


'''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        lowercase     :

  """simple docstring"""

  def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_=13   ,       UpperCamelCase_=7   ,       UpperCamelCase_=True   ,       UpperCamelCase_=True   ,       UpperCamelCase_=False   ,       UpperCamelCase_=True   ,       UpperCamelCase_=99   ,       UpperCamelCase_=64   ,       UpperCamelCase_=5   ,       UpperCamelCase_=4   ,       UpperCamelCase_=64   ,       UpperCamelCase_="gelu"   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=512   ,       UpperCamelCase_=16   ,       UpperCamelCase_=2   ,       UpperCamelCase_=0.02   ,       UpperCamelCase_=3   ,       UpperCamelCase_=4   ,       UpperCamelCase_=None   ,       ):

       '''simple docstring'''


       UpperCamelCase__       :List[str]							 =			parent
       UpperCamelCase__       :List[Any]							 =			batch_size
       UpperCamelCase__       :str							 =			seq_length
       UpperCamelCase__       :Tuple							 =			is_training
       UpperCamelCase__       :int							 =			use_input_mask
       UpperCamelCase__       :int							 =			use_token_type_ids
       UpperCamelCase__       :Any							 =			use_labels
       UpperCamelCase__       :Optional[int]							 =			vocab_size
       UpperCamelCase__       :List[str]							 =			hidden_size
       UpperCamelCase__       :Any							 =			num_hidden_layers
       UpperCamelCase__       :Tuple							 =			num_attention_heads
       UpperCamelCase__       :Tuple							 =			intermediate_size
       UpperCamelCase__       :List[Any]							 =			hidden_act
       UpperCamelCase__       :Tuple							 =			hidden_dropout_prob
       UpperCamelCase__       :List[Any]							 =			attention_probs_dropout_prob
       UpperCamelCase__       :str							 =			max_position_embeddings
       UpperCamelCase__       :Dict							 =			type_vocab_size
       UpperCamelCase__       :Dict							 =			type_sequence_label_size
       UpperCamelCase__       :int							 =			initializer_range
       UpperCamelCase__       :Tuple							 =			num_labels
       UpperCamelCase__       :Optional[Any]							 =			num_choices
       UpperCamelCase__       :int							 =			scope




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       return MPNetConfig.from_pretrained('''microsoft/mpnet-base'''							)




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :Optional[int]							 =			ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size							)

       UpperCamelCase__       :int							 =			None
       if self.use_input_mask:
            UpperCamelCase__       :Any							 =			random_attention_mask([self.batch_size, self.seq_length]							)

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

       UpperCamelCase__       :str							 =			self.get_config()
       return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       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   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

       '''simple docstring'''


       UpperCamelCase__       :Tuple							 =			MPNetModel(config=_a							)
       model.to(_a							)
       model.eval()
       UpperCamelCase__       :Union[str, Any]							 =			model(_a   ,       _a							)
       UpperCamelCase__       :Union[str, Any]							 =			model(_a							)
       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   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

       '''simple docstring'''


       UpperCamelCase__       :str							 =			MPNetForQuestionAnswering(config=_a							)
       model.to(_a							)
       model.eval()
       UpperCamelCase__       :Union[str, Any]							 =			model(
           _a   ,       attention_mask=_a   ,       start_positions=_a   ,       end_positions=_a   ,       )
       self.parent.assertEqual(result.start_logits.shape   ,       (self.batch_size, self.seq_length)							)
       self.parent.assertEqual(result.end_logits.shape   ,       (self.batch_size, self.seq_length)							)




  def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

       '''simple docstring'''


       UpperCamelCase__       :List[Any]							 =			self.num_labels
       UpperCamelCase__       :List[Any]							 =			MPNetForSequenceClassification(_a							)
       model.to(_a							)
       model.eval()
       UpperCamelCase__       :Optional[Any]							 =			model(_a   ,       attention_mask=_a   ,       labels=_a							)
       self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)							)




  def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

       '''simple docstring'''


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




  def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							):

       '''simple docstring'''


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




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :List[Any]							 =			self.prepare_config_and_inputs()
       (UpperCamelCase__)       :int							 =			config_and_inputs
       UpperCamelCase__       :Tuple							 =			{"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict



@require_torch
class        lowercase     (							UpperCamelCase_     ,		UpperCamelCase_     ,		unittest.TestCase ):

  """simple docstring"""

  _a   			=    (
      (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
      if is_torch_available()
      else ()
  )
  _a   			=    (
      {
          """feature-extraction""": MPNetModel,
          """fill-mask""": MPNetForMaskedLM,
          """question-answering""": MPNetForQuestionAnswering,
          """text-classification""": MPNetForSequenceClassification,
          """token-classification""": MPNetForTokenClassification,
          """zero-shot""": MPNetForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  _a   			=    False
  _a   			=    True




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :Union[str, Any]							 =			MPNetModelTester(self							)
       UpperCamelCase__       :Optional[int]							 =			ConfigTester(self   ,       config_class=_a   ,       hidden_size=37							)




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       self.config_tester.run_common_tests()




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :Optional[Any]							 =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_model(*_a							)




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :str							 =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_sequence_classification(*_a							)




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :Optional[Any]							 =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_multiple_choice(*_a							)




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :Tuple							 =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_token_classification(*_a							)




  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :Dict							 =			self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mpnet_for_question_answering(*_a							)



@require_torch
class        lowercase     (							unittest.TestCase ):

  """simple docstring"""

  @slow
  def    lowerCAmelCase__    (		self							):

       '''simple docstring'''


       UpperCamelCase__       :List[str]							 =			MPNetModel.from_pretrained('''microsoft/mpnet-base'''							)
       UpperCamelCase__       :List[str]							 =			torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]							)
       UpperCamelCase__       :Any							 =			model(_a							)[0]
       UpperCamelCase__       :Tuple							 =			torch.Size((1, 11, 768)							)
       self.assertEqual(output.shape   ,       _a							)
       UpperCamelCase__       :List[Any]							 =			torch.tensor(
           [[[-0.0550, 0.1943, -0.0740], [-0.0562, 0.2211, -0.0579], [-0.0437, 0.3337, -0.0641]]]							)
       # compare the actual values for a slice.
       self.assertTrue(torch.allclose(output[:, :3, :3]   ,       _a   ,       atol=1e-4							)							)



'''simple docstring'''
import socket


def       a				(      )  ->     Dict:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			socket.socket(socket.AF_INET			, socket.SOCK_STREAM       )
					UpperCamelCase__       :List[Any]							 =			socket.gethostname()
					UpperCamelCase__       :List[str]							 =			12312

					sock.connect((host, port)       )
					sock.send(B'''Hello server!'''       )

					with open('''Received_file'''			, '''wb'''       ) as out_file:
										print('''File opened'''       )
										print('''Receiving data...'''       )
										while True:
															UpperCamelCase__       :str							 =			sock.recv(1024       )
															if not data:
																				break
															out_file.write(__a       )

					print('''Successfully received the file'''       )
					sock.close()
					print('''Connection closed'''       )


if __name__ == "__main__":
		main()