

'''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 ,			a_ ,			a_=1_3 ,			a_=7 ,			a_=True ,			a_=True ,			a_=False ,			a_=True ,			a_=9_9 ,			a_=6_4 ,			a_=5 ,			a_=4 ,			a_=6_4 ,			a_="gelu" ,			a_=0.1 ,			a_=0.1 ,			a_=5_1_2 ,			a_=1_6 ,			a_=2 ,			a_=0.02 ,			a_=3 ,			a_=4 ,			a_=None ,			)				->					Optional[Any]:


    """simple docstring"""
    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     snake_case_      (  self  )				->					List[str]:


    """simple docstring"""
    return MPNetConfig.from_pretrained('microsoft/mpnet-base'  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    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     snake_case_      (  self  )				->					str:


    """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     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_ ,			a_ ,			a_  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						MPNetModel(config=a_  )
    model.to(a_  )
    model.eval()
    UpperCAmelCase    				=						model(a_ ,			a_  )
    UpperCAmelCase    				=						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     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_ ,			a_ ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						MPNetForQuestionAnswering(config=a_  )
    model.to(a_  )
    model.eval()
    UpperCAmelCase    				=						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     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_ ,			a_ ,			a_  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						self.num_labels
    UpperCAmelCase    				=						MPNetForSequenceClassification(a_  )
    model.to(a_  )
    model.eval()
    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_ ,			labels=a_  )
    self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)  )
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_ ,			a_ ,			a_  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.num_choices
    UpperCAmelCase    				=						MPNetForMultipleChoice(config=a_  )
    model.to(a_  )
    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(
        a_ ,			attention_mask=a_ ,			labels=a_ ,			)
    self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_choices)  )
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_ ,			a_ ,			a_ ,			a_  )				->					List[str]:


    """simple docstring"""
    UpperCAmelCase    				=						self.num_labels
    UpperCAmelCase    				=						MPNetForTokenClassification(config=a_  )
    model.to(a_  )
    model.eval()
    UpperCAmelCase    				=						model(a_ ,			attention_mask=a_ ,			labels=a_  )
    self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)  )





  def     snake_case_      (  self  )				->					Tuple:


    """simple docstring"""
    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 							lowercase_						(		a			,							a			,							unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Optional[Any]     	= (
      (
          MPNetForMaskedLM,
          MPNetForMultipleChoice,
          MPNetForQuestionAnswering,
          MPNetForSequenceClassification,
          MPNetForTokenClassification,
          MPNetModel,
      )
      if is_torch_available()
      else ()
  )
  __lowerCAmelCase       :			Any     	= (
      {
          "feature-extraction": MPNetModel,
          "fill-mask": MPNetForMaskedLM,
          "question-answering": MPNetForQuestionAnswering,
          "text-classification": MPNetForSequenceClassification,
          "token-classification": MPNetForTokenClassification,
          "zero-shot": MPNetForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  __lowerCAmelCase       :			Tuple     	= False
  __lowerCAmelCase       :			Optional[Any]     	= True
  def     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						MPNetModelTester(self  )
    UpperCAmelCase    				=						ConfigTester(self ,			config_class=a_ ,			hidden_size=3_7  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    self.config_tester.run_common_tests()
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_model(*a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_sequence_classification(*a_  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_multiple_choice(*a_  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_token_classification(*a_  )





  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						MPNetModel.from_pretrained('microsoft/mpnet-base'  )
    UpperCAmelCase    				=						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    				=						model(a_  )[0]
    UpperCAmelCase    				=						torch.Size((1, 1_1, 7_6_8)  )
    self.assertEqual(output.shape ,			a_  )
    UpperCAmelCase    				=						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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_a					:			Tuple							=    logging.get_logger(__name__)

_a					:			Optional[int]							=    '▁'

_a					:			str							=    {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

_a					:			Optional[Any]							=    {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

_a					:			Union[str, Any]							=    {'vinai/bartpho-syllable': 1_024}



class 							lowercase_						(		a			):

  '''simple docstring'''





  __lowerCAmelCase       :			Dict     	= VOCAB_FILES_NAMES
  __lowerCAmelCase       :			str     	= PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :			Any     	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :			int     	= ["input_ids", "attention_mask"]
  def __init__(  self ,			a_ ,			a_ ,			a_="<s>" ,			a_="</s>" ,			a_="</s>" ,			a_="<s>" ,			a_="<unk>" ,			a_="<pad>" ,			a_="<mask>" ,			a_ = None ,			**a_ ,			)				->					None:


    """simple docstring"""
    # Mask token behave like a normal word, i.e. include the space before it
    UpperCAmelCase    				=						AddedToken(a_ ,			lstrip=a_ ,			rstrip=a_  ) if isinstance(a_ ,			a_  ) else mask_token

