"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaVaForMaskedLM,
      TFDebertaVaForQuestionAnswering,
      TFDebertaVaForSequenceClassification,
      TFDebertaVaForTokenClassification,
      TFDebertaVaModel,
  )



class      _UpperCAmelCase						:






    """simple docstring"""







    def __init__(			self    , _lowercase    , _lowercase=13    , _lowercase=7    , _lowercase=True    , _lowercase=True    , _lowercase=True    , _lowercase=True    , _lowercase=99    , _lowercase=32    , _lowercase=2    , _lowercase=4    , _lowercase=37    , _lowercase="gelu"    , _lowercase=0.1    , _lowercase=0.1    , _lowercase=512    , _lowercase=16    , _lowercase=2    , _lowercase=0.02    , _lowercase=False    , _lowercase=True    , _lowercase="None"    , _lowercase=3    , _lowercase=4    , _lowercase=None    , )	->	Dict:
        _lowerCamelCase	: Any       =       parent
        _lowerCamelCase	: str       =       batch_size
        _lowerCamelCase	: Any       =       seq_length
        _lowerCamelCase	: List[str]       =       is_training
        _lowerCamelCase	: Any       =       use_input_mask
        _lowerCamelCase	: Optional[int]       =       use_token_type_ids
        _lowerCamelCase	: str       =       use_labels
        _lowerCamelCase	: Optional[Any]       =       vocab_size
        _lowerCamelCase	: Dict       =       hidden_size
        _lowerCamelCase	: int       =       num_hidden_layers
        _lowerCamelCase	: List[Any]       =       num_attention_heads
        _lowerCamelCase	: str       =       intermediate_size
        _lowerCamelCase	: Any       =       hidden_act
        _lowerCamelCase	: List[Any]       =       hidden_dropout_prob
        _lowerCamelCase	: Any       =       attention_probs_dropout_prob
        _lowerCamelCase	: List[str]       =       max_position_embeddings
        _lowerCamelCase	: int       =       type_vocab_size
        _lowerCamelCase	: Any       =       type_sequence_label_size
        _lowerCamelCase	: Union[str, Any]       =       initializer_range
        _lowerCamelCase	: Optional[int]       =       num_labels
        _lowerCamelCase	: List[Any]       =       num_choices
        _lowerCamelCase	: Union[str, Any]       =       relative_attention
        _lowerCamelCase	: str       =       position_biased_input
        _lowerCamelCase	: str       =       pos_att_type
        _lowerCamelCase	: Optional[int]       =       scope







    def 				a__					(			self					)	->	Union[str, Any]:
        _lowerCamelCase	: List[str]       =       ids_tensor([self.batch_size, self.seq_length]    , self.vocab_size					)

        _lowerCamelCase	: List[str]       =       None
        if self.use_input_mask:
            _lowerCamelCase	: List[str]       =       random_attention_mask([self.batch_size, self.seq_length]					)

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

        _lowerCamelCase	: Union[str, Any]       =       None
        _lowerCamelCase	: Optional[int]       =       None
        _lowerCamelCase	: Union[str, Any]       =       None
        if self.use_labels:
            _lowerCamelCase	: Optional[int]       =       ids_tensor([self.batch_size]    , self.type_sequence_label_size					)
            _lowerCamelCase	: List[Any]       =       ids_tensor([self.batch_size, self.seq_length]    , self.num_labels					)

        _lowerCamelCase	: Any       =       DebertaVaConfig(
            vocab_size=self.vocab_size    , hidden_size=self.hidden_size    , num_hidden_layers=self.num_hidden_layers    , num_attention_heads=self.num_attention_heads    , intermediate_size=self.intermediate_size    , hidden_act=self.hidden_act    , hidden_dropout_prob=self.hidden_dropout_prob    , attention_probs_dropout_prob=self.attention_probs_dropout_prob    , max_position_embeddings=self.max_position_embeddings    , type_vocab_size=self.type_vocab_size    , relative_attention=self.relative_attention    , position_biased_input=self.position_biased_input    , initializer_range=self.initializer_range    , return_dict=__lowerCAmelCase    , )

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase					)	->	int:
        _lowerCamelCase	: Optional[Any]       =       TFDebertaVaModel(config=__lowerCAmelCase					)
        _lowerCamelCase	: Union[str, Any]       =       {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

        _lowerCamelCase	: Optional[int]       =       [input_ids, input_mask]
        _lowerCamelCase	: Union[str, Any]       =       model(__lowerCAmelCase					)

        _lowerCamelCase	: Union[str, Any]       =       model(__lowerCAmelCase					)

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







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase					)	->	Union[str, Any]:
        _lowerCamelCase	: str       =       TFDebertaVaForMaskedLM(config=__lowerCAmelCase					)
        _lowerCamelCase	: str       =       {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        _lowerCamelCase	: Optional[int]       =       model(__lowerCAmelCase					)
        self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.vocab_size)					)







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase					)	->	int:
        _lowerCamelCase	: Tuple       =       self.num_labels
        _lowerCamelCase	: Dict       =       TFDebertaVaForSequenceClassification(config=__lowerCAmelCase					)
        _lowerCamelCase	: Tuple       =       {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

