




'''simple docstring'''







import os
from itertools import chain
from random import randrange, shuffle

import pytest

from .sola import PokerHand

A									=(
    '4S 3H 2C 7S 5H',
    '9D 8H 2C 6S 7H',
    '2D 6D 9D TH 7D',
    'TC 8C 2S JH 6C',
    'JH 8S TH AH QH',
    'TS KS 5S 9S AC',
    'KD 6S 9D TH AD',
    'KS 8D 4D 9S 4S',  # pair
    '8C 4S KH JS 4D',  # pair
    'QH 8H KD JH 8S',  # pair
    'KC 4H KS 2H 8D',  # pair
    'KD 4S KC 3H 8S',  # pair
    'AH 8S AS KC JH',  # pair
    '3H 4C 4H 3S 2H',  # 2 pairs
    '5S 5D 2C KH KH',  # 2 pairs
    '3C KH 5D 5S KH',  # 2 pairs
    'AS 3C KH AD KH',  # 2 pairs
    '7C 7S 3S 7H 5S',  # 3 of a kind
    '7C 7S KH 2H 7H',  # 3 of a kind
    'AC KH QH AH AS',  # 3 of a kind
    '2H 4D 3C AS 5S',  # straight (low ace)
    '3C 5C 4C 2C 6H',  # straight
    '6S 8S 7S 5H 9H',  # straight
    'JS QS 9H TS KH',  # straight
    'QC KH TS JS AH',  # straight (high ace)
    '8C 9C 5C 3C TC',  # flush
    '3S 8S 9S 5S KS',  # flush
    '4C 5C 9C 8C KC',  # flush
    'JH 8H AH KH QH',  # flush
    '3D 2H 3H 2C 2D',  # full house
    '2H 2C 3S 3H 3D',  # full house
    'KH KC 3S 3H 3D',  # full house
    'JC 6H JS JD JH',  # 4 of a kind
    'JC 7H JS JD JH',  # 4 of a kind
    'JC KH JS JD JH',  # 4 of a kind
    '2S AS 4S 5S 3S',  # straight flush (low ace)
    '2D 6D 3D 4D 5D',  # straight flush
    '5C 6C 3C 7C 4C',  # straight flush
    'JH 9H TH KH QH',  # straight flush
    'JH AH TH KH QH',  # royal flush (high ace straight flush)
)

A									=(
    ('2H 3H 4H 5H 6H', 'KS AS TS QS JS', 'Loss'),
    ('2H 3H 4H 5H 6H', 'AS AD AC AH JD', 'Win'),
    ('AS AH 2H AD AC', 'JS JD JC JH 3D', 'Win'),
    ('2S AH 2H AS AC', 'JS JD JC JH AD', 'Loss'),
    ('2S AH 2H AS AC', '2H 3H 5H 6H 7H', 'Win'),
    ('AS 3S 4S 8S 2S', '2H 3H 5H 6H 7H', 'Win'),
    ('2H 3H 5H 6H 7H', '2S 3H 4H 5S 6C', 'Win'),
    ('2S 3H 4H 5S 6C', '3D 4C 5H 6H 2S', 'Tie'),
    ('2S 3H 4H 5S 6C', 'AH AC 5H 6H AS', 'Win'),
    ('2S 2H 4H 5S 4C', 'AH AC 5H 6H AS', 'Loss'),
    ('2S 2H 4H 5S 4C', 'AH AC 5H 6H 7S', 'Win'),
    ('6S AD 7H 4S AS', 'AH AC 5H 6H 7S', 'Loss'),
    ('2S AH 4H 5S KC', 'AH AC 5H 6H 7S', 'Loss'),
    ('2S 3H 6H 7S 9C', '7H 3C TH 6H 9S', 'Loss'),
    ('4S 5H 6H TS AC', '3S 5H 6H TS AC', 'Win'),
    ('2S AH 4H 5S 6C', 'AD 4C 5H 6H 2C', 'Tie'),
    ('AS AH 3H AD AC', 'AS AH 2H AD AC', 'Win'),
    ('AH AC 5H 5C QS', 'AH AC 5H 5C KS', 'Loss'),
    ('AH AC 5H 5C QS', 'KH KC 5H 5C QS', 'Win'),
    ('7C 7S KH 2H 7H', '3C 3S AH 2H 3H', 'Win'),
    ('3C 3S AH 2H 3H', '7C 7S KH 2H 7H', 'Loss'),
    ('6H 5H 4H 3H 2H', '5H 4H 3H 2H AH', 'Win'),
    ('5H 4H 3H 2H AH', '5H 4H 3H 2H AH', 'Tie'),
    ('5H 4H 3H 2H AH', '6H 5H 4H 3H 2H', 'Loss'),
    ('AH AD KS KC AC', 'AH KD KH AC KC', 'Win'),
    ('2H 4D 3C AS 5S', '2H 4D 3C 6S 5S', 'Loss'),
    ('2H 3S 3C 3H 2S', '3S 3C 2S 2H 2D', 'Win'),
    ('4D 6D 5D 2D JH', '3S 8S 3H TC KH', 'Loss'),
    ('4S 6C 8S 3S 7S', 'AD KS 2D 7D 7C', 'Loss'),
    ('6S 4C 7H 8C 3H', '5H JC AH 9D 9C', 'Loss'),
    ('9D 9H JH TC QH', '3C 2S JS 5C 7H', 'Win'),
    ('2H TC 8S AD 9S', '4H TS 7H 2C 5C', 'Win'),
    ('9D 3S 2C 7S 7C', 'JC TD 3C TC 9H', 'Loss'),
)

