"""simple docstring"""


from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

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


if is_tf_available():
    import tensorflow as tf

    from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor


class 				a :


    def __init__(    self		:     Optional[int]      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Optional[int]=13      , __lowerCAmelCase		:     Any=30      , __lowerCAmelCase		:     Dict=2      , __lowerCAmelCase		:     str=3      , __lowerCAmelCase		:     str=True      , __lowerCAmelCase		:     Union[str, Any]=True      , __lowerCAmelCase		:     List[str]=32      , __lowerCAmelCase		:     List[Any]=2      , __lowerCAmelCase		:     int=4      , __lowerCAmelCase		:     Optional[int]=37      , __lowerCAmelCase		:     Tuple="gelu"      , __lowerCAmelCase		:     Optional[Any]=0.1      , __lowerCAmelCase		:     Optional[Any]=0.1      , __lowerCAmelCase		:     List[Any]=10      , __lowerCAmelCase		:     Dict=0.02      , __lowerCAmelCase		:     List[str]=3      , __lowerCAmelCase		:     Optional[Any]=0.6      , __lowerCAmelCase		:     Tuple=None      , ):
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					image_size
      _UpperCAmelCase         =					patch_size
      _UpperCAmelCase         =					num_channels
      _UpperCAmelCase         =					is_training
      _UpperCAmelCase         =					use_labels
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_hidden_layers
      _UpperCAmelCase         =					num_attention_heads
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dropout_prob
      _UpperCAmelCase         =					attention_probs_dropout_prob
      _UpperCAmelCase         =					type_sequence_label_size
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					mask_ratio
      _UpperCAmelCase         =					scope

      # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
      # (we add 1 for the [CLS] token)
      _UpperCAmelCase         =					(image_size // patch_size) ** 2
      _UpperCAmelCase         =					int(math.ceil((1 - mask_ratio) * (num_patches + 1)    )    )


    def    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      _UpperCAmelCase         =					None
      if self.use_labels:
        _UpperCAmelCase         =					ids_tensor([self.batch_size]      , self.type_sequence_label_size    )

      _UpperCAmelCase         =					self.get_config()

      return config, pixel_values, labels


    def    lowerCAmelCase_     (    self		:     int    ):
      return ViTMAEConfig(
          image_size=self.image_size      , patch_size=self.patch_size      , num_channels=self.num_channels      , hidden_size=self.hidden_size      , num_hidden_layers=self.num_hidden_layers      , num_attention_heads=self.num_attention_heads      , intermediate_size=self.intermediate_size      , decoder_hidden_size=self.hidden_size      , decoder_num_hidden_layers=self.num_hidden_layers      , decoder_num_attention_heads=self.num_attention_heads      , decoder_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      , is_decoder=__lowerCAmelCase      , initializer_range=self.initializer_range      , mask_ratio=self.mask_ratio      , )


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     List[Any]    ):
      _UpperCAmelCase         =					TFViTMAEModel(config=__lowerCAmelCase    )
      _UpperCAmelCase         =					model(__lowerCAmelCase      , training=__lowerCAmelCase    )
      self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)    )


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     Optional[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Any    ):
      _UpperCAmelCase         =					TFViTMAEForPreTraining(__lowerCAmelCase    )
      _UpperCAmelCase         =					model(__lowerCAmelCase      , training=__lowerCAmelCase    )
      # expected sequence length = num_patches
      _UpperCAmelCase         =					(self.image_size // self.patch_size) ** 2
      _UpperCAmelCase         =					self.patch_size**2 * self.num_channels
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, num_patches, expected_num_channels)    )

      # test greyscale images
      _UpperCAmelCase         =					1
      _UpperCAmelCase         =					TFViTMAEForPreTraining(__lowerCAmelCase    )

      _UpperCAmelCase         =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
      _UpperCAmelCase         =					model(__lowerCAmelCase      , training=__lowerCAmelCase    )
      _UpperCAmelCase         =					self.patch_size**2
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, num_patches, expected_num_channels)    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.prepare_config_and_inputs()
      ((_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase))         =					config_and_inputs
      _UpperCAmelCase         =					{"""pixel_values""": pixel_values}
      return config, inputs_dict



@require_tf
class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     Optional[Any]   							=							(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
    _snake_case   :     Union[str, Any]   							=							{'feature-extraction': TFViTMAEModel} if is_tf_available() else {}