        _lowerCamelCase	: Optional[Any]       =       model(__lowerCAmelCase					)
        self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels)					)







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase					)	->	int:
        _lowerCamelCase	: List[str]       =       self.num_labels
        _lowerCamelCase	: Optional[int]       =       TFDebertaVaForTokenClassification(config=__lowerCAmelCase					)
        _lowerCamelCase	: Any       =       {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }
        _lowerCamelCase	: List[Any]       =       model(__lowerCAmelCase					)
        self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.seq_length, self.num_labels)					)







    def 				a__					(			self    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase    , _lowercase					)	->	Union[str, Any]:
        _lowerCamelCase	: List[str]       =       TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase					)
        _lowerCamelCase	: int       =       {
            '''input_ids''': input_ids,
            '''attention_mask''': input_mask,
            '''token_type_ids''': token_type_ids,
        }

        _lowerCamelCase	: Dict       =       model(__lowerCAmelCase					)
        self.parent.assertEqual(result.start_logits.shape    , (self.batch_size, self.seq_length)					)
        self.parent.assertEqual(result.end_logits.shape    , (self.batch_size, self.seq_length)					)







    def 				a__					(			self					)	->	int:
        _lowerCamelCase	: Optional[int]       =       self.prepare_config_and_inputs()
        (
            (
            _lowerCamelCase       
        ),					(
            _lowerCamelCase       
        ),					(
            _lowerCamelCase       
        ),					(
            _lowerCamelCase       
        ),					(
            _lowerCamelCase       
        ),					(
            _lowerCamelCase       
        ),					(
            _lowerCamelCase       
        ),					       
        )	: Union[str, Any]       =       config_and_inputs
        _lowerCamelCase	: str       =       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
        return config, inputs_dict





@require_tf
class      _UpperCAmelCase						( UpperCAmelCase__ ,			UpperCAmelCase__ ,			unittest.TestCase						):






    """simple docstring"""
    __snake_case    =       (
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    __snake_case    =       (
        {
            '''feature-extraction''': TFDebertaVaModel,
            '''fill-mask''': TFDebertaVaForMaskedLM,
            '''question-answering''': TFDebertaVaForQuestionAnswering,
            '''text-classification''': TFDebertaVaForSequenceClassification,
            '''token-classification''': TFDebertaVaForTokenClassification,
            '''zero-shot''': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    __snake_case    =       False
    __snake_case    =       False







    def 				a__					(			self					)	->	Optional[int]:
        _lowerCamelCase	: Optional[int]       =       TFDebertaVaModelTester(self					)
        _lowerCamelCase	: Any       =       ConfigTester(self    , config_class=__lowerCAmelCase    , hidden_size=37					)







    def 				a__					(			self					)	->	Optional[int]:
        self.config_tester.run_common_tests()







    def 				a__					(			self					)	->	int:
        _lowerCamelCase	: Optional[Any]       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase					)







    def 				a__					(			self					)	->	int:
        _lowerCamelCase	: List[str]       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase					)







    def 				a__					(			self					)	->	List[str]:
        _lowerCamelCase	: Tuple       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase					)







    def 				a__					(			self					)	->	Any:
        _lowerCamelCase	: Tuple       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase					)







    def 				a__					(			self					)	->	Union[str, Any]:
        _lowerCamelCase	: str       =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase					)







    @slow
    def 				a__					(			self					)	->	Optional[Any]:
        _lowerCamelCase	: Optional[Any]       =       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
        self.assertIsNotNone(__lowerCAmelCase					)





@require_tf
class      _UpperCAmelCase						( unittest.TestCase						):






    """simple docstring"""







    @unittest.skip(reason='''Model not available yet'''					)
    def 				a__					(			self					)	->	List[str]:
        pass







    @slow
    def 				a__					(			self					)	->	Optional[Any]:
        _lowerCamelCase	: Tuple       =       TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
        _lowerCamelCase	: Tuple       =       tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
        _lowerCamelCase	: int       =       tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
        _lowerCamelCase	: Union[str, Any]       =       model(__lowerCAmelCase    , attention_mask=__lowerCAmelCase					)[0]

        _lowerCamelCase	: str       =       tf.constant(
            [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]					)
        tf.debugging.assert_near(output[:, 1:4, 1:4]    , __lowerCAmelCase    , atol=1E-4					)