A									=(
    ('2H 3H 4H 5H 6H', True),
    ('AS AH 2H AD AC', False),
    ('2H 3H 5H 6H 7H', True),
    ('KS AS TS QS JS', True),
    ('8H 9H QS JS TH', False),
    ('AS 3S 4S 8S 2S', True),
)

A									=(
    ('2H 3H 4H 5H 6H', True),
    ('AS AH 2H AD AC', False),
    ('2H 3H 5H 6H 7H', False),
    ('KS AS TS QS JS', True),
    ('8H 9H QS JS TH', True),
)

A									=(
    ('2H 4D 3C AS 5S', True, [5, 4, 3, 2, 14]),
    ('2H 5D 3C AS 5S', False, [14, 5, 5, 3, 2]),
    ('JH QD KC AS TS', False, [14, 13, 12, 11, 10]),
    ('9D 3S 2C 7S 7C', False, [9, 7, 7, 3, 2]),
)

A									=(
    ('JH AH TH KH QH', 0),
    ('JH 9H TH KH QH', 0),
    ('JC KH JS JD JH', 7),
    ('KH KC 3S 3H 3D', 6),
    ('8C 9C 5C 3C TC', 0),
    ('JS QS 9H TS KH', 0),
    ('7C 7S KH 2H 7H', 3),
    ('3C KH 5D 5S KH', 2),
    ('QH 8H KD JH 8S', 1),
    ('2D 6D 9D TH 7D', 0),
)

A									=(
    ('JH AH TH KH QH', 23),
    ('JH 9H TH KH QH', 22),
    ('JC KH JS JD JH', 21),
    ('KH KC 3S 3H 3D', 20),
    ('8C 9C 5C 3C TC', 19),
    ('JS QS 9H TS KH', 18),
    ('7C 7S KH 2H 7H', 17),
    ('3C KH 5D 5S KH', 16),
    ('QH 8H KD JH 8S', 15),
    ('2D 6D 9D TH 7D', 14),
)





def 		snake_case_							():
				UpperCAmelCase     =	randrange(len(SCREAMING_SNAKE_CASE_  )  ), randrange(len(SCREAMING_SNAKE_CASE_  )  )
				UpperCAmelCase     =	["Loss", "Tie", "Win"][(play >= oppo) + (play > oppo)]
				UpperCAmelCase     =	SORTED_HANDS[play], SORTED_HANDS[oppo]
				return hand, other, expected





def 		snake_case_							(_a			:							int = 1_0_0  ):
				return (generate_random_hand() for _ in range(SCREAMING_SNAKE_CASE_  ))





@pytest.mark.parametrize('''hand, expected'''					,     SCREAMING_SNAKE_CASE_  )
def 		snake_case_							(_a			:							Any					,     _a			:							Union[str, Any]  ):
				assert PokerHand(SCREAMING_SNAKE_CASE_  )._is_flush() == expected