    UpperCAmelCase    				=						{} if sp_model_kwargs is None else sp_model_kwargs

    super().__init__(
        bos_token=a_ ,			eos_token=a_ ,			unk_token=a_ ,			sep_token=a_ ,			cls_token=a_ ,			pad_token=a_ ,			mask_token=a_ ,			sp_model_kwargs=self.sp_model_kwargs ,			**a_ ,			)

    UpperCAmelCase    				=						vocab_file
    UpperCAmelCase    				=						monolingual_vocab_file
    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.Load(str(a_  )  )

    # Load the reduced vocab

    # Keep order of special tokens for backward compatibility
    UpperCAmelCase    				=						{}
    UpperCAmelCase    				=						0
    for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
      if str(a_  ) not in self.fairseq_tokens_to_ids:
        UpperCAmelCase    				=						cnt
        cnt += 1
    with open(a_ ,			'r' ,			encoding='utf-8'  ) as f:
      for line in f.readlines():
        UpperCAmelCase    				=						line.strip().split()[0]
        UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )
    if str(a_  ) not in self.fairseq_tokens_to_ids:
      UpperCAmelCase    				=						len(self.fairseq_tokens_to_ids  )

    UpperCAmelCase    				=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
  def __getstate__(  self  )				->					Any:


    """simple docstring"""
    UpperCAmelCase    				=						self.__dict__.copy()
    UpperCAmelCase    				=						None
    UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
    return state
  def __setstate__(  self ,			a_  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						d

    # for backward compatibility
    if not hasattr(self ,			'sp_model_kwargs'  ):
      UpperCAmelCase    				=						{}

    UpperCAmelCase    				=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto  )
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""

    if token_ids_a is None:
      return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]
    UpperCAmelCase    				=						[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = False  )				->					List[int]:


    """simple docstring"""

    if already_has_special_tokens:
      return super().get_special_tokens_mask(
          token_ids_a=a_ ,			token_ids_a=a_ ,			already_has_special_tokens=a_  )

    if token_ids_a is None:
      return [1] + ([0] * len(a_  )) + [1]
    return [1] + ([0] * len(a_  )) + [1, 1] + ([0] * len(a_  )) + [1]
  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					List[int]:


    """simple docstring"""
    UpperCAmelCase    				=						[self.sep_token_id]
    UpperCAmelCase    				=						[self.cls_token_id]

    if token_ids_a is None:
      return len(cls + token_ids_a + sep  ) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep  ) * [0]
  @property
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    return len(self.fairseq_ids_to_tokens  )
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						{self.convert_ids_to_tokens(a_  ): i for i in range(self.vocab_size  )}
    vocab.update(self.added_tokens_encoder  )
    return vocab
  def     snake_case_      (  self ,			a_  )				->					List[str]:


    """simple docstring"""
    return self.sp_model.encode(a_ ,			out_type=a_  )
  def     snake_case_      (  self ,			a_  )				->					Any:


    """simple docstring"""
    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    else:
      return self.unk_token_id
  def     snake_case_      (  self ,			a_  )				->					Dict:


    """simple docstring"""
    return self.fairseq_ids_to_tokens[index]
  def     snake_case_      (  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						''.join(a_  ).replace(a_ ,			' '  ).strip()
    return out_string





  def     snake_case_      (  self ,			a_ ,			a_ = None  )				->					Tuple[str]:


    """simple docstring"""
    if not os.path.isdir(a_  ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
      return
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )
    UpperCAmelCase    				=						os.path.join(
        a_ ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['monolingual_vocab_file'] ,			)

    if os.path.abspath(self.vocab_file  ) != os.path.abspath(a_  ) and os.path.isfile(self.vocab_file  ):
      copyfile(self.vocab_file ,			a_  )
    elif not os.path.isfile(self.vocab_file  ):
      with open(a_ ,			'wb'  ) as fi:
        UpperCAmelCase    				=						self.sp_model.serialized_model_proto()
        fi.write(a_  )

    if os.path.abspath(self.monolingual_vocab_file  ) != os.path.abspath(
        a_  ) and os.path.isfile(self.monolingual_vocab_file  ):
      copyfile(self.monolingual_vocab_file ,			a_  )
    elif not os.path.isfile(self.monolingual_vocab_file  ):
      with open(a_ ,			'w' ,			encoding='utf-8'  ) as fp:
        for token in self.fairseq_tokens_to_ids:
          if token not in self.all_special_tokens:
            fp.write(F'''{str(a_  )} \n'''  )

    return out_vocab_file, out_monolingual_vocab_file


