

import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import NatBackbone, NatForImageClassification, NatModel
  from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=13							,       lowerCAmelCase=64							,       lowerCAmelCase=4							,       lowerCAmelCase=3							,       lowerCAmelCase=16							,       lowerCAmelCase=[1, 2, 1]							,       lowerCAmelCase=[2, 4, 8]							,       lowerCAmelCase=3							,       lowerCAmelCase=2.0							,       lowerCAmelCase=True							,       lowerCAmelCase=0.0							,       lowerCAmelCase=0.0							,       lowerCAmelCase=0.1							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=True							,       lowerCAmelCase=0.02							,       lowerCAmelCase=1e-5							,       lowerCAmelCase=True							,       lowerCAmelCase=None							,       lowerCAmelCase=True							,       lowerCAmelCase=10							,       lowerCAmelCase=["stage1", "stage2"]							,       lowerCAmelCase=[1, 2]							,       )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=parent
        _lowercase     	=batch_size
        _lowercase     	=image_size
        _lowercase     	=patch_size
        _lowercase     	=num_channels
        _lowercase     	=embed_dim
        _lowercase     	=depths
        _lowercase     	=num_heads
        _lowercase     	=kernel_size
        _lowercase     	=mlp_ratio
        _lowercase     	=qkv_bias
        _lowercase     	=hidden_dropout_prob
        _lowercase     	=attention_probs_dropout_prob
        _lowercase     	=drop_path_rate
        _lowercase     	=hidden_act
        _lowercase     	=patch_norm
        _lowercase     	=layer_norm_eps
        _lowercase     	=initializer_range
        _lowercase     	=is_training
        _lowercase     	=scope
        _lowercase     	=use_labels
        _lowercase     	=num_labels
        _lowercase     	=out_features
        _lowercase     	=out_indices




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase     	=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

        _lowercase     	=None
        if self.use_labels:
              _lowercase     	=ids_tensor([self.batch_size]							,       self.num_labels )

        _lowercase     	=self.get_config()

        return config, pixel_values, labels




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        return NatConfig(
            num_labels=self.num_labels							,       image_size=self.image_size							,       patch_size=self.patch_size							,       num_channels=self.num_channels							,       embed_dim=self.embed_dim							,       depths=self.depths							,       num_heads=self.num_heads							,       kernel_size=self.kernel_size							,       mlp_ratio=self.mlp_ratio							,       qkv_bias=self.qkv_bias							,       hidden_dropout_prob=self.hidden_dropout_prob							,       attention_probs_dropout_prob=self.attention_probs_dropout_prob							,       drop_path_rate=self.drop_path_rate							,       hidden_act=self.hidden_act							,       patch_norm=self.patch_norm							,       layer_norm_eps=self.layer_norm_eps							,       initializer_range=self.initializer_range							,       out_features=self.out_features							,       out_indices=self.out_indices							,       )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=NatModel(config=lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.eval()
        _lowercase     	=model(lowerCAmelCase )

        _lowercase     	=_lowercase     	=(config.image_size // config.patch_size) // (2 ** (len(config.depths ) - 1))
        _lowercase     	=int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

        self.parent.assertEqual(
            result.last_hidden_state.shape							,       (self.batch_size, expected_height, expected_width, expected_dim) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=NatForImageClassification(lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.eval()
        _lowercase     	=model(lowerCAmelCase							,       labels=lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels) )

        # test greyscale images
        _lowercase     	=1
        _lowercase     	=NatForImageClassification(lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.eval()

        _lowercase     	=floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Any:
        '''simple docstring'''






        _lowercase     	=NatBackbone(config=lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.eval()
        _lowercase     	=model(lowerCAmelCase )

        # verify hidden states
        self.parent.assertEqual(len(result.feature_maps )							,       len(config.out_features ) )
        self.parent.assertListEqual(list(result.feature_maps[0].shape )							,       [self.batch_size, model.channels[0], 16, 16] )

        # verify channels
        self.parent.assertEqual(len(model.channels )							,       len(config.out_features ) )

        # verify backbone works with out_features=None
        _lowercase     	=None
        _lowercase     	=NatBackbone(config=lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.eval()
        _lowercase     	=model(lowerCAmelCase )

        # verify feature maps
        self.parent.assertEqual(len(result.feature_maps )							,       1 )
        self.parent.assertListEqual(list(result.feature_maps[0].shape )							,       [self.batch_size, model.channels[-1], 4, 4] )

        # verify channels
        self.parent.assertEqual(len(model.channels )							,       1 )




  def   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.prepare_config_and_inputs()
        _lowercase	,	_lowercase	,	_lowercase     	=config_and_inputs
        _lowercase     	={'pixel_values': pixel_values}
        return config, inputs_dict