@pytest.mark.parametrize('''hand, expected'''					,     SCREAMING_SNAKE_CASE_  )
def 		snake_case_							(_a			:							Tuple					,     _a			:							Dict  ):
				assert PokerHand(SCREAMING_SNAKE_CASE_  )._is_straight() == expected





@pytest.mark.parametrize('''hand, expected, card_values'''					,     SCREAMING_SNAKE_CASE_  )
def 		snake_case_							(_a			:							Dict					,     _a			:							Tuple					,     _a			:							Optional[Any]  ):
				UpperCAmelCase     =	PokerHand(SCREAMING_SNAKE_CASE_  )
				assert player._is_five_high_straight() == expected
				assert player._card_values == card_values





@pytest.mark.parametrize('''hand, expected'''					,     SCREAMING_SNAKE_CASE_  )
def 		snake_case_							(_a			:							Optional[int]					,     _a			:							List[str]  ):
				assert PokerHand(SCREAMING_SNAKE_CASE_  )._is_same_kind() == expected





@pytest.mark.parametrize('''hand, expected'''					,     SCREAMING_SNAKE_CASE_  )
def 		snake_case_							(_a			:							Any					,     _a			:							Optional[int]  ):
				assert PokerHand(SCREAMING_SNAKE_CASE_  )._hand_type == expected





@pytest.mark.parametrize('''hand, other, expected'''					,     SCREAMING_SNAKE_CASE_  )
def 		snake_case_							(_a			:							List[Any]					,     _a			:							Union[str, Any]					,     _a			:							Optional[Any]  ):
				assert PokerHand(SCREAMING_SNAKE_CASE_  ).compare_with(PokerHand(SCREAMING_SNAKE_CASE_  )  ) == expected





@pytest.mark.parametrize('''hand, other, expected'''					,     generate_random_hands()  )
def 		snake_case_							(_a			:							Optional[int]					,     _a			:							Tuple					,     _a			:							int  ):
				assert PokerHand(SCREAMING_SNAKE_CASE_  ).compare_with(PokerHand(SCREAMING_SNAKE_CASE_  )  ) == expected





def 		snake_case_							():
				UpperCAmelCase     =	[PokerHand(SCREAMING_SNAKE_CASE_  ) for hand in SORTED_HANDS]
				UpperCAmelCase     =	poker_hands.copy()
				shuffle(SCREAMING_SNAKE_CASE_  )
				UpperCAmelCase     =	chain(sorted(SCREAMING_SNAKE_CASE_  )  )
				for index, hand in enumerate(SCREAMING_SNAKE_CASE_  ):
								assert hand == poker_hands[index]





def 		snake_case_							():
				UpperCAmelCase     =	[PokerHand('''2D AC 3H 4H 5S'''  ), PokerHand('''2S 3H 4H 5S 6C'''  )]
				pokerhands.sort(reverse=SCREAMING_SNAKE_CASE_  )
				assert pokerhands[0].__str__() == "2S 3H 4H 5S 6C"





def 		snake_case_							():
				UpperCAmelCase     =	PokerHand('''2C 4S AS 3D 5C'''  )
				UpperCAmelCase     =	True
				UpperCAmelCase     =	[5, 4, 3, 2, 1_4]
				for _ in range(1_0  ):
								assert pokerhand._is_five_high_straight() == expected
								assert pokerhand._card_values == expected_card_values







def 		snake_case_							():
				UpperCAmelCase     =	0
				UpperCAmelCase     =	os.path.abspath(os.path.dirname(SCREAMING_SNAKE_CASE_  )  )
				UpperCAmelCase     =	os.path.join(SCREAMING_SNAKE_CASE_					,     '''poker_hands.txt'''  )
				with open(SCREAMING_SNAKE_CASE_  ) as file_hand:
								for line in file_hand:
												UpperCAmelCase     =	line[:1_4].strip()
												UpperCAmelCase     =	line[1_5:].strip()
												UpperCAmelCase     =	PokerHand(SCREAMING_SNAKE_CASE_  ), PokerHand(SCREAMING_SNAKE_CASE_  )
												UpperCAmelCase     =	player.compare_with(SCREAMING_SNAKE_CASE_  )
												if output == "Win":
																answer += 1
				assert answer == 3_7_6










