






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     _lowerCAmelCase:





       """simple docstring"""







       def __init__( self  ,      _lowerCamelCase  ,      _lowerCamelCase=1_3  ,      _lowerCamelCase=7  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=False  ,      _lowerCamelCase=True  ,      _lowerCamelCase=9_9  ,      _lowerCamelCase=6_4  ,      _lowerCamelCase=5  ,      _lowerCamelCase=4  ,      _lowerCamelCase=6_4  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=5_1_2  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase=2  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=3  ,      _lowerCamelCase=4  ,      _lowerCamelCase=None  ,      ):
         UpperCamelCase_: Optional[int]   				=    parent
         UpperCamelCase_: Dict   				=    batch_size
         UpperCamelCase_: List[Any]   				=    seq_length
         UpperCamelCase_: List[str]   				=    is_training
         UpperCamelCase_: List[str]   				=    use_input_mask
         UpperCamelCase_: List[str]   				=    use_token_type_ids
         UpperCamelCase_: Optional[Any]   				=    use_labels
         UpperCamelCase_: List[Any]   				=    vocab_size
         UpperCamelCase_: Optional[Any]   				=    hidden_size
         UpperCamelCase_: Any   				=    num_hidden_layers
         UpperCamelCase_: Union[str, Any]   				=    num_attention_heads
         UpperCamelCase_: Tuple   				=    intermediate_size
         UpperCamelCase_: List[Any]   				=    hidden_act
         UpperCamelCase_: Union[str, Any]   				=    hidden_dropout_prob
         UpperCamelCase_: List[str]   				=    attention_probs_dropout_prob
         UpperCamelCase_: List[Any]   				=    max_position_embeddings
         UpperCamelCase_: Optional[Any]   				=    type_vocab_size
         UpperCamelCase_: Optional[Any]   				=    type_sequence_label_size
         UpperCamelCase_: Dict   				=    initializer_range
         UpperCamelCase_: Optional[int]   				=    num_labels
         UpperCamelCase_: List[str]   				=    num_choices
         UpperCamelCase_: Dict   				=    scope





       def       _a   ( self							):
         return MPNetConfig.from_pretrained('microsoft/mpnet-base'							)





       def       _a   ( self							):
         UpperCamelCase_: List[Any]   				=    ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size							)

         UpperCamelCase_: Optional[int]   				=    None
         if self.use_input_mask:
           UpperCamelCase_: str   				=    random_attention_mask([self.batch_size, self.seq_length]							)

         UpperCamelCase_: Dict   				=    None
         UpperCamelCase_: Dict   				=    None
         UpperCamelCase_: Union[str, Any]   				=    None
         if self.use_labels:
           UpperCamelCase_: str   				=    ids_tensor([self.batch_size]  ,      self.type_sequence_label_size							)
           UpperCamelCase_: Tuple   				=    ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels							)
           UpperCamelCase_: Union[str, Any]   				=    ids_tensor([self.batch_size]  ,      self.num_choices							)

         UpperCamelCase_: Optional[int]   				=    self.get_config()
         return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





       def       _a   ( self							):
         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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[int]   				=    MPNetModel(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         UpperCamelCase_: Any   				=    model(_lowerCamelCase  ,      _lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Optional[Any]   				=    MPNetForQuestionAnswering(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         UpperCamelCase_: str   				=    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       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: str   				=    self.num_labels
         UpperCamelCase_: int   				=    MPNetForSequenceClassification(_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         UpperCamelCase_: List[str]   				=    model(_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      labels=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_labels)							)





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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Dict   				=    self.num_labels
         UpperCamelCase_: Dict   				=    MPNetForTokenClassification(config=_lowerCamelCase							)
         model.to(_lowerCamelCase							)
         model.eval()
         UpperCamelCase_: Tuple   				=    model(_lowerCamelCase  ,      attention_mask=_lowerCamelCase  ,      labels=_lowerCamelCase							)
         self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)							)





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.prepare_config_and_inputs()
         (UpperCamelCase_): List[Any]   				=    config_and_inputs
         UpperCamelCase_: int   				=    {'input_ids': input_ids, 'attention_mask': input_mask}
         return config, inputs_dict







@require_torch
class     _lowerCAmelCase(	UpperCAmelCase_							,     UpperCAmelCase_							,     unittest.TestCase ):





       """simple docstring"""







       a    :		Dict									=(
           (
               MPNetForMaskedLM,
               MPNetForMultipleChoice,
               MPNetForQuestionAnswering,
               MPNetForSequenceClassification,
               MPNetForTokenClassification,
               MPNetModel,
           )
           if is_torch_available()
           else ()
       )
       a    :		Tuple									=(
           {
               '''feature-extraction''': MPNetModel,
               '''fill-mask''': MPNetForMaskedLM,
               '''question-answering''': MPNetForQuestionAnswering,
               '''text-classification''': MPNetForSequenceClassification,
               '''token-classification''': MPNetForTokenClassification,
               '''zero-shot''': MPNetForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       a    :		str									=False
       a    :		Union[str, Any]									=True





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    MPNetModelTester(self							)
         UpperCamelCase_: List[Any]   				=    ConfigTester(self  ,      config_class=_lowerCamelCase  ,      hidden_size=3_7							)





       def       _a   ( self							):
         self.config_tester.run_common_tests()





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_model(*_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: List[str]   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_sequence_classification(*_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Dict   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_multiple_choice(*_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_token_classification(*_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_mpnet_for_question_answering(*_lowerCamelCase							)







@require_torch
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    MPNetModel.from_pretrained('microsoft/mpnet-base'							)
         UpperCamelCase_: Union[str, Any]   				=    torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]							)
         UpperCamelCase_: Tuple   				=    model(_lowerCamelCase							)[0]
         UpperCamelCase_: Optional[int]   				=    torch.Size((1, 1_1, 7_6_8)							)
         self.assertEqual(output.shape  ,      _lowerCamelCase							)
         UpperCamelCase_: str   				=    torch.tensor(
             [[[-0.0_5_5_0, 0.1_9_4_3, -0.0_7_4_0], [-0.0_5_6_2, 0.2_2_1_1, -0.0_5_7_9], [-0.0_4_3_7, 0.3_3_3_7, -0.0_6_4_1]]]							)
         # compare the actual values for a slice.
         self.assertTrue(torch.allclose(output[:, :3, :3]  ,      _lowerCamelCase  ,      atol=1e-4							)							)







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A_		:		List[str]		     =			'.'


if __name__ == "__main__":
   A_		:		Dict		     =			os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
   A_		:		Dict		     =			[]
   A_		:		Optional[Any]		     =			[]
   with open(doctest_file_path) as fp:
      for line in fp:
         A_		:		Tuple		     =			line.strip()
         A_		:		Any		     =			os.path.join(REPO_PATH, line)
         if not (os.path.isfile(path) or os.path.isdir(path)):
            non_existent_paths.append(line)
         all_paths.append(path)
   if len(non_existent_paths) > 0:
      A_		:		str		     =			'\n'.join(non_existent_paths)
      raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
   if all_paths != sorted(all_paths):
      raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')