


'''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_					:    Optional[int]          =    logging.getLogger(__name__)




@dataclass
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42





@dataclass
class  a     :
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	42
 _lowerCAmelCase 					=	None
 _lowerCAmelCase 					=	None





class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	"""train"""
 _lowerCAmelCase 					=	"""dev"""
 _lowerCAmelCase 					=	"""test"""





class  a     :
 @staticmethod
 def 							__UpperCAmelCase		( __magic_name__      ,				__magic_name__						)					->							List[InputExample]:
      raise NotImplementedError
 @staticmethod
 def 							__UpperCAmelCase		( __magic_name__						)					->							List[str]:
      raise NotImplementedError




 @staticmethod
 def 							__UpperCAmelCase		( __magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__=False      ,				__magic_name__="[CLS]"      ,				__magic_name__=1      ,				__magic_name__="[SEP]"      ,				__magic_name__=False      ,				__magic_name__=False      ,				__magic_name__=0      ,				__magic_name__=0      ,				__magic_name__=-1_00      ,				__magic_name__=0      ,				__magic_name__=True      ,				)					->							List[InputFeatures]:
      _a			      =							{label: i for i, label in enumerate(__magic_name__						)}

      _a			      =							[]
      for ex_index, example in enumerate(__magic_name__						):
           if ex_index % 1_00_00 == 0:
                logger.info('Writing example %d of %d'      ,				__magic_name__      ,				len(__magic_name__						)						)

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

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

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
           _a			      =							tokenizer.num_special_tokens_to_add()
           if len(__magic_name__						) > 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(__magic_name__						)

           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(__magic_name__						)

           # 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(__magic_name__						)

           # Zero-pad up to the sequence length.
           _a			      =							max_seq_length - len(__magic_name__						)
           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(__magic_name__						) == max_seq_length
           assert len(__magic_name__						) == max_seq_length
           assert len(__magic_name__						) == max_seq_length
           assert len(__magic_name__						) == max_seq_length

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

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

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


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




 class  a     (     _SCREAMING_SNAKE_CASE       ):
  _lowerCAmelCase 					=	42
  _lowerCAmelCase 					=	nn.CrossEntropyLoss().ignore_index
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__=False      ,				__magic_name__ = Split.train      ,				)					->							List[Any]:
       # Load data features from cache or dataset file
       _a			      =							os.path.join(
           __magic_name__      ,				'cached_{}_{}_{}'.format(mode.value      ,				tokenizer.__class__.__name__      ,				str(__magic_name__						)						)      ,				)

       # 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(__magic_name__						):
            if os.path.exists(__magic_name__						) and not overwrite_cache:
                 logger.info(f'Loading features from cached file {cached_features_file}'						)
                 _a			      =							torch.load(__magic_name__						)
            else:
                 logger.info(f'Creating features from dataset file at {data_dir}'						)
                 _a			      =							token_classification_task.read_examples_from_file(__magic_name__      ,				__magic_name__						)
                 # TODO clean up all this to leverage built-in features of tokenizers
                 _a			      =							token_classification_task.convert_examples_to_features(
                     __magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				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=__magic_name__      ,				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      ,				__magic_name__						)
  def __len__( self						)					->							str:
       return len(self.features						)




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


if is_tf_available():
 import tensorflow as tf




 class  a     :
  _lowerCAmelCase 					=	42
  _lowerCAmelCase 					=	-1_0_0
  def __init__( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__ = None      ,				__magic_name__=False      ,				__magic_name__ = Split.train      ,				)					->							int:
       _a			      =							token_classification_task.read_examples_from_file(__magic_name__      ,				__magic_name__						)
       # TODO clean up all this to leverage built-in features of tokenizers
       _a			      =							token_classification_task.convert_examples_to_features(
           __magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				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=__magic_name__      ,				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(
                __magic_name__      ,				({'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(
                __magic_name__      ,				({'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 							__UpperCAmelCase		( self						)					->							Dict:
       _a			      =							self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features						)						)						)

       return self.dataset
  def __len__( self						)					->							Dict:
       return len(self.features						)




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





'''simple docstring'''






import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict






@pytest.mark.parametrize(
    'split_dict'				,						[
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42				,						dataset_name='my_dataset'		)}		),
        SplitDict({'train': SplitInfo(name='train'				,						num_bytes=13_37				,						num_examples=42		)}		),
        SplitDict({'train': SplitInfo()}		),
    ]				,						)
def 	_A				(lowerCAmelCase__  :SplitDict		)   ->  Any:




     '''simple docstring'''



     _a			      =							split_dict._to_yaml_list()
     assert len(lowerCAmelCase__		) == len(lowerCAmelCase__		)
     _a			      =							SplitDict._from_yaml_list(lowerCAmelCase__		)
     for split_name, split_info in split_dict.items():
          # dataset_name field is deprecated, and is therefore not part of the YAML dump
          _a			      =							None
          # the split name of split_dict takes over the name of the split info object
          _a			      =							split_name
     assert split_dict == reloaded






@pytest.mark.parametrize(
    'split_info'				,						[SplitInfo(), SplitInfo(dataset_name=lowerCAmelCase__		), SplitInfo(dataset_name='my_dataset'		)]		)
def 	_A				(lowerCAmelCase__  :Optional[Any]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							asdict(SplitDict({'train': split_info}		)		)
     assert "dataset_name" in split_dict_asdict["train"]
     assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name

