'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowercase        =    {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}






@is_pipeline_test
class 							a__(   unittest.TestCase      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Tuple						=			MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
			UpperCAmelCase_     :	int						=			TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

			if model_mapping is not None:
						UpperCAmelCase_     :	List[Any]						=			{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
			if tf_model_mapping is not None:
						UpperCAmelCase_     :	List[Any]						=			{
						    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
						}



			@require_torch
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    pipeline(
					    task="""text-classification"""			,       model="""hf-internal-testing/tiny-random-distilbert"""			,       framework="""pt""")

					lowerCAmelCase     =    text_classifier("""This is great !""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """LABEL_0""", """score""": 0.504}])

					lowerCAmelCase     =    text_classifier("""This is great !"""			,       top_k=2)
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}])

					lowerCAmelCase     =    text_classifier(["""This is great !""", """This is bad"""]			,       top_k=2)
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [
					        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
					        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
					    ]			,       )

					lowerCAmelCase     =    text_classifier("""This is great !"""			,       top_k=1)
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """LABEL_0""", """score""": 0.504}])

					# Legacy behavior
					lowerCAmelCase     =    text_classifier("""This is great !"""			,       return_all_scores=__lowerCAmelCase)
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """LABEL_0""", """score""": 0.504}])

					lowerCAmelCase     =    text_classifier("""This is great !"""			,       return_all_scores=__lowerCAmelCase)
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [[{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}]])

					lowerCAmelCase     =    text_classifier(["""This is great !""", """Something else"""]			,       return_all_scores=__lowerCAmelCase)
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [
					        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
					        [{"""label""": """LABEL_0""", """score""": 0.504}, {"""label""": """LABEL_1""", """score""": 0.496}],
					    ]			,       )

					lowerCAmelCase     =    text_classifier(["""This is great !""", """Something else"""]			,       return_all_scores=__lowerCAmelCase)
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [
					        {"""label""": """LABEL_0""", """score""": 0.504},
					        {"""label""": """LABEL_0""", """score""": 0.504},
					    ]			,       )



			@require_torch
			def 				a_			(  self):




					"""simple docstring"""


					import torch

					lowerCAmelCase     =    pipeline(
					    task="""text-classification"""			,       model="""hf-internal-testing/tiny-random-distilbert"""			,       framework="""pt"""			,       device=torch.device("""cpu""")			,       )

					lowerCAmelCase     =    text_classifier("""This is great !""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """LABEL_0""", """score""": 0.504}])



			@require_tf
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    pipeline(
					    task="""text-classification"""			,       model="""hf-internal-testing/tiny-random-distilbert"""			,       framework="""tf""")

					lowerCAmelCase     =    text_classifier("""This is great !""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """LABEL_0""", """score""": 0.504}])



			@slow
			@require_torch
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    pipeline("""text-classification""")

					lowerCAmelCase     =    text_classifier("""This is great !""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """POSITIVE""", """score""": 1.0}])
					lowerCAmelCase     =    text_classifier("""This is bad !""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """NEGATIVE""", """score""": 1.0}])
					lowerCAmelCase     =    text_classifier("""Birds are a type of animal""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """POSITIVE""", """score""": 0.988}])



			@slow
			@require_tf
			def 				a_			(  self):




					"""simple docstring"""


					lowerCAmelCase     =    pipeline("""text-classification"""			,       framework="""tf""")

					lowerCAmelCase     =    text_classifier("""This is great !""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """POSITIVE""", """score""": 1.0}])
					lowerCAmelCase     =    text_classifier("""This is bad !""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """NEGATIVE""", """score""": 1.0}])
					lowerCAmelCase     =    text_classifier("""Birds are a type of animal""")
					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": """POSITIVE""", """score""": 0.988}])



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    TextClassificationPipeline(model=__lowerCAmelCase			,       tokenizer=__lowerCAmelCase)
					return text_classifier, ["HuggingFace is in", "This is another test"]







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    text_classifier.model
					# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
					lowerCAmelCase     =    """HuggingFace is in"""
					lowerCAmelCase     =    text_classifier(__lowerCAmelCase)

					self.assertEqual(nested_simplify(__lowerCAmelCase)			,       [{"""label""": ANY(__lowerCAmelCase), """score""": ANY(__lowerCAmelCase)}])
					self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values())

					lowerCAmelCase     =    ["""HuggingFace is in """, """Paris is in France"""]
					lowerCAmelCase     =    text_classifier(__lowerCAmelCase)
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [{"""label""": ANY(__lowerCAmelCase), """score""": ANY(__lowerCAmelCase)}, {"""label""": ANY(__lowerCAmelCase), """score""": ANY(__lowerCAmelCase)}]			,       )
					self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values())
					self.assertTrue(outputs[1]["""label"""] in model.config.idalabel.values())

					# Forcing to get all results with `top_k=None`
					# This is NOT the legacy format
					lowerCAmelCase     =    text_classifier(__lowerCAmelCase			,       top_k=__lowerCAmelCase)
					lowerCAmelCase     =    len(model.config.idalabel.values())
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [[{"""label""": ANY(__lowerCAmelCase), """score""": ANY(__lowerCAmelCase)}] * N, [{"""label""": ANY(__lowerCAmelCase), """score""": ANY(__lowerCAmelCase)}] * N]			,       )

					lowerCAmelCase     =    {"""text""": """HuggingFace is in """, """text_pair""": """Paris is in France"""}
					lowerCAmelCase     =    text_classifier(__lowerCAmelCase)
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       {"""label""": ANY(__lowerCAmelCase), """score""": ANY(__lowerCAmelCase)}			,       )
					self.assertTrue(outputs["""label"""] in model.config.idalabel.values())

					# This might be used a text pair, but tokenizer + pipe interaction
					# makes it hard to understand that it's not using the pair properly
					# https://github.com/huggingface/transformers/issues/17305
					# We disabled this usage instead as it was outputting wrong outputs.
					lowerCAmelCase     =    [["""HuggingFace is in """, """Paris is in France"""]]
					with self.assertRaises(__lowerCAmelCase):
							text_classifier(__lowerCAmelCase)

					# This used to be valid for doing text pairs
					# We're keeping it working because of backward compatibility
					lowerCAmelCase     =    text_classifier([[["""HuggingFace is in """, """Paris is in France"""]]])
					self.assertEqual(
					    nested_simplify(__lowerCAmelCase)			,       [{"""label""": ANY(__lowerCAmelCase), """score""": ANY(__lowerCAmelCase)}]			,       )
					self.assertTrue(outputs[0]["""label"""] in model.config.idalabel.values())





'''simple docstring'''




from __future__ import annotations

import math






def 							snake_case__				(     _A:   int					)       ->      list[int]:

		'''simple docstring'''


		if num <= 0:
				lowerCAmelCase     =    f"{num}: Invalid input, please enter a positive integer."
				raise ValueError(_A					)

		lowerCAmelCase     =    [True] * (num + 1)
		lowerCAmelCase     =    []
		lowerCAmelCase     =    2
		lowerCAmelCase     =    int(math.sqrt(_A					)					)

		while start <= end:
				# If start is a prime
				if sieve[start] is True:
						prime.append(_A					)

						# Set multiples of start be False
						for i in range(start * start ,  num + 1 ,  _A					):
								if sieve[i] is True:
										lowerCAmelCase     =    False

				start += 1

		for j in range(end + 1 ,  num + 1					):
				if sieve[j] is True:
						prime.append(_A					)

		return prime


if __name__ == "__main__":
						print(prime_sieve(int(input('''Enter a positive integer: ''').strip())))




