




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


    def        a				(	self					:					Optional[int]       )				->	Optional[int]:
     lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

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

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

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

     lowerCAmelCase__          =			self.get_config()

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


    def        a				(	self					:					Dict       )				->	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					:					Union[str, Any]       )				->	str:
     lowerCAmelCase__          =			self.get_config()
     lowerCAmelCase__          =			300
     return config


    def        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
     self.parent.assertListEqual(list(result.loss.size()       )       ,     []       )


    def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Dict:
     lowerCAmelCase__          =			DebertaModel(config=_lowerCamelCase       )
     model.to(_lowerCamelCase       )
     model.eval()
     lowerCAmelCase__          =			model(_lowerCamelCase       ,     attention_mask=_lowerCamelCase       ,     token_type_ids=_lowerCamelCase       )[0]
     lowerCAmelCase__          =			model(_lowerCamelCase       ,     token_type_ids=_lowerCamelCase       )[0]
     lowerCAmelCase__          =			model(_lowerCamelCase       )[0]

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


    def        a				(	self					:					str       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
     lowerCAmelCase__          =			DebertaForMaskedLM(config=_lowerCamelCase       )
     model.to(_lowerCamelCase       )
     model.eval()
     lowerCAmelCase__          =			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        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	List[Any]:
     lowerCAmelCase__          =			self.num_labels
     lowerCAmelCase__          =			DebertaForSequenceClassification(_lowerCamelCase       )
     model.to(_lowerCamelCase       )
     model.eval()
     lowerCAmelCase__          =			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        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Tuple:
     lowerCAmelCase__          =			self.num_labels
     lowerCAmelCase__          =			DebertaForTokenClassification(config=_lowerCamelCase       )
     model.to(_lowerCamelCase       )
     model.eval()
     lowerCAmelCase__          =			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        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					str       )				->	Optional[Any]:
     lowerCAmelCase__          =			DebertaForQuestionAnswering(config=_lowerCamelCase       )
     model.to(_lowerCamelCase       )
     model.eval()
     lowerCAmelCase__          =			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        a				(	self					:					Tuple       )				->	Any:
     lowerCAmelCase__          =			self.prepare_config_and_inputs()
     (
         (
         lowerCAmelCase__ 
     )     ,		(
         lowerCAmelCase__ 
     )     ,		(
         lowerCAmelCase__ 
     )     ,		(
         lowerCAmelCase__ 
     )     ,		(
         lowerCAmelCase__ 
     )     ,		(
         lowerCAmelCase__ 
     )     ,		(
         lowerCAmelCase__ 
     )     ,		 
     )          =			config_and_inputs
     lowerCAmelCase__          =			{"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        a				(	self					:					Tuple       )				->	Any:
     lowerCAmelCase__          =			DebertaModelTester(self       )
     lowerCAmelCase__          =			ConfigTester(self       ,     config_class=_lowerCamelCase       ,     hidden_size=37       )


    def        a				(	self					:					Optional[int]       )				->	int:
     self.config_tester.run_common_tests()


    def        a				(	self					:					Union[str, Any]       )				->	Union[str, Any]:
     lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_model(*_lowerCamelCase       )


    def        a				(	self					:					Tuple       )				->	str:
     lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_sequence_classification(*_lowerCamelCase       )


    def        a				(	self					:					Dict       )				->	List[Any]:
     lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_masked_lm(*_lowerCamelCase       )


    def        a				(	self					:					Optional[Any]       )				->	List[Any]:
     lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_question_answering(*_lowerCamelCase       )


    def        a				(	self					:					str       )				->	List[Any]:
     lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_token_classification(*_lowerCamelCase       )


    @slow
    def        a				(	self					:					Union[str, Any]       )				->	Any:
     for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
      lowerCAmelCase__          =			DebertaModel.from_pretrained(_lowerCamelCase       )
      self.assertIsNotNone(_lowerCamelCase       )






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




    """simple docstring"""

    @unittest.skip(reason="Model not available yet"       )
    def        a				(	self					:					List[Any]       )				->	Optional[Any]:
     pass


    @slow
    def        a				(	self					:					int       )				->	Optional[Any]:
     lowerCAmelCase__          =			DebertaModel.from_pretrained("microsoft/deberta-base"       )

     lowerCAmelCase__          =			torch.tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]       )
     lowerCAmelCase__          =			torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
     with torch.no_grad():
      lowerCAmelCase__          =			model(_lowerCamelCase       ,     attention_mask=_lowerCamelCase       )[0]
     # compare the actual values for a slice.
     lowerCAmelCase__          =			torch.tensor(
         [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]       )
     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,     _lowerCamelCase       ,     atol=1e-4       )       ,     f'{output[:, 1:4, 1:4]}'       )






import string



def 							_A       (						lowerCAmelCase_					: str      ):
     """simple docstring"""

     for key in range(len(string.ascii_uppercase      )      ):
          lowerCAmelCase__          =			""
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    lowerCAmelCase__          =			string.ascii_uppercase.find(lowerCAmelCase_      )
                    lowerCAmelCase__          =			num - key
                    if num < 0:
                         lowerCAmelCase__          =			num + len(string.ascii_uppercase      )
                    lowerCAmelCase__          =			translated + string.ascii_uppercase[num]
               else:
                    lowerCAmelCase__          =			translated + symbol
          print(F'Decryption using Key #{key}: {translated}'      )



def 							_A       (						):
     """simple docstring"""

     lowerCAmelCase__          =			input("Encrypted message: "      )
     lowerCAmelCase__          =			message.upper()
     decrypt(lowerCAmelCase_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()
