




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,
)


SCREAMING_SNAKE_CASE__     :     Tuple		    =       logging.getLogger(__name__)



@dataclass(frozen=SCREAMING_SNAKE_CASE__     )
class       a_       :
	A    							= 42
	A    							= 42
	A    							= None
	A    							= None
	A    							= None




@dataclass(frozen=SCREAMING_SNAKE_CASE__     )
class       a_       :
	A    							= 42
	A    							= None
	A    							= None
	A    							= None
	A    							= None


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



				class       a_       (     SCREAMING_SNAKE_CASE__     ):
					A    							= 42





					def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = None      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE = False      ,     )  ->					Optional[int]:




								"""simple docstring"""



								SCREAMING_SNAKE_CASE_								=						hans_processors[task]()

								SCREAMING_SNAKE_CASE_								=						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_								=						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_								=						label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE_								=						label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								SCREAMING_SNAKE_CASE_								=						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_								=						torch.load(SCREAMING_SNAKE_CASE      )
											else:
														logger.info(f'Creating features from dataset file at {data_dir}'      )

														SCREAMING_SNAKE_CASE_								=						(
														    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_								=						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      )  ->					int:




								"""simple docstring"""



								return len(self.features      )





					def __getitem__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					InputFeatures:




								"""simple docstring"""



								return self.features[i]





					def 						A_(							self      )  ->					str:




								"""simple docstring"""



								return self.label_list


if is_tf_available():
				import tensorflow as tf



				class       a_       :
					A    							= 42





					def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE = 128      ,     SCREAMING_SNAKE_CASE=False      ,     SCREAMING_SNAKE_CASE = False      ,     )  ->					Optional[Any]:




								"""simple docstring"""



								SCREAMING_SNAKE_CASE_								=						hans_processors[task]()
								SCREAMING_SNAKE_CASE_								=						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_								=						label_list[2], label_list[1]
								SCREAMING_SNAKE_CASE_								=						label_list

								SCREAMING_SNAKE_CASE_								=						processor.get_dev_examples(SCREAMING_SNAKE_CASE      ) if evaluate else processor.get_train_examples(SCREAMING_SNAKE_CASE      )
								SCREAMING_SNAKE_CASE_								=						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_0000 == 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_								=						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 						A_(							self      )  ->					Any:




								"""simple docstring"""



								return self.dataset





					def __len__(							self      )  ->					Tuple:




								"""simple docstring"""



								return len(self.features      )





					def __getitem__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					InputFeatures:




								"""simple docstring"""



								return self.features[i]





					def 						A_(							self      )  ->					str:




								"""simple docstring"""



								return self.label_list



class       a_       (     SCREAMING_SNAKE_CASE__     ):





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					List[Any]:




				"""simple docstring"""



				return self._create_examples(self._read_tsv(os.path.join(SCREAMING_SNAKE_CASE      ,     'heuristics_train_set.txt'      )      )      ,     'train'      )





	def 						A_(							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 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



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





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[]
				for i, line in enumerate(SCREAMING_SNAKE_CASE      ):
							if i == 0:
										continue
							SCREAMING_SNAKE_CASE_								=						'%s-%s' % (set_type, line[0])
							SCREAMING_SNAKE_CASE_								=						line[5]
							SCREAMING_SNAKE_CASE_								=						line[6]
							SCREAMING_SNAKE_CASE_								=						line[7][2:] if line[7].startswith('ex'      ) else line[7]
							SCREAMING_SNAKE_CASE_								=						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    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    )			->							List[str]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE )}

			SCREAMING_SNAKE_CASE_								=						[]
			for ex_index, example in tqdm.tqdm(enumerate(SCREAMING_SNAKE_CASE )       ,    desc='convert examples to features' ):
						if ex_index % 1_00_00 == 0:
									logger.info('Writing example %d' % (ex_index) )

						SCREAMING_SNAKE_CASE_								=						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       ,    )

						SCREAMING_SNAKE_CASE_								=						label_map[example.label] if example.label in label_map else 0

						SCREAMING_SNAKE_CASE_								=						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


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       {
    "hans": 3,
}
SCREAMING_SNAKE_CASE__     :     Any		    =       {
    "hans": HansProcessor,
}






from __future__ import annotations

from collections.abc import MutableSequence



class       a_       :





	def __init__(							self      ,     SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      )  ->					None:




				"""simple docstring"""



				if len(SCREAMING_SNAKE_CASE      ) != degree + 1:
							raise ValueError(
							    'The number of coefficients should be equal to the degree + 1.'      )

				SCREAMING_SNAKE_CASE_								=						list(SCREAMING_SNAKE_CASE      )
				SCREAMING_SNAKE_CASE_								=						degree





	def __add__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				if self.degree > polynomial_a.degree:
							SCREAMING_SNAKE_CASE_								=						self.coefficients[:]
							for i in range(polynomial_a.degree + 1      ):
										coefficients[i] += polynomial_a.coefficients[i]
							return Polynomial(self.degree      ,     SCREAMING_SNAKE_CASE      )
				else:
							SCREAMING_SNAKE_CASE_								=						polynomial_a.coefficients[:]
							for i in range(self.degree + 1      ):
										coefficients[i] += self.coefficients[i]
							return Polynomial(polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def __sub__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				return self + polynomial_a * Polynomial(0      ,     [-1]      )





	def __neg__(							self      )  ->					Polynomial:




				"""simple docstring"""



				return Polynomial(self.degree      ,     [-c for c in self.coefficients]      )





	def __mul__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + polynomial_a.degree + 1)
				for i in range(self.degree + 1      ):
							for j in range(polynomial_a.degree + 1      ):
										coefficients[i + j] += (
										    self.coefficients[i] * polynomial_a.coefficients[j]
										)

				return Polynomial(self.degree + polynomial_a.degree      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					int | float:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						0
				for i in range(self.degree + 1      ):
							result += self.coefficients[i] * (substitution**i)
				return result





	def __str__(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						''
				for i in range(self.degree      ,     -1      ,     -1      ):
							if self.coefficients[i] == 0:
										continue
							elif self.coefficients[i] > 0:
										if polynomial:
													polynomial += " + "
							else:
										polynomial += " - "

							if i == 0:
										polynomial += str(abs(self.coefficients[i]      )      )
							elif i == 1:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x"
							else:
										polynomial += str(abs(self.coefficients[i]      )      ) + "x^" + str(SCREAMING_SNAKE_CASE      )

				return polynomial





	def __repr__(							self      )  ->					str:




				"""simple docstring"""



				return self.__str__()





	def 						A_(							self      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * self.degree
				for i in range(self.degree      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i + 1] * (i + 1)
				return Polynomial(self.degree - 1      ,     SCREAMING_SNAKE_CASE      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE = 0      )  ->					Polynomial:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[0] * (self.degree + 2)
				SCREAMING_SNAKE_CASE_								=						constant
				for i in range(self.degree + 1      ):
							SCREAMING_SNAKE_CASE_								=						self.coefficients[i] / (i + 1)
				return Polynomial(self.degree + 1      ,     SCREAMING_SNAKE_CASE      )





	def __eq__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				if not isinstance(SCREAMING_SNAKE_CASE      ,     SCREAMING_SNAKE_CASE      ):
							return False

				if self.degree != polynomial_a.degree:
							return False

				for i in range(self.degree + 1      ):
							if self.coefficients[i] != polynomial_a.coefficients[i]:
										return False

				return True





	def __ne__(							self      ,     SCREAMING_SNAKE_CASE      )  ->					bool:




				"""simple docstring"""



				return not self.__eq__(SCREAMING_SNAKE_CASE      )
