
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_A						      =				"▁"

_A						      =				{"vocab_file": "spiece.model"}

_A						      =				{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

_A						      =				{
    "google/pegasus-xsum": 5_12,
}


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	VOCAB_FILES_NAMES

     UpperCAmelCase__       :							Tuple     =	VOCAB_FILES_NAMES
     UpperCAmelCase__       :							List[str]     =	PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase__       :							Optional[int]     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase__       :							Optional[Any]     =	["input_ids", "attention_mask"]





     def __init__(self   :					Optional[Any]       ,					_A   :					Any       ,					_A   :					List[Any]="<pad>"       ,					_A   :					int="</s>"       ,					_A   :					Dict="<unk>"       ,					_A   :					str="<mask_2>"       ,					_A   :					Optional[int]="<mask_1>"       ,					_A   :					Optional[Any]=None       ,					_A   :					Tuple=1_0_3       ,					_A   :					Optional[Dict[str, Any]] = None       ,					**_A   :					List[str]       ,					)		->		None:
         snake_case				  =      offset
         if additional_special_tokens is not None:
             if not isinstance(_A       ,					_A     ):
                 raise TypeError(
                     f'additional_special_tokens should be of type {type(_A     )}, but is'
                     f' {type(_A     )}'     )

             snake_case				  =      (
                 ([mask_token_sent] + additional_special_tokens)
                 if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                 else additional_special_tokens
             )
             # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
             additional_special_tokens_extended += [
                 f'<unk_{i}>' for i in range(len(_A     )       ,					self.offset - 1     )
             ]

             if len(set(_A     )     ) != len(_A     ):
                 raise ValueError(
                     "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                     f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'     )
             snake_case				  =      additional_special_tokens_extended
         else:
             snake_case				  =      [mask_token_sent] if mask_token_sent is not None else []
             additional_special_tokens += [f'<unk_{i}>' for i in range(2       ,					self.offset     )]

         snake_case				  =      {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             eos_token=_A       ,					unk_token=_A       ,					mask_token=_A       ,					pad_token=_A       ,					mask_token_sent=_A       ,					offset=_A       ,					additional_special_tokens=_A       ,					sp_model_kwargs=self.sp_model_kwargs       ,					**_A       ,					)
         snake_case				  =      mask_token_sent
         snake_case				  =      vocab_file
         snake_case				  =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(_A     )

         # add special tokens to encoder dict
         snake_case				  =      {
             0: self.pad_token,
             1: self.eos_token,
         }

         if self.mask_token_sent is not None:
             self.encoder.update(
                 {
                     2: self.mask_token_sent,
                     3: self.mask_token,
                 }     )

         if self.offset > 0:
             # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
             # mask_token_sent is already added to list -> so start at 1
             self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1       ,					self.offset - 1     )}     )

         snake_case				  =      {v: k for k, v in self.encoder.items()}





     @property
     def 	UpperCAmelCase(self   :					str     )		->		int:
         return len(self.sp_model     ) + self.offset





     def 	UpperCAmelCase(self   :					List[str]     )		->		Dict[str, int]:
         snake_case				  =      {self.convert_ids_to_tokens(_A     ): i for i in range(self.vocab_size     )}
         vocab.update(self.added_tokens_encoder     )
         return vocab





     def __getstate__(self   :					Any     )		->		List[Any]:
         snake_case				  =      self.__dict__.copy()
         snake_case				  =      None
         return state





     def __setstate__(self   :					str       ,					_A   :					Union[str, Any]     )		->		Tuple:
         snake_case				  =      d

         # for backward compatibility
         if not hasattr(self       ,					"sp_model_kwargs"     ):
             snake_case				  =      {}

         snake_case				  =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
         self.sp_model.Load(self.vocab_file     )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					str     )		->		List[str]:
         return self.sp_model.encode(_A       ,					out_type=_A     )





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					str     )		->		int:
         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
             return self.added_tokens_decoder[token]
         snake_case				  =      self.sp_model.piece_to_id(_A     )
         return sp_id + self.offset





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					int     )		->		str:
         if index in self.encoder:
             return self.encoder[index]
         elif index in self.added_tokens_encoder:
             return self.added_tokens_encoder[index]
         else:
             snake_case				  =      self.sp_model.IdToPiece(index - self.offset     )
         return token