'''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]     ,       lowercase		:			int     ,       lowercase		:			str=13     ,       lowercase		:			int=7     ,       lowercase		:			Optional[int]=True     ,       lowercase		:			Dict=True     ,       lowercase		:			Union[str, Any]=True     ,       lowercase		:			Union[str, Any]=99     ,       lowercase		:			Optional[Any]=32     ,       lowercase		:			List[Any]=5     ,       lowercase		:			List[Any]=4     ,       lowercase		:			int=37     ,       lowercase		:			int="gelu"     ,       lowercase		:			Tuple=0.1     ,       lowercase		:			Dict=0.1     ,       lowercase		:			Tuple=512     ,       lowercase		:			List[str]=16     ,       lowercase		:			Optional[int]=2     ,       lowercase		:			int=0.02     ,       lowercase		:			str=3     ,       lowercase		:			List[Any]=4     ,       lowercase		:			List[str]=None     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_labels
           UpperCAmelCase     =	num_choices
           UpperCAmelCase     =	scope
           UpperCAmelCase     =	self.vocab_size - 1
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

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

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

           UpperCAmelCase     =	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     ,       )

           UpperCAmelCase     =	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 					A   (			self		:			Any     ,       lowercase		:			Tuple     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]     ,       lowercase		:			List[str]     ,       *lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTModel(config=lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase     ,       head_mask=lowercase       )
           UpperCAmelCase     =	model(lowercase     ,       token_type_ids=lowercase       )
           UpperCAmelCase     =	model(lowercase       )

           self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, self.seq_length, self.hidden_size)       )
       def 					A   (			self		:			List[str]     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[Any]     ,       lowercase		:			str     ,       lowercase		:			Optional[int]     ,       *lowercase		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTLMHeadModel(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	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 					A   (			self		:			Optional[int]     ,       lowercase		:			Tuple     ,       lowercase		:			Tuple     ,       lowercase		:			Optional[int]     ,       lowercase		:			Optional[int]     ,       *lowercase		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTDoubleHeadsModel(lowercase       )
           model.to(lowercase       )
           model.eval()

           UpperCAmelCase     =	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 					A   (			self		:			Tuple     ,       lowercase		:			Dict     ,       lowercase		:			Any     ,       lowercase		:			Any     ,       lowercase		:			Optional[int]     ,       *lowercase		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.num_labels
           UpperCAmelCase     =	OpenAIGPTForSequenceClassification(lowercase       )
           model.to(lowercase       )
           model.eval()

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







       def 					A   (			self		:			List[str]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           (
               (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  (
               UpperCAmelCase
           ) ,  
           )     =	config_and_inputs
           UpperCAmelCase     =	{
               '''input_ids''': input_ids,
               '''token_type_ids''': token_type_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict






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



           '''simple docstring'''

           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 					A   (			self		:			List[Any]     ,       lowercase		:			int     ,       lowercase		:			List[Any]     ,       lowercase		:			Optional[Any]=False       ):



           '''simple docstring'''

           UpperCAmelCase     =	super()._prepare_for_class(lowercase     ,       lowercase     ,       return_labels=lowercase       )

           if return_labels:
               if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,       dtype=torch.long     ,       device=lowercase     ,       )
                   UpperCAmelCase     =	inputs_dict['''labels''']
                   UpperCAmelCase     =	inputs_dict['''labels''']
                   UpperCAmelCase     =	torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.num_choices)     ,       dtype=torch.long     ,       device=lowercase     ,       )
                   UpperCAmelCase     =	torch.zeros(
                       self.model_tester.batch_size     ,       dtype=torch.long     ,       device=lowercase       )
           return inputs_dict
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       n_embd=37       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_model(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_lm_head_model(*lowercase       )
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_double_lm_head_model(*lowercase       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase       )







       @slow
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     =	OpenAIGPTModel.from_pretrained(lowercase       )
               self.assertIsNotNone(lowercase       )






@require_torch
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''       )
           model.to(lowercase       )
           UpperCAmelCase     =	torch.tensor([[481, 4_735, 544]]     ,       dtype=torch.long     ,       device=lowercase       )  # the president is
           UpperCAmelCase     =	[
               481,
               4_735,
               544,
               246,
               963,
               870,
               762,
               239,
               244,
               40_477,
               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

           UpperCAmelCase     =	model.generate(lowercase     ,       do_sample=lowercase       )
           self.assertListEqual(output_ids[0].tolist()     ,       lowercase       )




