


'''simple docstring'''
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 			lowerCamelCase_   :






      def __init__(					self				:    Optional[Any]						,						_A				:    Union[str, Any]						,						_A				:    List[Any]=13						,						_A				:    str=7						,						_A				:    Optional[Any]=True						,						_A				:    Dict=True						,						_A				:    List[Any]=False						,						_A				:    Dict=True						,						_A				:    Optional[int]=99						,						_A				:    List[str]=32						,						_A				:    int=5						,						_A				:    str=4						,						_A				:    Dict=37						,						_A				:    Tuple="gelu"						,						_A				:    Union[str, Any]=0.1						,						_A				:    Any=0.1						,						_A				:    Dict=512						,						_A				:    Optional[int]=16						,						_A				:    str=2						,						_A				:    Union[str, Any]=0.0_2						,						_A				:    List[str]=3						,						_A				:    int=4						,						_A				:    Dict=None						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		int													=				parent
          UpperCAmelCase__			:		Optional[int]													=				batch_size
          UpperCAmelCase__			:		List[Any]													=				seq_length
          UpperCAmelCase__			:		int													=				is_training
          UpperCAmelCase__			:		Dict													=				use_input_mask
          UpperCAmelCase__			:		str													=				use_token_type_ids
          UpperCAmelCase__			:		str													=				use_labels
          UpperCAmelCase__			:		int													=				vocab_size
          UpperCAmelCase__			:		Union[str, Any]													=				hidden_size
          UpperCAmelCase__			:		List[Any]													=				num_hidden_layers
          UpperCAmelCase__			:		Optional[int]													=				num_attention_heads
          UpperCAmelCase__			:		Optional[int]													=				intermediate_size
          UpperCAmelCase__			:		Optional[int]													=				hidden_act
          UpperCAmelCase__			:		Optional[Any]													=				hidden_dropout_prob
          UpperCAmelCase__			:		Any													=				attention_probs_dropout_prob
          UpperCAmelCase__			:		Optional[Any]													=				max_position_embeddings
          UpperCAmelCase__			:		Union[str, Any]													=				type_vocab_size
          UpperCAmelCase__			:		int													=				type_sequence_label_size
          UpperCAmelCase__			:		Optional[int]													=				initializer_range
          UpperCAmelCase__			:		Tuple													=				num_labels
          UpperCAmelCase__			:		List[Any]													=				num_choices
          UpperCAmelCase__			:		List[Any]													=				scope






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size )

          UpperCAmelCase__			:		Union[str, Any]													=				None
          if self.use_input_mask:
              UpperCAmelCase__			:		int													=				random_attention_mask([self.batch_size, self.seq_length] )

          UpperCAmelCase__			:		Tuple													=				None
          if self.use_token_type_ids:
              UpperCAmelCase__			:		Union[str, Any]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.type_vocab_size )

          UpperCAmelCase__			:		Optional[Any]													=				None
          UpperCAmelCase__			:		Optional[int]													=				None
          UpperCAmelCase__			:		Union[str, Any]													=				None
          if self.use_labels:
              UpperCAmelCase__			:		str													=				ids_tensor([self.batch_size]						,						self.type_sequence_label_size )
              UpperCAmelCase__			:		Optional[int]													=				ids_tensor([self.batch_size, self.seq_length]						,						self.num_labels )
              UpperCAmelCase__			:		Optional[int]													=				ids_tensor([self.batch_size]						,						self.num_choices )

          UpperCAmelCase__			:		List[Any]													=				self.get_config()

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






      def lowercase_							(					self				:    Optional[int] ):


          '''simple docstring'''


          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=__a						,						initializer_range=self.initializer_range						,						use_stable_embedding=__a						,						)






      def lowercase_							(					self				:    str						,						_A				:    Dict						,						_A				:    Optional[Any]						,						_A				:    Optional[int]						,						_A				:    Dict						,						_A				:    int						,						_A				:    str						,						_A				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				OpenLlamaModel(config=__a )
          model.to(__a )
          model.eval()
          UpperCAmelCase__			:		Union[str, Any]													=				model(__a						,						attention_mask=__a )
          UpperCAmelCase__			:		List[Any]													=				model(__a )
          self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size) )






