




"""simple docstring"""




from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowerCAmelCase							:			Union[str, Any]     							=							10



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					int:
			for i in range(snake_case__       , snake_case__				):
						if array[i] == target:
									return i
			return -1



def     a__ (				snake_case__       , snake_case__				)						->					int:
			lowerCamelCase			      =					0
			lowerCamelCase			      =					len(snake_case__				)
			while left <= right:
						if right - left < precision:
									return lin_search(snake_case__       , snake_case__       , snake_case__       , snake_case__				)

						lowerCamelCase			      =					(left + right) // 3 + 1
						lowerCamelCase			      =					2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									lowerCamelCase			      =					one_third - 1
						elif array[two_third] < target:
									lowerCamelCase			      =					two_third + 1

						else:
									lowerCamelCase			      =					one_third + 1
									lowerCamelCase			      =					two_third - 1
			else:
						return -1



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					int:
			if left < right:
						if right - left < precision:
									return lin_search(snake_case__       , snake_case__       , snake_case__       , snake_case__				)
						lowerCamelCase			      =					(left + right) // 3 + 1
						lowerCamelCase			      =					2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									return rec_ternary_search(snake_case__       , one_third - 1       , snake_case__       , snake_case__				)
						elif array[two_third] < target:
									return rec_ternary_search(two_third + 1       , snake_case__       , snake_case__       , snake_case__				)
						else:
									return rec_ternary_search(one_third + 1       , two_third - 1       , snake_case__       , snake_case__				)
			else:
						return -1


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					lowerCAmelCase							:			List[Any]     							=							input("""Enter numbers separated by comma:\n""").strip()
					lowerCAmelCase							:			Dict     							=							[int(item.strip()) for item in user_input.split(""",""")]
					assert collection == sorted(collection), F"List must be ordered.\n{collection}."
					lowerCAmelCase							:			List[str]     							=							int(input("""Enter the number to be found in the list:\n""").strip())
					lowerCAmelCase							:			Tuple     							=							ite_ternary_search(collection, target)
					lowerCAmelCase							:			str     							=							rec_ternary_search(0, len(collection) - 1, collection, target)
					if resulta != -1:
										print(F"""Iterative search: {target} found at positions: {resulta}""")
										print(F"""Recursive search: {target} found at positions: {resulta}""")
					else:
										print("""Not found""")









"""simple docstring"""




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     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

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

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

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





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""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(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""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    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



