
'''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 			UpperCAmelCase__    :



    def __init__(						self   :							Union[str, Any],__A   :							Optional[int],__A   :							Union[str, Any]=1_3,__A   :							str=7,__A   :							str=True,__A   :							Dict=True,__A   :							int=False,__A   :							str=True,__A   :							Optional[Any]=9_9,__A   :							int=6_4,__A   :							Tuple=5,__A   :							Union[str, Any]=4,__A   :							Tuple=6_4,__A   :							Union[str, Any]="gelu",__A   :							Dict=0.1,__A   :							int=0.1,__A   :							List[str]=5_1_2,__A   :							Tuple=1_6,__A   :							List[str]=2,__A   :							int=0.02,__A   :							int=3,__A   :							int=4,__A   :							Any=None,):
          _lowerCamelCase					: Dict		    = parent
          _lowerCamelCase					: Tuple		    = batch_size
          _lowerCamelCase					: str		    = seq_length
          _lowerCamelCase					: Any		    = is_training
          _lowerCamelCase					: Dict		    = use_input_mask
          _lowerCamelCase					: int		    = use_token_type_ids
          _lowerCamelCase					: Optional[Any]		    = use_labels
          _lowerCamelCase					: int		    = vocab_size
          _lowerCamelCase					: Any		    = hidden_size
          _lowerCamelCase					: Tuple		    = num_hidden_layers
          _lowerCamelCase					: Optional[int]		    = num_attention_heads
          _lowerCamelCase					: List[Any]		    = intermediate_size
          _lowerCamelCase					: int		    = hidden_act
          _lowerCamelCase					: List[str]		    = hidden_dropout_prob
          _lowerCamelCase					: List[str]		    = attention_probs_dropout_prob
          _lowerCamelCase					: Optional[int]		    = max_position_embeddings
          _lowerCamelCase					: int		    = type_vocab_size
          _lowerCamelCase					: List[Any]		    = type_sequence_label_size
          _lowerCamelCase					: int		    = initializer_range
          _lowerCamelCase					: Any		    = num_labels
          _lowerCamelCase					: str		    = num_choices
          _lowerCamelCase					: Optional[int]		    = scope



    def 	lowerCamelCase_	(						self   :							str  ):
          return MPNetConfig.from_pretrained("microsoft/mpnet-base"  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          _lowerCamelCase					: Optional[Any]		    = ids_tensor([self.batch_size, self.seq_length],self.vocab_size  )

          _lowerCamelCase					: Optional[Any]		    = None
          if self.use_input_mask:
                _lowerCamelCase					: Union[str, Any]		    = random_attention_mask([self.batch_size, self.seq_length]  )

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

          _lowerCamelCase					: List[Any]		    = self.get_config()
          return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels



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



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



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							str,__A   :							List[str],__A   :							int,__A   :							Optional[int],__A   :							Any,__A   :							Any  ):
          _lowerCamelCase					: Any		    = self.num_labels
          _lowerCamelCase					: List[Any]		    = MPNetForTokenClassification(config=__A  )
          model.to(__A  )
          model.eval()
          _lowerCamelCase					: List[str]		    = 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   :							Any  ):
          _lowerCamelCase					: int		    = self.prepare_config_and_inputs()
          ((_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase)    ,		(_lowerCamelCase))					: List[Any]		    = config_and_inputs
          _lowerCamelCase					: Optional[int]		    = {"input_ids": input_ids, "attention_mask": input_mask}
          return config, inputs_dict


@require_torch
class 			UpperCAmelCase__    (							A    ,						A    ,						unittest.TestCase			):
    lowerCAmelCase_					     =		(
        (
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetModel,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase_					     =		(
        {
            'feature-extraction': MPNetModel,
            'fill-mask': MPNetForMaskedLM,
            'question-answering': MPNetForQuestionAnswering,
            'text-classification': MPNetForSequenceClassification,
            'token-classification': MPNetForTokenClassification,
            'zero-shot': MPNetForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCAmelCase_					     =		False
    lowerCAmelCase_					     =		True



    def 	lowerCamelCase_	(						self   :							Optional[Any]  ):
          _lowerCamelCase					: Any		    = MPNetModelTester(self  )
          _lowerCamelCase					: Tuple		    = ConfigTester(self,config_class=__A,hidden_size=3_7  )



    def 	lowerCamelCase_	(						self   :							List[Any]  ):
          self.config_tester.run_common_tests()



