






import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  A_						=   MODEL_FOR_MASKED_LM_MAPPING
  A_						=   TF_MODEL_FOR_MASKED_LM_MAPPING




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     super().tearDown()
     # clean-up as much as possible GPU memory occupied by PyTorch
     gc.collect()
     if is_torch_available():
        import torch

        torch.cuda.empty_cache()




  @require_tf
  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     pipeline(task='fill-mask'		,		model='sshleifer/tiny-distilroberta-base'		,		top_k=2		,		framework='tf' )
     UpperCamelCase_									=     unmasker('My name is <mask>' )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase		,		decimals=6 )		,		[
             {'sequence': 'My name is grouped', 'score': 2.1e-05, 'token': 38015, 'token_str': ' grouped'},
             {'sequence': 'My name is accuser', 'score': 2.1e-05, 'token': 25506, 'token_str': ' accuser'},
         ]		,		)

     UpperCamelCase_									=     unmasker('The largest city in France is <mask>' )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase		,		decimals=6 )		,		[
             {
                 'sequence': 'The largest city in France is grouped',
                 'score': 2.1e-05,
                 'token': 38015,
                 'token_str': ' grouped',
             },
             {
                 'sequence': 'The largest city in France is accuser',
                 'score': 2.1e-05,
                 'token': 25506,
                 'token_str': ' accuser',
             },
         ]		,		)

     UpperCamelCase_									=     unmasker('My name is <mask>'		,		targets=[' Patrick', ' Clara', ' Teven']		,		top_k=3 )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase		,		decimals=6 )		,		[
             {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 13606, 'token_str': ' Clara'},
             {'sequence': 'My name is Patrick', 'score': 2e-05, 'token': 3499, 'token_str': ' Patrick'},
             {'sequence': 'My name is Te', 'score': 1.9e-05, 'token': 2941, 'token_str': ' Te'},
         ]		,		)




  @require_torch
  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     pipeline(task='fill-mask'		,		model='sshleifer/tiny-distilroberta-base'		,		top_k=2		,		framework='pt' )

     UpperCamelCase_									=     unmasker('My name is <mask>' )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase		,		decimals=6 )		,		[
             {'sequence': 'My name is Maul', 'score': 2.2e-05, 'token': 35676, 'token_str': ' Maul'},
             {'sequence': 'My name isELS', 'score': 2.2e-05, 'token': 16416, 'token_str': 'ELS'},
         ]		,		)

     UpperCamelCase_									=     unmasker('The largest city in France is <mask>' )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase		,		decimals=6 )		,		[
             {
                 'sequence': 'The largest city in France is Maul',
                 'score': 2.2e-05,
                 'token': 35676,
                 'token_str': ' Maul',
             },
             {'sequence': 'The largest city in France isELS', 'score': 2.2e-05, 'token': 16416, 'token_str': 'ELS'},
         ]		,		)

     UpperCamelCase_									=     unmasker('My name is <mask>'		,		targets=[' Patrick', ' Clara', ' Teven']		,		top_k=3 )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase		,		decimals=6 )		,		[
             {'sequence': 'My name is Patrick', 'score': 2.1e-05, 'token': 3499, 'token_str': ' Patrick'},
             {'sequence': 'My name is Te', 'score': 2e-05, 'token': 2941, 'token_str': ' Te'},
             {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 13606, 'token_str': ' Clara'},
         ]		,		)

     UpperCamelCase_									=     unmasker('My name is <mask> <mask>'		,		top_k=2 )

     self.assertEqual(
         nested_simplify(_UpperCAmelCase		,		decimals=6 )		,		[
             [
                 {
                     'score': 2.2e-05,
                     'token': 35676,
                     'token_str': ' Maul',
                     'sequence': '<s>My name is Maul<mask></s>',
                 },
                 {'score': 2.2e-05, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
             ],
             [
                 {
                     'score': 2.2e-05,
                     'token': 35676,
                     'token_str': ' Maul',
                     'sequence': '<s>My name is<mask> Maul</s>',
                 },
                 {'score': 2.2e-05, 'token': 16416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
             ],
         ]		,		)




  @require_torch_gpu
  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     pipeline('fill-mask'		,		model='hf-internal-testing/tiny-random-distilbert'		,		device=0		,		framework='pt' )

