


def 	lowerCamelCase_							(       UpperCAmelCase__   ):



						"""simple docstring"""




						if not isinstance(UpperCamelCase__							,     UpperCamelCase__   ) or number < 0:
												raise ValueError("""Input must be a non-negative integer"""   )

						a_		      =		0
						while number:
												# This way we arrive at next set bit (next 1) instead of looping
												# through each bit and checking for 1s hence the
												# loop won't run 32 times it will only run the number of `1` times
												number &= number - 1
												count += 1
						return count


if __name__ == "__main__":
	import doctest

	doctest.testmod()
'''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


__lowerCAmelCase						:							int    							=						logging.getLogger(__name__)






@dataclass
class     A :
							a_										=   42
							a_										=   42
							a_										=   42






@dataclass
class     A :
							a_										=   42
							a_										=   42
							a_										=   None
							a_										=   None






class     A ( UpperCAmelCase ):
							a_										=   '''train'''
							a_										=   '''dev'''
							a_										=   '''test'''






class     A :





							@staticmethod
							def    snake_case__    (   __a					:				List[Any]  ,		__a					:				Union[Split, str]						)				->						List[InputExample]:
								raise NotImplementedError





							@staticmethod
							def    snake_case__    (   __a					:				str						)				->						List[str]:
								raise NotImplementedError






							@staticmethod
							def    snake_case__    (   __a					:				List[InputExample]  ,		__a					:				List[str]  ,		__a					:				int  ,		__a					:				PreTrainedTokenizer  ,		__a					:				Dict=False  ,		__a					:				int="[CLS]"  ,		__a					:				Dict=1  ,		__a					:				Tuple="[SEP]"  ,		__a					:				Any=False  ,		__a					:				Union[str, Any]=False  ,		__a					:				Any=0  ,		__a					:				Optional[int]=0  ,		__a					:				Tuple=-1_0_0  ,		__a					:				Optional[Any]=0  ,		__a					:				int=True  ,		)				->						List[InputFeatures]:
								__UpperCAmelCase      					=      {label: i for i, label in enumerate(__a						)}

								__UpperCAmelCase      					=      []
								for ex_index, example in enumerate(__a						):
									if ex_index % 1_0_0_0_0 == 0:
										logger.info('''Writing example %d of %d'''  ,		__a  ,		len(__a						)						)

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

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

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

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

									__UpperCAmelCase      					=      tokenizer.convert_tokens_to_ids(__a						)

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

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

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

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

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


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






					class     A ( UpperCAmelCase ):
												a_										=   42
												a_										=   nn.CrossEntropyLoss().ignore_index





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

													# 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(__a						):
														if os.path.exists(__a						) and not overwrite_cache:
															logger.info(f"""Loading features from cached file {cached_features_file}"""						)
															__UpperCAmelCase      					=      torch.load(__a						)
														else:
															logger.info(f"""Creating features from dataset file at {data_dir}"""						)
															__UpperCAmelCase      					=      token_classification_task.read_examples_from_file(__a  ,		__a						)
															# TODO clean up all this to leverage built-in features of tokenizers
															__UpperCAmelCase      					=      token_classification_task.convert_examples_to_features(
															    __a  ,		__a  ,		__a  ,		__a  ,		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=__a  ,		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  ,		__a						)





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






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


if is_tf_available():
					import tensorflow as tf






					class     A :
												a_										=   42
												a_										=   -1_0_0





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

													return self.dataset





												def __len__(   self					:				int						)				->						str:
													return len(self.features						)






												def __getitem__(   self					:				int  ,		__a					:				List[Any]						)				->						InputFeatures:
													return self.features[i]
