






'''simple docstring'''
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

__magic_name__		:     List[Any]					=    {
    # 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						:						Any							,    __A						:						int = 1_4    ):







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

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




		def       snake_case     ( self						:						Optional[int]    ):







							"""simple docstring"""
							return hex(self.__private_key    )[2:]




		def       snake_case     ( self						:						Union[str, Any]    ):







							"""simple docstring"""
							_lowercase			= pow(self.generator							,    self.__private_key							,    self.prime    )
							return hex(_a    )[2:]




		def       snake_case     ( self						:						Optional[Any]							,    __A						:						List[str]    ):







							"""simple docstring"""
							# 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       snake_case     ( self						:						int							,    __A						:						Optional[Any]    ):







							"""simple docstring"""
							_lowercase			= int(_a							,    base=1_6    )
							if not self.is_valid_public_key(_a    ):
												raise ValueError("Invalid public key"    )
							_lowercase			= pow(_a							,    self.__private_key							,    self.prime    )
							return shaaaa(str(_a    ).encode()    ).hexdigest()




		@staticmethod
		def       snake_case     ( __A						:						Optional[int]							,    __A						:						Any    ):







							"""simple docstring"""
							# 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       snake_case     ( __A						:						Tuple							,    __A						:						List[Any]							,    __A						:						int = 1_4    ):







							"""simple docstring"""
							_lowercase			= int(_a							,    base=1_6    )
							_lowercase			= int(_a							,    base=1_6    )
							_lowercase			= primes[group]["prime"]
							if not DiffieHellman.is_valid_public_key_static(_a							,    _a    ):
												raise ValueError("Invalid public key"    )
							_lowercase			= pow(_a							,    _a							,    _a    )
							return shaaaa(str(_a    ).encode()    ).hexdigest()


if __name__ == "__main__":
	import doctest

	doctest.testmod()



"""simple docstring"""




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						a__		( a_,     unittest.TestCase		):
   __lowerCAmelCase				=							GPTaTokenizer
   __lowerCAmelCase				=							GPTaTokenizerFast
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							{"""add_prefix_space""": True}
   __lowerCAmelCase				=							False
   def      __magic_name__     (     self							):
          super().setUp()

          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          lowercase       :							Any									=				[
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
              "<|endoftext|>",
          ]
          lowercase       :							Any									=				dict(zip(_a      ,       range(len(_a							)							)							)							)
          lowercase       :							Dict									=				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          lowercase       :							int									=				{"unk_token": "<unk>"}

          lowercase       :							Tuple									=				os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["vocab_file"]							)
          lowercase       :							Optional[Any]									=				os.path.join(self.tmpdirname      ,       VOCAB_FILES_NAMES["merges_file"]							)
          with open(self.vocab_file      ,       "w"      ,       encoding="utf-8"							) as fp:
                 fp.write(json.dumps(_a							) + "\n"							)
          with open(self.merges_file      ,       "w"      ,       encoding="utf-8"							) as fp:
                 fp.write("\n".join(_a							)							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizer.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,       **_a							)
   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Tuple									=				"lower newer"
          lowercase       :							Dict									=				"lower newer"
          return input_text, output_text
   def      __magic_name__     (     self							):
          lowercase       :							str									=				GPTaTokenizer(self.vocab_file      ,       self.merges_file      ,       **self.special_tokens_map							)
          lowercase       :							Union[str, Any]									=				"lower newer"
          lowercase       :							Any									=				["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
          lowercase       :							int									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          self.assertListEqual(_a      ,       _a							)

          lowercase       :							int									=				tokens + [tokenizer.unk_token]
          lowercase       :							List[Any]									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self							):
          if not self.test_rust_tokenizer:
                 return

          lowercase       :							Union[str, Any]									=				self.get_tokenizer()
          lowercase       :							int									=				self.get_rust_tokenizer(add_prefix_space=_a							)

          lowercase       :							Union[str, Any]									=				"lower newer"

