

import math




def 			UpperCAmelCase__				(			_A      : Tuple	,      _A      : Any							):





  '''simple docstring'''







  if 0 not in (x, y):
    # We use the relation x^y = y*log10(x), where 10 is the base.
    return y * math.logaa(_A							)
  else:
    if x == 0:  # 0 raised to any number is 0
      return 0
    elif y == 0:
      return 1  # any number raised to 0 is 1
  raise AssertionError('''This should never happen'''							)


if __name__ == "__main__":  # Main function
       # Read two numbers from input and typecast them to int using map function.
       # Here x is the base and y is the power.
       lowerCamelCase           =					'''Enter the base and the power separated by a comma: '''
       lowerCamelCase     ,       lowerCamelCase           =					map(int, input(prompt).split(''','''))
       lowerCamelCase     ,       lowerCamelCase           =					map(int, input(prompt).split(''','''))

       # We find the log of each number, using the function res(), which takes two
       # arguments.
       lowerCamelCase           =					res(xa, ya)
       lowerCamelCase           =					res(xa, ya)

       # We check for the largest number
       if resa > resa:
              print('''Largest number is''', xa, '''^''', ya)
       elif resa > resa:
              print('''Largest number is''', xa, '''^''', ya)
       else:
              print('''Both are equal''')



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 						__magic_name__			:

   '''simple docstring'''




   def __init__(				self,    lowercase_,    lowercase_=13,    lowercase_=7,    lowercase_=True,    lowercase_=True,    lowercase_=False,    lowercase_=True,    lowercase_=99,    lowercase_=32,    lowercase_=5,    lowercase_=4,    lowercase_=37,    lowercase_="gelu",    lowercase_=0.1,    lowercase_=0.1,    lowercase_=512,    lowercase_=16,    lowercase_=2,    lowercase_=0.02,    lowercase_=3,    lowercase_=4,    lowercase_=None,    )							->    List[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






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

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

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

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

     a__  =self.get_config()

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



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """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=lowercase_,    initializer_range=self.initializer_range,    use_stable_embedding=lowercase_,    )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_       )							->    List[str]:






     """simple docstring"""






     a__  =OpenLlamaModel(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    attention_mask=lowercase_       )
     a__  =model(lowercase_       )
     self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )							->    Any:






     """simple docstring"""






     a__  =True
     a__  =OpenLlamaModel(lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(
         lowercase_,    attention_mask=lowercase_,    encoder_hidden_states=lowercase_,    encoder_attention_mask=lowercase_,    )
     a__  =model(
         lowercase_,    attention_mask=lowercase_,    encoder_hidden_states=lowercase_,    )
     a__  =model(lowercase_,    attention_mask=lowercase_       )
     self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )							->    List[str]:






     """simple docstring"""






     a__  =OpenLlamaForCausalLM(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    attention_mask=lowercase_,    labels=lowercase_       )
     self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.vocab_size)       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    )							->    List[Any]:






     """simple docstring"""






     a__  =True
     a__  =True
     a__  =OpenLlamaForCausalLM(config=lowercase_       )
     model.to(lowercase_       )
     model.eval()

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

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

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

     a__  =model(
         lowercase_,    attention_mask=lowercase_,    encoder_hidden_states=lowercase_,    encoder_attention_mask=lowercase_,    output_hidden_states=lowercase_,    )['''hidden_states'''][0]
     a__  =model(
         lowercase_,    attention_mask=lowercase_,    encoder_hidden_states=lowercase_,    encoder_attention_mask=lowercase_,    past_key_values=lowercase_,    output_hidden_states=lowercase_,    )['''hidden_states'''][0]

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



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






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







@require_torch
class 						__magic_name__			(							lowerCamelCase__		,       lowerCamelCase__		,       lowerCamelCase__		,       unittest.TestCase							):

   '''simple docstring'''




   lowerCamelCase__ :	Optional[Any]    =   (
       (OpenLlamaModel, OpenLlamaForCausalLM, OpenLlamaForSequenceClassification) if is_torch_available() else ()
   )
   lowerCamelCase__ :	Optional[Any]    =   (OpenLlamaForCausalLM,) if is_torch_available() else ()
   lowerCamelCase__ :	List[str]    =   (
       {
           'feature-extraction': OpenLlamaModel,
           'text-classification': OpenLlamaForSequenceClassification,
           'text-generation': OpenLlamaForCausalLM,
           'zero-shot': OpenLlamaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase__ :	int    =   False
   lowerCamelCase__ :	Any    =   False



   def  _UpperCAmelCase (				self       )							->    List[str]:






     """simple docstring"""






     a__  =OpenLlamaModelTester(self       )
     a__  =ConfigTester(self,    config_class=lowercase_,    hidden_size=37       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






     self.config_tester.run_common_tests()



   def  _UpperCAmelCase (				self       )							->    Optional[Any]:






     """simple docstring"""






     a__  =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_       )



   def  _UpperCAmelCase (				self       )							->    str:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    int:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    Any:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self       )							->    Optional[int]:






     """simple docstring"""






     a__,      a__  =self.model_tester.prepare_config_and_inputs_for_common()
     a__  =3
     a__  ='''multi_label_classification'''
     a__  =input_dict['''input_ids''']
     a__  =input_ids.ne(1       ).to(lowercase_       )
     a__  =ids_tensor(
         [self.model_tester.batch_size, config.num_labels],    self.model_tester.type_sequence_label_size       ).to(torch.float       )
     a__  =OpenLlamaForSequenceClassification(lowercase_       )
     model.to(lowercase_       )
     model.eval()
     a__  =model(lowercase_,    attention_mask=lowercase_,    labels=lowercase_       )
     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       )							->    List[str]:






     """simple docstring"""






     pass



   @parameterized.expand([('''linear''',), ('''dynamic''',)]       )
   def  _UpperCAmelCase (				self,    lowercase_       )							->    Optional[Any]:






     """simple docstring"""






     a__,      a__  =self.model_tester.prepare_config_and_inputs_for_common()
     a__  =ids_tensor([1, 10],    config.vocab_size       )
     a__  =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
     a__  =OpenLlamaModel(lowercase_       )
     original_model.to(lowercase_       )
     original_model.eval()
     a__  =original_model(lowercase_       ).last_hidden_state
     a__  =original_model(lowercase_       ).last_hidden_state

     set_seed(42       )  # Fixed seed at init time so the two models get the same random weights
     a__  ={'''type''': scaling_type, '''factor''': 10.0}
     a__  =OpenLlamaModel(lowercase_       )
     scaled_model.to(lowercase_       )
     scaled_model.eval()
     a__  =scaled_model(lowercase_       ).last_hidden_state
     a__  =scaled_model(lowercase_       ).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(lowercase_,    lowercase_,    atol=1E-5       )       )
     else:
       self.assertFalse(torch.allclose(lowercase_,    lowercase_,    atol=1E-5       )       )

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