



import unittest

from parameterized import parameterized

from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, 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 OpenLlamaForCausalLM, OpenLlamaForSequenceClassification, OpenLlamaModel






class 						__a :



	def __init__(						self							:    str				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[str]=13				,	snake_case_							:    Tuple=7				,	snake_case_							:    Optional[int]=True				,	snake_case_							:    List[str]=True				,	snake_case_							:    List[str]=False				,	snake_case_							:    List[Any]=True				,	snake_case_							:    Union[str, Any]=99				,	snake_case_							:    Union[str, Any]=32				,	snake_case_							:    str=5				,	snake_case_							:    str=4				,	snake_case_							:    Union[str, Any]=37				,	snake_case_							:    List[str]="gelu"				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    Union[str, Any]=0.1				,	snake_case_							:    Any=5_12				,	snake_case_							:    Union[str, Any]=16				,	snake_case_							:    str=2				,	snake_case_							:    Optional[Any]=0.0_2				,	snake_case_							:    List[str]=3				,	snake_case_							:    Union[str, Any]=4				,	snake_case_							:    Tuple=None				,	)->							Union[str, Any]:
						__lowerCAmelCase	   =parent
						__lowerCAmelCase	   =batch_size
						__lowerCAmelCase	   =seq_length
						__lowerCAmelCase	   =is_training
						__lowerCAmelCase	   =use_input_mask
						__lowerCAmelCase	   =use_token_type_ids
						__lowerCAmelCase	   =use_labels
						__lowerCAmelCase	   =vocab_size
						__lowerCAmelCase	   =hidden_size
						__lowerCAmelCase	   =num_hidden_layers
						__lowerCAmelCase	   =num_attention_heads
						__lowerCAmelCase	   =intermediate_size
						__lowerCAmelCase	   =hidden_act
						__lowerCAmelCase	   =hidden_dropout_prob
						__lowerCAmelCase	   =attention_probs_dropout_prob
						__lowerCAmelCase	   =max_position_embeddings
						__lowerCAmelCase	   =type_vocab_size
						__lowerCAmelCase	   =type_sequence_label_size
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =num_labels
						__lowerCAmelCase	   =num_choices
						__lowerCAmelCase	   =scope



	def 		UpperCamelCase							(						self							:    Tuple)->							Optional[int]:
						__lowerCAmelCase	   =ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)

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

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

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

						__lowerCAmelCase	   =self.get_config()

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



	def 		UpperCamelCase							(						self							:    Any)->							List[str]:
						return OpenLlamaConfig(
						    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=snake_case_				,	initializer_range=self.initializer_range				,	use_stable_embedding=snake_case_				,	)



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Dict)->							Union[str, Any]:
						__lowerCAmelCase	   =OpenLlamaModel(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_)
						__lowerCAmelCase	   =model(snake_case_)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    int				,	snake_case_							:    List[str]				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Optional[int]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    int				,	)->							Optional[Any]:
						__lowerCAmelCase	   =True
						__lowerCAmelCase	   =OpenLlamaModel(snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	encoder_hidden_states=snake_case_				,	encoder_attention_mask=snake_case_				,	)
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	encoder_hidden_states=snake_case_				,	)
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    int				,	snake_case_							:    int				,	snake_case_							:    List[Any]				,	snake_case_							:    Tuple				,	)->							Any:
						__lowerCAmelCase	   =OpenLlamaForCausalLM(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.seq_length, self.vocab_size))



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	snake_case_							:    List[str]				,	)->							int:
						__lowerCAmelCase	   =True
						__lowerCAmelCase	   =True
						__lowerCAmelCase	   =OpenLlamaForCausalLM(config=snake_case_)
						model.to(snake_case_)
						model.eval()

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

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

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

						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	encoder_hidden_states=snake_case_				,	encoder_attention_mask=snake_case_				,	output_hidden_states=snake_case_				,	)["""hidden_states"""][0]
						__lowerCAmelCase	   =model(
						    snake_case_				,	attention_mask=snake_case_				,	encoder_hidden_states=snake_case_				,	encoder_attention_mask=snake_case_				,	past_key_values=snake_case_				,	output_hidden_states=snake_case_				,	)["""hidden_states"""][0]

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



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							List[str]:
						__lowerCAmelCase	   =self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	
						)	   =config_and_inputs
						__lowerCAmelCase	   ={"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict






@require_torch
class 						__a (     SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       (
	    (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
	)
	SCREAMING_SNAKE_CASE       =       (OpenLlamaForCausalLM,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE       =       (
	    {
	        "feature-extraction": OpenLlamaModel,
	        "text-classification": OpenLlamaForSequenceClassification,
	        "text-generation": OpenLlamaForCausalLM,
	        "zero-shot": OpenLlamaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE       =       False
	SCREAMING_SNAKE_CASE       =       False



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							int:
						__lowerCAmelCase	   =OpenLlamaModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=snake_case_				,	hidden_size=37)



