


'''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			(	SCREAMING_SNAKE_CASE__	):
    """simple docstring"""


    def __init__(  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple=13				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=7				,							SCREAMING_SNAKE_CASE      :							List[str]=True				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=True				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=True				,							SCREAMING_SNAKE_CASE      :							List[Any]=True				,							SCREAMING_SNAKE_CASE      :							str=99				,							SCREAMING_SNAKE_CASE      :							Any=32				,							SCREAMING_SNAKE_CASE      :							Tuple=5				,							SCREAMING_SNAKE_CASE      :							List[str]=4				,							SCREAMING_SNAKE_CASE      :							List[str]=37				,							SCREAMING_SNAKE_CASE      :							Optional[int]="gelu"				,							SCREAMING_SNAKE_CASE      :							Optional[int]=0.1				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=0.1				,							SCREAMING_SNAKE_CASE      :							Any=512				,							SCREAMING_SNAKE_CASE      :							Optional[int]=16				,							SCREAMING_SNAKE_CASE      :							Tuple=2				,							SCREAMING_SNAKE_CASE      :							Tuple=0.02				,							SCREAMING_SNAKE_CASE      :							Any=False				,							SCREAMING_SNAKE_CASE      :							List[str]=True				,							SCREAMING_SNAKE_CASE      :							List[Any]="None"				,							SCREAMING_SNAKE_CASE      :							Optional[int]=3				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=4				,							SCREAMING_SNAKE_CASE      :							Optional[int]=None				,							):
           _A					:     Optional[Any]     = parent
           _A					:     Optional[Any]     = batch_size
           _A					:     List[Any]     = seq_length
           _A					:     Optional[int]     = is_training
           _A					:     List[Any]     = use_input_mask
           _A					:     Optional[Any]     = use_token_type_ids
           _A					:     int     = use_labels
           _A					:     Tuple     = vocab_size
           _A					:     Union[str, Any]     = hidden_size
           _A					:     str     = num_hidden_layers
           _A					:     Dict     = num_attention_heads
           _A					:     Tuple     = intermediate_size
           _A					:     Optional[Any]     = hidden_act
           _A					:     str     = hidden_dropout_prob
           _A					:     List[str]     = attention_probs_dropout_prob
           _A					:     Dict     = max_position_embeddings
           _A					:     int     = type_vocab_size
           _A					:     Union[str, Any]     = type_sequence_label_size
           _A					:     str     = initializer_range
           _A					:     List[str]     = num_labels
           _A					:     Dict     = num_choices
           _A					:     Optional[int]     = relative_attention
           _A					:     Optional[int]     = position_biased_input
           _A					:     Optional[Any]     = pos_att_type
           _A					:     List[str]     = scope


    def        A   (  self      :							List[Any]):
           _A					:     Optional[Any]     = ids_tensor([self.batch_size, self.seq_length]				,							self.vocab_size)

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

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

           _A					:     Union[str, Any]     = None
           _A					:     List[Any]     = None
           _A					:     Tuple     = None
           if self.use_labels:
                  _A					:     List[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					:     Optional[int]     = ids_tensor([self.batch_size]				,							self.num_choices)

           _A					:     Any     = self.get_config()

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


    def        A   (  self      :							Optional[Any]):
           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        A   (  self      :							int):
           _A					:     Dict     = self.get_config()
           _A					:     Tuple     = 300
           return config


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict):
           self.parent.assertListEqual(list(result.loss.size())				,							[])


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]):
           _A					:     Optional[Any]     = DebertaModel(config=_SCREAMING_SNAKE_CASE)
           model.to(_SCREAMING_SNAKE_CASE)
           model.eval()
           _A					:     Optional[int]     = model(_SCREAMING_SNAKE_CASE				,							attention_mask=_SCREAMING_SNAKE_CASE				,							token_type_ids=_SCREAMING_SNAKE_CASE)[0]
           _A					:     Dict     = model(_SCREAMING_SNAKE_CASE				,							token_type_ids=_SCREAMING_SNAKE_CASE)[0]
           _A					:     Optional[int]     = model(_SCREAMING_SNAKE_CASE)[0]

