"""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

lowerCAmelCase__  :  Optional[int]						     =	{
    # 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 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	str    ,lowerCamelCase__   :	int = 14			):
									if group not in primes:
													raise ValueError('Unsupported Group'			)
									UpperCAmelCase__             =							primes[group]['''prime''']
									UpperCAmelCase__             =							primes[group]['''generator''']

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







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									return hex(self.__private_key			)[2:]







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							pow(self.generator    ,self.__private_key    ,self.prime			)
									return hex(lowercase_			)[2:]







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	int			):
									# check if the other public key is valid based on NIST SP800-56
									return (
									    2 <= key <= self.prime - 2
									    and pow(lowercase_    ,(self.prime - 1) // 2    ,self.prime			) == 1
									)







					def 		__lowerCAmelCase						(				self   :	int    ,lowerCamelCase__   :	str			):
									UpperCAmelCase__             =							int(lowercase_    ,base=16			)
									if not self.is_valid_public_key(lowercase_			):
													raise ValueError('Invalid public key'			)
									UpperCAmelCase__             =							pow(lowercase_    ,self.__private_key    ,self.prime			)
									return shaaaa(str(lowercase_			).encode()			).hexdigest()







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







					@staticmethod
					def 		__lowerCAmelCase						(				lowerCamelCase__   :	str    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	int = 14			):
									UpperCAmelCase__             =							int(lowercase_    ,base=16			)
									UpperCAmelCase__             =							int(lowercase_    ,base=16			)
									UpperCAmelCase__             =							primes[group]['''prime''']
									if not DiffieHellman.is_valid_public_key_static(lowercase_    ,lowercase_			):
													raise ValueError('Invalid public key'			)
									UpperCAmelCase__             =							pow(lowercase_    ,lowercase_    ,lowercase_			)
									return shaaaa(str(lowercase_			).encode()			).hexdigest()


if __name__ == "__main__":
						import doctest

						doctest.testmod()








"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )




class        _UpperCAmelCase       :

 def __init__(     self		:       Union[str, Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       int=13  ,     lowercase_		:       Optional[int]=7  ,     lowercase_		:       Any=True  ,     lowercase_		:       Dict=True  ,     lowercase_		:       Dict=True  ,     lowercase_		:       Optional[Any]=99  ,     lowercase_		:       Union[str, Any]=32  ,     lowercase_		:       str=5  ,     lowercase_		:       Union[str, Any]=4  ,     lowercase_		:       Any=37  ,     lowercase_		:       Tuple="gelu"  ,     lowercase_		:       Dict=0.1  ,     lowercase_		:       Tuple=0.1  ,     lowercase_		:       Optional[int]=512  ,     lowercase_		:       Optional[Any]=16  ,     lowercase_		:       Optional[Any]=2  ,     lowercase_		:       Optional[Any]=0.02  ,     lowercase_		:       List[Any]=3  ,     lowercase_		:       Union[str, Any]=4  ,     lowercase_		:       List[Any]=None  ,     ):
     snake_case_ :     Any				=	parent
     snake_case_ :     List[str]				=	batch_size
     snake_case_ :     List[Any]				=	seq_length
     snake_case_ :     Optional[int]				=	is_training
     snake_case_ :     Union[str, Any]				=	use_token_type_ids
     snake_case_ :     Optional[Any]				=	use_labels
     snake_case_ :     Union[str, Any]				=	vocab_size
     snake_case_ :     Any				=	hidden_size
     snake_case_ :     List[Any]				=	num_hidden_layers
     snake_case_ :     Any				=	num_attention_heads
     snake_case_ :     Dict				=	intermediate_size
     snake_case_ :     Union[str, Any]				=	hidden_act
     snake_case_ :     Optional[int]				=	hidden_dropout_prob
     snake_case_ :     Optional[Any]				=	attention_probs_dropout_prob
     snake_case_ :     Tuple				=	max_position_embeddings
     snake_case_ :     int				=	type_vocab_size
     snake_case_ :     Tuple				=	type_sequence_label_size
     snake_case_ :     str				=	initializer_range
     snake_case_ :     Tuple				=	num_labels
     snake_case_ :     str				=	num_choices
     snake_case_ :     Any				=	scope
     snake_case_ :     Dict				=	self.vocab_size - 1

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Optional[Any]				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

     snake_case_ :     Optional[Any]				=	None
     if self.use_token_type_ids:
         snake_case_ :     Tuple				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.type_vocab_size				)

     snake_case_ :     str				=	None
     snake_case_ :     Dict				=	None
     snake_case_ :     str				=	None
     if self.use_labels:
         snake_case_ :     Optional[int]				=	ids_tensor([self.batch_size]  ,     self.type_sequence_label_size				)
         snake_case_ :     List[Any]				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.num_labels				)
         snake_case_ :     Tuple				=	ids_tensor([self.batch_size]  ,     self.num_choices				)

     snake_case_ :     int				=	OpenAIGPTConfig(
         vocab_size=self.vocab_size  ,     n_embd=self.hidden_size  ,     n_layer=self.num_hidden_layers  ,     n_head=self.num_attention_heads  ,     n_positions=self.max_position_embeddings  ,     pad_token_id=self.pad_token_id  ,     )

     snake_case_ :     Any				=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,     2				)

     return (
         config,
         input_ids,
         head_mask,
         token_type_ids,
         sequence_labels,
         token_labels,
         choice_labels,
     )