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



	def 		UpperCamelCase							(						self							:    int)->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case_)



	def 		UpperCamelCase							(						self							:    Optional[int])->							Any:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__lowerCAmelCase	   =type
											self.model_tester.create_and_check_model(*snake_case_)



	def 		UpperCamelCase							(						self							:    Optional[Any])->							Any:
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase	   =3
						__lowerCAmelCase	   =input_dict["""input_ids"""]
						__lowerCAmelCase	   =input_ids.ne(1).to(snake_case_)
						__lowerCAmelCase	   =ids_tensor([self.model_tester.batch_size]				,	self.model_tester.type_sequence_label_size)
						__lowerCAmelCase	   =OpenLlamaForSequenceClassification(snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	labels=snake_case_)
						self.assertEqual(result.logits.shape				,	(self.model_tester.batch_size, self.model_tester.num_labels))



	def 		UpperCamelCase							(						self							:    Any)->							List[str]:
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase	   =3
						__lowerCAmelCase	   ="""single_label_classification"""
						__lowerCAmelCase	   =input_dict["""input_ids"""]
						__lowerCAmelCase	   =input_ids.ne(1).to(snake_case_)
						__lowerCAmelCase	   =ids_tensor([self.model_tester.batch_size]				,	self.model_tester.type_sequence_label_size)
						__lowerCAmelCase	   =OpenLlamaForSequenceClassification(snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	labels=snake_case_)
						self.assertEqual(result.logits.shape				,	(self.model_tester.batch_size, self.model_tester.num_labels))



	def 		UpperCamelCase							(						self							:    int)->							List[str]:
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase	   =3
						__lowerCAmelCase	   ="""multi_label_classification"""
						__lowerCAmelCase	   =input_dict["""input_ids"""]
						__lowerCAmelCase	   =input_ids.ne(1).to(snake_case_)
						__lowerCAmelCase	   =ids_tensor(
						    [self.model_tester.batch_size, config.num_labels]				,	self.model_tester.type_sequence_label_size).to(torch.float)
						__lowerCAmelCase	   =OpenLlamaForSequenceClassification(snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	attention_mask=snake_case_				,	labels=snake_case_)
						self.assertEqual(result.logits.shape				,	(self.model_tester.batch_size, self.model_tester.num_labels))



	@unittest.skip("""Open-Llama buffers include complex numbers, which breaks this test""")
	def 		UpperCamelCase							(						self							:    str)->							int:
						pass



	@parameterized.expand([("""linear""",), ("""dynamic""",)])
	def 		UpperCamelCase							(						self							:    Dict				,	snake_case_							:    List[str])->							Tuple:
						__lowerCAmelCase   ,	__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs_for_common()
						__lowerCAmelCase	   =ids_tensor([1, 10]				,	config.vocab_size)
						__lowerCAmelCase	   =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
						__lowerCAmelCase	   =OpenLlamaModel(snake_case_)
						original_model.to(snake_case_)
						original_model.eval()
						__lowerCAmelCase	   =original_model(snake_case_).last_hidden_state
						__lowerCAmelCase	   =original_model(snake_case_).last_hidden_state

						set_seed(42)  # Fixed seed at init time so the two models get the same random weights
						__lowerCAmelCase	   ={"""type""": scaling_type, """factor""": 1_0.0}
						__lowerCAmelCase	   =OpenLlamaModel(snake_case_)
						scaled_model.to(snake_case_)
						scaled_model.eval()
						__lowerCAmelCase	   =scaled_model(snake_case_).last_hidden_state
						__lowerCAmelCase	   =scaled_model(snake_case_).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(snake_case_				,	snake_case_				,	atol=1e-5))
						else:
											self.assertFalse(torch.allclose(snake_case_				,	snake_case_				,	atol=1e-5))

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









def 					__lowerCAmelCase		(  __lowerCamelCase	:    List[Any]	)  ->						Any:
					__lowerCAmelCase	   =[]
					__lowerCAmelCase	   =set({"""(""", """[""", """{"""}	)
					__lowerCAmelCase	   =set({""")""", """]""", """}"""}	)
					__lowerCAmelCase	   ={"""{""": """}""", """[""": """]""", """(""": """)"""}

					for i in range(len(__lowerCamelCase	)	):
										if s[i] in open_brackets:
															stack.append(s[i]	)

										elif s[i] in closed_brackets and (
										    len(__lowerCamelCase	) == 0 or (len(__lowerCamelCase	) > 0 and open_to_closed[stack.pop()] != s[i])
										):
															return False

					return len(__lowerCamelCase	) == 0






def 					__lowerCAmelCase		(  )  ->						List[str]:
					__lowerCAmelCase	   =input("""Enter sequence of brackets: """	)
					if is_balanced(__lowerCamelCase	):
										print(__lowerCamelCase				,   """is balanced"""	)
					else:
										print(__lowerCamelCase				,   """is not balanced"""	)


if __name__ == "__main__":
				main()




