




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,
)


lowercase__  		=		logging.getLogger(__name__)






@dataclass(frozen=lowercase__		)
class lowerCAmelCase__			:






			'''simple docstring'''



			lowerCamelCase__   =    42
			lowerCamelCase__   =    42
			lowerCamelCase__   =    None
			lowerCamelCase__   =    None
			lowerCamelCase__   =    None






@dataclass(frozen=lowercase__		)
class lowerCAmelCase__			:






			'''simple docstring'''



			lowerCamelCase__   =    42
			lowerCamelCase__   =    None
			lowerCamelCase__   =    None
			lowerCamelCase__   =    None
			lowerCamelCase__   =    None


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






			class lowerCAmelCase__			(  lowercase__		):






						'''simple docstring'''



						lowerCamelCase__   =    42
						def __init__(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase = None						,   lowercase=False						,   lowercase = False						,   ):
										_lowerCamelCase  :    int					=					hans_processors[task]()

										_lowerCamelCase  :    Optional[Any]					=					os.path.join(
										    lowercase						,   'cached_{}_{}_{}_{}'.format(
										        'dev' if evaluate else 'train'						,   tokenizer.__class__.__name__						,   str(lowercase						)						,   lowercase						,   )						,   )
										_lowerCamelCase  :    List[Any]					=					processor.get_labels()
										if tokenizer.__class__ in (
										    RobertaTokenizer,
										    RobertaTokenizerFast,
										    XLMRobertaTokenizer,
										    BartTokenizer,
										    BartTokenizerFast,
										):
														# HACK(label indices are swapped in RoBERTa pretrained model)
														_lowerCamelCase,				_lowerCamelCase  :    Union[str, Any]					=					label_list[2], label_list[1]
										_lowerCamelCase  :    str					=					label_list

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

																		_lowerCamelCase  :    str					=					(
																		    processor.get_dev_examples(lowercase						) if evaluate else processor.get_train_examples(lowercase						)
																		)

																		logger.info('Training examples: %s'						,   len(lowercase						)						)
																		_lowerCamelCase  :    Dict					=					hans_convert_examples_to_features(lowercase						,   lowercase						,   lowercase						,   lowercase						)
																		logger.info('Saving features into cached file %s'						,   lowercase						)
																		torch.save(self.features						,   lowercase						)
						def __len__(						self						):
										return len(self.features						)
						def __getitem__(						self						,   lowercase						):
										return self.features[i]






						def       A_					(						self						):
										return self.label_list


if is_tf_available():
			import tensorflow as tf






			class lowerCAmelCase__			:






						'''simple docstring'''



						lowerCamelCase__   =    42
						def __init__(						self						,   lowercase						,   lowercase						,   lowercase						,   lowercase = 128						,   lowercase=False						,   lowercase = False						,   ):
										_lowerCamelCase  :    Tuple					=					hans_processors[task]()
										_lowerCamelCase  :    Tuple					=					processor.get_labels()
										if tokenizer.__class__ in (
										    RobertaTokenizer,
										    RobertaTokenizerFast,
										    XLMRobertaTokenizer,
										    BartTokenizer,
										    BartTokenizerFast,
										):
														# HACK(label indices are swapped in RoBERTa pretrained model)
														_lowerCamelCase,				_lowerCamelCase  :    Tuple					=					label_list[2], label_list[1]
										_lowerCamelCase  :    Union[str, Any]					=					label_list

										_lowerCamelCase  :    Tuple					=					processor.get_dev_examples(lowercase						) if evaluate else processor.get_train_examples(lowercase						)
										_lowerCamelCase  :    List[str]					=					hans_convert_examples_to_features(lowercase						,   lowercase						,   lowercase						,   lowercase						)

										def gen():
														for ex_index, ex in tqdm.tqdm(enumerate(self.features						)						,   desc='convert examples to features'						):
																		if ex_index % 10000 == 0:
																						logger.info('Writing example %d of %d' % (ex_index, len(lowercase						))						)

