


'''simple docstring'''
import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 					lowerCAmelCase_     (    UpperCAmelCase			,       unittest.TestCase  ):
       __UpperCAmelCase				=MPNetTokenizer
       __UpperCAmelCase				=MPNetTokenizerFast
       __UpperCAmelCase				=True
       __UpperCAmelCase				=True






       def      UpperCamelCase	(   self      )->			int:
            super().setUp()

            _A				=       [
                '''[UNK]''',
                '''[CLS]''',
                '''[SEP]''',
                '''[PAD]''',
                '''[MASK]''',
                '''want''',
                '''##want''',
                '''##ed''',
                '''wa''',
                '''un''',
                '''runn''',
                '''##ing''',
                ''',''',
                '''low''',
                '''lowest''',
            ]
            _A				=       os.path.join(self.tmpdirname ,    VOCAB_FILES_NAMES['vocab_file']      )
            with open(self.vocab_file ,    'w' ,    encoding='utf-8'      ) as vocab_writer:
                 vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )






       def      UpperCamelCase	(   self ,    _UpperCamelCase      )->			Union[str, Any]:
            _A				=       '''UNwant\u00E9d,running'''
            _A				=       '''unwanted, running'''
            return input_text, output_text






       def      UpperCamelCase	(   self      )->			Any:
            _A				=       self.tokenizer_class(self.vocab_file      )

            _A				=       tokenizer.tokenize('UNwant\u00E9d,running'      )
            self.assertListEqual(_UpperCamelCase ,    ['un', '##want', '##ed', ',', 'runn', '##ing']      )
            self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase      ) ,    [9, 6, 7, 12, 10, 11]      )






       @slow
       def      UpperCamelCase	(   self      )->			List[str]:
            _A				=       self.tokenizer_class.from_pretrained('microsoft/mpnet-base'      )

            _A				=       tokenizer.encode('sequence builders' ,    add_special_tokens=_UpperCamelCase      )
            _A				=       tokenizer.encode('multi-sequence build' ,    add_special_tokens=_UpperCamelCase      )

            _A				=       tokenizer.build_inputs_with_special_tokens(_UpperCamelCase      )
            _A				=       tokenizer.build_inputs_with_special_tokens(_UpperCamelCase ,    _UpperCamelCase      )

            assert encoded_sentence == [0] + text + [2]
            assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]









'''simple docstring'''
from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
    import torch

lowerCAmelCase  =				logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
    from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

    def 				lowerCamelCase_   (   cls				:					Tuple		)				->  Dict:







         """simple docstring"""
         return cls

def 				lowerCamelCase_   (   __UpperCamelCase				:					Union[Tuple, List]       ,						__UpperCamelCase				:					Optional[Union[List["torch.Generator"], "torch.Generator"]] = None       ,						__UpperCamelCase				:					Optional["torch.device"] = None       ,						__UpperCamelCase				:					Optional["torch.dtype"] = None       ,						__UpperCamelCase				:					Optional["torch.layout"] = None       ,						)				->  Tuple:







     """simple docstring"""
     _A				=       device
     _A				=       shape[0]

     _A				=       layout or torch.strided
     _A				=       device or torch.device('cpu'		)

     if generator is not None:
          _A				=       generator.device.type if not isinstance(__UpperCamelCase       ,						__UpperCamelCase		) else generator[0].device.type
          if gen_device_type != device.type and gen_device_type == "cpu":
               _A				=       'cpu'
               if device != "mps":
                    logger.info(
                        F'The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'
                        F' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'
                        F' slighly speed up this function by passing a generator that was created on the {device} device.'		)
          elif gen_device_type != device.type and gen_device_type == "cuda":
               raise ValueError(F'Cannot generate a {device} tensor from a generator of type {gen_device_type}.'		)

     if isinstance(__UpperCamelCase       ,						__UpperCamelCase		):
          _A				=       (1,) + shape[1:]
          _A				=       [
              torch.randn(__UpperCamelCase       ,						generator=generator[i]       ,						device=__UpperCamelCase       ,						dtype=__UpperCamelCase       ,						layout=__UpperCamelCase		)
              for i in range(__UpperCamelCase		)
          ]
          _A				=       torch.cat(__UpperCamelCase       ,						dim=0		).to(__UpperCamelCase		)
     else:
          _A				=       torch.randn(__UpperCamelCase       ,						generator=__UpperCamelCase       ,						device=__UpperCamelCase       ,						dtype=__UpperCamelCase       ,						layout=__UpperCamelCase		).to(__UpperCamelCase		)

     return latents

def 				lowerCamelCase_   (   __UpperCamelCase				:					Any		)				->  Optional[int]:







     """simple docstring"""
     if is_torch_version('<'       ,						'2.0.0'		) or not hasattr(__UpperCamelCase       ,						'_dynamo'		):
          return False
     return isinstance(__UpperCamelCase       ,						torch._dynamo.eval_frame.OptimizedModule		)





