"""simple docstring"""







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    :



				def __init__(self       :   Any			,						_A       :   Tuple			,						_A       :   Any=13			,						_A       :   Union[str, Any]=7			,						_A       :   Dict=True			,						_A       :   Any=True			,						_A       :   str=True			,						_A       :   Tuple=True			,						_A       :   Dict=99			,						_A       :   int=32			,						_A       :   int=5			,						_A       :   str=4			,						_A       :   Optional[int]=4			,						_A       :   List[str]="gelu"			,						_A       :   List[str]=0.0			,						_A       :   str=0.1			,						_A       :   str=True			,						_A       :   Any=5_12			,						_A       :   List[str]=16			,						_A       :   List[str]=2			,						_A       :   Tuple=0.02			,						_A       :   Any=3			,						_A       :   List[Any]=4			,						_A       :   int=None			,						)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				parent
					__snake_case    :							str   	=				batch_size
					__snake_case    :							int   	=				seq_length
					__snake_case    :							Union[str, Any]   	=				is_training
					__snake_case    :							int   	=				use_input_mask
					__snake_case    :							List[str]   	=				use_token_type_ids
					__snake_case    :							Optional[Any]   	=				use_labels
					__snake_case    :							int   	=				vocab_size
					__snake_case    :							str   	=				hidden_size
					__snake_case    :							Optional[int]   	=				num_hidden_layers
					__snake_case    :							List[str]   	=				num_attention_heads
					__snake_case    :							Dict   	=				intermediate_multiple_size
					__snake_case    :							Dict   	=				hidden_act
					__snake_case    :							Union[str, Any]   	=				hidden_dropout
					__snake_case    :							Any   	=				attention_dropout
					__snake_case    :							str   	=				weight_tying
					__snake_case    :							Optional[int]   	=				max_position_embeddings
					__snake_case    :							Optional[int]   	=				type_vocab_size
					__snake_case    :							int   	=				type_sequence_label_size
					__snake_case    :							Tuple   	=				initializer_range
					__snake_case    :							Optional[int]   	=				num_labels
					__snake_case    :							Tuple   	=				num_choices
					__snake_case    :							Union[str, Any]   	=				scope



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

					__snake_case    :							str   	=				None
					if self.use_input_mask:
						__snake_case    :							Optional[Any]   	=				random_attention_mask([self.batch_size, self.seq_length])

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

					__snake_case    :							Optional[Any]   	=				self.get_config()

					return config, input_ids, input_mask, token_labels



				def       _lowercase  (self       :   Dict)					->			int:
					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       _lowercase  (self       :   str)					->			Optional[int]:
					__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							Dict   	=				self.prepare_config_and_inputs()

					__snake_case    :							List[str]   	=				True

					return config, input_ids, input_mask, token_labels



				def       _lowercase  (self       :   Any			,						_A       :   Optional[Any]			,						_A       :   Any			,						_A       :   List[str])					->			Dict:
					__snake_case    :							Optional[int]   	=				GPTNeoXJapaneseModel(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Dict   	=				model(_A			,						attention_mask=_A)
					__snake_case    :							int   	=				model(_A)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   int			,						_A       :   Optional[Any]			,						_A       :   Optional[Any]			,						_A       :   Tuple)					->			Dict:
					__snake_case    :							int   	=				True
					__snake_case    :							int   	=				GPTNeoXJapaneseModel(_A)
					model.to(_A)
					model.eval()
					__snake_case    :							Optional[Any]   	=				model(_A			,						attention_mask=_A)
					self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size))



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   Any			,						_A       :   List[str]			,						_A       :   Any			,						_A       :   str)					->			Optional[Any]:
					__snake_case    :							List[str]   	=				GPTNeoXJapaneseForCausalLM(config=_A)
					model.to(_A)
					model.eval()
					__snake_case    :							int   	=				model(_A			,						attention_mask=_A			,						labels=_A)
					self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size))



				def       _lowercase  (self       :   str			,						_A       :   Any			,						_A       :   Optional[Any]			,						_A       :   Optional[Any])					->			Union[str, Any]:
					__snake_case    :							Tuple   	=				True
					__snake_case    :							Union[str, Any]   	=				GPTNeoXJapaneseForCausalLM(config=_A)
					model.to(_A)
					model.eval()

					# first forward pass
					__snake_case    :							List[Any]   	=				model(_A			,						attention_mask=_A			,						use_cache=_A)
					__snake_case    :							str   	=				outputs.past_key_values

					# create hypothetical multiple next token and extent to next_input_ids
					__snake_case    :							Optional[int]   	=				ids_tensor((self.batch_size, 3)			,						config.vocab_size)
					__snake_case    :							str   	=				ids_tensor((self.batch_size, 3)			,						vocab_size=2)

					# append to next input_ids and
					__snake_case    :							Optional[int]   	=				torch.cat([input_ids, next_tokens]			,						dim=-1)
					__snake_case    :							List[Any]   	=				torch.cat([input_mask, next_mask]			,						dim=-1)

					__snake_case    :							Optional[Any]   	=				model(_A			,						attention_mask=_A			,						output_hidden_states=_A)
					__snake_case    :							List[Any]   	=				output_from_no_past['hidden_states'][0]
					__snake_case    :							Any   	=				model(
					    _A			,						attention_mask=_A			,						past_key_values=_A			,						output_hidden_states=_A			,						)['hidden_states'][0]

