


"""simple docstring"""

import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a         =      get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')





@require_sentencepiece
@require_tokenizers
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Optional[int]				  =     PegasusTokenizer
 UpperCAmelCase   :					List[str]				  =     PegasusTokenizerFast
 UpperCAmelCase   :					Optional[int]				  =     True
 UpperCAmelCase   :					List[str]				  =     True

 def       lowerCAmelCase_							(			self	:	List[str]		):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _A        =       PegasusTokenizer(_UpperCAmelCase		)
       tokenizer.save_pretrained(self.tmpdirname		)

 @cached_property
 def       lowerCAmelCase_							(			self	:	Any		):
       return PegasusTokenizer.from_pretrained('google/pegasus-large'		)

 def       lowerCAmelCase_							(			self	:	str   ,							**_UpperCAmelCase	:	int		):
       return PegasusTokenizer.from_pretrained(self.tmpdirname   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Tuple		):
       return ("This is a test", "This is a test")

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       '</s>'
       _A        =       1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase		)   ,							_UpperCAmelCase		)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       list(self.get_tokenizer().get_vocab().keys()		)

       self.assertEqual(vocab_keys[0]   ,							'<pad>'		)
       self.assertEqual(vocab_keys[1]   ,							'</s>'		)
       self.assertEqual(vocab_keys[-1]   ,							'v'		)
       self.assertEqual(len(_UpperCAmelCase		)   ,							1_103		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       self.assertEqual(self.get_tokenizer().vocab_size   ,							1_103		)

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       self.tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       (
           'Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'
           ' </s> <pad> <pad> <pad>'
       )
       _A        =       rust_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       _A        =       py_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       self._large_tokenizer
       # <mask_1> masks whole sentence while <mask_2> masks single word
       _A        =       '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
       _A        =       [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
       _A        =       tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       self._large_tokenizer
       # The tracebacks for the following asserts are **better** without messages or self.assertEqual
       assert tokenizer.vocab_size == 96_103
       assert tokenizer.pad_token_id == 0
       assert tokenizer.eos_token_id == 1
       assert tokenizer.offset == 103
       assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
       assert tokenizer.unk_token == "<unk>"
       assert tokenizer.model_max_length == 1_024
       _A        =       'To ensure a smooth flow of bank resolutions.'
       _A        =       [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
       _A        =       tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)
       assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]		) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

 @require_torch
 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       ['This is going to be way too long.' * 150, 'short example']
       _A        =       ['not super long but more than 5 tokens', 'tiny']
       _A        =       self._large_tokenizer(_UpperCAmelCase   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)
       _A        =       self._large_tokenizer(
           text_target=_UpperCAmelCase   ,							max_length=5   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)

       assert batch.input_ids.shape == (2, 1_024)
       assert batch.attention_mask.shape == (2, 1_024)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase		) == 2  # input_ids, attention_mask.



 @slow
 def       lowerCAmelCase_							(			self	:	Dict		):
       # fmt: off
       _A        =       {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=_UpperCAmelCase   ,							model_name='google/bigbird-pegasus-large-arxiv'   ,							revision='ba85d0851d708441f91440d509690f1ab6353415'   ,							)





@require_sentencepiece
@require_tokenizers
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     PegasusTokenizer
 UpperCAmelCase   :					Tuple				  =     PegasusTokenizerFast
 UpperCAmelCase   :					Union[str, Any]				  =     True
 UpperCAmelCase   :					Dict				  =     True

 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _A        =       PegasusTokenizer(_UpperCAmelCase   ,							offset=0   ,							mask_token_sent=_UpperCAmelCase   ,							mask_token='[MASK]'		)
       tokenizer.save_pretrained(self.tmpdirname		)

 @cached_property
 def       lowerCAmelCase_							(			self	:	int		):
       return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							**_UpperCAmelCase	:	Any		):
       return PegasusTokenizer.from_pretrained(self.tmpdirname   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Optional[int]		):
       return ("This is a test", "This is a test")

 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.rust_tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       self.tokenizer_class.from_pretrained(self.tmpdirname		)
       _A        =       (
           'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
           ' <pad> <pad> <pad>'
       )
       _A        =       rust_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       _A        =       py_tokenizer([raw_input_str]   ,							return_tensors=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase		).input_ids[0]
       self.assertListEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       ['This is going to be way too long.' * 1_000, 'short example']
       _A        =       ['not super long but more than 5 tokens', 'tiny']
       _A        =       self._large_tokenizer(_UpperCAmelCase   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)
       _A        =       self._large_tokenizer(
           text_target=_UpperCAmelCase   ,							max_length=5   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							return_tensors='pt'		)

       assert batch.input_ids.shape == (2, 4_096)
       assert batch.attention_mask.shape == (2, 4_096)
       assert targets["input_ids"].shape == (2, 5)
       assert len(_UpperCAmelCase		) == 2  # input_ids, attention_mask.