    _snake_case   :     List[str]   							=							False
    _snake_case   :     Optional[Any]   							=							False
    _snake_case   :     Union[str, Any]   							=							False
    _snake_case   :     List[Any]   							=							False


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					TFViTMAEModelTester(self    )
      _UpperCAmelCase         =					ConfigTester(self      , config_class=__lowerCAmelCase      , has_text_modality=__lowerCAmelCase      , hidden_size=37    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      self.config_tester.run_common_tests()


    @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""    )
    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      pass


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase       , _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        _UpperCAmelCase         =					model_class(__lowerCAmelCase    )
        self.assertIsInstance(model.get_input_embeddings()      , (tf.keras.layers.Layer)    )
        _UpperCAmelCase         =					model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__lowerCAmelCase      , tf.keras.layers.Layer    )    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase       , _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

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

        _UpperCAmelCase         =					["""pixel_values"""]
        self.assertListEqual(arg_names[:1]      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]    ):
      # make the mask reproducible
      np.random.seed(2    )

      _UpperCAmelCase       , _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase         =					int((config.image_size // config.patch_size) ** 2    )
      _UpperCAmelCase         =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

      for model_class in self.all_model_classes:
        _UpperCAmelCase         =					model_class(__lowerCAmelCase    )
        _UpperCAmelCase         =					self._prepare_for_class(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					model(__lowerCAmelCase      , noise=__lowerCAmelCase    )

        _UpperCAmelCase         =					copy.deepcopy(self._prepare_for_class(__lowerCAmelCase      , __lowerCAmelCase    )    )
        _UpperCAmelCase         =					model(**__lowerCAmelCase      , noise=__lowerCAmelCase    )
        _UpperCAmelCase         =					outputs_dict[0].numpy()
        _UpperCAmelCase         =					outputs_keywords[0].numpy()

        self.assertLess(np.sum(np.abs(output_dict - output_keywords    )    )      , 1e-6    )


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      # make the mask reproducible
      np.random.seed(2    )

      _UpperCAmelCase       , _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase         =					int((config.image_size // config.patch_size) ** 2    )
      _UpperCAmelCase         =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

      def prepare_numpy_arrays(__lowerCAmelCase		:     Any    ):
        _UpperCAmelCase         =					{}
        for k, v in inputs_dict.items():
          if tf.is_tensor(__lowerCAmelCase    ):
            _UpperCAmelCase         =					v.numpy()
          else:
            _UpperCAmelCase         =					np.array(__lowerCAmelCase    )

        return inputs_np_dict

      for model_class in self.all_model_classes:
        _UpperCAmelCase         =					model_class(__lowerCAmelCase    )

        _UpperCAmelCase         =					self._prepare_for_class(__lowerCAmelCase      , __lowerCAmelCase    )
        _UpperCAmelCase         =					prepare_numpy_arrays(__lowerCAmelCase    )

        _UpperCAmelCase         =					model(__lowerCAmelCase      , noise=__lowerCAmelCase    )
        _UpperCAmelCase         =					model(**__lowerCAmelCase      , noise=__lowerCAmelCase    )
        self.assert_outputs_same(__lowerCAmelCase      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Any      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     Tuple    ):
      # make masks reproducible
      np.random.seed(2    )

      _UpperCAmelCase         =					int((tf_model.config.image_size // tf_model.config.patch_size) ** 2    )
      _UpperCAmelCase         =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
      _UpperCAmelCase         =					tf.constant(__lowerCAmelCase    )

      # Add `noise` argument.
      # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
      _UpperCAmelCase         =					tf_noise

      super().check_pt_tf_models(__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # make mask reproducible
      np.random.seed(2    )

      _UpperCAmelCase       , _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase         =					{
          module_member
          for model_class in self.all_model_classes
          for module in (import_module(model_class.__module__    ),)
          for module_member_name in dir(__lowerCAmelCase    )
          if module_member_name.endswith("""MainLayer"""    )
          # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
          and module_member_name[: -len("""MainLayer"""    )] == model_class.__name__[: -len("""Model"""    )]
          for module_member in (getattr(__lowerCAmelCase      , __lowerCAmelCase    ),)
          if isinstance(__lowerCAmelCase      , __lowerCAmelCase    )
          and tf.keras.layers.Layer in module_member.__bases__
          and getattr(__lowerCAmelCase      , """_keras_serializable"""      , __lowerCAmelCase    )
      }

