






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_						:						Optional[Any]						      =					get_tests_dir('fixtures/test_sentencepiece_no_bos.model')




@require_sentencepiece
@require_tokenizers
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						str										=PegasusTokenizer
       a							:						List[Any]										=PegasusTokenizerFast
       a							:						Optional[Any]										=True
       a							:						List[Any]										=True





       def      _a       (   self				):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCamelCase_:   List[Any]        =							PegasusTokenizer(_lowerCamelCase				)
         tokenizer.save_pretrained(self.tmpdirname				)





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





       def      _a       (   self    ,     **_lowerCamelCase				):
         return PegasusTokenizer.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return ("This is a test", "This is a test")





       def      _a       (   self				):
         UpperCamelCase_:   Optional[Any]        =							'</s>'
         UpperCamelCase_:   Dict        =							1

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





       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							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(_lowerCamelCase				)    ,     1_1_0_3				)





       def      _a       (   self				):
         self.assertEqual(self.get_tokenizer().vocab_size    ,     1_1_0_3				)





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Dict        =							self.tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   int        =							(
             '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>'
         )
         UpperCamelCase_:   Any        =							rust_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         UpperCamelCase_:   int        =							py_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         UpperCamelCase_:   int        =							'<mask_1> To ensure a <mask_2> flow of bank resolutions.'
         UpperCamelCase_:   Optional[int]        =							[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
         UpperCamelCase_:   Dict        =							tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)





       def      _a       (   self				):
         UpperCamelCase_:   List[Any]        =							self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 9_6_1_0_3
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 1_0_3
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1_0_2_4
         UpperCamelCase_:   Union[str, Any]        =							'To ensure a smooth flow of bank resolutions.'
         UpperCamelCase_:   List[Any]        =							[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
         UpperCamelCase_:   Optional[Any]        =							tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]				) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]





       @require_torch
       def      _a       (   self				):
         UpperCamelCase_:   Optional[int]        =							['This is going to be way too long.' * 1_5_0, 'short example']
         UpperCamelCase_:   Optional[Any]        =							['not super long but more than 5 tokens', 'tiny']
         UpperCamelCase_:   Union[str, Any]        =							self._large_tokenizer(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)
         UpperCamelCase_:   int        =							self._large_tokenizer(
             text_target=_lowerCamelCase    ,     max_length=5    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)

         assert batch.input_ids.shape == (2, 1_0_2_4)
         assert batch.attention_mask.shape == (2, 1_0_2_4)
         assert targets["input_ids"].shape == (2, 5)
         assert len(_lowerCamelCase				) == 2  # input_ids, attention_mask.





       @slow
       def      _a       (   self				):
         # fmt: off
         UpperCamelCase_:   Optional[Any]        =							{'input_ids': [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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=_lowerCamelCase    ,     model_name='google/bigbird-pegasus-large-arxiv'    ,     revision='ba85d0851d708441f91440d509690f1ab6353415'    ,     )







@require_sentencepiece
@require_tokenizers
class 							_lowerCAmelCase(			UpperCAmelCase_				,      unittest.TestCase   ):





       """simple docstring"""







       a							:						Optional[int]										=PegasusTokenizer
       a							:						int										=PegasusTokenizerFast
       a							:						List[str]										=True
       a							:						List[Any]										=True





       def      _a       (   self				):
         super().setUp()

         # We have a SentencePiece fixture for testing
         UpperCamelCase_:   Optional[int]        =							PegasusTokenizer(_lowerCamelCase    ,     offset=0    ,     mask_token_sent=_lowerCamelCase    ,     mask_token='[MASK]'				)
         tokenizer.save_pretrained(self.tmpdirname				)





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





       def      _a       (   self    ,     **_lowerCamelCase				):
         return PegasusTokenizer.from_pretrained(self.tmpdirname    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return ("This is a test", "This is a test")





       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Any        =							self.tokenizer_class.from_pretrained(self.tmpdirname				)
         UpperCamelCase_:   Tuple        =							(
             'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
             ' <pad> <pad> <pad>'
         )
         UpperCamelCase_:   str        =							rust_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         UpperCamelCase_:   Dict        =							py_tokenizer([raw_input_str]    ,     return_tensors=_lowerCamelCase    ,     add_special_tokens=_lowerCamelCase				).input_ids[0]
         self.assertListEqual(_lowerCamelCase    ,     _lowerCamelCase				)





       @require_torch
       def      _a       (   self				):
         UpperCamelCase_:   List[str]        =							['This is going to be way too long.' * 1_0_0_0, 'short example']
         UpperCamelCase_:   Optional[Any]        =							['not super long but more than 5 tokens', 'tiny']
         UpperCamelCase_:   Dict        =							self._large_tokenizer(_lowerCamelCase    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)
         UpperCamelCase_:   Optional[int]        =							self._large_tokenizer(
             text_target=_lowerCamelCase    ,     max_length=5    ,     padding=_lowerCamelCase    ,     truncation=_lowerCamelCase    ,     return_tensors='pt'				)

         assert batch.input_ids.shape == (2, 4_0_9_6)
         assert batch.attention_mask.shape == (2, 4_0_9_6)
         assert targets["input_ids"].shape == (2, 5)
         assert len(_lowerCamelCase				) == 2  # input_ids, attention_mask.