@require_natten
@require_torch
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  (
      (
          NatModel,
          NatForImageClassification,
          NatBackbone,
      )
      if is_torch_available()
      else ()
  )
  _a						      =  (
      {"""feature-extraction""": NatModel, """image-classification""": NatForImageClassification}
      if is_torch_available()
      else {}
  )
  _a						      =  False

  _a						      =  False
  _a						      =  False
  _a						      =  False
  _a						      =  False




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=NatModelTester(self )
        _lowercase     	=ConfigTester(self							,       config_class=lowerCAmelCase							,       embed_dim=37 )




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        self.create_and_test_config_common_properties()
        self.config_tester.create_and_test_config_to_json_string()
        self.config_tester.create_and_test_config_to_json_file()
        self.config_tester.create_and_test_config_from_and_save_pretrained()
        self.config_tester.create_and_test_config_with_num_labels()
        self.config_tester.check_config_can_be_init_without_params()
        self.config_tester.check_config_arguments_init()




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        return




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase )




  def   A__					(    self )  ->			List[str]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowerCAmelCase )




  @unittest.skip(reason='Nat does not use inputs_embeds' )
  def   A__					(    self )  ->			str:
        '''simple docstring'''






        pass




  @unittest.skip(reason='Nat does not use feedforward chunking' )
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        pass




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase	,	_lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _lowercase     	=model_class(lowerCAmelCase )
              self.assertIsInstance(model.get_input_embeddings()							,       (nn.Module) )
              _lowercase     	=model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(lowerCAmelCase							,       nn.Linear ) )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase	,	_lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              _lowercase     	=model_class(lowerCAmelCase )
              _lowercase     	=inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _lowercase     	=[*signature.parameters.keys()]

              _lowercase     	=['pixel_values']
              self.assertListEqual(arg_names[:1]							,       lowerCAmelCase )




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        self.skipTest('Nat\'s attention operation is handled entirely by NATTEN.' )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Any:
        '''simple docstring'''






        _lowercase     	=model_class(lowerCAmelCase )
        model.to(lowerCAmelCase )
        model.eval()

        with torch.no_grad():
              _lowercase     	=model(**self._prepare_for_class(lowerCAmelCase							,       lowerCAmelCase ) )

        _lowercase     	=outputs.hidden_states

        _lowercase     	=getattr(
            self.model_tester							,       'expected_num_hidden_layers'							,       len(self.model_tester.depths ) + 1 )
        self.assertEqual(len(lowerCAmelCase )							,       lowerCAmelCase )

        # Nat has a different seq_length
        _lowercase     	=(
            config.patch_size
            if isinstance(config.patch_size							,       collections.abc.Iterable )
            else (config.patch_size, config.patch_size)
        )

        _lowercase     	=image_size[0] // patch_size[0]
        _lowercase     	=image_size[1] // patch_size[1]

        self.assertListEqual(
            list(hidden_states[0].shape[-3:] )							,       [height, width, self.model_tester.embed_dim]							,       )

        if model_class.__name__ != "NatBackbone":
              _lowercase     	=outputs.reshaped_hidden_states
              self.assertEqual(len(lowerCAmelCase )							,       lowerCAmelCase )

              _lowercase	,	_lowercase	,	_lowercase	,	_lowercase     	=reshaped_hidden_states[0].shape
              _lowercase     	=(
                  reshaped_hidden_states[0].view(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase ).permute(0							,       2							,       3							,       1 )
              )
              self.assertListEqual(
                  list(reshaped_hidden_states.shape[-3:] )							,       [height, width, self.model_tester.embed_dim]							,       )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase	,	_lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

        _lowercase     	=(
            self.model_tester.image_size
            if isinstance(self.model_tester.image_size							,       collections.abc.Iterable )
            else (self.model_tester.image_size, self.model_tester.image_size)
        )

        for model_class in self.all_model_classes:
              _lowercase     	=True
              self.check_hidden_states_output(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _lowercase     	=True

              self.check_hidden_states_output(lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )




  @slow
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowercase     	=NatModel.from_pretrained(lowerCAmelCase )
              self.assertIsNotNone(lowerCAmelCase )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        _lowercase	,	_lowercase     	=self.model_tester.prepare_config_and_inputs_for_common()

        _lowercase     	=_config_zero_init(lowerCAmelCase )
        for model_class in self.all_model_classes:
              _lowercase     	=model_class(config=lowerCAmelCase )
              for name, param in model.named_parameters():
                    if "embeddings" not in name and param.requires_grad:
                          self.assertIn(
                              ((param.data.mean() * 1e9).round() / 1e9).item()							,       [0.0, 1.0]							,       msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,       )