          # Testing tokenization
          lowercase       :							Union[str, Any]									=				tokenizer.tokenize(_a      ,       add_prefix_space=_a							)
          lowercase       :							List[Any]									=				rust_tokenizer.tokenize(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids without special tokens
          lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a      ,       add_prefix_space=_a							)
          lowercase       :							Dict									=				rust_tokenizer.encode(_a      ,       add_special_tokens=_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing conversion to ids with special tokens
          lowercase       :							Any									=				self.get_rust_tokenizer(add_prefix_space=_a							)
          lowercase       :							Any									=				tokenizer.encode(_a      ,       add_prefix_space=_a							)
          lowercase       :							Any									=				rust_tokenizer.encode(_a							)
          self.assertListEqual(_a      ,       _a							)

          # Testing the unknown token
          lowercase       :							Union[str, Any]									=				tokens + [rust_tokenizer.unk_token]
          lowercase       :							str									=				[14, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_a							)      ,       _a							)
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          # It's very difficult to mix/test pretokenization with byte-level
          # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
          pass
   def      __magic_name__     (     self      ,       _a=15							):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                        lowercase       :							int									=				self.rust_tokenizer_class.from_pretrained(_a      ,       **_a							)

                        # Simple input
                        lowercase       :							int									=				"This is a simple input"
                        lowercase       :							Optional[int]									=				["This is a simple input 1", "This is a simple input 2"]
                        lowercase       :							Union[str, Any]									=				("This is a simple input", "This is a pair")
                        lowercase       :							Any									=				[
                            ("This is a simple input 1", "This is a simple input 2"),
                            ("This is a simple pair 1", "This is a simple pair 2"),
                        ]

                        # Simple input tests
                        self.assertRaises(_a      ,       tokenizer_r.encode      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(_a      ,       tokenizer_r.encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(
                            _a      ,       tokenizer_r.batch_encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"      ,       )

                        # Pair input
                        self.assertRaises(_a      ,       tokenizer_r.encode      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(_a      ,       tokenizer_r.encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(
                            _a      ,       tokenizer_r.batch_encode_plus      ,       _a      ,       max_length=_a      ,       padding="max_length"      ,       )
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       pad_token="<pad>"							)

          # Simple input
          lowercase       :							int									=				"This is a simple input"
          lowercase       :							Tuple									=				["This is a simple input looooooooong", "This is a simple input"]
          lowercase       :							Any									=				("This is a simple input", "This is a pair")
          lowercase       :							str									=				[
              ("This is a simple input loooooong", "This is a simple input"),
              ("This is a simple pair loooooong", "This is a simple pair"),
          ]

          lowercase       :							List[Any]									=				tokenizer.pad_token_id

          lowercase       :							List[Any]									=				tokenizer(_a      ,       padding="max_length"      ,       max_length=30      ,       return_tensors="np"							)
          lowercase       :							List[str]									=				tokenizer(_a      ,       padding=_a      ,       truncate=_a      ,       return_tensors="np"							)
          lowercase       :							List[Any]									=				tokenizer(*_a      ,       padding="max_length"      ,       max_length=60      ,       return_tensors="np"							)
          lowercase       :							Any									=				tokenizer(_a      ,       padding=_a      ,       truncate=_a      ,       return_tensors="np"							)

          # s
          # test single string max_length padding
          self.assertEqual(out_s["input_ids"].shape[-1]      ,       30							)
          self.assertTrue(pad_token_id in out_s["input_ids"]							)
          self.assertTrue(0 in out_s["attention_mask"]							)

          # s2
          # test automatic padding
          self.assertEqual(out_sa["input_ids"].shape[-1]      ,       33							)
          # long slice doesn't have padding
          self.assertFalse(pad_token_id in out_sa["input_ids"][0]							)
          self.assertFalse(0 in out_sa["attention_mask"][0]							)
          # short slice does have padding
          self.assertTrue(pad_token_id in out_sa["input_ids"][1]							)
          self.assertTrue(0 in out_sa["attention_mask"][1]							)

          # p
          # test single pair max_length padding
          self.assertEqual(out_p["input_ids"].shape[-1]      ,       60							)
          self.assertTrue(pad_token_id in out_p["input_ids"]							)
          self.assertTrue(0 in out_p["attention_mask"]							)

