case
        )						,    
        ) 						=   config_and_inputs
        snake_case 						=   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
        return config, inputs_dict




@require_torch
class 			_lowerCAmelCase		(			A__							,     A__							,     unittest.TestCase	):






     """simple docstring"""






     snake_case_						      =   (
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     snake_case_						      =   (
         {
             "feature-extraction": DebertaModel,
             "fill-mask": DebertaForMaskedLM,
             "question-answering": DebertaForQuestionAnswering,
             "text-classification": DebertaForSequenceClassification,
             "token-classification": DebertaForTokenClassification,
             "zero-shot": DebertaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     snake_case_						      =   True
     snake_case_						      =   False
     snake_case_						      =   False
     snake_case_						      =   False
     snake_case_						      =   False



     def      lowerCAmelCase			(   self							:			int						)->	str:
        snake_case 						=   DebertaModelTester(self						)
        snake_case 						=   ConfigTester(self   ,       config_class=__snake_case   ,       hidden_size=37						)



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



     def      lowerCAmelCase			(   self							:			int						)->	Optional[Any]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_model(*__snake_case						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	Union[str, Any]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_sequence_classification(*__snake_case						)



     def      lowerCAmelCase			(   self							:			List[Any]						)->	Optional[Any]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_masked_lm(*__snake_case						)



     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Any:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_question_answering(*__snake_case						)



     def      lowerCAmelCase			(   self							:			List[str]						)->	List[str]:
        snake_case 						=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_deberta_for_token_classification(*__snake_case						)



     @slow
     def      lowerCAmelCase			(   self							:			Optional[int]						)->	Optional[Any]:
        for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           snake_case 						=   DebertaModel.from_pretrained(__snake_case						)
           self.assertIsNotNone(__snake_case						)




@require_torch
@require_sentencepiece
@require_tokenizers
class 			_lowerCAmelCase		(			unittest.TestCase	):






     """simple docstring"""






     @unittest.skip(reason="""Model not available yet"""						)
     def      lowerCAmelCase			(   self							:			Any						)->	Tuple:
        pass



     @slow
     def      lowerCAmelCase			(   self							:			int						)->	Any:
        snake_case 						=   DebertaModel.from_pretrained("""microsoft/deberta-base"""						)

        snake_case 						=   torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]						)
        snake_case 						=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
        with torch.no_grad():
           snake_case 						=   model(__snake_case   ,       attention_mask=__snake_case						)[0]
        # compare the actual values for a slice.
        snake_case 						=   torch.tensor(
            [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]						)
        self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,       __snake_case   ,       atol=1e-4						)   ,       f'''{output[:, 1:4, 1:4]}'''						)








'''simple docstring'''




from __future__ import annotations





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float		)		->  float:
   if days_between_payments <= 0:
      raise ValueError("""days_between_payments must be > 0"""		)
   if daily_interest_rate < 0:
      raise ValueError("""daily_interest_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)
   return principal * daily_interest_rate * days_between_payments





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_compounding_periods <= 0:
      raise ValueError("""number_of_compounding_periods must be > 0"""		)
   if nominal_annual_interest_rate_percentage < 0:
      raise ValueError("""nominal_annual_interest_rate_percentage must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return principal * (
       (1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
       - 1
   )





def 	__lowerCamelCase			(      __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    __lowerCAmelCase       :							float      ,    )		->  float:
   if number_of_years <= 0:
      raise ValueError("""number_of_years must be > 0"""		)
   if nominal_annual_percentage_rate < 0:
      raise ValueError("""nominal_annual_percentage_rate must be >= 0"""		)
   if principal <= 0:
      raise ValueError("""principal must be > 0"""		)

   return compound_interest(
       __lowerCAmelCase      ,    nominal_annual_percentage_rate / 3_65      ,    number_of_years * 3_65		)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