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Any  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       str  ,     lowercase_		:       Dict  ,     *lowercase_		:       Dict				):
     snake_case_ :     List[Any]				=	OpenAIGPTModel(config=lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Any				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     head_mask=lowercase_				)
     snake_case_ :     Optional[Any]				=	model(lowercase_  ,     token_type_ids=lowercase_				)
     snake_case_ :     Optional[Any]				=	model(lowercase_				)

     self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)				)

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Dict  ,     lowercase_		:       str  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       List[Any]  ,     *lowercase_		:       Optional[Any]				):
     snake_case_ :     Union[str, Any]				=	OpenAIGPTLMHeadModel(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Union[str, Any]				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     labels=lowercase_				)
     self.parent.assertEqual(result.loss.shape  ,     ()				)
     self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)				)

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Dict  ,     lowercase_		:       List[str]  ,     lowercase_		:       Any  ,     lowercase_		:       Dict  ,     *lowercase_		:       Union[str, Any]				):
     snake_case_ :     Tuple				=	OpenAIGPTDoubleHeadsModel(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Dict				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     labels=lowercase_				)
     self.parent.assertEqual(result.loss.shape  ,     ()				)
     self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)				)

 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       str  ,     lowercase_		:       List[str]  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       Optional[Any]  ,     *lowercase_		:       Any				):
     snake_case_ :     int				=	self.num_labels
     snake_case_ :     Any				=	OpenAIGPTForSequenceClassification(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Optional[int]				=	ids_tensor([self.batch_size]  ,     self.type_sequence_label_size				)
     snake_case_ :     Optional[Any]				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     labels=lowercase_				)
     self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)				)






 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Dict				=	self.prepare_config_and_inputs()
     (
         (
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	
     ) :     str				=	config_and_inputs
     snake_case_ :     str				=	{
         '''input_ids''': input_ids,
         '''token_type_ids''': token_type_ids,
         '''head_mask''': head_mask,
     }

     return config, inputs_dict





@require_torch
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			Dict							=		(
     (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase   :			int							=		(
     (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 _lowerCAmelCase   :			Union[str, Any]							=		(
     {
         """feature-extraction""": OpenAIGPTModel,
         """text-classification""": OpenAIGPTForSequenceClassification,
         """text-generation""": OpenAIGPTLMHeadModel,
         """zero-shot""": OpenAIGPTForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       int  ,     lowercase_		:       List[Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Union[str, Any]				):
     if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
         # tiny config could not be created.
         return True

     return False

 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]=False				):
     snake_case_ :     Dict				=	super()._prepare_for_class(lowercase_  ,     lowercase_  ,     return_labels=lowercase_				)

     if return_labels:
         if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
             snake_case_ :     List[str]				=	torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)  ,     dtype=torch.long  ,     device=lowercase_  ,     )
             snake_case_ :     int				=	inputs_dict['''labels''']
             snake_case_ :     Optional[Any]				=	inputs_dict['''labels''']
             snake_case_ :     int				=	torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.num_choices)  ,     dtype=torch.long  ,     device=lowercase_  ,     )
             snake_case_ :     Tuple				=	torch.zeros(
                 self.model_tester.batch_size  ,     dtype=torch.long  ,     device=lowercase_				)
     return inputs_dict

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     List[str]				=	OpenAIGPTModelTester(self				)
     snake_case_ :     Dict				=	ConfigTester(self  ,     config_class=lowercase_  ,     n_embd=37				)

 def 						_snake_case					(     self		:       List[str]				):
     self.config_tester.run_common_tests()

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Tuple				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_model(*lowercase_				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     List[str]				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head_model(*lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Any				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_double_lm_head_model(*lowercase_				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Optional[int]				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_				)






 @slow
 def 						_snake_case					(     self		:       Dict				):
     for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_ :     Optional[Any]				=	OpenAIGPTModel.from_pretrained(lowercase_				)
         self.assertIsNotNone(lowercase_				)





@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 @slow
 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Optional[Any]				=	OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''				)
     model.to(lowercase_				)
     snake_case_ :     List[str]				=	torch.tensor([[481, 4735, 544]]  ,     dtype=torch.long  ,     device=lowercase_				)  # the president is
     snake_case_ :     List[Any]				=	[
         481,
         4735,
         544,
         246,
         963,
         870,
         762,
         239,
         244,
         40477,
         244,
         249,
         719,
         881,
         487,
         544,
         240,
         244,
         603,
         481,
     ]  # the president is a very good man. " \n " i\'m sure he is, " said the

     snake_case_ :     Optional[Any]				=	model.generate(lowercase_  ,     do_sample=lowercase_				)
     self.assertListEqual(output_ids[0].tolist()  ,     lowercase_				)





