






import os

lowercase  							=					{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							0

							snake_case_				=							0
							while index < len(a_) - 1:
														snake_case_				=							SYMBOLS[numerals[index]]
														snake_case_				=							SYMBOLS[numerals[index + 1]]
														if current_value < next_value:
																					total_value -= current_value
														else:
																					total_value += current_value
														index += 1
							total_value += SYMBOLS[numerals[index]]

							return total_value





def 						__UpperCAmelCase			(     a_):
							snake_case_				=							''

							snake_case_				=							num // 10_00
							numerals += m_count * "M"
							num %= 10_00

							snake_case_				=							num // 1_00
							if c_count == 9:
														numerals += "CM"
														c_count -= 9
							elif c_count == 4:
														numerals += "CD"
														c_count -= 4
							if c_count >= 5:
														numerals += "D"
														c_count -= 5
							numerals += c_count * "C"
							num %= 1_00

							snake_case_				=							num // 10
							if x_count == 9:
														numerals += "XC"
														x_count -= 9
							elif x_count == 4:
														numerals += "XL"
														x_count -= 4
							if x_count >= 5:
														numerals += "L"
														x_count -= 5
							numerals += x_count * "X"
							num %= 10

							if num == 9:
														numerals += "IX"
														num -= 9
							elif num == 4:
														numerals += "IV"
														num -= 4
							if num >= 5:
														numerals += "V"
														num -= 5
							numerals += num * "I"

							return numerals





def 						__UpperCAmelCase			(     a_ = "/p089_roman.txt"):
							snake_case_				=							0

							with open(os.path.dirname(a_) + roman_numerals_filename) as filea:
														snake_case_				=							filea.readlines()

							for line in lines:
														snake_case_				=							line.strip()
														snake_case_				=							parse_roman_numerals(a_)
														snake_case_				=							generate_roman_numerals(a_)
														savings += len(a_) - len(a_)

							return savings


if __name__ == "__main__":
	print(f'{solution() = }')













import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel




class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=4       ,			a="gelu"       ,			a=0.0       ,			a=0.1       ,			a=True       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=3       ,			a=4       ,			a=None       ,			)       ->    Optional[Any]:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_input_mask
								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_multiple_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout
								snake_case_				=							attention_dropout
								snake_case_				=							weight_tying
								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




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_input_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

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

								snake_case_				=							self.get_config()

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								return GPTNeoXJapaneseConfig(
								    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_multiple_size=self.intermediate_multiple_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout=self.hidden_dropout       ,			attention_dropout=self.attention_dropout       ,			weight_tying=self.weight_tying       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			is_decoder=a       ,			initializer_range=self.initializer_range       ,			)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.prepare_config_and_inputs()

								snake_case_				=							True

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Any:
								snake_case_				=							GPTNeoXJapaneseModel(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								snake_case_				=							model(a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							True
								snake_case_				=							GPTNeoXJapaneseModel(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    int:
								snake_case_				=							GPTNeoXJapaneseForCausalLM(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							True
								snake_case_				=							GPTNeoXJapaneseForCausalLM(config=a					)
								model.to(a					)
								model.eval()

								# first forward pass
								snake_case_				=							model(a       ,			attention_mask=a       ,			use_cache=a					)
								snake_case_				=							outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								snake_case_				=							ids_tensor((self.batch_size, 3)       ,			config.vocab_size					)
								snake_case_				=							ids_tensor((self.batch_size, 3)       ,			vocab_size=2					)

								# append to next input_ids and
								snake_case_				=							torch.cat([input_ids, next_tokens]       ,			dim=-1					)
								snake_case_				=							torch.cat([input_mask, next_mask]       ,			dim=-1					)

								snake_case_				=							model(a       ,			attention_mask=a       ,			output_hidden_states=a					)
								snake_case_				=							output_from_no_past['hidden_states'][0]
								snake_case_				=							model(
								    a       ,			attention_mask=a       ,			past_key_values=a       ,			output_hidden_states=a       ,			)['hidden_states'][0]

								# select random slice
								snake_case_				=							ids_tensor((1,)       ,			output_from_past.shape[-1]					).item()
								snake_case_				=							output_from_no_past[:, -3:, random_slice_idx].detach()
								snake_case_				=							output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(a       ,			a       ,			atol=1E-3					)					)






	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict






@require_torch
class  UpperCamelCase_      (			snake_case_       ,					snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
	lowerCAmelCase													= (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
	lowerCAmelCase													= (
	    {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							GPTNeoXJapaneseModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			hidden_size=37					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								# This regression test was failing with PyTorch < 1.3
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case_				=							None

								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*a					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							'abeja/gpt-neox-japanese-2.7b'

								snake_case_				=							['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

								snake_case_				=							[
								    'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
								    '100年後に必要とされる会社は、「人」が中心の会社です。',
								    'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
								    '国境の長いトンネルを抜けると、そこは雪国だった。',
								    '美味しい日本食といえば、やっぱりお寿司ですよね。',
								]

								snake_case_				=							GPTNeoXJapaneseTokenizer.from_pretrained(a					)
								snake_case_				=							GPTNeoXJapaneseForCausalLM.from_pretrained(a					)

								snake_case_				=							[]
								for prompt in prompts:
															snake_case_				=							tokenizer(a       ,			return_tensors='pt'					).input_ids
															snake_case_				=							model.generate(a       ,			max_length=50					)
															snake_case_				=							tokenizer.batch_decode(a       ,			skip_special_tokens=a					)
															predicted_outputs += generated_string
								self.assertListEqual(a       ,			a					)