 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       _A        =       (
           'This is an example string that is used to test the original TF implementation against the HF'
           ' implementation'
       )

       _A        =       self._large_tokenizer(_UpperCAmelCase		).input_ids

       self.assertListEqual(
           _UpperCAmelCase   ,							[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]   ,							)







"""simple docstring"""

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


a         =      logging.getLogger(__name__)





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					str
 UpperCAmelCase   :					List[str]
 UpperCAmelCase   :					Optional[List[str]]





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					List[int]
 UpperCAmelCase   :					List[int]
 UpperCAmelCase   :					Optional[List[int]]				  =     None
 UpperCAmelCase   :					Optional[List[int]]				  =     None





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     '''train'''
 UpperCAmelCase   :					Tuple				  =     '''dev'''
 UpperCAmelCase   :					int				  =     '''test'''





class     lowercase_	:



 '''simple docstring'''

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Union[Split, str]		):
       raise NotImplementedError

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	str		):
       raise NotImplementedError



 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	List[InputExample]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]=False   ,							_UpperCAmelCase	:	List[str]="[CLS]"   ,							_UpperCAmelCase	:	List[Any]=1   ,							_UpperCAmelCase	:	Tuple="[SEP]"   ,							_UpperCAmelCase	:	List[str]=False   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	str=0   ,							_UpperCAmelCase	:	Optional[int]=0   ,							_UpperCAmelCase	:	Any=-100   ,							_UpperCAmelCase	:	Union[str, Any]=0   ,							_UpperCAmelCase	:	List[Any]=True   ,							):
       _A        =       {label: i for i, label in enumerate(_UpperCAmelCase		)}

       _A        =       []
       for ex_index, example in enumerate(_UpperCAmelCase		):
             if ex_index % 10_000 == 0:
                   logger.info('Writing example %d of %d'   ,							_UpperCAmelCase   ,							len(_UpperCAmelCase		)		)

             _A        =       []
             _A        =       []
             for word, label in zip(example.words   ,							example.labels		):
                   _A        =       tokenizer.tokenize(_UpperCAmelCase		)

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(_UpperCAmelCase		) > 0:
                         tokens.extend(_UpperCAmelCase		)
                         # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                         label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_UpperCAmelCase		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
             _A        =       tokenizer.num_special_tokens_to_add()
             if len(_UpperCAmelCase		) > max_seq_length - special_tokens_count:
                   _A        =       tokens[: (max_seq_length - special_tokens_count)]
                   _A        =       label_ids[: (max_seq_length - special_tokens_count)]

             # The convention in BERT is:
             # (a) For sequence pairs:
             #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
             #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
             # (b) For single sequences:
             #  tokens:   [CLS] the dog is hairy . [SEP]
             #  type_ids:   0   0   0   0  0     0   0
             #
             # Where "type_ids" are used to indicate whether this is the first
             # sequence or the second sequence. The embedding vectors for `type=0` and
             # `type=1` were learned during pre-training and are added to the wordpiece
             # embedding vector (and position vector). This is not *strictly* necessary
             # since the [SEP] token unambiguously separates the sequences, but it makes
             # it easier for the model to learn the concept of sequences.
             #
             # For classification tasks, the first vector (corresponding to [CLS]) is
             # used as the "sentence vector". Note that this only makes sense because
             # the entire model is fine-tuned.
             tokens += [sep_token]
             label_ids += [pad_token_label_id]
             if sep_token_extra:
                   # roberta uses an extra separator b/w pairs of sentences
                   tokens += [sep_token]
                   label_ids += [pad_token_label_id]
             _A        =       [sequence_a_segment_id] * len(_UpperCAmelCase		)

             if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
             else:
                   _A        =       [cls_token] + tokens
                   _A        =       [pad_token_label_id] + label_ids
                   _A        =       [cls_token_segment_id] + segment_ids

             _A        =       tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)

             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             _A        =       [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase		)

             # Zero-pad up to the sequence length.
             _A        =       max_seq_length - len(_UpperCAmelCase		)
             if pad_on_left:
                   _A        =       ([pad_token] * padding_length) + input_ids
                   _A        =       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   _A        =       ([pad_token_segment_id] * padding_length) + segment_ids
                   _A        =       ([pad_token_label_id] * padding_length) + label_ids
             else:
                   input_ids += [pad_token] * padding_length
                   input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                   segment_ids += [pad_token_segment_id] * padding_length
                   label_ids += [pad_token_label_id] * padding_length

             assert len(_UpperCAmelCase		) == max_seq_length
             assert len(_UpperCAmelCase		) == max_seq_length
             assert len(_UpperCAmelCase		) == max_seq_length
             assert len(_UpperCAmelCase		) == max_seq_length

             if ex_index < 5:
                   logger.info('*** Example ***'		)
                   logger.info('guid: %s'   ,							example.guid		)
                   logger.info('tokens: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in tokens]		)		)
                   logger.info('input_ids: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in input_ids]		)		)
                   logger.info('input_mask: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in input_mask]		)		)
                   logger.info('segment_ids: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in segment_ids]		)		)
                   logger.info('label_ids: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in label_ids]		)		)

             if "token_type_ids" not in tokenizer.model_input_names:
                   _A        =       None

             features.append(
                 InputFeatures(
                     input_ids=_UpperCAmelCase   ,							attention_mask=_UpperCAmelCase   ,							token_type_ids=_UpperCAmelCase   ,							label_ids=_UpperCAmelCase		)		)
       return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset





       class     lowercase_	(					__lowerCAmelCase							):



