


"""simple docstring"""







def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       len(lowerCAmelCase_     )
					for i in range(length - 1     ):
										__SCREAMING_SNAKE_CASE		     =       i
										for k in range(i + 1						,		lowerCAmelCase_     ):
															if collection[k] < collection[least]:
																				__SCREAMING_SNAKE_CASE		     =       k
										if least != i:
															__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       (collection[i], collection[least])
					return collection


if __name__ == "__main__":
		a__       :							Optional[int]        =  input('''Enter numbers separated by a comma:\n''').strip()
		a__       :							str        =  [int(item) for item in user_input.split(''',''')]
		print(selection_sort(unsorted))




"""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.
a__       :							Dict        =  {'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 				UpperCamelCase_			(				unittest.TestCase):

	"""simple docstring"""





	snake_case__       :						str					      =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	snake_case__       :						Tuple					      =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		snake_case__       :						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:
		snake_case__       :						Dict					      =    {
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}




	@require_torch
	def 						UpperCAmelCase_   (				self    :       str     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="pt"     )

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

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

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

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

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

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

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

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




	@require_torch
	def 						UpperCAmelCase_   (				self    :       Union[str, Any]     ) ->			Optional[int]:
						import torch

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

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




	@require_tf
	def 						UpperCAmelCase_   (				self    :       Dict     ) ->			Optional[int]:
						__SCREAMING_SNAKE_CASE		     =       pipeline(
						    task="text-classification"    ,    model="hf-internal-testing/tiny-random-distilbert"    ,    framework="tf"     )

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




	@slow
	@require_torch
	def 						UpperCAmelCase_   (				self    :       Optional[Any]     ) ->			int:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"     )

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




	@slow
	@require_tf
	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			List[str]:
						__SCREAMING_SNAKE_CASE		     =       pipeline("text-classification"    ,    framework="tf"     )

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




	def 						UpperCAmelCase_   (				self    :       Any    ,    UpperCAmelCase__    :       str    ,    UpperCAmelCase__    :       Any    ,    UpperCAmelCase__    :       Tuple     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       TextClassificationPipeline(model=UpperCAmelCase__    ,    tokenizer=UpperCAmelCase__     )
						return text_classifier, ["HuggingFace is in", "This is another test"]




	def 						UpperCAmelCase_   (				self    :       str    ,    UpperCAmelCase__    :       Optional[int]    ,    UpperCAmelCase__    :       Optional[int]     ) ->			Dict:
						__SCREAMING_SNAKE_CASE		     =       text_classifier.model
						# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
						__SCREAMING_SNAKE_CASE		     =       "HuggingFace is in"
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )

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

						__SCREAMING_SNAKE_CASE		     =       ["HuggingFace is in ", "Paris is in France"]
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}, {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}]    ,    )
						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
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__    ,    top_k=UpperCAmelCase__     )
						__SCREAMING_SNAKE_CASE		     =       len(model.config.idalabel.values()     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    [[{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N, [{"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}] * N]    ,    )

						__SCREAMING_SNAKE_CASE		     =       {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
						__SCREAMING_SNAKE_CASE		     =       text_classifier(UpperCAmelCase__     )
						self.assertEqual(
						    nested_simplify(UpperCAmelCase__     )    ,    {"label": ANY(UpperCAmelCase__     ), "score": ANY(UpperCAmelCase__     )}    ,    )
						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.
						__SCREAMING_SNAKE_CASE		     =       [["HuggingFace is in ", "Paris is in France"]]
						with self.assertRaises(UpperCAmelCase__     ):
											text_classifier(UpperCAmelCase__     )

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