'''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 						a_				:




	def __init__(       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]=1_3       ,		__lowerCAmelCase     :  Tuple=7       ,		__lowerCAmelCase     :  Optional[int]=True       ,		__lowerCAmelCase     :  Any=True       ,		__lowerCAmelCase     :  List[Any]=True       ,		__lowerCAmelCase     :  Union[str, Any]=9_9       ,		__lowerCAmelCase     :  Any=3_2       ,		__lowerCAmelCase     :  Tuple=5       ,		__lowerCAmelCase     :  int=4       ,		__lowerCAmelCase     :  List[str]=3_7       ,		__lowerCAmelCase     :  List[Any]="gelu"       ,		__lowerCAmelCase     :  Union[str, Any]=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Any=5_1_2       ,		__lowerCAmelCase     :  Dict=1_6       ,		__lowerCAmelCase     :  Dict=2       ,		__lowerCAmelCase     :  Union[str, Any]=0.02       ,		__lowerCAmelCase     :  Optional[Any]=3       ,		__lowerCAmelCase     :  str=4       ,		__lowerCAmelCase     :  List[str]=None       ,		):
							__snake_case						 =   parent
							__snake_case						 =   batch_size
							__snake_case						 =   seq_length
							__snake_case						 =   is_training
							__snake_case						 =   use_token_type_ids
							__snake_case						 =   use_labels
							__snake_case						 =   vocab_size
							__snake_case						 =   hidden_size
							__snake_case						 =   num_hidden_layers
							__snake_case						 =   num_attention_heads
							__snake_case						 =   intermediate_size
							__snake_case						 =   hidden_act
							__snake_case						 =   hidden_dropout_prob
							__snake_case						 =   attention_probs_dropout_prob
							__snake_case						 =   max_position_embeddings
							__snake_case						 =   type_vocab_size
							__snake_case						 =   type_sequence_label_size
							__snake_case						 =   initializer_range
							__snake_case						 =   num_labels
							__snake_case						 =   num_choices
							__snake_case						 =   scope
							__snake_case						 =   self.vocab_size - 1




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size      )

							__snake_case						 =   None
							if self.use_token_type_ids:
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.type_vocab_size      )

							__snake_case						 =   None
							__snake_case						 =   None
							__snake_case						 =   None
							if self.use_labels:
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
													__snake_case						 =   ids_tensor([self.batch_size, self.seq_length]       ,		self.num_labels      )
													__snake_case						 =   ids_tensor([self.batch_size]       ,		self.num_choices      )

							__snake_case						 =   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						 =   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   lowercase__   (       self     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Union[str, Any]       ,		*__lowerCAmelCase     :  List[str]      ):
							__snake_case						 =   OpenAIGPTModel(config=__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		head_mask=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase      )
							__snake_case						 =   model(__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Dict       ,		*__lowerCAmelCase     :  str      ):
							__snake_case						 =   OpenAIGPTLMHeadModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.loss.shape       ,		()      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  List[Any]       ,		*__lowerCAmelCase     :  Tuple      ):
							__snake_case						 =   OpenAIGPTDoubleHeadsModel(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.loss.shape       ,		()      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int       ,		*__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   OpenAIGPTForSequenceClassification(__lowerCAmelCase      )
							model.to(__lowerCAmelCase      )
							model.eval()

							__snake_case						 =   ids_tensor([self.batch_size]       ,		self.type_sequence_label_size      )
							__snake_case						 =   model(__lowerCAmelCase       ,		token_type_ids=__lowerCAmelCase       ,		labels=__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)      )




	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   self.prepare_config_and_inputs()
							(
							    (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       (
							    __snake_case
							) ,       
							)						 =   config_and_inputs
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'token_type_ids': token_type_ids,
							    'head_mask': head_mask,
							}

							return config, inputs_dict





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




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  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   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]=False      ):
							__snake_case						 =   super()._prepare_for_class(__lowerCAmelCase       ,		__lowerCAmelCase       ,		return_labels=__lowerCAmelCase      )

							if return_labels:
													if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   inputs_dict['labels']
																			__snake_case						 =   torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.num_choices)       ,		dtype=torch.long       ,		device=__lowerCAmelCase       ,		)
																			__snake_case						 =   torch.zeros(
																			    self.model_tester.batch_size       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )
							return inputs_dict




	def   lowercase__   (       self     :  Tuple      ):
							__snake_case						 =   OpenAIGPTModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		n_embd=3_7      )




	def   lowercase__   (       self     :  int      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_double_lm_head_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  str      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  str      ):
							for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__snake_case						 =   OpenAIGPTModel.from_pretrained(__lowerCAmelCase      )
													self.assertIsNotNone(__lowerCAmelCase      )





@require_torch
class 						a_				(			unittest.TestCase						):




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'      )
							model.to(__lowerCAmelCase      )
							__snake_case						 =   torch.tensor([[4_8_1, 4_7_3_5, 5_4_4]]       ,		dtype=torch.long       ,		device=__lowerCAmelCase      )  # the president is
							__snake_case						 =   [
							    4_8_1,
							    4_7_3_5,
							    5_4_4,
							    2_4_6,
							    9_6_3,
							    8_7_0,
							    7_6_2,
							    2_3_9,
							    2_4_4,
							    4_0_4_7_7,
							    2_4_4,
							    2_4_9,
							    7_1_9,
							    8_8_1,
							    4_8_7,
							    5_4_4,
							    2_4_0,
							    2_4_4,
							    6_0_3,
							    4_8_1,
							]  # the president is a very good man. " \n " i\'m sure he is, " said the

							__snake_case						 =   model.generate(__lowerCAmelCase       ,		do_sample=__lowerCAmelCase      )
							self.assertListEqual(output_ids[0].tolist()       ,		__lowerCAmelCase      )