     # convert model to fp16
     pipe.model.half()

     UpperCamelCase_									=     pipe('Paris is the [MASK] of France.' )
     # We actually don't care about the result, we just want to make sure
     # it works, meaning the float16 tensor got casted back to float32
     # for postprocessing.
     self.assertIsInstance(_UpperCAmelCase		,		_UpperCAmelCase )




  @slow
  @require_torch
  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     pipeline(task='fill-mask'		,		model='distilroberta-base'		,		top_k=2		,		framework='pt' )
     self.run_large_test(_UpperCAmelCase )




  @slow
  @require_tf
  def    _UpperCAmelCase     (     self )			-> str:
     UpperCamelCase_									=     pipeline(task='fill-mask'		,		model='distilroberta-base'		,		top_k=2		,		framework='tf' )
     self.run_large_test(_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Optional[int]:
     UpperCamelCase_									=     unmasker('My name is <mask>' )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase )		,		[
             {'sequence': 'My name is John', 'score': 0.0_0_8, 'token': 610, 'token_str': ' John'},
             {'sequence': 'My name is Chris', 'score': 0.0_0_7, 'token': 1573, 'token_str': ' Chris'},
         ]		,		)
     UpperCamelCase_									=     unmasker('The largest city in France is <mask>' )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase )		,		[
             {
                 'sequence': 'The largest city in France is Paris',
                 'score': 0.2_5_1,
                 'token': 2201,
                 'token_str': ' Paris',
             },
             {
                 'sequence': 'The largest city in France is Lyon',
                 'score': 0.2_1_4,
                 'token': 12790,
                 'token_str': ' Lyon',
             },
         ]		,		)

     UpperCamelCase_									=     unmasker('My name is <mask>'		,		targets=[' Patrick', ' Clara', ' Teven']		,		top_k=3 )
     self.assertEqual(
         nested_simplify(_UpperCAmelCase )		,		[
             {'sequence': 'My name is Patrick', 'score': 0.0_0_5, 'token': 3499, 'token_str': ' Patrick'},
             {'sequence': 'My name is Clara', 'score': 0.0_0_0, 'token': 13606, 'token_str': ' Clara'},
             {'sequence': 'My name is Te', 'score': 0.0_0_0, 'token': 2941, 'token_str': ' Te'},
         ]		,		)




  @require_torch
  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     pipeline(task='fill-mask'		,		model='sshleifer/tiny-distilroberta-base'		,		framework='pt' )
     UpperCamelCase_									=     None
     UpperCamelCase_									=     None
     self.run_pipeline_test(_UpperCAmelCase		,		[] )




  @require_tf
  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     pipeline(task='fill-mask'		,		model='sshleifer/tiny-distilroberta-base'		,		framework='tf' )
     UpperCamelCase_									=     None
     UpperCamelCase_									=     None
     self.run_pipeline_test(_UpperCAmelCase		,		[] )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Tuple:
     if tokenizer is None or tokenizer.mask_token_id is None:
        self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)' )

     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase )
     UpperCamelCase_									=     [
         f"""This is another {tokenizer.mask_token} test""",
     ]
     return fill_masker, examples




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[Any]:
     UpperCamelCase_									=     fill_masker.tokenizer
     UpperCamelCase_									=     fill_masker.model

     UpperCamelCase_									=     fill_masker(
         f"""This is a {tokenizer.mask_token}"""		,		)
     self.assertEqual(
         _UpperCAmelCase		,		[
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
         ]		,		)

     UpperCamelCase_									=     fill_masker([f"""This is a {tokenizer.mask_token}"""] )
     self.assertEqual(
         _UpperCAmelCase		,		[
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
         ]		,		)

