

import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer

class 				__UpperCamelCase				:







	def __init__(   self: int  , __UpperCamelCase: Dict  , __UpperCamelCase: Optional[int]=13  , __UpperCamelCase: Any=7  , __UpperCamelCase: List[Any]=True  , __UpperCamelCase: Tuple=True  , __UpperCamelCase: Union[str, Any]=False  , __UpperCamelCase: Any=True  , __UpperCamelCase: Union[str, Any]=99  , __UpperCamelCase: Any=32  , __UpperCamelCase: int=5  , __UpperCamelCase: int=4  , __UpperCamelCase: Any=37  , __UpperCamelCase: Dict="gelu"  , __UpperCamelCase: List[Any]=0.1  , __UpperCamelCase: Optional[int]=0.1  , __UpperCamelCase: Dict=5_12  , __UpperCamelCase: Tuple=16  , __UpperCamelCase: int=2  , __UpperCamelCase: Optional[int]=0.02  , __UpperCamelCase: str=3  , __UpperCamelCase: str=4  , __UpperCamelCase: str=None  , ):

						'''simple docstring'''





						__magic_name__							=    parent
						__magic_name__							=    batch_size
						__magic_name__							=    seq_length
						__magic_name__							=    is_training
						__magic_name__							=    use_input_mask
						__magic_name__							=    use_token_type_ids
						__magic_name__							=    use_labels
						__magic_name__							=    vocab_size
						__magic_name__							=    hidden_size
						__magic_name__							=    num_hidden_layers
						__magic_name__							=    num_attention_heads
						__magic_name__							=    intermediate_size
						__magic_name__							=    hidden_act
						__magic_name__							=    hidden_dropout_prob
						__magic_name__							=    attention_probs_dropout_prob
						__magic_name__							=    max_position_embeddings
						__magic_name__							=    type_vocab_size
						__magic_name__							=    type_sequence_label_size
						__magic_name__							=    initializer_range
						__magic_name__							=    num_labels
						__magic_name__							=    num_choices
						__magic_name__							=    scope







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





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

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

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

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

						__magic_name__							=    self.get_config()

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







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						return LlamaConfig(
						    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  , is_decoder=__UpperCamelCase  , initializer_range=self.initializer_range  , )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: str  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[Any]      ):

						'''simple docstring'''





						__magic_name__							=    LlamaModel(config=__UpperCamelCase      )
						model.to(__UpperCamelCase      )
						model.eval()
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase      )
						__magic_name__							=    model(__UpperCamelCase      )
						self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size)      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Dict  , __UpperCamelCase: List[str]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: List[str]  , __UpperCamelCase: List[str]  , __UpperCamelCase: str  , __UpperCamelCase: Any  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Union[str, Any]  , ):

						'''simple docstring'''





						__magic_name__							=    True
						__magic_name__							=    LlamaModel(__UpperCamelCase      )
						model.to(__UpperCamelCase      )
						model.eval()
						__magic_name__							=    model(
						    __UpperCamelCase  , attention_mask=__UpperCamelCase  , encoder_hidden_states=__UpperCamelCase  , encoder_attention_mask=__UpperCamelCase  , )
						__magic_name__							=    model(
						    __UpperCamelCase  , attention_mask=__UpperCamelCase  , encoder_hidden_states=__UpperCamelCase  , )
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase      )
						self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size)      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Any  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Dict  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Dict  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Optional[Any]  , ):

						'''simple docstring'''





						__magic_name__							=    LlamaForCausalLM(config=__UpperCamelCase      )
						model.to(__UpperCamelCase      )
						model.eval()
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase  , labels=__UpperCamelCase      )
						self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length, self.vocab_size)      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: str  , __UpperCamelCase: Tuple  , __UpperCamelCase: List[Any]  , __UpperCamelCase: int  , __UpperCamelCase: Tuple  , __UpperCamelCase: Tuple  , __UpperCamelCase: Optional[int]  , ):

						'''simple docstring'''





						__magic_name__							=    True
						__magic_name__							=    True
						__magic_name__							=    LlamaForCausalLM(config=__UpperCamelCase      )
						model.to(__UpperCamelCase      )
						model.eval()

						# first forward pass
						__magic_name__							=    model(
						    __UpperCamelCase  , attention_mask=__UpperCamelCase  , encoder_hidden_states=__UpperCamelCase  , encoder_attention_mask=__UpperCamelCase  , use_cache=__UpperCamelCase  , )
						__magic_name__							=    outputs.past_key_values

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

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

						__magic_name__							=    model(
						    __UpperCamelCase  , attention_mask=__UpperCamelCase  , encoder_hidden_states=__UpperCamelCase  , encoder_attention_mask=__UpperCamelCase  , output_hidden_states=__UpperCamelCase  , )['hidden_states'][0]
						__magic_name__							=    model(
						    __UpperCamelCase  , attention_mask=__UpperCamelCase  , encoder_hidden_states=__UpperCamelCase  , encoder_attention_mask=__UpperCamelCase  , past_key_values=__UpperCamelCase  , output_hidden_states=__UpperCamelCase  , )['hidden_states'][0]