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: List[str]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_model(*__A  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Tuple		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_sequence_classification(*__A  )



    def 	lowerCamelCase_	(						self   :							List[str]  ):
          _lowerCamelCase					: Dict		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_multiple_choice(*__A  )



    def 	lowerCamelCase_	(						self   :							Dict  ):
          _lowerCamelCase					: Any		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_token_classification(*__A  )



    def 	lowerCamelCase_	(						self   :							Union[str, Any]  ):
          _lowerCamelCase					: Union[str, Any]		    = self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_mpnet_for_question_answering(*__A  )


@require_torch
class 			UpperCAmelCase__    (							unittest.TestCase			):



    @slow
    def 	lowerCamelCase_	(						self   :							str  ):
          _lowerCamelCase					: List[Any]		    = MPNetModel.from_pretrained("microsoft/mpnet-base"  )
          _lowerCamelCase					: Optional[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]]  )
          _lowerCamelCase					: Dict		    = model(__A  )[0]
          _lowerCamelCase					: int		    = torch.Size((1, 1_1, 7_6_8)  )
          self.assertEqual(output.shape,__A  )
          _lowerCamelCase					: int		    = 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 List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{"vocab_file": "sentencepiece.model"}

_UpperCamelCase								=		{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
}

_UpperCamelCase								=		{
    "google/rembert": 2_56,
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				VOCAB_FILES_NAMES
			_UpperCamelCase  :						List[str]					     =				PRETRAINED_VOCAB_FILES_MAP
			_UpperCamelCase  :						Any					     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





			def __init__(			self   ,      snake_case_   ,      snake_case_=False   ,      snake_case_=True   ,      snake_case_=True   ,      snake_case_="[CLS]"   ,      snake_case_="[SEP]"   ,      snake_case_="[UNK]"   ,      snake_case_="[SEP]"   ,      snake_case_="[PAD]"   ,      snake_case_="[CLS]"   ,      snake_case_="[MASK]"   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(
										    do_lower_case=snake_case_   ,      remove_space=snake_case_   ,      keep_accents=snake_case_   ,      bos_token=snake_case_   ,      eos_token=snake_case_   ,      unk_token=snake_case_   ,      sep_token=snake_case_   ,      pad_token=snake_case_   ,      cls_token=snake_case_   ,      mask_token=snake_case_   ,      **snake_case_   ,      )

										A__       :     List[Any]								=			do_lower_case
										A__       :     Dict								=			remove_space
										A__       :     Optional[int]								=			keep_accents
										A__       :     Tuple								=			vocab_file

										A__       :     List[str]								=			spm.SentencePieceProcessor()
										self.sp_model.Load(snake_case_							)





			@property
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return len(self.sp_model							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			{self.convert_ids_to_tokens(snake_case_							): i for i in range(self.vocab_size							)}
										vocab.update(self.added_tokens_encoder							)
										return vocab





			def __getstate__(			self							):







										'''simple docstring'''


										A__       :     str								=			self.__dict__.copy()
										A__       :     Tuple								=			None
										return state





			def __setstate__(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Dict								=			d
										A__       :     int								=			spm.SentencePieceProcessor()
										self.sp_model.Load(self.vocab_file							)





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=False							):







										'''simple docstring'''


										A__       :     Tuple								=			self.sp_model.EncodeAsPieces(snake_case_							)
										return pieces





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.PieceToId(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										return self.sp_model.IdToPiece(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     str								=			self.sp_model.decode_pieces(snake_case_							)
										return out_string





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     List[Any]								=			[self.sep_token_id]
										A__       :     str								=			[self.cls_token_id]
										if token_ids_a is None:
																	return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None   ,      snake_case_ = False							):







										'''simple docstring'''


										if already_has_special_tokens:
																	if token_ids_a is not None:
																								raise ValueError(
																								    """You should not supply a second sequence if the provided sequence of """
																								    """ids is already formatted with special tokens for the model."""							)
																	return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

										if token_ids_a is not None:
																	return [1] + ([0] * len(snake_case_							)) + [1] + ([0] * len(snake_case_							)) + [1]
										return [1] + ([0] * len(snake_case_							)) + [1]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										A__       :     int								=			[self.sep_token_id]
										A__       :     Any								=			[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							) * [0] + len(token_ids_a + sep							) * [1]





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_ = None							):







										'''simple docstring'''


										if not os.path.isdir(snake_case_							):
																	logger.error("""Vocabulary path ({}) should be a directory""".format(snake_case_							)							)
																	return
										A__       :     Any								=			os.path.join(
										    snake_case_   ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(snake_case_							):
																	copyfile(self.vocab_file   ,      snake_case_							)

										return (out_vocab_file,)





