



'''simple docstring'''




from bisect import bisect
from itertools import accumulate



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase			,       _lowercase	)   ->    str:
							UpperCAmelCase  :	int							  =	sorted(zip(_lowercase			,       _lowercase	)			,       key=lambda _lowercase	: x[0] / x[1]			,       reverse=_lowercase	)
							UpperCAmelCase  ,  UpperCAmelCase  :	Tuple							  =	[i[0] for i in r], [i[1] for i in r]
							UpperCAmelCase  :	Any							  =	list(accumulate(_lowercase	)	)
							UpperCAmelCase  :	int							  =	bisect(_lowercase			,       _lowercase	)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]	) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]	)
							)


if __name__ == "__main__":
							import doctest

							doctest.testmod()






'''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     UpperCamelCase_				:





	def __init__(						self	,	A	,	A=13	,	A=7	,	A=True	,	A=True	,	A=False	,	A=True	,	A=99	,	A=32	,	A=5	,	A=4	,	A=37	,	A="gelu"	,	A=0.1	,	A=0.1	,	A=512	,	A=16	,	A=2	,	A=0.0_2	,	A=3	,	A=4	,	A=None	,	)    ->    Any:
								UpperCAmelCase  :	Optional[int]							  =	parent
								UpperCAmelCase  :	List[Any]							  =	batch_size
								UpperCAmelCase  :	Union[str, Any]							  =	seq_length
								UpperCAmelCase  :	Optional[int]							  =	is_training
								UpperCAmelCase  :	str							  =	use_input_mask
								UpperCAmelCase  :	Optional[int]							  =	use_token_type_ids
								UpperCAmelCase  :	Dict							  =	use_labels
								UpperCAmelCase  :	str							  =	vocab_size
								UpperCAmelCase  :	Optional[int]							  =	hidden_size
								UpperCAmelCase  :	str							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	num_attention_heads
								UpperCAmelCase  :	Union[str, Any]							  =	intermediate_size
								UpperCAmelCase  :	str							  =	hidden_act
								UpperCAmelCase  :	Any							  =	hidden_dropout_prob
								UpperCAmelCase  :	str							  =	attention_probs_dropout_prob
								UpperCAmelCase  :	Tuple							  =	max_position_embeddings
								UpperCAmelCase  :	Optional[Any]							  =	type_vocab_size
								UpperCAmelCase  :	Optional[Any]							  =	type_sequence_label_size
								UpperCAmelCase  :	str							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	num_labels
								UpperCAmelCase  :	Dict							  =	num_choices
								UpperCAmelCase  :	Tuple							  =	scope





	def 				_lowercase(						self					)    ->    Dict:
								UpperCAmelCase  :	Dict							  =	ids_tensor([self.batch_size, self.seq_length]	,	self.vocab_size					)

								UpperCAmelCase  :	Optional[Any]							  =	None
								if self.use_input_mask:
															UpperCAmelCase  :	Optional[int]							  =	random_attention_mask([self.batch_size, self.seq_length]					)

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

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

								UpperCAmelCase  :	int							  =	self.get_config()

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





	def 				_lowercase(						self					)    ->    Dict:
								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	,	A	,	A	,	A	,	A	,	A	,	A	,	A					)    ->    str:
								UpperCAmelCase  :	Union[str, Any]							  =	OpenLlamaModel(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Dict							  =	model(A	,	attention_mask=A					)
								UpperCAmelCase  :	Optional[int]							  =	model(A					)
								self.parent.assertEqual(result.last_hidden_state.shape	,	(self.batch_size, self.seq_length, self.hidden_size)					)





	def 				_lowercase(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	)    ->    List[Any]:
								UpperCAmelCase  :	Optional[int]							  =	True
								UpperCAmelCase  :	Union[str, Any]							  =	OpenLlamaModel(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	List[Any]							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	)
								UpperCAmelCase  :	str							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	)
								UpperCAmelCase  :	List[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	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	)    ->    int:
								UpperCAmelCase  :	Optional[int]							  =	OpenLlamaForCausalLM(config=A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Tuple							  =	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	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	A	,	)    ->    int:
								UpperCAmelCase  :	Dict							  =	True
								UpperCAmelCase  :	Tuple							  =	True
								UpperCAmelCase  :	str							  =	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  :	str							  =	outputs.past_key_values

