


import unittest

from transformers import DebertaVaConfig, 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 (
           DebertaVaForMaskedLM,
           DebertaVaForMultipleChoice,
           DebertaVaForQuestionAnswering,
           DebertaVaForSequenceClassification,
           DebertaVaForTokenClassification,
           DebertaVaModel,
       )
       from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     List[Any]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Union[str, Any]=1_3      ,							lowerCamelCase_      :     str=7      ,							lowerCamelCase_      :     List[str]=True      ,							lowerCamelCase_      :     Optional[Any]=True      ,							lowerCamelCase_      :     Optional[int]=True      ,							lowerCamelCase_      :     int=True      ,							lowerCamelCase_      :     Tuple=9_9      ,							lowerCamelCase_      :     List[Any]=3_2      ,							lowerCamelCase_      :     str=5      ,							lowerCamelCase_      :     Union[str, Any]=4      ,							lowerCamelCase_      :     List[str]=3_7      ,							lowerCamelCase_      :     Union[str, Any]="gelu"      ,							lowerCamelCase_      :     Dict=0.1      ,							lowerCamelCase_      :     Optional[int]=0.1      ,							lowerCamelCase_      :     Any=5_1_2      ,							lowerCamelCase_      :     Any=1_6      ,							lowerCamelCase_      :     Dict=2      ,							lowerCamelCase_      :     Tuple=0.0_2      ,							lowerCamelCase_      :     Tuple=False      ,							lowerCamelCase_      :     List[str]=True      ,							lowerCamelCase_      :     List[Any]="None"      ,							lowerCamelCase_      :     str=3      ,							lowerCamelCase_      :     str=4      ,							lowerCamelCase_      :     List[str]=None      ,							):
            a_    :     Dict										=    parent
            a_    :     Optional[Any]										=    batch_size
            a_    :     Tuple										=    seq_length
            a_    :     Any										=    is_training
            a_    :     Tuple										=    use_input_mask
            a_    :     Any										=    use_token_type_ids
            a_    :     Any										=    use_labels
            a_    :     List[str]										=    vocab_size
            a_    :     Any										=    hidden_size
            a_    :     Dict										=    num_hidden_layers
            a_    :     int										=    num_attention_heads
            a_    :     str										=    intermediate_size
            a_    :     Optional[int]										=    hidden_act
            a_    :     Tuple										=    hidden_dropout_prob
            a_    :     Tuple										=    attention_probs_dropout_prob
            a_    :     Tuple										=    max_position_embeddings
            a_    :     Optional[Any]										=    type_vocab_size
            a_    :     Dict										=    type_sequence_label_size
            a_    :     int										=    initializer_range
            a_    :     int										=    num_labels
            a_    :     Any										=    num_choices
            a_    :     Union[str, Any]										=    relative_attention
            a_    :     str										=    position_biased_input
            a_    :     Any										=    pos_att_type
            a_    :     str										=    scope

       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            a_    :     Union[str, Any]										=    ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size    )

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

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

            a_    :     Tuple										=    None
            a_    :     Dict										=    None
            a_    :     Optional[int]										=    None
            if self.use_labels:
                 a_    :     Union[str, Any]										=    ids_tensor([self.batch_size]      ,							self.type_sequence_label_size    )
                 a_    :     Tuple										=    ids_tensor([self.batch_size, self.seq_length]      ,							self.num_labels    )
                 a_    :     int										=    ids_tensor([self.batch_size]      ,							self.num_choices    )

            a_    :     Union[str, Any]										=    self.get_config()

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

       def 						UpperCAmelCase(   self      :     Any    ):
            return DebertaVaConfig(
                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 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     List[str]    ):
            self.parent.assertListEqual(list(result.loss.size()    )      ,							[]    )

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     int										=    DebertaVaModel(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     Optional[int]										=    model(lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )[0]
            a_    :     Any										=    model(lowerCamelCase_      ,							token_type_ids=lowerCamelCase_    )[0]
            a_    :     int										=    model(lowerCamelCase_    )[0]

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

       def 						UpperCAmelCase(   self      :     Optional[int]      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Optional[Any]    ):
            a_    :     Tuple										=    DebertaVaForMaskedLM(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     Union[str, Any]										=    model(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      :     Tuple      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     Union[str, Any]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Tuple    ):
            a_    :     int										=    self.num_labels
            a_    :     Any										=    DebertaVaForSequenceClassification(lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     List[str]										=    model(lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_      ,							labels=lowerCamelCase_    )
            self.parent.assertListEqual(list(result.logits.size()    )      ,							[self.batch_size, self.num_labels]    )
            self.check_loss_output(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Dict      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     Optional[int]										=    self.num_labels
            a_    :     Tuple										=    DebertaVaForTokenClassification(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     Any										=    model(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      :     Union[str, Any]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[int]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[Any]    ):
            a_    :     str										=    DebertaVaForQuestionAnswering(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     List[str]										=    model(
                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      :     Any      ,							lowerCamelCase_      :     List[Any]      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Optional[Any]      ,							lowerCamelCase_      :     Any      ,							lowerCamelCase_      :     List[str]      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     Optional[int]    ):
            a_    :     Any										=    DebertaVaForMultipleChoice(config=lowerCamelCase_    )
            model.to(lowerCamelCase_    )
            model.eval()
            a_    :     Union[str, Any]										=    input_ids.unsqueeze(1    ).expand(-1      ,							self.num_choices      ,							-1    ).contiguous()
            a_    :     Optional[Any]										=    token_type_ids.unsqueeze(1    ).expand(-1      ,							self.num_choices      ,							-1    ).contiguous()
            a_    :     Tuple										=    input_mask.unsqueeze(1    ).expand(-1      ,							self.num_choices      ,							-1    ).contiguous()
            a_    :     Optional[Any]										=    model(
                lowerCamelCase_      ,							attention_mask=lowerCamelCase_      ,							token_type_ids=lowerCamelCase_      ,							labels=lowerCamelCase_      ,							)
            self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_choices)    )

       def 						UpperCAmelCase(   self      :     str    ):
            a_    :     Union[str, Any]										=    self.prepare_config_and_inputs()
            (
                (
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			(
                a_
            )			,			
            )    :     str										=    config_and_inputs
            a_    :     Optional[Any]										=    {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
            return config, inputs_dict