     UpperCamelCase_									=     fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""] )
     self.assertEqual(
         _UpperCAmelCase		,		[
             [
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             ],
             [
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             ],
         ]		,		)

     with self.assertRaises(_UpperCAmelCase ):
        fill_masker([None] )
     # No mask_token is not supported
     with self.assertRaises(_UpperCAmelCase ):
        fill_masker('This is' )

     self.run_test_top_k(_UpperCAmelCase		,		_UpperCAmelCase )
     self.run_test_targets(_UpperCAmelCase		,		_UpperCAmelCase )
     self.run_test_top_k_targets(_UpperCAmelCase		,		_UpperCAmelCase )
     self.fill_mask_with_duplicate_targets_and_top_k(_UpperCAmelCase		,		_UpperCAmelCase )
     self.fill_mask_with_multiple_masks(_UpperCAmelCase		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Union[str, Any]:
     UpperCamelCase_									=     tokenizer.get_vocab()
     UpperCamelCase_									=     sorted(vocab.keys() )[:2]
     # Pipeline argument
     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		targets=_UpperCAmelCase )
     UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}""" )
     self.assertEqual(
         _UpperCAmelCase		,		[
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
         ]		,		)
     UpperCamelCase_									=     {vocab[el] for el in targets}
     self.assertEqual({el['token'] for el in outputs}		,		_UpperCAmelCase )
     UpperCamelCase_									=     [tokenizer.decode([x] ) for x in target_ids]
     self.assertEqual({el['token_str'] for el in outputs}		,		set(_UpperCAmelCase ) )

     # Call argument
     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase )
     UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		targets=_UpperCAmelCase )
     self.assertEqual(
         _UpperCAmelCase		,		[
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
         ]		,		)
     UpperCamelCase_									=     {vocab[el] for el in targets}
     self.assertEqual({el['token'] for el in outputs}		,		_UpperCAmelCase )
     UpperCamelCase_									=     [tokenizer.decode([x] ) for x in target_ids]
     self.assertEqual({el['token_str'] for el in outputs}		,		set(_UpperCAmelCase ) )

     # Score equivalence
     UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		targets=_UpperCAmelCase )
     UpperCamelCase_									=     [top_mask['token_str'] for top_mask in outputs]
     UpperCamelCase_									=     [top_mask['score'] for top_mask in outputs]

     # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
     if set(_UpperCAmelCase ) == set(_UpperCAmelCase ):
        UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		targets=_UpperCAmelCase )
        UpperCamelCase_									=     [top_mask['score'] for top_mask in unmasked_targets]
        self.assertEqual(nested_simplify(_UpperCAmelCase )		,		nested_simplify(_UpperCAmelCase ) )

     # Raises with invalid
     with self.assertRaises(_UpperCAmelCase ):
        UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		targets=[] )
     # For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
     if "" not in tokenizer.get_vocab():
        with self.assertRaises(_UpperCAmelCase ):
           UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		targets=[''] )
        with self.assertRaises(_UpperCAmelCase ):
           UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		targets='' )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase		,		top_k=2 )
     UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}""" )
     self.assertEqual(
         _UpperCAmelCase		,		[
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
         ]		,		)

     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase )
     UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		top_k=2 )
     self.assertEqual(
         _UpperCAmelCase		,		[
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
         ]		,		)
     self.assertEqual(nested_simplify(_UpperCAmelCase )		,		nested_simplify(_UpperCAmelCase ) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> List[str]:
     UpperCamelCase_									=     tokenizer.get_vocab()
     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase )

     # top_k=2, ntargets=3
     UpperCamelCase_									=     sorted(vocab.keys() )[:3]
     UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		top_k=2		,		targets=_UpperCAmelCase )

     # If we use the most probably targets, and filter differently, we should still
     # have the same results
     UpperCamelCase_									=     [el['token_str'] for el in sorted(_UpperCAmelCase		,		key=lambda _UpperCAmelCase : x["score"]		,		reverse=_UpperCAmelCase )]
     # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
     if set(_UpperCAmelCase ).issubset(_UpperCAmelCase ):
        UpperCamelCase_									=     fill_masker(f"""This is a {tokenizer.mask_token}"""		,		top_k=3		,		targets=_UpperCAmelCase )
        # They should yield exactly the same result
        self.assertEqual(nested_simplify(_UpperCAmelCase )		,		nested_simplify(_UpperCAmelCase ) )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Any:
     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase )
     UpperCamelCase_									=     tokenizer.get_vocab()
     # String duplicates + id duplicates
     UpperCamelCase_									=     sorted(vocab.keys() )[:3]
     UpperCamelCase_									=     [targets[0], targets[1], targets[0], targets[2], targets[1]]
     UpperCamelCase_									=     fill_masker(f"""My name is {tokenizer.mask_token}"""		,		targets=_UpperCAmelCase		,		top_k=10 )

