"""simple docstring"""




import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class     UpperCamelCase__(			__A ):
						# to overwrite at feature extractactor specific tests
						lowerCAmelCase__  :						Tuple													=		None
						lowerCAmelCase__  :						List[Any]													=		None





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													return self.feat_extract_tester.prepare_feat_extract_dict()





						def 				snake_case__	(       self     )					->				int:
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'feature_size'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'sampling_rate'     )     )
													self.assertTrue(hasattr(__UpperCAmelCase		,'padding_value'     )     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common()
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}     )

													self.assertTrue(all(len(__UpperCAmelCase     ) == len(__UpperCAmelCase     ) for x, y in zip(__UpperCAmelCase		,processed_features[input_name]     )     )     )

													A__	     =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase     )
													A__	     =							BatchFeature({input_name: speech_inputs}		,tensor_type='np'     )

													A__	     =							processed_features[input_name]

													if len(batch_features_input.shape     ) < 3:
																				A__	     =							batch_features_input[:, :, None]

													self.assertTrue(
													    batch_features_input.shape
													    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )





						@require_torch
						def 				snake_case__	(       self     )					->				Any:
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase     )
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}		,tensor_type='pt'     )

													A__	     =							processed_features[input_name]

													if len(batch_features_input.shape     ) < 3:
																				A__	     =							batch_features_input[:, :, None]

													self.assertTrue(
													    batch_features_input.shape
													    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )





						@require_tf
						def 				snake_case__	(       self     )					->				Any:
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common(equal_length=__UpperCAmelCase     )
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}		,tensor_type='tf'     )

													A__	     =							processed_features[input_name]

													if len(batch_features_input.shape     ) < 3:
																				A__	     =							batch_features_input[:, :, None]

													self.assertTrue(
													    batch_features_input.shape
													    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]     ), self.feat_extract_tester.feature_size)     )





						def 				snake_case__	(       self		,__UpperCAmelCase=False     )					->				Optional[Any]:
													def _inputs_have_equal_length(__UpperCAmelCase     ):
																				A__	     =							len(input[0]     )
																				for input_slice in input[1:]:
																											if len(__UpperCAmelCase     ) != length:
																																		return False
																				return True

													def _inputs_are_equal(__UpperCAmelCase		,__UpperCAmelCase     ):
																				if len(__UpperCAmelCase     ) != len(__UpperCAmelCase     ):
																											return False

																				for input_slice_a, input_slice_a in zip(__UpperCAmelCase		,__UpperCAmelCase     ):
																											if not np.allclose(np.asarray(__UpperCAmelCase     )		,np.asarray(__UpperCAmelCase     )		,atol=1e-3     ):
																																		return False
																				return True

													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCAmelCase     )
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}     )

													A__	     =							self.feat_extract_tester.seq_length_diff
													A__	     =							self.feat_extract_tester.max_seq_length + pad_diff
													A__	     =							self.feat_extract_tester.min_seq_length
													A__	     =							self.feat_extract_tester.batch_size
													A__	     =							self.feat_extract_tester.feature_size

													# test padding for List[int] + numpy
													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding=__UpperCAmelCase     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[-1]     )     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'		,return_tensors='np'     )
													A__	     =							input_a[input_name]

													# max_length parameter has to be provided when setting `padding="max_length"`
													with self.assertRaises(__UpperCAmelCase     ):
																				feat_extract.pad(__UpperCAmelCase		,padding='max_length'     )[input_name]

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=__UpperCAmelCase		,return_tensors='np'     )
													A__	     =							input_a[input_name]

													self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertTrue(_inputs_are_equal(__UpperCAmelCase		,__UpperCAmelCase     )     )
													self.assertTrue(len(input_a[0]     ) == pad_min_length     )
													self.assertTrue(len(input_a[1]     ) == pad_min_length + pad_diff     )
													self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0]     ))     )
													self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length)     )

													if feature_size > 1:
																				self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size     )

													# test padding for `pad_to_multiple_of` for List[int] + numpy
													A__	     =							feat_extract.pad(__UpperCAmelCase		,pad_to_multiple_of=10     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'		,pad_to_multiple_of=10     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,pad_to_multiple_of=10		,max_length=__UpperCAmelCase     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,pad_to_multiple_of=10		,max_length=__UpperCAmelCase		,return_tensors='np'		,)
													A__	     =							input_a[input_name]

													self.assertTrue(all(len(__UpperCAmelCase     ) % 10 == 0 for x in input_a     )     )
													self.assertTrue(_inputs_are_equal(__UpperCAmelCase		,__UpperCAmelCase     )     )