@require_natten
@require_vision
@require_torch
class 			__lowerCAmelCase					( unittest.TestCase							):




  @cached_property
  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        return AutoImageProcessor.from_pretrained('shi-labs/nat-mini-in1k-224' ) if is_vision_available() else None




  @slow
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=NatForImageClassification.from_pretrained('shi-labs/nat-mini-in1k-224' ).to(lowerCAmelCase )
        _lowercase     	=self.default_image_processor

        _lowercase     	=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
        _lowercase     	=image_processor(images=lowerCAmelCase							,       return_tensors='pt' ).to(lowerCAmelCase )

        # forward pass
        with torch.no_grad():
              _lowercase     	=model(**lowerCAmelCase )

        # verify the logits
        _lowercase     	=torch.Size((1, 1_000) )
        self.assertEqual(outputs.logits.shape							,       lowerCAmelCase )
        _lowercase     	=torch.tensor([0.3805, -0.8676, -0.3912] ).to(lowerCAmelCase )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]							,       lowerCAmelCase							,       atol=1e-4 ) )





@require_torch
@require_natten
class 			__lowerCAmelCase					( unittest.TestCase      ,      SCREAMING_SNAKE_CASE							):
  _a						      =  (NatBackbone,) if is_torch_available() else ()
  _a						      =  NatConfig




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=NatModelTester(self )





from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=13							,       lowerCAmelCase=7							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=99							,       lowerCAmelCase=32							,       lowerCAmelCase=2							,       lowerCAmelCase=4							,       lowerCAmelCase=37							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=0.1							,       lowerCAmelCase=0.1							,       lowerCAmelCase=512							,       lowerCAmelCase=16							,       lowerCAmelCase=2							,       lowerCAmelCase=0.02							,       lowerCAmelCase=3							,       lowerCAmelCase=4							,       lowerCAmelCase=None							,       )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=parent
        _lowercase     	=13
        _lowercase     	=7
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=99
        _lowercase     	=32
        _lowercase     	=2
        _lowercase     	=4
        _lowercase     	=37
        _lowercase     	='gelu'
        _lowercase     	=0.1
        _lowercase     	=0.1
        _lowercase     	=512
        _lowercase     	=16
        _lowercase     	=False
        _lowercase     	=-1
        _lowercase     	=True
        _lowercase     	=2
        _lowercase     	=0.02
        _lowercase     	=3
        _lowercase     	=4
        _lowercase     	=None




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=ids_tensor([self.batch_size, self.seq_length]							,       self.vocab_size )

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

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

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

        _lowercase     	=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							,       relative_attention=self.relative_attention							,       max_relative_positions=self.max_relative_positions							,       position_biased_input=self.position_biased_input							,       initializer_range=self.initializer_range							,       return_dict=lowerCAmelCase							,       )

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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel(config=lowerCAmelCase )
        _lowercase     	={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        _lowercase     	=[input_ids, input_mask]
        _lowercase     	=model(lowerCAmelCase )

        _lowercase     	=model(lowerCAmelCase )

        self.parent.assertEqual(result.last_hidden_state.shape							,       (self.batch_size, self.seq_length, self.hidden_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForMaskedLM(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForSequenceClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForTokenClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForQuestionAnswering(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(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 )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.prepare_config_and_inputs()
        (
            (
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	
        )     	=config_and_inputs
        _lowercase     	={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict





@require_tf
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  (
      (
          TFDebertaModel,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _a						      =  (
      {
          """feature-extraction""": TFDebertaModel,
          """fill-mask""": TFDebertaForMaskedLM,
          """question-answering""": TFDebertaForQuestionAnswering,
          """text-classification""": TFDebertaForSequenceClassification,
          """token-classification""": TFDebertaForTokenClassification,
          """zero-shot""": TFDebertaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  _a						      =  False
  _a						      =  False




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModelTester(self )
        _lowercase     	=ConfigTester(self							,       config_class=lowerCAmelCase							,       hidden_size=37 )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase )




  @slow
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        self.assertIsNotNone(lowerCAmelCase )





@require_tf
class 			__lowerCAmelCase					( unittest.TestCase							):




  @unittest.skip(reason='Model not available yet' )
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        pass




  @slow
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        _lowercase     	=tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
        _lowercase     	=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
        _lowercase     	=model(lowerCAmelCase							,       attention_mask=lowerCAmelCase )[0]

        _lowercase     	=tf.constant(
            [
                [
                    [-0.59855896, -0.80552566, -0.8462135],
                    [1.4484025, -0.93483794, -0.80593085],
                    [0.3122741, 0.00316059, -1.4131377],
                ]
            ] )
        tf.debugging.assert_near(output[:, 1:4, 1:4]							,       lowerCAmelCase							,       atol=1e-4 )