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

										_lowerCamelCase  :    str					=					tf.data.Dataset.from_generator(
										    lowercase						,   (
										        {
										            '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       A_					(						self						):
										return self.dataset
						def __len__(						self						):
										return len(self.features						)
						def __getitem__(						self						,   lowercase						):
										return self.features[i]






						def       A_					(						self						):
										return self.label_list






class lowerCAmelCase__			(  lowercase__		):






			'''simple docstring'''



			def       A_					(						self						,   lowercase						):
							return self._create_examples(self._read_tsv(os.path.join(lowercase						,   'heuristics_train_set.txt'						)						)						,   'train'						)
			def       A_					(						self						,   lowercase						):
							return self._create_examples(self._read_tsv(os.path.join(lowercase						,   'heuristics_evaluation_set.txt'						)						)						,   'dev'						)
			def       A_					(						self						):
							return ["contradiction", "entailment", "neutral"]






			def       A_					(						self						,   lowercase						,   lowercase						):
							_lowerCamelCase  :    str					=					[]
							for i, line in enumerate(lowercase						):
											if i == 0:
															continue
											_lowerCamelCase  :    Union[str, Any]					=					'%s-%s' % (set_type, line[0])
											_lowerCamelCase  :    Dict					=					line[5]
											_lowerCamelCase  :    List[Any]					=					line[6]
											_lowerCamelCase  :    List[Any]					=					line[7][2:] if line[7].startswith('ex'						) else line[7]
											_lowerCamelCase  :    List[str]					=					line[0]
											examples.append(InputExample(guid=lowercase						,   text_a=lowercase						,   text_b=lowercase						,   label=lowercase						,   pairID=lowercase						)						)
							return examples







def   _snake_case    (       lowercase__							,					lowercase__							,					lowercase__							,					lowercase__							,					):
				_lowerCamelCase  :    Dict					=					{label: i for i, label in enumerate(__SCREAMING_SNAKE_CASE	)}

				_lowerCamelCase  :    Dict					=					[]
				for ex_index, example in tqdm.tqdm(enumerate(__SCREAMING_SNAKE_CASE	)							,					desc='convert examples to features'	):
								if ex_index % 10000 == 0:
												logger.info('Writing example %d' % (ex_index)	)

								_lowerCamelCase  :    str					=					tokenizer(
								    example.text_a							,					example.text_b							,					add_special_tokens=__SCREAMING_SNAKE_CASE							,					max_length=__SCREAMING_SNAKE_CASE							,					padding='max_length'							,					truncation=__SCREAMING_SNAKE_CASE							,					return_overflowing_tokens=__SCREAMING_SNAKE_CASE							,					)

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

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

								features.append(InputFeatures(**__SCREAMING_SNAKE_CASE							,					label=__SCREAMING_SNAKE_CASE							,					pairID=__SCREAMING_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


lowercase__  		=		{
    """hans""": 3,
}
lowercase__  		=		{
    """hans""": HansProcessor,
}





"""simple docstring"""






from __future__ import annotations

lowercase__  		=		10







def   _snake_case    (       lowercase__	):
				_lowerCamelCase  :    List[Any]					=					1
				_lowerCamelCase  :    Optional[int]					=					max(lowercase__	)
				while placement <= max_digit:
								# declare and initialize empty buckets
								_lowerCamelCase  :    list[list]					=					[[] for _ in range(lowercase__	)]
								# split list_of_ints between the buckets
								for i in list_of_ints:
												_lowerCamelCase  :    Dict					=					int((i / placement) % RADIX	)
												buckets[tmp].append(lowercase__	)
								# put each buckets' contents into list_of_ints
								_lowerCamelCase  :    str					=					0
								for b in range(lowercase__	):
												for i in buckets[b]:
																_lowerCamelCase  :    str					=					i
																a += 1
        # move to next
								placement *= RADIX
				return list_of_ints


if __name__ == "__main__":
			import doctest

			doctest.testmod()