      _UpperCAmelCase         =					int((config.image_size // config.patch_size) ** 2    )
      _UpperCAmelCase         =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )
      _UpperCAmelCase         =					tf.convert_to_tensor(__lowerCAmelCase    )
      inputs_dict.update({"""noise""": noise}    )

      for main_layer_class in tf_main_layer_classes:
        _UpperCAmelCase         =					main_layer_class(__lowerCAmelCase    )

        _UpperCAmelCase         =					{
            name: tf.keras.Input(tensor.shape[1:]      , dtype=tensor.dtype    ) for name, tensor in inputs_dict.items()
        }

        _UpperCAmelCase         =					tf.keras.Model(__lowerCAmelCase      , outputs=main_layer(__lowerCAmelCase    )    )
        _UpperCAmelCase         =					model(__lowerCAmelCase    )

        with tempfile.TemporaryDirectory() as tmpdirname:
          _UpperCAmelCase         =					os.path.join(__lowerCAmelCase      , """keras_model.h5"""    )
          model.save(__lowerCAmelCase    )
          _UpperCAmelCase         =					tf.keras.models.load_model(
              __lowerCAmelCase      , custom_objects={main_layer_class.__name__: main_layer_class}    )
          assert isinstance(__lowerCAmelCase      , tf.keras.Model    )
          _UpperCAmelCase         =					model(__lowerCAmelCase    )
          self.assert_outputs_same(__lowerCAmelCase      , __lowerCAmelCase    )


    @slow
    def    lowerCAmelCase_     (    self		:     int    ):
      # make mask reproducible
      np.random.seed(2    )

      _UpperCAmelCase       , _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase         =					int((config.image_size // config.patch_size) ** 2    )
      _UpperCAmelCase         =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

      for model_class in self.all_model_classes:
        _UpperCAmelCase         =					model_class(__lowerCAmelCase    )
        _UpperCAmelCase         =					self._prepare_for_class(__lowerCAmelCase      , __lowerCAmelCase    )
        _UpperCAmelCase         =					model(__lowerCAmelCase      , noise=__lowerCAmelCase    )

        if model_class.__name__ == "TFViTMAEModel":
          _UpperCAmelCase         =					outputs.last_hidden_state.numpy()
          _UpperCAmelCase         =					0
        else:
          _UpperCAmelCase         =					outputs.logits.numpy()
          _UpperCAmelCase         =					0

        with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(__lowerCAmelCase      , saved_model=__lowerCAmelCase    )
          _UpperCAmelCase         =					model_class.from_pretrained(__lowerCAmelCase    )
          _UpperCAmelCase         =					model(__lowerCAmelCase      , noise=__lowerCAmelCase    )

          if model_class.__name__ == "TFViTMAEModel":
            _UpperCAmelCase         =					after_outputs["""last_hidden_state"""].numpy()
            _UpperCAmelCase         =					0
          else:
            _UpperCAmelCase         =					after_outputs["""logits"""].numpy()
            _UpperCAmelCase         =					0

          _UpperCAmelCase         =					np.amax(np.abs(out_a - out_a    )    )
          self.assertLessEqual(__lowerCAmelCase      , 1e-5    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      # make mask reproducible
      np.random.seed(2    )

      _UpperCAmelCase       , _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs_for_common()

      _UpperCAmelCase         =					int((config.image_size // config.patch_size) ** 2    )
      _UpperCAmelCase         =					np.random.uniform(size=(self.model_tester.batch_size, num_patches)    )

      for model_class in self.all_model_classes:
        _UpperCAmelCase         =					model_class(__lowerCAmelCase    )
        _UpperCAmelCase         =					self._prepare_for_class(__lowerCAmelCase      , __lowerCAmelCase    )

        _UpperCAmelCase         =					model(__lowerCAmelCase      , noise=__lowerCAmelCase    )
        _UpperCAmelCase         =					model.get_config()
        # make sure that returned config is jsonifiable, which is required by keras
        json.dumps(__lowerCAmelCase    )
        _UpperCAmelCase         =					model_class.from_config(model.get_config()    )
        # make sure it also accepts a normal config
        _UpperCAmelCase         =					model_class.from_config(model.config    )
        _UpperCAmelCase         =					new_model(__lowerCAmelCase    )  # Build model
        new_model.set_weights(model.get_weights()    )
        _UpperCAmelCase         =					new_model(__lowerCAmelCase      , noise=__lowerCAmelCase    )

        self.assert_outputs_same(__lowerCAmelCase      , __lowerCAmelCase    )