      def lowercase_							(					self				:    Dict						,						_A				:    Dict						,						_A				:    str						,						_A				:    Tuple						,						_A				:    str						,						_A				:    Dict						,						_A				:    List[str]						,						_A				:    List[str]						,						_A				:    Optional[Any]						,						_A				:    Tuple						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[int]													=				True
          UpperCAmelCase__			:		str													=				OpenLlamaModel(__a )
          model.to(__a )
          model.eval()
          UpperCAmelCase__			:		Any													=				model(
              __a						,						attention_mask=__a						,						encoder_hidden_states=__a						,						encoder_attention_mask=__a						,						)
          UpperCAmelCase__			:		Any													=				model(
              __a						,						attention_mask=__a						,						encoder_hidden_states=__a						,						)
          UpperCAmelCase__			:		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				:    List[Any]						,						_A				:    List[Any]						,						_A				:    Optional[int]						,						_A				:    Optional[Any]						,						_A				:    Optional[int]						,						_A				:    Optional[Any]						,						_A				:    Tuple						,						_A				:    Union[str, Any]						,						_A				:    List[str]						,						_A				:    Tuple						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				OpenLlamaForCausalLM(config=__a )
          model.to(__a )
          model.eval()
          UpperCAmelCase__			:		Optional[Any]													=				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				:    List[Any]						,						_A				:    Optional[Any]						,						_A				:    List[str]						,						_A				:    Optional[Any]						,						_A				:    Union[str, Any]						,						_A				:    Union[str, Any]						,						_A				:    int						,						_A				:    Tuple						,						_A				:    Tuple						,						_A				:    Dict						,						):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				True
          UpperCAmelCase__			:		Dict													=				True
          UpperCAmelCase__			:		List[Any]													=				OpenLlamaForCausalLM(config=__a )
          model.to(__a )
          model.eval()

          # first forward pass
          UpperCAmelCase__			:		Union[str, Any]													=				model(
              __a						,						attention_mask=__a						,						encoder_hidden_states=__a						,						encoder_attention_mask=__a						,						use_cache=__a						,						)
          UpperCAmelCase__			:		int													=				outputs.past_key_values

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

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

          UpperCAmelCase__			:		int													=				model(
              __a						,						attention_mask=__a						,						encoder_hidden_states=__a						,						encoder_attention_mask=__a						,						output_hidden_states=__a						,						)['''hidden_states'''][0]
          UpperCAmelCase__			:		List[str]													=				model(
              __a						,						attention_mask=__a						,						encoder_hidden_states=__a						,						encoder_attention_mask=__a						,						past_key_values=__a						,						output_hidden_states=__a						,						)['''hidden_states'''][0]

          # select random slice
          UpperCAmelCase__			:		List[Any]													=				ids_tensor((1,)						,						output_from_past.shape[-1] ).item()
          UpperCAmelCase__			:		Dict													=				output_from_no_past[:, -3:, random_slice_idx].detach()
          UpperCAmelCase__			:		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				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Optional[Any]													=				self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase__	
          )						,						(
              UpperCAmelCase__	
          )						,						(
              UpperCAmelCase__	
          )						,						(
              UpperCAmelCase__	
          )						,						(
              UpperCAmelCase__	
          )						,						(
              UpperCAmelCase__	
          )						,						(
              UpperCAmelCase__	
          )						,							
          )			:		Tuple													=				config_and_inputs
          UpperCAmelCase__			:		Any													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
          return config, inputs_dict




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






      def lowercase_							(					self				:    List[str] ):


          '''simple docstring'''


          UpperCAmelCase__			:		Union[str, Any]													=				OpenLlamaModelTester(self )
          UpperCAmelCase__			:		int													=				ConfigTester(self						,						config_class=__a						,						hidden_size=37 )






      def lowercase_							(					self				:    str ):


          '''simple docstring'''


          self.config_tester.run_common_tests()