          # p2
          # test automatic padding pair
          self.assertEqual(out_pa["input_ids"].shape[-1]      ,       52							)
          # long slice pair doesn't have padding
          self.assertFalse(pad_token_id in out_pa["input_ids"][0]							)
          self.assertFalse(0 in out_pa["attention_mask"][0]							)
          # short slice pair does have padding
          self.assertTrue(pad_token_id in out_pa["input_ids"][1]							)
          self.assertTrue(0 in out_pa["attention_mask"][1]							)
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				"$$$"
          lowercase       :							List[str]									=				GPTaTokenizer.from_pretrained(self.tmpdirname      ,       bos_token=_a      ,       add_bos_token=_a							)

          lowercase       :							List[Any]									=				"This is a simple input"
          lowercase       :							str									=				["This is a simple input 1", "This is a simple input 2"]

          lowercase       :							Union[str, Any]									=				tokenizer.bos_token_id

          lowercase       :							List[str]									=				tokenizer(_a							)
          lowercase       :							Any									=				tokenizer(_a							)

          self.assertEqual(out_s.input_ids[0]      ,       _a							)
          self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids							)							)

          lowercase       :							List[Any]									=				tokenizer.decode(out_s.input_ids							)
          lowercase       :							Tuple									=				tokenizer.batch_decode(out_sa.input_ids							)

          self.assertEqual(decode_s.split()[0]      ,       _a							)
          self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa							)							)
   def      __magic_name__     (     self							):
          pass

   def      __magic_name__     (     self							):
          # TODO: change to self.get_tokenizers() when the fast version is implemented
          lowercase       :							Dict									=				[self.get_tokenizer(do_lower_case=_a      ,       add_bos_token=_a							)]
          for tokenizer in tokenizers:
                 with self.subTest(f"""{tokenizer.__class__.__name__}"""							):
                        lowercase       :							Optional[Any]									=				"Encode this."
                        lowercase       :							Union[str, Any]									=				"This one too please."
                        lowercase       :							Dict									=				tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        encoded_sequence += tokenizer.encode(_a      ,       add_special_tokens=_a							)
                        lowercase       :							Optional[Any]									=				tokenizer.encode_plus(
                            _a      ,       _a      ,       add_special_tokens=_a      ,       return_special_tokens_mask=_a      ,       )
                        lowercase       :							Tuple									=				encoded_sequence_dict["input_ids"]
                        lowercase       :							int									=				encoded_sequence_dict["special_tokens_mask"]
                        self.assertEqual(len(_a							)      ,       len(_a							)							)

                        lowercase       :							List[str]									=				[
                            (x if not special_tokens_mask[i] else None) for i, x in enumerate(_a							)
                        ]
                        lowercase       :							List[str]									=				[x for x in filtered_sequence if x is not None]
                        self.assertEqual(_a      ,       _a							)


@require_tokenizers
class 						a__		( unittest.TestCase		):
   def      __magic_name__     (     self							):
          # More context:
          # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
          # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
          # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)
          lowercase       :							Any									=				"A photo of a cat"

          lowercase       :							List[str]									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("test_opt"							)

          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("./test_opt"							)
          lowercase       :							Tuple									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       use_slow=_a							)
          lowercase       :							Union[str, Any]									=				"A photo of a cat"

          lowercase       :							Union[str, Any]									=				tokenizer.encode(
              _a      ,       )
          # Same as above
          self.assertEqual(_a      ,       [2, 250, 1_345, 9, 10, 4_758]							)

   @unittest.skip("This test is failing because of a bug in the fast tokenizer"							)
   def      __magic_name__     (     self							):
          lowercase       :							int									=				AutoTokenizer.from_pretrained("facebook/opt-350m"      ,       from_slow=_a							)

          lowercase       :							List[Any]									=				"bos"
          lowercase       :							Optional[int]									=				tokenizer.get_vocab()["bos"]

          lowercase       :							List[str]									=				"A photo of a cat"
          lowercase       :							Dict									=				tokenizer.encode(
              _a      ,       )
          # We changed the bos token
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)
          tokenizer.save_pretrained("./tok"							)
          lowercase       :							Tuple									=				AutoTokenizer.from_pretrained("./tok"							)
          self.assertTrue(tokenizer.is_fast							)
          lowercase       :							str									=				tokenizer.encode(
              _a      ,       )
          self.assertEqual(_a      ,       [31_957, 250, 1_345, 9, 10, 4_758]							)