    @unittest.skip(
        reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""    )
    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      pass


    @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""    )
    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      pass


    @slow
    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224"""    )
      self.assertIsNotNone(__lowerCAmelCase    )






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  _UpperCAmelCase         =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
  return image


@require_tf
@require_vision
class 				a (						unittest.TestCase      ):


    @cached_property
    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""    ) if is_vision_available() else None


    @slow
    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      # make random mask reproducible across the PT and TF model
      np.random.seed(2    )

      _UpperCAmelCase         =					TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""    )

      _UpperCAmelCase         =					self.default_image_processor
      _UpperCAmelCase         =					prepare_img()
      _UpperCAmelCase         =					image_processor(images=__lowerCAmelCase      , return_tensors="""tf"""    )

      # prepare a noise vector that will be also used for testing the TF model
      # (this way we can ensure that the PT and TF models operate on the same inputs)
      _UpperCAmelCase         =					ViTMAEConfig()
      _UpperCAmelCase         =					int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2    )
      _UpperCAmelCase         =					np.random.uniform(size=(1, num_patches)    )

      # forward pass
      _UpperCAmelCase         =					model(**__lowerCAmelCase      , noise=__lowerCAmelCase    )

      # verify the logits
      _UpperCAmelCase         =					tf.convert_to_tensor([1, 196, 768]    )
      self.assertEqual(outputs.logits.shape      , __lowerCAmelCase    )

      _UpperCAmelCase         =					tf.convert_to_tensor(
          [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]    )

      tf.debugging.assert_near(outputs.logits[0, :3, :3]      , __lowerCAmelCase      , atol=1e-4    )

"""simple docstring"""


import unittest

from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import (
        MPNetForMaskedLM,
        MPNetForMultipleChoice,
        MPNetForQuestionAnswering,
        MPNetForSequenceClassification,
        MPNetForTokenClassification,
        MPNetModel,
    )


class 				a :


    def __init__(    self		:     Optional[int]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     List[str]=13      , __lowerCAmelCase		:     List[Any]=7      , __lowerCAmelCase		:     Any=True      , __lowerCAmelCase		:     List[Any]=True      , __lowerCAmelCase		:     Tuple=False      , __lowerCAmelCase		:     List[str]=True      , __lowerCAmelCase		:     Optional[Any]=99      , __lowerCAmelCase		:     int=64      , __lowerCAmelCase		:     Optional[int]=5      , __lowerCAmelCase		:     Union[str, Any]=4      , __lowerCAmelCase		:     Union[str, Any]=64      , __lowerCAmelCase		:     Optional[Any]="gelu"      , __lowerCAmelCase		:     int=0.1      , __lowerCAmelCase		:     Optional[int]=0.1      , __lowerCAmelCase		:     str=512      , __lowerCAmelCase		:     Any=16      , __lowerCAmelCase		:     Tuple=2      , __lowerCAmelCase		:     Tuple=0.02      , __lowerCAmelCase		:     List[str]=3      , __lowerCAmelCase		:     int=4      , __lowerCAmelCase		:     str=None      , ):
      _UpperCAmelCase         =					parent
      _UpperCAmelCase         =					batch_size
      _UpperCAmelCase         =					seq_length
      _UpperCAmelCase         =					is_training
      _UpperCAmelCase         =					use_input_mask
      _UpperCAmelCase         =					use_token_type_ids
      _UpperCAmelCase         =					use_labels
      _UpperCAmelCase         =					vocab_size
      _UpperCAmelCase         =					hidden_size
      _UpperCAmelCase         =					num_hidden_layers
      _UpperCAmelCase         =					num_attention_heads
      _UpperCAmelCase         =					intermediate_size
      _UpperCAmelCase         =					hidden_act
      _UpperCAmelCase         =					hidden_dropout_prob
      _UpperCAmelCase         =					attention_probs_dropout_prob
      _UpperCAmelCase         =					max_position_embeddings
      _UpperCAmelCase         =					type_vocab_size
      _UpperCAmelCase         =					type_sequence_label_size
      _UpperCAmelCase         =					initializer_range
      _UpperCAmelCase         =					num_labels
      _UpperCAmelCase         =					num_choices
      _UpperCAmelCase         =					scope


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return MPNetConfig.from_pretrained("""microsoft/mpnet-base"""    )


