




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__							:	Optional[int]											=		logging.getLogger(__name__)
@dataclass(frozen=UpperCAmelCase__     )
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None







@dataclass(frozen=UpperCAmelCase__     )
class 						UpperCAmelCase  :







						'''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 						UpperCAmelCase  (							UpperCAmelCase__     ):







								'''simple docstring'''





								lowerCAmelCase_							=  42






								def __init__(		self				:	List[Any]			,      __lowercase				:	str			,      __lowercase				:	PreTrainedTokenizer			,      __lowercase				:	str			,      __lowercase				:	Optional[int] = None			,      __lowercase				:	Union[str, Any]=False			,      __lowercase				:	bool = False			,      ):






									"""simple docstring"""






									snake_case_						       =	hans_processors[task]()

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

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

											snake_case_						       =	(
											    processor.get_dev_examples(__lowercase		) if evaluate else processor.get_train_examples(__lowercase		)
											)

											logger.info("Training examples: %s"			,      len(__lowercase		)		)
											snake_case_						       =	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				:	List[Any]		):






									"""simple docstring"""






									return len(self.features		)






								def __getitem__(		self				:	List[str]			,      __lowercase				:	Optional[Any]		):






									"""simple docstring"""






									return self.features[i]






								def 			snake_case__		(		self				:	Dict		):






									"""simple docstring"""






									return self.label_list


if is_tf_available():
		import tensorflow as tf
		class 						UpperCAmelCase  :







								'''simple docstring'''





								lowerCAmelCase_							=  42






								def __init__(		self				:	Optional[int]			,      __lowercase				:	str			,      __lowercase				:	PreTrainedTokenizer			,      __lowercase				:	str			,      __lowercase				:	Optional[int] = 1_28			,      __lowercase				:	List[Any]=False			,      __lowercase				:	bool = False			,      ):






									"""simple docstring"""






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

									snake_case_						       =	processor.get_dev_examples(__lowercase		) if evaluate else processor.get_train_examples(__lowercase		)
									snake_case_						       =	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 % 1_00_00 == 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,
											)

									snake_case_						       =	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 			snake_case__		(		self				:	Optional[Any]		):






									"""simple docstring"""






									return self.dataset






								def __len__(		self				:	Dict		):






									"""simple docstring"""






									return len(self.features		)






								def __getitem__(		self				:	Optional[Any]			,      __lowercase				:	Dict		):






									"""simple docstring"""






									return self.features[i]






								def 			snake_case__		(		self				:	Dict		):






									"""simple docstring"""






									return self.label_list
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def 			snake_case__		(		self				:	Tuple			,      __lowercase				:	Union[str, Any]		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Dict			,      __lowercase				:	List[Any]		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






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






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	Any			,      __lowercase				:	Optional[int]		):






							"""simple docstring"""






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







def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A	,      ):







	'''simple docstring'''





	snake_case_						       =	{label: i for i, label in enumerate(_A				)}

	snake_case_						       =	[]
	for ex_index, example in tqdm.tqdm(enumerate(_A				)	,      desc="convert examples to features"				):
		if ex_index % 10000 == 0:
			logger.info("Writing example %d" % (ex_index)				)

		snake_case_						       =	tokenizer(
		    example.text_a	,      example.text_b	,      add_special_tokens=_A	,      max_length=_A	,      padding="max_length"	,      truncation=_A	,      return_overflowing_tokens=_A	,      )

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

		snake_case_						       =	int(example.pairID				)

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

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

	return features


lowercase__							:	Optional[Any]											=		{
    "hans": 3,
}






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






from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

lowercase__							:	Dict											=		{
    # 1536-bit
    5: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 2048-bit
    14: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 3072-bit
    15: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 4096-bit
    16: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
            + "FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 6144-bit
    17: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
            + "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
            + "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
            + "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
            + "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
            + "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
            + "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
            + "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
            + "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
            + "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
            + "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
            + "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
            + "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
            + "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
            + "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
            + "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
            + "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
            + "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
            + "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
            + "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
            + "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
            + "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
            + "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
            + "6DCC4024FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 8192-bit
    18: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
            + "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
            + "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
            + "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
            + "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
            + "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
            + "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
            + "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
            + "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
            + "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
            + "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
            + "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
            + "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
            + "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
            + "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
            + "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
            + "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
            + "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
            + "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
            + "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
            + "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
}
class 						UpperCAmelCase  :







						'''simple docstring'''





						def __init__(		self				:	Tuple			,      __lowercase				:	int = 14		):






							"""simple docstring"""






							if group not in primes:
								raise ValueError("Unsupported Group"		)
							snake_case_						       =	primes[group]["prime"]
							snake_case_						       =	primes[group]["generator"]

							snake_case_						       =	int(hexlify(urandom(32		)		)			,      base=16		)






						def 			snake_case__		(		self				:	Tuple		):






							"""simple docstring"""






							return hex(self.__private_key		)[2:]






						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							snake_case_						       =	pow(self.generator			,      self.__private_key			,      self.prime		)
							return hex(__lowercase		)[2:]






						def 			snake_case__		(		self				:	int			,      __lowercase				:	int		):






							"""simple docstring"""






							return (
							    2 <= key <= self.prime - 2
							    and pow(__lowercase			,      (self.prime - 1) // 2			,      self.prime		) == 1
							)






						def 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	str		):






							"""simple docstring"""






							snake_case_						       =	int(__lowercase			,      base=16		)
							if not self.is_valid_public_key(__lowercase		):
								raise ValueError("Invalid public key"		)
							snake_case_						       =	pow(__lowercase			,      self.__private_key			,      self.prime		)
							return shaaaa(str(__lowercase		).encode()		).hexdigest()






						@staticmethod
						def 			snake_case__		(		__lowercase				:	int			,      __lowercase				:	int		):






							"""simple docstring"""






							return (
							    2 <= remote_public_key_str <= prime - 2
							    and pow(__lowercase			,      (prime - 1) // 2			,      __lowercase		) == 1
							)






						@staticmethod
						def 			snake_case__		(		__lowercase				:	str			,      __lowercase				:	str			,      __lowercase				:	int = 14		):






							"""simple docstring"""






							snake_case_						       =	int(__lowercase			,      base=16		)
							snake_case_						       =	int(__lowercase			,      base=16		)
							snake_case_						       =	primes[group]["prime"]
							if not DiffieHellman.is_valid_public_key_static(__lowercase			,      __lowercase		):
								raise ValueError("Invalid public key"		)
							snake_case_						       =	pow(__lowercase			,      __lowercase			,      __lowercase		)
							return shaaaa(str(__lowercase		).encode()		).hexdigest()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
