"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
           TFDebertaVaModel,
       )






class      _A	:




    """simple docstring"""




    def __init__(		self							:     List[str]	,      A_							:     Any	,      A_							:     Optional[Any]=13	,      A_							:     Union[str, Any]=7	,      A_							:     Optional[Any]=True	,      A_							:     Tuple=True	,      A_							:     Dict=True	,      A_							:     Dict=True	,      A_							:     Any=99	,      A_							:     List[str]=32	,      A_							:     Dict=2	,      A_							:     Optional[int]=4	,      A_							:     Any=37	,      A_							:     List[str]="gelu"	,      A_							:     List[Any]=0.1	,      A_							:     Tuple=0.1	,      A_							:     List[str]=512	,      A_							:     List[str]=16	,      A_							:     Dict=2	,      A_							:     int=0.02	,      A_							:     List[Any]=False	,      A_							:     str=True	,      A_							:     Tuple="None"	,      A_							:     Union[str, Any]=3	,      A_							:     List[str]=4	,      A_							:     int=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  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

         __snake_case				=							None
         if self.use_input_mask:
              __snake_case				=							random_attention_mask([self.batch_size, self.seq_length]    )

         __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				=							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	,      relative_attention=self.relative_attention	,      position_biased_input=self.position_biased_input	,      initializer_range=self.initializer_range	,      return_dict=A_	,      )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     int	,      A_							:     List[str]	,      A_							:     Optional[int]	,      A_							:     List[Any]	,      A_							:     int	,      A_							:     Optional[int]	,      A_							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							TFDebertaVaModel(config=A_    )
         __snake_case				=							{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

         __snake_case				=							[input_ids, input_mask]
         __snake_case				=							model(A_    )

         __snake_case				=							model(A_    )

         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     Optional[Any]	,      A_							:     Dict	,      A_							:     List[Any]	,      A_							:     List[str]	,      A_							:     Union[str, Any]	,      A_							:     int	,      A_							:     List[Any]	,      A_							:     Optional[Any]    )    ->       Union[str, Any]:
         __snake_case				=							TFDebertaVaForMaskedLM(config=A_    )
         __snake_case				=							{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)    )
    def  lowercase					(		self							:     int	,      A_							:     int	,      A_							:     int	,      A_							:     int	,      A_							:     Union[str, Any]	,      A_							:     Optional[Any]	,      A_							:     str	,      A_							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.num_labels
         __snake_case				=							TFDebertaVaForSequenceClassification(config=A_    )
         __snake_case				=							{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)    )
    def  lowercase					(		self							:     int	,      A_							:     Optional[Any]	,      A_							:     Any	,      A_							:     int	,      A_							:     Dict	,      A_							:     Dict	,      A_							:     Optional[Any]	,      A_							:     List[Any]    )    ->       Optional[int]:
         __snake_case				=							self.num_labels
         __snake_case				=							TFDebertaVaForTokenClassification(config=A_    )
         __snake_case				=							{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)    )
    def  lowercase					(		self							:     Tuple	,      A_							:     Optional[Any]	,      A_							:     Tuple	,      A_							:     Dict	,      A_							:     List[Any]	,      A_							:     List[str]	,      A_							:     Optional[int]	,      A_							:     Optional[int]    )    ->       Optional[int]:
         __snake_case				=							TFDebertaVaForQuestionAnswering(config=A_    )
         __snake_case				=							{
             """input_ids""": input_ids,
             """attention_mask""": input_mask,
             """token_type_ids""": token_type_ids,
         }

         __snake_case				=							model(A_    )
         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  lowercase					(		self							:     Union[str, Any]    )    ->       int:
         __snake_case				=							self.prepare_config_and_inputs()
         (
             __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_tf
class      _A	(						UpperCAmelCase__							, UpperCAmelCase__							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Optional[int]							=					(
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    UpperCamelCase_	:					int							=					(
        {
            'feature-extraction': TFDebertaVaModel,
            'fill-mask': TFDebertaVaForMaskedLM,
            'question-answering': TFDebertaVaForQuestionAnswering,
            'text-classification': TFDebertaVaForSequenceClassification,
            'token-classification': TFDebertaVaForTokenClassification,
            'zero-shot': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    UpperCamelCase_	:					Union[str, Any]							=					False
    UpperCamelCase_	:					str							=					False
    def  lowercase					(		self							:     Any    )    ->       Optional[Any]:
         __snake_case				=							TFDebertaVaModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     Any    )    ->       Any:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       str:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A_    )
    def  lowercase					(		self							:     int    )    ->       Optional[int]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A_    )
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A_    )







    @slow
    def  lowercase					(		self							:     List[str]    )    ->       Optional[Any]:
         __snake_case				=							TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''    )
         self.assertIsNotNone(A_    )






@require_tf
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @unittest.skip(reason='''Model not available yet'''    )
    def  lowercase					(		self							:     Optional[Any]    )    ->       Union[str, Any]:
         pass







    @slow
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Union[str, Any]:
         __snake_case				=							TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''    )
         __snake_case				=							tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    )
         __snake_case				=							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
         __snake_case				=							model(A_	,      attention_mask=A_    )[0]

         __snake_case				=							tf.constant(
             [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]    )
         tf.debugging.assert_near(output[:, 1:4, 1:4]	,      A_	,      atol=1E-4    )
"""simple docstring"""



from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate



@dataclass(frozen=_UpperCAmelCase					)
class      _A	(						_UpperCAmelCase					):




    """simple docstring"""




    UpperCamelCase_	:					str							=					field(default='''summarization'''							, metadata={'''include_in_asdict_even_if_is_default''': True}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''text''': Value('''string'''					)}					)
    UpperCamelCase_	:					ClassVar[Features]							=					Features({'''summary''': Value('''string'''					)}					)
    UpperCamelCase_	:					str							=					"text"
    UpperCamelCase_	:					str							=					"summary"
    @property
    def  lowercase					(		self							:     List[Any]    )    ->       Dict[str, str]:
         return {self.text_column: "text", self.summary_column: "summary"}