
"""simple docstring"""




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

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


UpperCAmelCase__   :					str												=				logging.getLogger(__name__)



@dataclass(frozen=a__						)
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		str
				__UpperCamelCase   :		str
				__UpperCamelCase   :		Optional[str]       =     None
				__UpperCamelCase   :		Optional[str]       =     None
				__UpperCamelCase   :		Optional[str]       =     None






@dataclass(frozen=a__						)
class 			lowerCAmelCase_		:


				"""simple docstring"""



				__UpperCamelCase   :		List[int]
				__UpperCamelCase   :		Optional[List[int]]       =     None
				__UpperCamelCase   :		Optional[List[int]]       =     None
				__UpperCamelCase   :		Optional[Union[int, float]]       =     None
				__UpperCamelCase   :		Optional[int]       =     None


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



					class 			lowerCAmelCase_		(a__						):


									"""simple docstring"""



									__UpperCamelCase   :		List[InputFeatures]



									def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__ = False     , )      ->	Dict:



																"""simple docstring"""




																SCREAMING_SNAKE_CASE__		: int     =     hans_processors[task]()

																SCREAMING_SNAKE_CASE__		: Dict     =     os.path.join(
																    SCREAMING_SNAKE_CASE__     , """cached_{}_{}_{}_{}""".format(
																        """dev""" if evaluate else """train"""     , tokenizer.__class__.__name__     , str(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__     , )     , )
																SCREAMING_SNAKE_CASE__		: Tuple     =     processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																							# HACK(label indices are swapped in RoBERTa pretrained model)
																							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Tuple     =     label_list[2], label_list[1]
																SCREAMING_SNAKE_CASE__		: int     =     label_list

																# Make sure only the first process in distributed training processes the dataset,
																# and the others will use the cache.
																SCREAMING_SNAKE_CASE__		: Optional[int]     =     cached_features_file + """.lock"""
																with FileLock(SCREAMING_SNAKE_CASE__					):
																							if os.path.exists(SCREAMING_SNAKE_CASE__					) and not overwrite_cache:
																														logger.info(F'''Loading features from cached file {cached_features_file}'''					)
																														SCREAMING_SNAKE_CASE__		: Tuple     =     torch.load(SCREAMING_SNAKE_CASE__					)
																							else:
																														logger.info(F'''Creating features from dataset file at {data_dir}'''					)

																														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     (
																														    processor.get_dev_examples(SCREAMING_SNAKE_CASE__					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE__					)
																														)

																														logger.info("""Training examples: %s"""     , len(SCREAMING_SNAKE_CASE__					)					)
																														SCREAMING_SNAKE_CASE__		: Optional[int]     =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																														logger.info("""Saving features into cached file %s"""     , SCREAMING_SNAKE_CASE__					)
																														torch.save(self.features     , SCREAMING_SNAKE_CASE__					)



									def __len__(self					)      ->	Optional[Any]:



																"""simple docstring"""




																return len(self.features					)



									def __getitem__(self     , SCREAMING_SNAKE_CASE__					)      ->	InputFeatures:



																"""simple docstring"""




																return self.features[i]



									def 			__magic_name__					(self					)      ->	Optional[int]:



																"""simple docstring"""




																return self.label_list


if is_tf_available():
					import tensorflow as tf



					class 			lowerCAmelCase_		:


									"""simple docstring"""



									__UpperCamelCase   :		List[InputFeatures]



									def __init__(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = 1_28     , SCREAMING_SNAKE_CASE__=False     , SCREAMING_SNAKE_CASE__ = False     , )      ->	str:



																"""simple docstring"""




																SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     hans_processors[task]()
																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																							# HACK(label indices are swapped in RoBERTa pretrained model)
																							SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     label_list[2], label_list[1]
																SCREAMING_SNAKE_CASE__		: Dict     =     label_list

																SCREAMING_SNAKE_CASE__		: List[Any]     =     processor.get_dev_examples(SCREAMING_SNAKE_CASE__					) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE__					)
																SCREAMING_SNAKE_CASE__		: Dict     =     hans_convert_examples_to_features(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

																def gen():
																							for ex_index, ex in tqdm.tqdm(enumerate(self.features					)     , desc="""convert examples to features"""					):
																														if ex_index % 1_00_00 == 0:
																																					logger.info("""Writing example %d of %d""" % (ex_index, len(SCREAMING_SNAKE_CASE__					))					)

																														yield (
																														    {
																														        "example_id": 0,
																														        "input_ids": ex.input_ids,
																														        "attention_mask": ex.attention_mask,
																														        "token_type_ids": ex.token_type_ids,
																														    },
																														    ex.label,
																														)

