


"""simple docstring"""

import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors





@require_tokenizers
class     lowercase_	(					__SCREAMING_SNAKE_CASE      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Dict				  =     MvpTokenizer
 UpperCAmelCase   :					str				  =     MvpTokenizerFast
 UpperCAmelCase   :					Optional[int]				  =     True
 UpperCAmelCase   :					Tuple				  =     filter_roberta_detectors

 def       lowerCAmelCase_							(			self	:	Tuple		):
       super().setUp()
       _A        =       [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           '\u0120',
           '\u0120l',
           '\u0120n',
           '\u0120lo',
           '\u0120low',
           'er',
           '\u0120lowest',
           '\u0120newer',
           '\u0120wider',
           '<unk>',
       ]
       _A        =       dict(zip(_a   ,							range(len(_a		)		)		)		)
       _A        =       ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
       _A        =       {'unk_token': '<unk>'}

       _A        =       os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES['vocab_file']		)
       _A        =       os.path.join(self.tmpdirname   ,							VOCAB_FILES_NAMES['merges_file']		)
       with open(self.vocab_file   ,							'w'   ,							encoding='utf-8'		) as fp:
             fp.write(json.dumps(_a		) + '\n'		)
       with open(self.merges_file   ,							'w'   ,							encoding='utf-8'		) as fp:
             fp.write('\n'.join(_a		)		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       kwargs.update(self.special_tokens_map		)
       return self.tokenizer_class.from_pretrained(self.tmpdirname   ,							**_a		)

 def       lowerCAmelCase_							(			self	:	Tuple   ,							**_UpperCAmelCase	:	Optional[Any]		):
       kwargs.update(self.special_tokens_map		)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,							**_a		)

 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	List[str]		):
       return "lower newer", "lower newer"

 @cached_property
 def       lowerCAmelCase_							(			self	:	List[str]		):
       return MvpTokenizer.from_pretrained('RUCAIBox/mvp'		)

 @cached_property
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
       _A        =       [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             _A        =       tokenizer(_a   ,							max_length=len(_a		)   ,							padding=_a   ,							return_tensors='pt'		)
             self.assertIsInstance(_a   ,							_a		)

             self.assertEqual((2, 9)   ,							batch.input_ids.shape		)
             self.assertEqual((2, 9)   ,							batch.attention_mask.shape		)
             _A        =       batch.input_ids.tolist()[0]
             self.assertListEqual(_a   ,							_a		)
             # Test that special tokens are reset

 @require_torch
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       ['A long paragraph for summarization.', 'Another paragraph for summarization.']
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             _A        =       tokenizer(_a   ,							padding=_a   ,							return_tensors='pt'		)
             # check if input_ids are returned and no labels
             self.assertIn('input_ids'   ,							_a		)
             self.assertIn('attention_mask'   ,							_a		)
             self.assertNotIn('labels'   ,							_a		)
             self.assertNotIn('decoder_attention_mask'   ,							_a		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       [
           'Summary of the text.',
           'Another summary.',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             _A        =       tokenizer(text_target=_a   ,							max_length=32   ,							padding='max_length'   ,							return_tensors='pt'		)
             self.assertEqual(32   ,							targets['input_ids'].shape[1]		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	Tuple		):
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             _A        =       tokenizer(
                 ['I am a small frog' * 1_024, 'I am a small frog']   ,							padding=_a   ,							truncation=_a   ,							return_tensors='pt'		)
             self.assertIsInstance(_a   ,							_a		)
             self.assertEqual(batch.input_ids.shape   ,							(2, 1_024)		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       ['A long paragraph for summarization.']
       _A        =       [
           'Summary of the text.',
       ]
       for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             _A        =       tokenizer(_a   ,							text_target=_a   ,							return_tensors='pt'		)
             _A        =       inputs['input_ids']
             _A        =       inputs['labels']
             self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
             self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
             self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
             self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)

 def       lowerCAmelCase_							(			self	:	Any		):
       pass



 def       lowerCAmelCase_							(			self	:	Any		):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                   _A        =       self.rust_tokenizer_class.from_pretrained(_a   ,							**_a		)
                   _A        =       self.tokenizer_class.from_pretrained(_a   ,							**_a		)
                   _A        =       'A, <mask> AllenNLP sentence.'
                   _A        =       tokenizer_r.encode_plus(_a   ,							add_special_tokens=_a   ,							return_token_type_ids=_a		)
                   _A        =       tokenizer_p.encode_plus(_a   ,							add_special_tokens=_a   ,							return_token_type_ids=_a		)

                   # token_type_ids should put 0 everywhere
                   self.assertEqual(sum(tokens_r['token_type_ids']		)   ,							sum(tokens_p['token_type_ids']		)		)

                   # attention_mask should put 1 everywhere, so sum over length should be 1
                   self.assertEqual(
                       sum(tokens_r['attention_mask']		) / len(tokens_r['attention_mask']		)   ,							sum(tokens_p['attention_mask']		) / len(tokens_p['attention_mask']		)   ,							)

                   _A        =       tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
                   _A        =       tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)

                   # Rust correctly handles the space before the mask while python doesnt
                   self.assertSequenceEqual(tokens_p['input_ids']   ,							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)
                   self.assertSequenceEqual(tokens_r['input_ids']   ,							[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]		)

                   self.assertSequenceEqual(
                       _a   ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)
                   self.assertSequenceEqual(
                       _a   ,							['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']		)







"""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]

