code: 
  '''simple docstring'''
  
  
  
  from __future__ import annotations
  
  import unittest
  
  from transformers import MPNetConfig, 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.models.mpnet.modeling_tf_mpnet import (
  				    TFMPNetForMaskedLM,
  				    TFMPNetForMultipleChoice,
  				    TFMPNetForQuestionAnswering,
  				    TFMPNetForSequenceClassification,
  				    TFMPNetForTokenClassification,
  				    TFMPNetModel,
  				)
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__=13       ,     lowerCamelCase__=7       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     lowerCamelCase__=False       ,     lowerCamelCase__=True       ,     lowerCamelCase__=99       ,     lowerCamelCase__=64       ,     lowerCamelCase__=2       ,     lowerCamelCase__=4       ,     lowerCamelCase__=64       ,     lowerCamelCase__="gelu"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=512       ,     lowerCamelCase__=16       ,     lowerCamelCase__=2       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=3       ,     lowerCamelCase__=4       ,     lowerCamelCase__=None       ,     )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       parent
  									lowercase__							 =       batch_size
  									lowercase__							 =       seq_length
  									lowercase__							 =       is_training
  									lowercase__							 =       use_input_mask
  									lowercase__							 =       use_token_type_ids
  									lowercase__							 =       use_labels
  									lowercase__							 =       vocab_size
  									lowercase__							 =       hidden_size
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       num_attention_heads
  									lowercase__							 =       intermediate_size
  									lowercase__							 =       hidden_act
  									lowercase__							 =       hidden_dropout_prob
  									lowercase__							 =       attention_probs_dropout_prob
  									lowercase__							 =       max_position_embeddings
  									lowercase__							 =       type_vocab_size
  									lowercase__							 =       type_sequence_label_size
  									lowercase__							 =       initializer_range
  									lowercase__							 =       num_labels
  									lowercase__							 =       num_choices
  									lowercase__							 =       scope
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''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
  									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__							 =       ids_tensor([self.batch_size]       ,     self.num_choices       )
  
  									lowercase__							 =       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       ,     )
  									return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		List[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMPNetModel(config=lowerCamelCase__       )
  									lowercase__							 =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
  									lowercase__							 =       model(lowerCamelCase__       )
  									lowercase__							 =       [input_ids, input_mask]
  									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__       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMPNetForMaskedLM(config=lowerCamelCase__       )
  									lowercase__							 =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
  									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__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMPNetForQuestionAnswering(config=lowerCamelCase__       )
  									lowercase__							 =       {
  									    """input_ids""": input_ids,
  									    """attention_mask""": input_mask,
  									}
  									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       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       TFMPNetForSequenceClassification(lowerCamelCase__       )
  									lowercase__							 =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_choices
  									lowercase__							 =       TFMPNetForMultipleChoice(lowerCamelCase__       )
  									lowercase__							 =       tf.tile(tf.expand_dims(lowerCamelCase__       ,     1       )       ,     (1, self.num_choices, 1)       )
  									lowercase__							 =       tf.tile(tf.expand_dims(lowerCamelCase__       ,     1       )       ,     (1, self.num_choices, 1)       )
  									lowercase__							 =       {
  									    """input_ids""": multiple_choice_inputs_ids,
  									    """attention_mask""": multiple_choice_input_mask,
  									}
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_choices)       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.num_labels
  									lowercase__							 =       TFMPNetForTokenClassification(lowerCamelCase__       )
  									lowercase__							 =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
  									lowercase__							 =       model(lowerCamelCase__       )
  									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.seq_length, self.num_labels)       )
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.prepare_config_and_inputs()
  									((lowercase__) ,						(lowercase__) ,						(lowercase__) ,						(lowercase__) ,						(lowercase__) ,						(lowercase__))							 =       config_and_inputs
  									lowercase__							 =       {"""input_ids""": input_ids, """attention_mask""": input_mask}
  									return config, inputs_dict
  
  
  
  
  
  @require_tf
  class 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
  			lowerCamelCase							:							Tuple       		=  (
  			    (
  			        TFMPNetForMaskedLM,
  			        TFMPNetForMultipleChoice,
  			        TFMPNetForQuestionAnswering,
  			        TFMPNetForSequenceClassification,
  			        TFMPNetForTokenClassification,
  			        TFMPNetModel,
  			    )
  			    if is_tf_available()
  			    else ()
  			)
  			lowerCamelCase							:							Optional[int]       		=  (
  			    {
  			        """feature-extraction""": TFMPNetModel,
  			        """fill-mask""": TFMPNetForMaskedLM,
  			        """question-answering""": TFMPNetForQuestionAnswering,
  			        """text-classification""": TFMPNetForSequenceClassification,
  			        """token-classification""": TFMPNetForTokenClassification,
  			        """zero-shot""": TFMPNetForSequenceClassification,
  			    }
  			    if is_tf_available()
  			    else {}
  			)
  			lowerCamelCase							:							List[Any]       		=  False
  			lowerCamelCase							:							List[str]       		=  False
  
  
  
  			def A__ (					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMPNetModelTester(self       )
  									lowercase__							 =       ConfigTester(self       ,     config_class=lowerCamelCase__       ,     hidden_size=37       )
  
  
  
  			def A__ (					self       )			->		int:
  
  
  
  
  									'''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_mpnet_model(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Dict:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_mpnet_for_masked_lm(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_mpnet_for_question_answering(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_mpnet_for_sequence_classification(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_mpnet_for_multiple_choice(*lowerCamelCase__       )
  
  
  
  			def A__ (					self       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self.model_tester.prepare_config_and_inputs()
  									self.model_tester.create_and_check_mpnet_for_token_classification(*lowerCamelCase__       )
  
  
  
  			@slow
  			def A__ (					self       )			->		str:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									for model_name in ["microsoft/mpnet-base"]:
  															lowercase__							 =       TFMPNetModel.from_pretrained(lowerCamelCase__       )
  															self.assertIsNotNone(lowerCamelCase__       )
  
  
  
  
  
  @require_tf
  class 			A							(       unittest.TestCase				):
  
  
  
  			@slow
  			def A__ (					self       )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       TFMPNetModel.from_pretrained("""microsoft/mpnet-base"""       )
  									lowercase__							 =       tf.constant([[0, 1, 2, 3, 4, 5]]       )
  									lowercase__							 =       model(lowerCamelCase__       )[0]
  
  									lowercase__							 =       [1, 6, 768]
  									self.assertEqual(output.shape       ,     lowerCamelCase__       )
  
  									lowercase__							 =       tf.constant(
  									    [
  									        [
  									            [-0.1_06_71_72, 0.08_21_64_73, 0.0_02_45_43],
  									            [-0.03_46_58_79, 0.8_35_41_18, -0.03_25_22_88],
  									            [-0.06_56_94_76, -0.12_42_41_11, -0.0_49_44_36],
  									        ]
  									    ]       )
  									tf.debugging.assert_near(output[:, :3, :3]       ,     lowerCamelCase__       ,     atol=1e-4       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  __A          =    {"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    ["DetrFeatureExtractor"]
  				__A          =    ["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				__A          =    [
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				__A          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  
  