     # The target list contains duplicates, so we can't output more
     # than them
     self.assertEqual(len(_UpperCAmelCase )		,		3 )




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> str:
     UpperCamelCase_									=     FillMaskPipeline(model=_UpperCAmelCase		,		tokenizer=_UpperCAmelCase )

     UpperCamelCase_									=     fill_masker(
         f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""		,		top_k=2 )
     self.assertEqual(
         _UpperCAmelCase		,		[
             [
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             ],
             [
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             ],
             [
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
                 {'sequence': ANY(_UpperCAmelCase ), 'score': ANY(_UpperCAmelCase ), 'token': ANY(_UpperCAmelCase ), 'token_str': ANY(_UpperCAmelCase )},
             ],
         ]		,		)













import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


snake_case__      :	Any       	=      logging.get_logger(__name__)


@dataclass
class 						_a       :






  """simple docstring"""





  A_						=   field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()    )}    )
  A_						=   field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}    )
  A_						=   field(
      default=128						,       metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }						,       )
  A_						=   field(
      default=UpperCAmelCase__						,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.task_name.lower()




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   """train"""
  A_						=   """dev"""
  A_						=   """test"""




class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  A_						=   42
  A_						=   42
  A_						=   42




  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase = None		,		_UpperCAmelCase = Split.train		,		_UpperCAmelCase = None		,		)			-> Tuple:
     warnings.warn(
         'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
         'library. You can have a look at this example script for pointers: '
         'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'		,		_UpperCAmelCase		,		)
     UpperCamelCase_									=     args
     UpperCamelCase_									=     glue_processors[args.task_name]()
     UpperCamelCase_									=     glue_output_modes[args.task_name]
     if isinstance(_UpperCAmelCase		,		_UpperCAmelCase ):
        try:
           UpperCamelCase_									=     Split[mode]
        except KeyError:
           raise KeyError('mode is not a valid split name' )
        # Load data features from cache or dataset file
     UpperCamelCase_									=     os.path.join(
         cache_dir if cache_dir is not None else args.data_dir		,		f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""		,		)
     UpperCamelCase_									=     self.processor.get_labels()
     if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
         "RobertaTokenizer",
         "RobertaTokenizerFast",
         "XLMRobertaTokenizer",
         "BartTokenizer",
         "BartTokenizerFast",
     ):
        # HACK(label indices are swapped in RoBERTa pretrained model)
        UpperCamelCase_		,      UpperCamelCase_									=     label_list[2], label_list[1]
     UpperCamelCase_									=     label_list

     # Make sure only the first process in distributed training processes the dataset,
     # and the others will use the cache.
     UpperCamelCase_									=     cached_features_file + '.lock'
     with FileLock(_UpperCAmelCase ):
        if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
           UpperCamelCase_									=     time.time()
           UpperCamelCase_									=     torch.load(_UpperCAmelCase )
           logger.info(
               f"""Loading features from cached file {cached_features_file} [took %.3f s]"""		,		time.time() - start )
        else:
           logger.info(f"""Creating features from dataset file at {args.data_dir}""" )

           if mode == Split.dev:
              UpperCamelCase_									=     self.processor.get_dev_examples(args.data_dir )
           elif mode == Split.test:
              UpperCamelCase_									=     self.processor.get_test_examples(args.data_dir )
           else:
              UpperCamelCase_									=     self.processor.get_train_examples(args.data_dir )
           if limit_length is not None:
              UpperCamelCase_									=     examples[:limit_length]
           UpperCamelCase_									=     glue_convert_examples_to_features(
               _UpperCAmelCase		,		_UpperCAmelCase		,		max_length=args.max_seq_length		,		label_list=_UpperCAmelCase		,		output_mode=self.output_mode		,		)
           UpperCamelCase_									=     time.time()
           torch.save(self.features		,		_UpperCAmelCase )
           # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
           logger.info(
               f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]""" )




  def __len__(     self )			-> List[str]:
     return len(self.features )




  def __getitem__(     self		,		_UpperCAmelCase )			-> InputFeatures:
     return self.features[i]




  def    _UpperCAmelCase     (     self )			-> Tuple:
     return self.label_list