        '''simple docstring'''
        UpperCAmelCase   :					List[InputFeatures]
        UpperCAmelCase   :					int				  =     nn.CrossEntropyLoss().ignore_index

        def __init__(			self	:	int   ,							_UpperCAmelCase	:	TokenClassificationTask   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	Split = Split.train   ,							):
              # Load data features from cache or dataset file
              _A        =       os.path.join(
                  _UpperCAmelCase   ,							'cached_{}_{}_{}'.format(mode.value   ,							tokenizer.__class__.__name__   ,							str(_UpperCAmelCase		)		)   ,							)

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              _A        =       cached_features_file + '.lock'
              with FileLock(_UpperCAmelCase		):
                    if os.path.exists(_UpperCAmelCase		) and not overwrite_cache:
                          logger.info(F'''Loading features from cached file {cached_features_file}'''		)
                          _A        =       torch.load(_UpperCAmelCase		)
                    else:
                          logger.info(F'''Creating features from dataset file at {data_dir}'''		)
                          _A        =       token_classification_task.read_examples_from_file(_UpperCAmelCase   ,							_UpperCAmelCase		)
                          # TODO clean up all this to leverage built-in features of tokenizers
                          _A        =       token_classification_task.convert_examples_to_features(
                              _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							cls_token_at_end=bool(model_type in ['xlnet']		)   ,							cls_token=tokenizer.cls_token   ,							cls_token_segment_id=2 if model_type in ['xlnet'] else 0   ,							sep_token=tokenizer.sep_token   ,							sep_token_extra=_UpperCAmelCase   ,							pad_on_left=bool(tokenizer.padding_side == 'left'		)   ,							pad_token=tokenizer.pad_token_id   ,							pad_token_segment_id=tokenizer.pad_token_type_id   ,							pad_token_label_id=self.pad_token_label_id   ,							)
                          logger.info(F'''Saving features into cached file {cached_features_file}'''		)
                          torch.save(self.features   ,							_UpperCAmelCase		)

        def __len__(			self	:	Dict		):
              return len(self.features		)



        def __getitem__(			self	:	int   ,							_UpperCAmelCase	:	Union[str, Any]		):
              return self.features[i]


if is_tf_available():
       import tensorflow as tf





       class     lowercase_	:



        '''simple docstring'''
        UpperCAmelCase   :					List[InputFeatures]
        UpperCAmelCase   :					int				  =     -100

        def __init__(			self	:	int   ,							_UpperCAmelCase	:	TokenClassificationTask   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	Split = Split.train   ,							):
              _A        =       token_classification_task.read_examples_from_file(_UpperCAmelCase   ,							_UpperCAmelCase		)
              # TODO clean up all this to leverage built-in features of tokenizers
              _A        =       token_classification_task.convert_examples_to_features(
                  _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							cls_token_at_end=bool(model_type in ['xlnet']		)   ,							cls_token=tokenizer.cls_token   ,							cls_token_segment_id=2 if model_type in ['xlnet'] else 0   ,							sep_token=tokenizer.sep_token   ,							sep_token_extra=_UpperCAmelCase   ,							pad_on_left=bool(tokenizer.padding_side == 'left'		)   ,							pad_token=tokenizer.pad_token_id   ,							pad_token_segment_id=tokenizer.pad_token_type_id   ,							pad_token_label_id=self.pad_token_label_id   ,							)

              def gen():
                    for ex in self.features:
                          if ex.token_type_ids is None:
                                yield (
                                    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                    ex.label_ids,
                                )
                          else:
                                yield (
                                    {
                                        "input_ids": ex.input_ids,
                                        "attention_mask": ex.attention_mask,
                                        "token_type_ids": ex.token_type_ids,
                                    },
                                    ex.label_ids,
                                )

              if "token_type_ids" not in tokenizer.model_input_names:
                    _A        =       tf.data.Dataset.from_generator(
                        _UpperCAmelCase   ,							({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)   ,							(
                            {'input_ids': tf.TensorShape([None]		), 'attention_mask': tf.TensorShape([None]		)},
                            tf.TensorShape([None]		),
                        )   ,							)
              else:
                    _A        =       tf.data.Dataset.from_generator(
                        _UpperCAmelCase   ,							({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)   ,							(
                            {
                                'input_ids': tf.TensorShape([None]		),
                                'attention_mask': tf.TensorShape([None]		),
                                'token_type_ids': tf.TensorShape([None]		),
                            },
                            tf.TensorShape([None]		),
                        )   ,							)

        def       lowerCAmelCase_							(			self	:	Dict		):
              _A        =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

              return self.dataset

        def __len__(			self	:	Tuple		):
              return len(self.features		)



        def __getitem__(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]		):
              return self.features[i]