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


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     Tuple     = DebertaForMaskedLM(config=_SCREAMING_SNAKE_CASE)
           model.to(_SCREAMING_SNAKE_CASE)
           model.eval()
           _A					:     str     = model(_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        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     Union[str, Any]     = self.num_labels
           _A					:     Tuple     = DebertaForSequenceClassification(_SCREAMING_SNAKE_CASE)
           model.to(_SCREAMING_SNAKE_CASE)
           model.eval()
           _A					:     int     = model(_SCREAMING_SNAKE_CASE				,							attention_mask=_SCREAMING_SNAKE_CASE				,							token_type_ids=_SCREAMING_SNAKE_CASE				,							labels=_SCREAMING_SNAKE_CASE)
           self.parent.assertListEqual(list(result.logits.size())				,							[self.batch_size, self.num_labels])
           self.check_loss_output(_SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							str				,							SCREAMING_SNAKE_CASE      :							int):
           _A					:     Optional[int]     = self.num_labels
           _A					:     str     = DebertaForTokenClassification(config=_SCREAMING_SNAKE_CASE)
           model.to(_SCREAMING_SNAKE_CASE)
           model.eval()
           _A					:     Dict     = model(_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        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							List[str]				,							SCREAMING_SNAKE_CASE      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple				,							SCREAMING_SNAKE_CASE      :							Optional[Any]):
           _A					:     List[str]     = DebertaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE)
           model.to(_SCREAMING_SNAKE_CASE)
           model.eval()
           _A					:     Optional[Any]     = model(
               _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        A   (  self      :							List[str]):
           _A					:     Any     = self.prepare_config_and_inputs()
           (
               _A    
           )					:     Tuple     = config_and_inputs
           _A					:     str     = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
           return config, inputs_dict




@require_torch
class 			__lowerCamelCase			(	SCREAMING_SNAKE_CASE__    , SCREAMING_SNAKE_CASE__    , unittest.TestCase	):
    """simple docstring"""
    a													=   (
        (
            DebertaModel,
            DebertaForMaskedLM,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    a													=   (
        {
            'feature-extraction': DebertaModel,
            'fill-mask': DebertaForMaskedLM,
            'question-answering': DebertaForQuestionAnswering,
            'text-classification': DebertaForSequenceClassification,
            'token-classification': DebertaForTokenClassification,
            'zero-shot': DebertaForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )

    a													=   True
    a													=   False
    a													=   False
    a													=   False
    a													=   False


    def        A   (  self      :							Tuple):
           _A					:     Union[str, Any]     = DebertaModelTester(self)
           _A					:     Optional[Any]     = ConfigTester(self				,							config_class=_SCREAMING_SNAKE_CASE				,							hidden_size=37)


    def        A   (  self      :							str):
           self.config_tester.run_common_tests()


    def        A   (  self      :							str):
           _A					:     List[str]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_model(*_SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]):
           _A					:     Dict     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_sequence_classification(*_SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]):
           _A					:     Dict     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_masked_lm(*_SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Dict):
           _A					:     Union[str, Any]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_question_answering(*_SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):
           _A					:     Optional[int]     = self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_deberta_for_token_classification(*_SCREAMING_SNAKE_CASE)


    @slow
    def        A   (  self      :							Optional[int]):
           for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  _A					:     Dict     = DebertaModel.from_pretrained(_SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(_SCREAMING_SNAKE_CASE)




@require_torch
@require_sentencepiece
@require_tokenizers
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    @unittest.skip(reason='Model not available yet')
    def        A   (  self      :							List[Any]):
           pass


    @slow
    def        A   (  self      :							List[Any]):
           _A					:     Optional[Any]     = DebertaModel.from_pretrained('microsoft/deberta-base')

           _A					:     Optional[int]     = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
           _A					:     int     = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
           with torch.no_grad():
                  _A					:     int     = model(_SCREAMING_SNAKE_CASE				,							attention_mask=_SCREAMING_SNAKE_CASE)[0]
           # compare the actual values for a slice.
           _A					:     str     = torch.tensor(
               [[[-0.5986, -0.8055, -0.8462], [1.4484, -0.9348, -0.8059], [0.3123, 0.0032, -1.4131]]])
           self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,							_SCREAMING_SNAKE_CASE				,							atol=1e-4)				,							F'{output[:, 1:4, 1:4]}')










'''simple docstring'''







class 			__lowerCamelCase			:  # Public class to implement a graph
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           _A					:     List[Any]     = row
           _A					:     Union[str, Any]     = col
           _A					:     List[str]     = graph


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           return (
               0 <= i < self.ROW
               and 0 <= j < self.COL
               and not visited[i][j]
               and self.graph[i][j]
           )


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							list[list[bool]]):
           # Checking all 8 elements surrounding nth element
           _A					:     Tuple     = [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
           _A					:     Dict     = [-1, 0, 1, -1, 1, -1, 0, 1]
           _A					:     List[Any]     = True  # Make those cells visited
           for k in range(8):
                  if self.is_safe(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE):
                         self.diffs(i + row_nbr[k]				,							j + col_nbr[k]				,							SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int):  # And finally, count all islands.
           _A					:     Dict     = [[False for j in range(self.COL)] for i in range(self.ROW)]
           _A					:     Union[str, Any]     = 0
           for i in range(self.ROW):
                  for j in range(self.COL):
                         if visited[i][j] is False and self.graph[i][j] == 1:
                                self.diffs(SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE)
                                count += 1
           return count






