

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       snake_case_  :







			'''simple docstring'''

			def __init__(	self					:							Tuple				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							Any=1_3				,  _UpperCamelCase					:							int=7				,  _UpperCamelCase					:							List[Any]=True				,  _UpperCamelCase					:							int=True				,  _UpperCamelCase					:							int=False				,  _UpperCamelCase					:							List[str]=True				,  _UpperCamelCase					:							Any=9_9				,  _UpperCamelCase					:							Any=3_2				,  _UpperCamelCase					:							int=5				,  _UpperCamelCase					:							List[str]=4				,  _UpperCamelCase					:							Optional[Any]=3_7				,  _UpperCamelCase					:							Any="gelu"				,  _UpperCamelCase					:							Tuple=0.1				,  _UpperCamelCase					:							Tuple=0.1				,  _UpperCamelCase					:							Tuple=5_1_2				,  _UpperCamelCase					:							int=1_6				,  _UpperCamelCase					:							int=2				,  _UpperCamelCase					:							Dict=0.02				,  _UpperCamelCase					:							Dict=3				,  _UpperCamelCase					:							Any=4				,  _UpperCamelCase					:							Optional[int]=None				,  ) ->Optional[int]:
									snake_case_  =      parent
									snake_case_  =      batch_size
									snake_case_  =      seq_length
									snake_case_  =      is_training
									snake_case_  =      use_input_mask
									snake_case_  =      use_token_type_ids
									snake_case_  =      use_labels
									snake_case_  =      vocab_size
									snake_case_  =      hidden_size
									snake_case_  =      num_hidden_layers
									snake_case_  =      num_attention_heads
									snake_case_  =      intermediate_size
									snake_case_  =      hidden_act
									snake_case_  =      hidden_dropout_prob
									snake_case_  =      attention_probs_dropout_prob
									snake_case_  =      max_position_embeddings
									snake_case_  =      type_vocab_size
									snake_case_  =      type_sequence_label_size
									snake_case_  =      initializer_range
									snake_case_  =      num_labels
									snake_case_  =      num_choices
									snake_case_  =      scope







			def 							snake_case__(	self					:							Optional[int]				) ->Dict:
									snake_case_  =      ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size				)

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

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

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

									snake_case_  =      self.get_config()

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







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







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







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







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Optional[int]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Dict				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							Optional[Any]				,  ) ->Optional[int]:
									snake_case_  =      True
									snake_case_  =      True
									snake_case_  =      OpenLlamaForCausalLM(config=__A				)
									model.to(__A				)
									model.eval()

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

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

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

									snake_case_  =      model(
									    __A				,  attention_mask=__A				,  encoder_hidden_states=__A				,  encoder_attention_mask=__A				,  output_hidden_states=__A				,  )['''hidden_states'''][0]
									snake_case_  =      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
									snake_case_  =      ids_tensor((1,)				,  output_from_past.shape[-1]				).item()
									snake_case_  =      output_from_no_past[:, -3:, random_slice_idx].detach()
									snake_case_  =      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 							snake_case__(	self					:							List[str]				) ->List[Any]:
									snake_case_  =      self.prepare_config_and_inputs()
									(
									    snake_case_     
									)  =      config_and_inputs
									snake_case_  =      {'''input_ids''': input_ids, '''attention_mask''': input_mask}
									return config, inputs_dict






@require_torch
class       snake_case_  (						__A      ,			__A      ,			__A      ,			unittest.TestCase   ):







			'''simple docstring'''

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







			def 							snake_case__(	self					:							Optional[int]				) ->List[Any]:
									snake_case_  =      OpenLlamaModelTester(self				)
									snake_case_  =      ConfigTester(self				,  config_class=__A				,  hidden_size=3_7				)







			def 							snake_case__(	self					:							int				) ->Tuple:
									self.config_tester.run_common_tests()







			def 							snake_case__(	self					:							List[str]				) ->Tuple:
									snake_case_  =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__A				)







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







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







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







			def 							snake_case__(	self					:							Union[str, Any]				) ->Any:
									snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_  =      3
									snake_case_  =      '''multi_label_classification'''
									snake_case_  =      input_dict['''input_ids''']
									snake_case_  =      input_ids.ne(1				).to(__A				)
									snake_case_  =      ids_tensor(
									    [self.model_tester.batch_size, config.num_labels]				,  self.model_tester.type_sequence_label_size				).to(torch.float				)
									snake_case_  =      OpenLlamaForSequenceClassification(__A				)
									model.to(__A				)
									model.eval()
									snake_case_  =      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 							snake_case__(	self					:							List[Any]				) ->Dict:
									pass







			@parameterized.expand([('''linear''',), ('''dynamic''',)]				)
			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]				) ->str:
									snake_case_  =      self.model_tester.prepare_config_and_inputs_for_common()
									snake_case_  =      ids_tensor([1, 1_0]				,  config.vocab_size				)
									snake_case_  =      ids_tensor([1, int(config.max_position_embeddings * 1.5				)]				,  config.vocab_size				)

									set_seed(4_2				)  # Fixed seed at init time so the two models get the same random weights
									snake_case_  =      OpenLlamaModel(__A				)
									original_model.to(__A				)
									original_model.eval()
									snake_case_  =      original_model(__A				).last_hidden_state
									snake_case_  =      original_model(__A				).last_hidden_state

									set_seed(4_2				)  # Fixed seed at init time so the two models get the same random weights
									snake_case_  =      {'''type''': scaling_type, '''factor''': 10.0}
									snake_case_  =      OpenLlamaModel(__A				)
									scaled_model.to(__A				)
									scaled_model.eval()
									snake_case_  =      scaled_model(__A				).last_hidden_state
									snake_case_  =      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				)				)

from math import isqrt, loga
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						list[int]:
       SCREAMING_SNAKE_CASE_							:				int		=[True] * max_number
       for i in range(2			,							isqrt(max_number - 1				) + 1				):
              if is_prime[i]:
                     for j in range(i**2			,							UpperCAmelCase_			,							UpperCAmelCase_				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=False

       return [i for i in range(2			,							UpperCAmelCase_				) if is_prime[i]]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8_0_0_8_0_0			,							UpperCAmelCase_	:     int = 8_0_0_8_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				str		=degree * loga(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=int(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				int		=calculate_prime_numbers(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				int		=0
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0
       SCREAMING_SNAKE_CASE_							:				Any		=len(UpperCAmelCase_				) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]				)
                  + prime_numbers[left] * loga(prime_numbers[right]				)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


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