													A__	     =							pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
													self.assertTrue(all(len(__UpperCAmelCase     ) == expected_mult_pad_length for x in input_a     )     )
													self.assertEqual(input_a.shape[:2]		,(batch_size, expected_mult_pad_length)     )

													if feature_size > 1:
																				self.assertTrue(input_a.shape[2] == feature_size     )

													# Check padding value is correct
													A__	     =							(np.ones(self.feat_extract_tester.feature_size     ) * feat_extract.padding_value).sum()
													self.assertTrue(
													    abs(np.asarray(input_a[0]     )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)     )
													    < 1e-3     )
													self.assertTrue(
													    abs(
													        np.asarray(input_a[1]     )[pad_min_length + pad_diff :].sum()
													        - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff)     )
													    < 1e-3     )
													self.assertTrue(
													    abs(
													        np.asarray(input_a[2]     )[pad_min_length + 2 * pad_diff :].sum()
													        - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff)     )
													    < 1e-3     )
													self.assertTrue(
													    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)     ) < 1e-3     )
													self.assertTrue(
													    abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length)     )
													    < 1e-3     )





						def 				snake_case__	(       self		,__UpperCAmelCase=False     )					->				Union[str, Any]:
													def _inputs_have_equal_length(__UpperCAmelCase     ):
																				A__	     =							len(input[0]     )
																				for input_slice in input[1:]:
																											if len(__UpperCAmelCase     ) != length:
																																		return False
																				return True

													def _inputs_are_equal(__UpperCAmelCase		,__UpperCAmelCase     ):
																				if len(__UpperCAmelCase     ) != len(__UpperCAmelCase     ):
																											return False

																				for input_slice_a, input_slice_a in zip(__UpperCAmelCase		,__UpperCAmelCase     ):
																											if not np.allclose(np.asarray(__UpperCAmelCase     )		,np.asarray(__UpperCAmelCase     )		,atol=1e-3     ):
																																		return False
																				return True

													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common(numpify=__UpperCAmelCase     )
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}     )

													# truncate to smallest
													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[0]     )		,truncation=__UpperCAmelCase     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[0]     )     )
													A__	     =							input_a[input_name]

													self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase     )     )

													# truncate to smallest with np
													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[0]     )		,return_tensors='np'		,truncation=__UpperCAmelCase		,)
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[0]     )		,return_tensors='np'     )
													A__	     =							input_a[input_name]

													self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertTrue(input_a.shape[1] == len(speech_inputs[0]     )     )

													# since truncation forces padding to be smaller than longest input
													# function can't return `np.ndarray`, but has to return list
													self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase     )     )

													# truncate to middle
													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[1]     )		,truncation=__UpperCAmelCase		,return_tensors='np'		,)
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[1]     )		,truncation=__UpperCAmelCase     )
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[1]     )		,return_tensors='np'     )
													A__	     =							input_a[input_name]

													self.assertTrue(input_a.shape[1] == len(speech_inputs[1]     )     )
													self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertTrue(_inputs_are_equal(__UpperCAmelCase		,__UpperCAmelCase     )     )

													# since truncation forces padding to be smaller than longest input
													# function can't return `np.ndarray`, but has to return list
													self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertTrue(len(input_a[-1]     ) == len(speech_inputs[-1]     )     )

													# padding has to be max_length when setting `truncation=True`
													with self.assertRaises(__UpperCAmelCase     ):
																				feat_extract.pad(__UpperCAmelCase		,truncation=__UpperCAmelCase     )[input_name]

													# padding has to be max_length when setting `truncation=True`
													with self.assertRaises(__UpperCAmelCase     ):
																				feat_extract.pad(__UpperCAmelCase		,padding='longest'		,truncation=__UpperCAmelCase     )[input_name]

													# padding has to be max_length when setting `truncation=True`
													with self.assertRaises(__UpperCAmelCase     ):
																				feat_extract.pad(__UpperCAmelCase		,padding='longest'		,truncation=__UpperCAmelCase     )[input_name]

													# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
													with self.assertRaises(__UpperCAmelCase     ):
																				feat_extract.pad(__UpperCAmelCase		,padding='max_length'		,truncation=__UpperCAmelCase     )[input_name]

													# test truncation for `pad_to_multiple_of` for List[int] + numpy
													A__	     =							12
													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[0]     )		,pad_to_multiple_of=__UpperCAmelCase		,truncation=__UpperCAmelCase		,)
													A__	     =							input_a[input_name]

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=len(speech_inputs[0]     )		,pad_to_multiple_of=__UpperCAmelCase		,)
													A__	     =							input_a[input_name]

													# retrieve expected_length as multiple of pad_to_multiple_of
													A__	     =							len(speech_inputs[0]     )
													if expected_length % pad_to_multiple_of != 0:
																				A__	     =							((len(speech_inputs[0]     ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

													self.assertTrue(len(input_a[0]     ) == expected_length     )
													self.assertTrue(_inputs_have_equal_length(__UpperCAmelCase     )     )
													self.assertFalse(_inputs_have_equal_length(__UpperCAmelCase     )     )





						def 				snake_case__	(       self     )					->				Any:
													self._check_padding(numpify=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													self._check_padding(numpify=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				int:
													self._check_truncation(numpify=__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				List[str]:
													self._check_truncation(numpify=__UpperCAmelCase     )





						@require_torch
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common()
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}     )

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'		,return_tensors='np'     )[input_name]
													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'		,return_tensors='pt'     )[input_name]

													self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_pt.numpy().astype(np.floataa     ).sum()     ) < 1e-2     )





						@require_tf
						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							self.feature_extraction_class(**self.feat_extract_dict     )
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common()
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}     )

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'		,return_tensors='np'     )[input_name]
													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'		,return_tensors='tf'     )[input_name]

													self.assertTrue(abs(input_np.astype(np.floataa     ).sum() - input_tf.numpy().astype(np.floataa     ).sum()     ) < 1e-2     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							self.feat_extract_dict
													A__	     =							True
													A__	     =							self.feature_extraction_class(**__UpperCAmelCase     )
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common()
													A__	     =							[len(__UpperCAmelCase     ) for x in speech_inputs]
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}     )

													A__	     =							feat_extract.pad(__UpperCAmelCase		,padding='longest'		,return_tensors='np'     )
													self.assertIn('attention_mask'		,__UpperCAmelCase     )
													self.assertListEqual(list(processed.attention_mask.shape     )		,list(processed[input_name].shape[:2]     )     )
													self.assertListEqual(processed.attention_mask.sum(-1     ).tolist()		,__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							self.feat_extract_dict
													A__	     =							True
													A__	     =							self.feature_extraction_class(**__UpperCAmelCase     )
													A__	     =							self.feat_extract_tester.prepare_inputs_for_common()
													A__	     =							[len(__UpperCAmelCase     ) for x in speech_inputs]
													A__	     =							feat_extract.model_input_names[0]

													A__	     =							BatchFeature({input_name: speech_inputs}     )
													A__	     =							min(__UpperCAmelCase     )

													A__	     =							feat_extract.pad(
													    __UpperCAmelCase		,padding='max_length'		,max_length=__UpperCAmelCase		,truncation=__UpperCAmelCase		,return_tensors='np'     )
													self.assertIn('attention_mask'		,__UpperCAmelCase     )
													self.assertListEqual(
													    list(processed_pad.attention_mask.shape     )		,[processed_pad[input_name].shape[0], max_length]     )
													self.assertListEqual(
													    processed_pad.attention_mask[:, :max_length].sum(-1     ).tolist()		,[max_length for x in speech_inputs]     )






"""simple docstring"""




def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__     ,  A__	     =							[], []
							while len(UpperCamelCase__				) > 1:
														A__     ,  A__	     =							min(UpperCamelCase__				), max(UpperCamelCase__				)
														start.append(UpperCamelCase__				)
														end.append(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
														collection.remove(UpperCamelCase__				)
							end.reverse()
							return start + collection + end


if __name__ == "__main__":
				__lowerCamelCase											=  input("Enter numbers separated by a comma:\n").strip()
				__lowerCamelCase											=  [int(item) for item in user_input.split(",")]
				print(*merge_sort(unsorted), sep=",")