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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.prepare_config_and_inputs()
						(
						    (
						    __magic_name__       
						),	(
						    __magic_name__       
						),	(
						    __magic_name__       
						),	(
						    __magic_name__       
						),	(
						    __magic_name__       
						),	(
						    __magic_name__       
						),	(
						    __magic_name__       
						),	       
						)							=    config_and_inputs
						__magic_name__							=    {'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict




@require_torch
class 				__UpperCamelCase				(					__snake_case  ,						__snake_case  ,						__snake_case  ,						unittest.TestCase			):
	_lowercase  :   Dict       				=  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
	_lowercase  :   Dict       				=  (LlamaForCausalLM,) if is_torch_available() else ()
	_lowercase  :   Optional[Any]       				=  (
	    {
	        "feature-extraction": LlamaModel,
	        "text-classification": LlamaForSequenceClassification,
	        "text-generation": LlamaForCausalLM,
	        "zero-shot": LlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	_lowercase  :   Dict       				=  False
	_lowercase  :   int       				=  False







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    LlamaModelTester(self      )
						__magic_name__							=    ConfigTester(self  , config_class=__UpperCamelCase  , hidden_size=37      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						self.config_tester.run_common_tests()







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						__magic_name__							=    self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__magic_name__							=    type
											self.model_tester.create_and_check_model(*__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.model_tester.prepare_config_and_inputs_for_common()
						__magic_name__							=    3
						__magic_name__							=    input_dict['input_ids']
						__magic_name__							=    input_ids.ne(1      ).to(__UpperCamelCase      )
						__magic_name__							=    ids_tensor([self.model_tester.batch_size]  , self.model_tester.type_sequence_label_size      )
						__magic_name__							=    LlamaForSequenceClassification(__UpperCamelCase      )
						model.to(__UpperCamelCase      )
						model.eval()
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase  , labels=__UpperCamelCase      )
						self.assertEqual(result.logits.shape  , (self.model_tester.batch_size, self.model_tester.num_labels)      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.model_tester.prepare_config_and_inputs_for_common()
						__magic_name__							=    3
						__magic_name__							=    'single_label_classification'
						__magic_name__							=    input_dict['input_ids']
						__magic_name__							=    input_ids.ne(1      ).to(__UpperCamelCase      )
						__magic_name__							=    ids_tensor([self.model_tester.batch_size]  , self.model_tester.type_sequence_label_size      )
						__magic_name__							=    LlamaForSequenceClassification(__UpperCamelCase      )
						model.to(__UpperCamelCase      )
						model.eval()
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase  , labels=__UpperCamelCase      )
						self.assertEqual(result.logits.shape  , (self.model_tester.batch_size, self.model_tester.num_labels)      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.model_tester.prepare_config_and_inputs_for_common()
						__magic_name__							=    3
						__magic_name__							=    'multi_label_classification'
						__magic_name__							=    input_dict['input_ids']
						__magic_name__							=    input_ids.ne(1      ).to(__UpperCamelCase      )
						__magic_name__							=    ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]  , self.model_tester.type_sequence_label_size      ).to(torch.float      )
						__magic_name__							=    LlamaForSequenceClassification(__UpperCamelCase      )
						model.to(__UpperCamelCase      )
						model.eval()
						__magic_name__							=    model(__UpperCamelCase  , attention_mask=__UpperCamelCase  , labels=__UpperCamelCase      )
						self.assertEqual(result.logits.shape  , (self.model_tester.batch_size, self.model_tester.num_labels)      )







	@unittest.skip('LLaMA buffers include complex numbers, which breaks this test'      )
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]      ):

						'''simple docstring'''





						pass







	@parameterized.expand([('linear',), ('dynamic',)]      )
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: str      ):

						'''simple docstring'''





						__magic_name__,	__magic_name__							=    self.model_tester.prepare_config_and_inputs_for_common()
						__magic_name__							=    ids_tensor([1, 10]  , config.vocab_size      )
						__magic_name__							=    ids_tensor([1, int(config.max_position_embeddings * 1.5      )]  , config.vocab_size      )

						set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
						__magic_name__							=    LlamaModel(__UpperCamelCase      )
						original_model.to(__UpperCamelCase      )
						original_model.eval()
						__magic_name__							=    original_model(__UpperCamelCase      ).last_hidden_state
						__magic_name__							=    original_model(__UpperCamelCase      ).last_hidden_state

						set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
						__magic_name__							=    {'type': scaling_type, 'factor': 10.0}
						__magic_name__							=    LlamaModel(__UpperCamelCase      )
						scaled_model.to(__UpperCamelCase      )
						scaled_model.eval()
						__magic_name__							=    scaled_model(__UpperCamelCase      ).last_hidden_state
						__magic_name__							=    scaled_model(__UpperCamelCase      ).last_hidden_state

						# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
						# maximum sequence length, so the outputs for the short input should match.
						if scaling_type == "dynamic":
											self.assertTrue(torch.allclose(__UpperCamelCase  , __UpperCamelCase  , atol=1E-5      )      )
						else:
											self.assertFalse(torch.allclose(__UpperCamelCase  , __UpperCamelCase  , atol=1E-5      )      )

						# The output should be different for long inputs
						self.assertFalse(torch.allclose(__UpperCamelCase  , __UpperCamelCase  , atol=1E-5      )      )




@require_torch
class 				__UpperCamelCase				(					unittest.TestCase			):







	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						__magic_name__							=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
						__magic_name__							=    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'  , device_map='auto'      )
						__magic_name__							=    model(torch.tensor([input_ids]      )      )
						# Expected mean on dim = -1
						__magic_name__							=    torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]      )
						torch.testing.assert_close(out.mean(-1      )  , __UpperCamelCase  , atol=1E-2  , rtol=1E-2      )
						# slicing logits[0, 0, 0:30]
						# fmt: off
						__magic_name__							=    torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]      )
						# fmt: on
						torch.testing.assert_close(out[0, 0, :30]  , __UpperCamelCase  , atol=1E-5  , rtol=1E-5      )







	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[Any]      ):

						'''simple docstring'''





						__magic_name__							=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
						__magic_name__							=    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'  , device_map='auto'      )
						__magic_name__							=    model(torch.tensor(__UpperCamelCase      )      )
						# Expected mean on dim = -1
						__magic_name__							=    torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]      )
						torch.testing.assert_close(out.mean(-1      )  , __UpperCamelCase  , atol=1E-2  , rtol=1E-2      )
						# slicing logits[0, 0, 0:30]
						# fmt: off
						__magic_name__							=    torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]      )
						# fmt: on
						torch.testing.assert_close(out[0, 0, :30]  , __UpperCamelCase  , atol=1E-5  , rtol=1E-5      )







	@unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'      )
	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
						__magic_name__							=    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'  , device_map='auto'      )
						__magic_name__							=    model(torch.tensor(__UpperCamelCase      )      )
						# Expected mean on dim = -1
						__magic_name__							=    torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]      )
						torch.testing.assert_close(out.mean(-1      )  , __UpperCamelCase  , atol=1E-2  , rtol=1E-2      )
						# slicing logits[0, 0, 0:30]
						# fmt: off
						__magic_name__							=    torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]      )
						# fmt: on
						torch.testing.assert_close(out.mean(-1      )  , __UpperCamelCase  , atol=1E-2  , rtol=1E-2      )







	@unittest.skip(
	    'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'      )
	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						__magic_name__							=    [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
						__magic_name__							=    LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'  , device_map='auto'      )
						__magic_name__							=    model(torch.tensor(__UpperCamelCase      )      )

						__magic_name__							=    torch.tensor(
						    [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]  , dtype=torch.floataa      )
						torch.testing.assert_close(out.mean(-1      )  , __UpperCamelCase  , atol=1E-2  , rtol=1E-2      )
						# fmt: off
						__magic_name__							=    torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]      )
						# fmt: on
						torch.testing.assert_close(out[0, 0, :30]  , __UpperCamelCase  , atol=1E-5  , rtol=1E-5      )







	@unittest.skip('Model is curently gated'      )
	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						__magic_name__							=    'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
						__magic_name__							=    'Simply put, the theory of relativity states that '
						__magic_name__							=    LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'      )
						__magic_name__							=    tokenizer.encode(__UpperCamelCase  , return_tensors='pt'      )
						__magic_name__							=    LlamaForCausalLM.from_pretrained(
						    'meta-llama/Llama-2-13b-chat-hf'  , device_map='sequential'  , use_safetensors=__UpperCamelCase      )

						# greedy generation outputs
						__magic_name__							=    model.generate(__UpperCamelCase  , max_new_tokens=64  , top_p=__UpperCamelCase  , temperature=1  , do_sample=__UpperCamelCase      )
						__magic_name__							=    tokenizer.decode(generated_ids[0]  , skip_special_tokens=__UpperCamelCase      )
						self.assertEqual(__UpperCamelCase  , __UpperCamelCase      )







from __future__ import annotations




def 					_lowercase     (				a_				:					int				)				->	bool:




					'''simple docstring'''







					__magic_name__							=    str(a_				)
					return len(a_				) == 9 and set(a_				) == set('123456789'				)




def 					_lowercase     (				)				->	int | None:




					'''simple docstring'''







					for base_num in range(9_9_9_9					,4_9_9_9					,-1				):
										__magic_name__							=    1_0_0_0_0_2 * base_num
										if is_9_pandigital(a_				):
															return candidate

					for base_num in range(3_3_3					,9_9					,-1				):
										__magic_name__							=    1_0_0_2_0_0_3 * base_num
										if is_9_pandigital(a_				):
															return candidate

					return None


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