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

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

								UpperCAmelCase  :	List[Any]							  =	model(
								    A	,	attention_mask=A	,	encoder_hidden_states=A	,	encoder_attention_mask=A	,	output_hidden_states=A	,	)["""hidden_states"""][0]
								UpperCAmelCase  :	Optional[Any]							  =	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  :	str							  =	ids_tensor((1,)	,	output_from_past.shape[-1]					).item()
								UpperCAmelCase  :	Any							  =	output_from_no_past[:, -3:, random_slice_idx].detach()
								UpperCAmelCase  :	Dict							  =	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					)    ->    Union[str, Any]:
								UpperCAmelCase  :	Union[str, Any]							  =	self.prepare_config_and_inputs()
								(
								    (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  (
								    UpperCAmelCase
								)  ,  
								)  :	Tuple							  =	config_and_inputs
								UpperCAmelCase  :	str							  =	{"""input_ids""": input_ids, """attention_mask""": input_mask}
								return config, inputs_dict




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





	def 				_lowercase(						self					)    ->    Tuple:
								UpperCAmelCase  :	Dict							  =	OpenLlamaModelTester(self					)
								UpperCAmelCase  :	List[str]							  =	ConfigTester(self	,	config_class=A	,	hidden_size=37					)





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





	def 				_lowercase(						self					)    ->    int:
								UpperCAmelCase  :	Tuple							  =	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs()
								for type in ["absolute", "relative_key", "relative_key_query"]:
															UpperCAmelCase  :	int							  =	type
															self.model_tester.create_and_check_model(*A					)





	def 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  ,  UpperCAmelCase  :	List[str]							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	List[str]							  =	3
								UpperCAmelCase  :	Optional[Any]							  =	input_dict["""input_ids"""]
								UpperCAmelCase  :	str							  =	input_ids.ne(1					).to(A					)
								UpperCAmelCase  :	Tuple							  =	ids_tensor([self.model_tester.batch_size]	,	self.model_tester.type_sequence_label_size					)
								UpperCAmelCase  :	Optional[Any]							  =	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					)    ->    Union[str, Any]:
								UpperCAmelCase  ,  UpperCAmelCase  :	Union[str, Any]							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	Dict							  =	3
								UpperCAmelCase  :	Any							  =	"""single_label_classification"""
								UpperCAmelCase  :	Dict							  =	input_dict["""input_ids"""]
								UpperCAmelCase  :	Optional[Any]							  =	input_ids.ne(1					).to(A					)
								UpperCAmelCase  :	str							  =	ids_tensor([self.model_tester.batch_size]	,	self.model_tester.type_sequence_label_size					)
								UpperCAmelCase  :	Optional[Any]							  =	OpenLlamaForSequenceClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Tuple							  =	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					)    ->    int:
								UpperCAmelCase  ,  UpperCAmelCase  :	Dict							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	Tuple							  =	3
								UpperCAmelCase  :	Optional[Any]							  =	"""multi_label_classification"""
								UpperCAmelCase  :	Dict							  =	input_dict["""input_ids"""]
								UpperCAmelCase  :	int							  =	input_ids.ne(1					).to(A					)
								UpperCAmelCase  :	int							  =	ids_tensor(
								    [self.model_tester.batch_size, config.num_labels]	,	self.model_tester.type_sequence_label_size					).to(torch.float					)
								UpperCAmelCase  :	Any							  =	OpenLlamaForSequenceClassification(A					)
								model.to(A					)
								model.eval()
								UpperCAmelCase  :	Dict							  =	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					)    ->    Dict:
								pass






	@parameterized.expand([("""linear""",), ("""dynamic""",)]					)
	def 				_lowercase(						self	,	A					)    ->    str:
								UpperCAmelCase  ,  UpperCAmelCase  :	Any							  =	self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase  :	Dict							  =	ids_tensor([1, 10]	,	config.vocab_size					)
								UpperCAmelCase  :	List[str]							  =	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  :	Any							  =	OpenLlamaModel(A					)
								original_model.to(A					)
								original_model.eval()
								UpperCAmelCase  :	List[str]							  =	original_model(A					).last_hidden_state
								UpperCAmelCase  :	List[Any]							  =	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""": 1_0.0}
								UpperCAmelCase  :	str							  =	OpenLlamaModel(A					)
								scaled_model.to(A					)
								scaled_model.eval()
								UpperCAmelCase  :	List[str]							  =	scaled_model(A					).last_hidden_state
								UpperCAmelCase  :	Optional[int]							  =	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					)					)