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

         UpperCamelCase_:   int        =							self._large_tokenizer(_lowerCamelCase				).input_ids

         self.assertListEqual(
             _lowerCamelCase    ,     [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 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        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						42







@dataclass
class        UpperCAmelCase_			:
    """simple docstring"""







    lowercase   =						42
    lowercase   =						42
    lowercase   =						None
    lowercase   =						None







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"train"
    lowercase   =						"dev"
    lowercase   =						"test"







class        UpperCAmelCase_			:
    """simple docstring"""







    @staticmethod
    def lowerCamelCase			(       snake_case_   :	Optional[Any]					,	snake_case_   :	Union[Split, str]		):
          raise NotImplementedError




    @staticmethod
    def lowerCamelCase			(       snake_case_   :	str		):
          raise NotImplementedError




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

          snake_case__    :       List[Any]       		= []
          for ex_index, example in enumerate(snake_case_		):
                if ex_index % 10_000 == 0:
                      logger.info("""Writing example %d of %d"""					,	snake_case_					,	len(snake_case_		)		)

                snake_case__    :       Tuple       		= []
                snake_case__    :       Dict       		= []
                for word, label in zip(example.words					,	example.labels		):
                      snake_case__    :       Any       		= tokenizer.tokenize(snake_case_		)

                      # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                      if len(snake_case_		) > 0:
                            tokens.extend(snake_case_		)
                            # 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(snake_case_		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                snake_case__    :       Dict       		= tokenizer.num_special_tokens_to_add()
                if len(snake_case_		) > max_seq_length - special_tokens_count:
                      snake_case__    :       Tuple       		= tokens[: (max_seq_length - special_tokens_count)]
                      snake_case__    :       Tuple       		= 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]
                snake_case__    :       Dict       		= [sequence_a_segment_id] * len(snake_case_		)

                if cls_token_at_end:
                      tokens += [cls_token]
                      label_ids += [pad_token_label_id]
                      segment_ids += [cls_token_segment_id]
                else:
                      snake_case__    :       str       		= [cls_token] + tokens
                      snake_case__    :       Union[str, Any]       		= [pad_token_label_id] + label_ids
                      snake_case__    :       Any       		= [cls_token_segment_id] + segment_ids

                snake_case__    :       Union[str, Any]       		= tokenizer.convert_tokens_to_ids(snake_case_		)

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

                # Zero-pad up to the sequence length.
                snake_case__    :       Dict       		= max_seq_length - len(snake_case_		)
                if pad_on_left:
                      snake_case__    :       Optional[Any]       		= ([pad_token] * padding_length) + input_ids
                      snake_case__    :       Union[str, Any]       		= ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                      snake_case__    :       Union[str, Any]       		= ([pad_token_segment_id] * padding_length) + segment_ids
                      snake_case__    :       Union[str, Any]       		= ([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(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length
                assert len(snake_case_		) == max_seq_length

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

                if "token_type_ids" not in tokenizer.model_input_names:
                      snake_case__    :       Tuple       		= None

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


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







       class        UpperCAmelCase_			(			_a      ):
           """simple docstring"""







           lowercase   =						42
           lowercase   =						nn.CrossEntropyLoss().ignore_index




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

                 # Make sure only the first process in distributed training processes the dataset,
                 # and the others will use the cache.
                 snake_case__    :       Dict       		= cached_features_file + """.lock"""
                 with FileLock(snake_case_		):
                       if os.path.exists(snake_case_		) and not overwrite_cache:
                             logger.info(f"Loading features from cached file {cached_features_file}"		)
                             snake_case__    :       Tuple       		= torch.load(snake_case_		)
                       else:
                             logger.info(f"Creating features from dataset file at {data_dir}"		)
                             snake_case__    :       Any       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                             # TODO clean up all this to leverage built-in features of tokenizers
                             snake_case__    :       Any       		= token_classification_task.convert_examples_to_features(
                                 snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	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=snake_case_					,	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					,	snake_case_		)




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




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


if is_tf_available():
       import tensorflow as tf







       class        UpperCAmelCase_			:
           """simple docstring"""







           lowercase   =						42
           lowercase   =						-1_00




           def __init__(       self   :	List[str]					,	snake_case_   :	TokenClassificationTask					,	snake_case_   :	str					,	snake_case_   :	PreTrainedTokenizer					,	snake_case_   :	List[str]					,	snake_case_   :	str					,	snake_case_   :	Optional[int] = None					,	snake_case_   :	Any=False					,	snake_case_   :	Split = Split.train					,	):
                 snake_case__    :       int       		= token_classification_task.read_examples_from_file(snake_case_					,	snake_case_		)
                 # TODO clean up all this to leverage built-in features of tokenizers
                 snake_case__    :       Optional[Any]       		= token_classification_task.convert_examples_to_features(
                     snake_case_					,	snake_case_					,	snake_case_					,	snake_case_					,	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=snake_case_					,	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:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)					,	(
                               {"""input_ids""": tf.TensorShape([None]		), """attention_mask""": tf.TensorShape([None]		)},
                               tf.TensorShape([None]		),
                           )					,	)
                 else:
                       snake_case__    :       str       		= tf.data.Dataset.from_generator(
                           snake_case_					,	({"""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   :	Optional[Any]		):
                 snake_case__    :       Optional[Any]       		= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

                 return self.dataset




           def __len__(       self   :	Optional[Any]		):
                 return len(self.features		)




           def __getitem__(       self   :	str					,	snake_case_   :	List[str]		):
                 return self.features[i]