					# select random slice
					__snake_case    :							List[Any]   	=				ids_tensor((1,)			,						output_from_past.shape[-1]).item()
					__snake_case    :							Dict   	=				output_from_no_past[:, -3:, random_slice_idx].detach()
					__snake_case    :							Union[str, Any]   	=				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       _lowercase  (self       :   int)					->			Any:
					__snake_case    :							List[Any]   	=				self.prepare_config_and_inputs()
					__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							str   	=				config_and_inputs
					__snake_case    :							List[Any]   	=				{'input_ids': input_ids, 'attention_mask': input_mask}
					return config, inputs_dict







@require_torch
class 	UpperCamelCase    (					lowercase   ,			lowercase   ,			unittest.TestCase		):
				UpperCAmelCase       :   Dict					=	(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
				UpperCAmelCase       :   Tuple					=	(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
				UpperCAmelCase       :   Any					=	(
				    {"""feature-extraction""": GPTNeoXJapaneseModel, """text-generation""": GPTNeoXJapaneseForCausalLM}
				    if is_torch_available()
				    else {}
				)
				UpperCAmelCase       :   str					=	False
				UpperCAmelCase       :   Union[str, Any]					=	False
				UpperCAmelCase       :   str					=	False
				UpperCAmelCase       :   List[str]					=	False



				def       _lowercase  (self       :   Any)					->			str:
					__snake_case    :							List[Any]   	=				GPTNeoXJapaneseModelTester(self)
					__snake_case    :							Union[str, Any]   	=				ConfigTester(self			,						config_class=_A			,						hidden_size=37)



				def       _lowercase  (self       :   Optional[int])					->			List[Any]:
					self.config_tester.run_common_tests()



				def       _lowercase  (self       :   Optional[Any])					->			List[str]:
					__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							Any   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(_A			,						_A			,						_A)



				def       _lowercase  (self       :   Any)					->			List[str]:
					__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							Dict   	=				self.model_tester.prepare_config_and_inputs_for_decoder()
					self.model_tester.create_and_check_model_as_decoder(_A			,						_A			,						_A)



				def       _lowercase  (self       :   Dict)					->			Dict:
					# This regression test was failing with PyTorch < 1.3
					__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							Any   	=				self.model_tester.prepare_config_and_inputs_for_decoder()

					__snake_case    :							str   	=				None

					self.model_tester.create_and_check_model_as_decoder(_A			,						_A			,						_A)



				def       _lowercase  (self       :   Tuple)					->			str:
					__snake_case	,       __snake_case	,       __snake_case	,       __snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_decoder_model_past_large_inputs(_A			,						_A			,						_A)



				def       _lowercase  (self       :   List[Any])					->			List[Any]:
					__snake_case    :							List[str]   	=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_causal_lm(*_A)






				@slow
				def       _lowercase  (self       :   Any)					->			Any:
					__snake_case    :							Dict   	=				'abeja/gpt-neox-japanese-2.7b'

					__snake_case    :							List[Any]   	=				['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    :							List[str]   	=				[
					    '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    :							int   	=				GPTNeoXJapaneseTokenizer.from_pretrained(_A)
					__snake_case    :							Union[str, Any]   	=				GPTNeoXJapaneseForCausalLM.from_pretrained(_A)

					__snake_case    :							List[str]   	=				[]
					for prompt in prompts:
						__snake_case    :							Union[str, Any]   	=				tokenizer(_A			,						return_tensors='pt').input_ids
						__snake_case    :							Tuple   	=				model.generate(_A			,						max_length=50)
						__snake_case    :							List[str]   	=				tokenizer.batch_decode(_A			,						skip_special_tokens=_A)
						predicted_outputs += generated_string
					self.assertListEqual(_A			,						_A)






"""simple docstring"""







def 						__UpperCAmelCase       (					UpperCAmelCase_					:       list[int]    ,							UpperCAmelCase_					:       str						)							->    list[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				int(UpperCAmelCase_						)

	# Initialize Result
	__snake_case    :							int   	=				[]

	# Traverse through all denomination
	for denomination in reversed(UpperCAmelCase_						):
		# Find denominations
		while int(UpperCAmelCase_						) >= int(UpperCAmelCase_						):
			total_value -= int(UpperCAmelCase_						)
			answer.append(UpperCAmelCase_						)  # Append the "answers" array

	return answer


# Driver Code
if __name__ == "__main__":
	_a  :  Optional[int]=				[]
	_a  :  Optional[int]=				"0"

	if (
	    input("Do you want to enter your denominations ? (yY/n): ").strip().lower()
	    == "y"
	):
		_a  :  int=				int(input("Enter the number of denominations you want to add: ").strip())

		for i in range(0, n):
			denominations.append(int(input(f'''Denomination {i}: ''').strip()))
		_a  :  Optional[int]=				input("Enter the change you want to make in Indian Currency: ").strip()
	else:
		# All denominations of Indian Currency if user does not enter
		_a  :  Tuple=				[1, 2, 5, 10, 20, 50, 100, 500, 2_000]
		_a  :  List[str]=				input("Enter the change you want to make: ").strip()

	if int(value) == 0 or int(value) < 0:
		print("The total value cannot be zero or negative.")

	else:
		print(f'''Following is minimal change for {value}: ''')
		_a  :  List[Any]=				find_minimum_change(denominations, value)
		# Print result
		for i in range(len(answer)):
			print(answer[i], end=" ")