																SCREAMING_SNAKE_CASE__		: int     =     tf.data.Dataset.from_generator(
																    SCREAMING_SNAKE_CASE__     , (
																        {
																            """example_id""": tf.intaa,
																            """input_ids""": tf.intaa,
																            """attention_mask""": tf.intaa,
																            """token_type_ids""": tf.intaa,
																        },
																        tf.intaa,
																    )     , (
																        {
																            """example_id""": tf.TensorShape([]					),
																            """input_ids""": tf.TensorShape([None, None]					),
																            """attention_mask""": tf.TensorShape([None, None]					),
																            """token_type_ids""": tf.TensorShape([None, None]					),
																        },
																        tf.TensorShape([]					),
																    )     , )



									def 			__magic_name__					(self					)      ->	Optional[int]:



																"""simple docstring"""




																return self.dataset



									def __len__(self					)      ->	List[str]:



																"""simple docstring"""




																return len(self.features					)



									def __getitem__(self     , SCREAMING_SNAKE_CASE__					)      ->	InputFeatures:



																"""simple docstring"""




																return self.features[i]



									def 			__magic_name__					(self					)      ->	Optional[Any]:



																"""simple docstring"""




																return self.label_list



class 			lowerCAmelCase_		(a__						):


				"""simple docstring"""



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




											return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE__     , """heuristics_train_set.txt"""					)					)     , """train"""					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__					)      ->	int:



											"""simple docstring"""




											return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE__     , """heuristics_evaluation_set.txt"""					)					)     , """dev"""					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											return ["contradiction", "entailment", "neutral"]



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     []
											for i, line in enumerate(SCREAMING_SNAKE_CASE__					):
																		if i == 0:
																									continue
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     """%s-%s""" % (set_type, line[0])
																		SCREAMING_SNAKE_CASE__		: int     =     line[5]
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     line[6]
																		SCREAMING_SNAKE_CASE__		: str     =     line[7][2:] if line[7].startswith("""ex"""					) else line[7]
																		SCREAMING_SNAKE_CASE__		: int     =     line[0]
																		examples.append(InputExample(guid=SCREAMING_SNAKE_CASE__     , text_a=SCREAMING_SNAKE_CASE__     , text_b=SCREAMING_SNAKE_CASE__     , label=SCREAMING_SNAKE_CASE__     , pairID=SCREAMING_SNAKE_CASE__					)					)
											return examples






def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case     ,_snake_case     ,):
							SCREAMING_SNAKE_CASE__		: str     =     {label: i for i, label in enumerate(_snake_case		)}

							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     []
							for ex_index, example in tqdm.tqdm(enumerate(_snake_case		)     ,desc="""convert examples to features"""		):
														if ex_index % 10_000 == 0:
																					logger.info("""Writing example %d""" % (ex_index)		)

														SCREAMING_SNAKE_CASE__		: Any     =     tokenizer(
														    example.text_a     ,example.text_b     ,add_special_tokens=_snake_case     ,max_length=_snake_case     ,padding="""max_length"""     ,truncation=_snake_case     ,return_overflowing_tokens=_snake_case     ,)

														SCREAMING_SNAKE_CASE__		: Dict     =     label_map[example.label] if example.label in label_map else 0

														SCREAMING_SNAKE_CASE__		: Optional[int]     =     int(example.pairID		)

														features.append(InputFeatures(**_snake_case     ,label=_snake_case     ,pairID=_snake_case		)		)

							for i, example in enumerate(examples[:5]		):
														logger.info("""*** Example ***"""		)
														logger.info(f'''guid: {example}'''		)
														logger.info(f'''features: {features[i]}'''		)

							return features


UpperCAmelCase__   :					Any												=				{
    'hans': 3,
}

UpperCAmelCase__   :					Union[str, Any]												=				{
    'hans': HansProcessor,
}






"""simple docstring"""




import baseaa

def lowercase_						(     _snake_case		):
							return baseaa.baaencode(string.encode("""utf-8"""		)		)

def lowercase_						(     _snake_case		):
							return baseaa.baadecode(_snake_case		).decode("""utf-8"""		)


if __name__ == "__main__":
					UpperCAmelCase__   :					Dict												=				'Hello World!'
					UpperCAmelCase__   :					Tuple												=				baseaa_encode(test)
					print(encoded)

					UpperCAmelCase__   :					Tuple												=				baseaa_decode(encoded)
					print(decoded)