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

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

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

      _UpperCAmelCase         =					self.get_config()
      return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      return MPNetConfig(
          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      , initializer_range=self.initializer_range      , )


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Union[str, Any]      , __lowerCAmelCase		:     str    ):
      _UpperCAmelCase         =					MPNetModel(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(__lowerCAmelCase      , __lowerCAmelCase    )
      _UpperCAmelCase         =					model(__lowerCAmelCase    )
      self.parent.assertEqual(result.last_hidden_state.shape      , (self.batch_size, self.seq_length, self.hidden_size)    )
      self.parent.assertEqual(result.pooler_output.shape      , (self.batch_size, self.hidden_size)    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     List[str]    ):
      _UpperCAmelCase         =					MPNetForQuestionAnswering(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(
          __lowerCAmelCase      , attention_mask=__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    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     str      , __lowerCAmelCase		:     Optional[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					self.num_labels
      _UpperCAmelCase         =					MPNetForSequenceClassification(__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(__lowerCAmelCase      , attention_mask=__lowerCAmelCase      , labels=__lowerCAmelCase    )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_labels)    )


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     List[str]      , __lowerCAmelCase		:     Union[str, Any]    ):
      _UpperCAmelCase         =					self.num_choices
      _UpperCAmelCase         =					MPNetForMultipleChoice(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					input_ids.unsqueeze(1    ).expand(-1      , self.num_choices      , -1    ).contiguous()
      _UpperCAmelCase         =					input_mask.unsqueeze(1    ).expand(-1      , self.num_choices      , -1    ).contiguous()
      _UpperCAmelCase         =					model(
          __lowerCAmelCase      , attention_mask=__lowerCAmelCase      , labels=__lowerCAmelCase      , )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.num_choices)    )


    def    lowerCAmelCase_     (    self		:     Optional[Any]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     Optional[int]      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     Tuple      , __lowerCAmelCase		:     Any    ):
      _UpperCAmelCase         =					self.num_labels
      _UpperCAmelCase         =					MPNetForTokenClassification(config=__lowerCAmelCase    )
      model.to(__lowerCAmelCase    )
      model.eval()
      _UpperCAmelCase         =					model(__lowerCAmelCase      , attention_mask=__lowerCAmelCase      , labels=__lowerCAmelCase    )
      self.parent.assertEqual(result.logits.shape      , (self.batch_size, self.seq_length, self.num_labels)    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					self.prepare_config_and_inputs()
      ((_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase)       , (_UpperCAmelCase))         =					config_and_inputs
      _UpperCAmelCase         =					{"""input_ids""": input_ids, """attention_mask""": input_mask}
      return config, inputs_dict



@require_torch
class 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     List[Any]   							=							(
        (
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetModel,
        )
        if is_torch_available()
        else ()
    )
    _snake_case   :     Union[str, Any]   							=							(
        {
            'feature-extraction': MPNetModel,
            'fill-mask': MPNetForMaskedLM,
            'question-answering': MPNetForQuestionAnswering,
            'text-classification': MPNetForSequenceClassification,
            'token-classification': MPNetForTokenClassification,
            'zero-shot': MPNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    _snake_case   :     int   							=							False
    _snake_case   :     List[Any]   							=							True


    def    lowerCAmelCase_     (    self		:     Dict    ):
      _UpperCAmelCase         =					MPNetModelTester(self    )
      _UpperCAmelCase         =					ConfigTester(self      , config_class=__lowerCAmelCase      , hidden_size=37    )


    def    lowerCAmelCase_     (    self		:     Dict    ):
      self.config_tester.run_common_tests()


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_model(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_sequence_classification(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_multiple_choice(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_token_classification(*__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     str    ):
      _UpperCAmelCase         =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_mpnet_for_question_answering(*__lowerCAmelCase    )



@require_torch
class 				a (						unittest.TestCase      ):


    @slow
    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					MPNetModel.from_pretrained("""microsoft/mpnet-base"""    )
      _UpperCAmelCase         =					torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]    )
      _UpperCAmelCase         =					model(__lowerCAmelCase    )[0]
      _UpperCAmelCase         =					torch.Size((1, 11, 768)    )
      self.assertEqual(output.shape      , __lowerCAmelCase    )
      _UpperCAmelCase         =					torch.tensor(
          [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]    )
      # compare the actual values for a slice.
      self.assertTrue(torch.allclose(output[:, :3, :3]      , __lowerCAmelCase      , atol=1e-4    )    )