@require_torch
class   a__	(						lowerCAmelCase_						,       lowerCAmelCase_						,       unittest.TestCase    ):
       lowerCamelCase__:     int							    =     (
           (
               DebertaVaModel,
               DebertaVaForMaskedLM,
               DebertaVaForSequenceClassification,
               DebertaVaForTokenClassification,
               DebertaVaForQuestionAnswering,
               DebertaVaForMultipleChoice,
           )
           if is_torch_available()
           else ()
       )
       lowerCamelCase__:     Union[str, Any]							    =     (
           {
               """feature-extraction""": DebertaVaModel,
               """fill-mask""": DebertaVaForMaskedLM,
               """question-answering""": DebertaVaForQuestionAnswering,
               """text-classification""": DebertaVaForSequenceClassification,
               """token-classification""": DebertaVaForTokenClassification,
               """zero-shot""": DebertaVaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       lowerCamelCase__:     int							    =     True
       lowerCamelCase__:     List[str]							    =     False
       lowerCamelCase__:     Union[str, Any]							    =     False
       lowerCamelCase__:     Any							    =     False
       lowerCamelCase__:     Union[str, Any]							    =     False

       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Union[str, Any]										=    DebertaVaModelTester(self    )
            a_    :     Any										=    ConfigTester(self      ,							config_class=lowerCamelCase_      ,							hidden_size=3_7    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            self.config_tester.run_common_tests()

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     int										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_model(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     List[Any]    ):
            a_    :     Optional[int]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Optional[Any]    ):
            a_    :     Optional[Any]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_question_answering(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     List[str]										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_token_classification(*lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     int    ):
            a_    :     Any										=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_multiple_choice(*lowerCamelCase_    )

       @slow
       def 						UpperCAmelCase(   self      :     Any    ):
            for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a_    :     Any										=    DebertaVaModel.from_pretrained(lowerCamelCase_    )
                 self.assertIsNotNone(lowerCamelCase_    )


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

       @unittest.skip(reason="""Model not available yet"""    )
       def 						UpperCAmelCase(   self      :     Union[str, Any]    ):
            pass

       @slow
       def 						UpperCAmelCase(   self      :     Dict    ):
            a_    :     Any										=    DebertaVaModel.from_pretrained("""microsoft/deberta-v2-xlarge"""    )

            a_    :     Optional[int]										=    torch.tensor([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    )
            a_    :     Tuple										=    torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
            with torch.no_grad():
                 a_    :     int										=    model(lowerCamelCase_      ,							attention_mask=lowerCamelCase_    )[0]
            # compare the actual values for a slice.
            a_    :     int										=    torch.tensor(
                [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]    )
            self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,							lowerCamelCase_      ,							atol=1E-4    )      ,							F'''{output[:, 1:4, 1:4]}'''    )







from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt



def  _a			(						__UpperCamelCase ):
     a_    :     int										=    int(number**0.5 )
     return number == sq * sq



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase ):
     a_    :     int										=    x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     a_    :     int										=    x_den * y_den * z_den
     a_    :     int										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
     top //= hcf
     bottom //= hcf
     return top, bottom



def  _a			(						__UpperCamelCase = 3_5 ):
     a_    :     set										=    set()
     a_    :     int
     a_    :     Fraction										=    Fraction(0 )
     a_    :     tuple[int, int]

     for x_num in range(1  ,  order + 1 ):
          for x_den in range(x_num + 1  ,  order + 1 ):
               for y_num in range(1  ,  order + 1 ):
                    for y_den in range(y_num + 1  ,  order + 1 ):
                         # n=1
                         a_    :     int										=    x_num * y_den + x_den * y_num
                         a_    :     Optional[int]										=    x_den * y_den
                         a_    :     Optional[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Optional[int]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

                         # n=2
                         a_    :     List[Any]										=    (
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         a_    :     str										=    x_den * x_den * y_den * y_den
                         if is_sq(__UpperCamelCase ) and is_sq(__UpperCamelCase ):
                              a_    :     Optional[Any]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Optional[int]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Optional[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   a_    :     str										=    add_three(
                                       __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                                   unique_s.add(__UpperCamelCase )

                    # n=-1
                         a_    :     Union[str, Any]										=    x_num * y_num
                         a_    :     Tuple										=    x_den * y_num + x_num * y_den
                         a_    :     List[Any]										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              a_    :     Union[str, Any]										=    add_three(
                                  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                              unique_s.add(__UpperCamelCase )

                         # n=2
                         a_    :     Union[str, Any]										=    x_num * x_num * y_num * y_num
                         a_    :     Dict										=    (
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(__UpperCamelCase ) and is_sq(__UpperCamelCase ):
                              a_    :     Dict										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Union[str, Any]										=    int(sqrt(__UpperCamelCase ) )
                              a_    :     Dict										=    gcd(__UpperCamelCase  ,  __UpperCamelCase )
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   a_    :     List[Any]										=    add_three(
                                       __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase  ,  __UpperCamelCase )
                                   unique_s.add(__UpperCamelCase )

     for num, den in unique_s:
          total += Fraction(__UpperCamelCase  ,  __UpperCamelCase )

     return total.denominator + total.numerator


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