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__			:		str													=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__a )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__			:		Dict													=				self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase__			:		Any													=				type
              self.model_tester.create_and_check_model(*__a )






      def lowercase_							(					self				:    Optional[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Dict													=				self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__			:		Tuple													=				3
          UpperCAmelCase__			:		Union[str, Any]													=				input_dict['''input_ids''']
          UpperCAmelCase__			:		Optional[int]													=				input_ids.ne(1 ).to(__a )
          UpperCAmelCase__			:		List[str]													=				ids_tensor([self.model_tester.batch_size]						,						self.model_tester.type_sequence_label_size )
          UpperCAmelCase__			:		int													=				OpenLlamaForSequenceClassification(__a )
          model.to(__a )
          model.eval()
          UpperCAmelCase__			:		int													=				model(__a						,						attention_mask=__a						,						labels=__a )
          self.assertEqual(result.logits.shape						,						(self.model_tester.batch_size, self.model_tester.num_labels) )






      def lowercase_							(					self				:    List[Any] ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Any													=				self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__			:		int													=				3
          UpperCAmelCase__			:		List[str]													=				'''single_label_classification'''
          UpperCAmelCase__			:		int													=				input_dict['''input_ids''']
          UpperCAmelCase__			:		List[str]													=				input_ids.ne(1 ).to(__a )
          UpperCAmelCase__			:		Union[str, Any]													=				ids_tensor([self.model_tester.batch_size]						,						self.model_tester.type_sequence_label_size )
          UpperCAmelCase__			:		List[str]													=				OpenLlamaForSequenceClassification(__a )
          model.to(__a )
          model.eval()
          UpperCAmelCase__			:		List[Any]													=				model(__a						,						attention_mask=__a						,						labels=__a )
          self.assertEqual(result.logits.shape						,						(self.model_tester.batch_size, self.model_tester.num_labels) )






      def lowercase_							(					self				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		List[str]													=				self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__			:		Optional[int]													=				3
          UpperCAmelCase__			:		Optional[int]													=				'''multi_label_classification'''
          UpperCAmelCase__			:		Dict													=				input_dict['''input_ids''']
          UpperCAmelCase__			:		int													=				input_ids.ne(1 ).to(__a )
          UpperCAmelCase__			:		Tuple													=				ids_tensor(
              [self.model_tester.batch_size, config.num_labels]						,						self.model_tester.type_sequence_label_size ).to(torch.float )
          UpperCAmelCase__			:		Union[str, Any]													=				OpenLlamaForSequenceClassification(__a )
          model.to(__a )
          model.eval()
          UpperCAmelCase__			:		Optional[Any]													=				model(__a						,						attention_mask=__a						,						labels=__a )
          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 lowercase_							(					self				:    Any ):


          '''simple docstring'''


          pass






      @parameterized.expand([('''linear''',), ('''dynamic''',)] )
      def lowercase_							(					self				:    List[Any]						,						_A				:    Tuple ):


          '''simple docstring'''


          UpperCAmelCase__						,						UpperCAmelCase__			:		Any													=				self.model_tester.prepare_config_and_inputs_for_common()
          UpperCAmelCase__			:		Union[str, Any]													=				ids_tensor([1, 10]						,						config.vocab_size )
          UpperCAmelCase__			:		Union[str, Any]													=				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
          UpperCAmelCase__			:		Optional[Any]													=				OpenLlamaModel(__a )
          original_model.to(__a )
          original_model.eval()
          UpperCAmelCase__			:		Tuple													=				original_model(__a ).last_hidden_state
          UpperCAmelCase__			:		Optional[int]													=				original_model(__a ).last_hidden_state

          set_seed(42 )  # Fixed seed at init time so the two models get the same random weights
          UpperCAmelCase__			:		Union[str, Any]													=				{'''type''': scaling_type, '''factor''': 10.0}
          UpperCAmelCase__			:		Dict													=				OpenLlamaModel(__a )
          scaled_model.to(__a )
          scaled_model.eval()
          UpperCAmelCase__			:		Optional[Any]													=				scaled_model(__a ).last_hidden_state
          UpperCAmelCase__			:		List[Any]													=				scaled_model(__a ).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(__a						,						__a						,						atol=1e-5 ) )
          else:
              self.assertFalse(torch.allclose(__a						,						__a						,						atol=1e-5 ) )

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




"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase = 6008_5147_5143      ):

			'''simple docstring'''




			try:
						__lowerCAmelCase				    =			int(_UpperCamelCase      )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."      )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."      )
			__lowerCAmelCase				    =			2
			__lowerCAmelCase				    =			0
			if n == 2:
						return 2
			while n > 2:
						while n % i != 0:
									i += 1
						__lowerCAmelCase				    =			i
						while n % i == 0:
									__lowerCAmelCase				    =			n // i
						i += 1
			return int(_UpperCamelCase      )


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