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Tuple     )		->		Tuple:
         snake_case				  =      []
         snake_case				  =      ""
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += self.sp_model.decode(_A     ) + token
                 snake_case				  =      []
             else:
                 current_sub_tokens.append(_A     )
         out_string += self.sp_model.decode(_A     )
         return out_string.strip()





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					Tuple=False     )		->		Tuple:
         return 1





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[int]     )		->		Tuple:
         snake_case				  =      set(self.all_special_ids     )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id     )  # <unk> is only sometimes special

         return [1 if x in all_special_ids else 0 for x in seq]





     def 	UpperCAmelCase(self   :					str       ,					_A   :					List       ,					_A   :					Optional[List] = None       ,					_A   :					bool = False     )		->		List[int]:
         if already_has_special_tokens:
             return self._special_token_mask(_A     )
         elif token_ids_a is None:
             return self._special_token_mask(_A     ) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a     ) + [1]





     def 	UpperCAmelCase(self   :					int       ,					_A   :					Dict       ,					_A   :					List[Any]=None     )		->		List[int]:
         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]





     def 	UpperCAmelCase(self   :					Optional[Any]       ,					_A   :					str       ,					_A   :					Optional[str] = None     )		->		Tuple[str]:
         if not os.path.isdir(_A     ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
             return
         snake_case				  =      os.path.join(
             _A       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

         if os.path.abspath(self.vocab_file     ) != os.path.abspath(_A     ) and os.path.isfile(self.vocab_file     ):
             copyfile(self.vocab_file       ,					_A     )
         elif not os.path.isfile(self.vocab_file     ):
             with open(_A       ,					"wb"     ) as fi:
                 snake_case				  =      self.sp_model.serialized_model_proto()
                 fi.write(_A     )

         return (out_vocab_file,)



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

_A						      =				{
    # 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   lowerCamelCase	:





     def __init__(self   :					Any       ,					_A   :					int = 1_4     )		->		None:
         if group not in primes:
             raise ValueError("Unsupported Group"     )
         snake_case				  =      primes[group]["prime"]
         snake_case				  =      primes[group]["generator"]

         snake_case				  =      int(hexlify(urandom(3_2     )     )       ,					base=1_6     )





     def 	UpperCAmelCase(self   :					Any     )		->		str:
         return hex(self.__private_key     )[2:]





     def 	UpperCAmelCase(self   :					Tuple     )		->		str:
         snake_case				  =      pow(self.generator       ,					self.__private_key       ,					self.prime     )
         return hex(_A     )[2:]





     def 	UpperCAmelCase(self   :					Optional[int]       ,					_A   :					int     )		->		bool:
         # check if the other public key is valid based on NIST SP800-56
         return (
             2 <= key <= self.prime - 2
             and pow(_A       ,					(self.prime - 1) // 2       ,					self.prime     ) == 1
         )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					str     )		->		str:
         snake_case				  =      int(_A       ,					base=1_6     )
         if not self.is_valid_public_key(_A     ):
             raise ValueError("Invalid public key"     )
         snake_case				  =      pow(_A       ,					self.__private_key       ,					self.prime     )
         return shaaaa(str(_A     ).encode()     ).hexdigest()





     @staticmethod
     def 	UpperCAmelCase(_A   :					int       ,					_A   :					int     )		->		bool:
         # check if the other public key is valid based on NIST SP800-56
         return (
             2 <= remote_public_key_str <= prime - 2
             and pow(_A       ,					(prime - 1) // 2       ,					_A     ) == 1
         )





     @staticmethod
     def 	UpperCAmelCase(_A   :					str       ,					_A   :					str       ,					_A   :					int = 1_4     )		->		str:
         snake_case				  =      int(_A       ,					base=1_6     )
         snake_case				  =      int(_A       ,					base=1_6     )
         snake_case				  =      primes[group]["prime"]
         if not DiffieHellman.is_valid_public_key_static(_A       ,					_A     ):
             raise ValueError("Invalid public key"     )
         snake_case				  =      pow(_A       ,					_A       ,					_A     )
         return shaaaa(str(_A     ).encode()     ).hexdigest()


if __name__ == "__main__":
   import doctest

   doctest.testmod()

