

"""simple docstring"""





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat




def   _A  (  )	->						tuple[list[int], int]:
   '''simple docstring'''


   __lowercase								=						[randint(-1000,			1000) for i in range(10)]
   __lowercase								=						randint(-5000,			5000)
   return (arr, r)


_a       =							make_dataset()




def   _A  (  UpperCamelCase_     :       list[int],			UpperCamelCase_     :       int)	->						tuple[int, ...]:
   '''simple docstring'''


   for triplet in permutations(UpperCamelCase_,			3):
      if sum(UpperCamelCase_) == target:
         return tuple(sorted(UpperCamelCase_))
   return (0, 0, 0)




def   _A  (  UpperCamelCase_     :       list[int],			UpperCamelCase_     :       int)	->						tuple[int, int, int]:
   '''simple docstring'''


   arr.sort()
   __lowercase								=						len(UpperCamelCase_)
   for i in range(n - 1):
      __lowercase		,__lowercase								=						i + 1, n - 1
      while left < right:
         if arr[i] + arr[left] + arr[right] == target:
            return (arr[i], arr[left], arr[right])
         elif arr[i] + arr[left] + arr[right] < target:
            left += 1
         elif arr[i] + arr[left] + arr[right] > target:
            right -= 1
   return (0, 0, 0)




def   _A  (  )	->						tuple[float, float]:
   '''simple docstring'''


   __lowercase								=						"\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
   __lowercase								=						"\ntriplet_sum1(*dataset)\n"
   __lowercase								=						"\ntriplet_sum2(*dataset)\n"
   __lowercase								=						repeat(setup=UpperCamelCase_,			stmt=UpperCamelCase_,			repeat=5,			number=10000)
   __lowercase								=						repeat(setup=UpperCamelCase_,			stmt=UpperCamelCase_,			repeat=5,			number=10000)
   return (min(UpperCamelCase_), min(UpperCamelCase_))


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   _a       =							solution_times()
   print(F"The time for naive implementation is {times[0]}.")
   print(F"The time for optimized implementation is {times[1]}.")







import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
						import torch



@is_pipeline_test
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			_UpperCAmelCase   :							str										=     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       TextaTextGenerationPipeline(model=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)
									return generator, ["Something to write", "Something else"]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there")
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ANY(lowerCAmelCase__)}])
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))

									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator(["This is great !", "Something else"]   ,						num_return_sequences=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									SCREAMING_SNAKE_CASE_: Dict  		=       generator(
									    ["This is great !", "Something else"]   ,						num_return_sequences=2   ,						batch_size=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

									with self.assertRaises(lowerCAmelCase__):
															generator(4)



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="pt")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       3
									SCREAMING_SNAKE_CASE_: Any  		=       generator(
									    "Something there"   ,						num_return_sequences=lowerCAmelCase__   ,						num_beams=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       [
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
									    {"generated_text": ""},
									]
									self.assertEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       generator("This is a test"   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						return_tensors=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        {"generated_token_ids": ANY(torch.Tensor)},
									        {"generated_token_ids": ANY(torch.Tensor)},
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: str  		=       generator.model.config.eos_token_id
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: Tuple  		=       generator(
									    ["This is a test", "This is a second test"]   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						batch_size=2   ,						return_tensors=lowerCAmelCase__   ,						)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									        [
									            {"generated_token_ids": ANY(torch.Tensor)},
									            {"generated_token_ids": ANY(torch.Tensor)},
									        ],
									    ]   ,						)




			@require_tf
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="tf")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])






