




'''simple docstring'''





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


__lowerCAmelCase	:       List[str]     =	logging.get_logger(__name__)




class 					snake_case__							(_UpperCamelCase     ):
				"""simple docstring"""



				def __init__(   self       :  Tuple      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  float      ,   **__lowerCamelCase       :  Any							)      ->  int:
							a					=    feature_size
							a					=    sampling_rate
							a					=    padding_value

							a					=    kwargs.pop("padding_side"      ,   "right"							)
							a					=    kwargs.pop("return_attention_mask"      ,   __lowerCamelCase							)

							super().__init__(**__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[Any]      ,   __lowerCamelCase       :  Union[
				        BatchFeature,
				        List[BatchFeature],
				        Dict[str, BatchFeature],
				        Dict[str, List[BatchFeature]],
				        List[Dict[str, BatchFeature]],
				    ]      ,   __lowerCamelCase       :  Union[bool, str, PaddingStrategy] = True      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  bool = False      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  Optional[bool] = None      ,   __lowerCamelCase       :  Optional[Union[str, TensorType]] = None      ,   )      ->  BatchFeature:
							# If we have a list of dicts, let's convert it in a dict of lists
							# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
							if isinstance(__lowerCamelCase      ,   (list, tuple)							) and isinstance(processed_features[0]      ,   (dict, BatchFeature)							):
										a					=    {
										    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
										}

							# The model's main input name, usually `input_values`, has be passed for padding
							if self.model_input_names[0] not in processed_features:
										raise ValueError(
										    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
										    f""" to this method that includes {self.model_input_names[0]}, but you provided"""
										    f""" {list(processed_features.keys()							)}"""							)

							a					=    processed_features[self.model_input_names[0]]
							a					=    (
							    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
							)

							if len(__lowerCamelCase							) == 0:
										if return_attention_mask:
													a					=    []
										return processed_features

							# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
							# and rebuild them afterwards if no return_tensors is specified
							# Note that we lose the specific device the tensor may be on for PyTorch

							a					=    required_input[0]
							if isinstance(__lowerCamelCase      ,   (list, tuple)							):
										# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
										a					=    0
										while len(required_input[index]							) == 0:
													index += 1
										if index < len(__lowerCamelCase							):
													a					=    required_input[index][0]

							if return_tensors is None:
										if is_tf_tensor(__lowerCamelCase							):
													a					=    "tf"
										elif is_torch_tensor(__lowerCamelCase							):
													a					=    "pt"
										elif isinstance(__lowerCamelCase      ,   (int, float, list, tuple, np.ndarray)							):
													a					=    "np"
										else:
													raise ValueError(
													    f"""type of {first_element} unknown: {type(__lowerCamelCase							)}. """
													    "Should be one of a python, numpy, pytorch or tensorflow object."							)

							for key, value in processed_features.items():
										if isinstance(value[0]      ,   (int, float)							):
													a					=    to_numpy(__lowerCamelCase							)
										else:
													a					=    [to_numpy(__lowerCamelCase							) for v in value]

        # Convert padding_strategy in PaddingStrategy
							a					=    self._get_padding_strategies(padding=__lowerCamelCase      ,   max_length=__lowerCamelCase							)

							a					=    processed_features[self.model_input_names[0]]

							a					=    len(__lowerCamelCase							)
							if not all(len(__lowerCamelCase							) == batch_size for v in processed_features.values()							):
										raise ValueError("Some items in the output dictionary have a different batch size than others."							)

							a					=    []
							for i in range(__lowerCamelCase							):
										a					=    {k: v[i] for k, v in processed_features.items()}
										# truncation
										a					=    self._truncate(
										    __lowerCamelCase      ,   max_length=__lowerCamelCase      ,   pad_to_multiple_of=__lowerCamelCase      ,   truncation=__lowerCamelCase      ,   )
										truncated_inputs.append(__lowerCamelCase							)

							if padding_strategy == PaddingStrategy.LONGEST:
										# make sure that `max_length` cannot be longer than the longest truncated length
										a					=    max(len(input_slice[self.model_input_names[0]]							) for input_slice in truncated_inputs							)
										a					=    PaddingStrategy.MAX_LENGTH

							a					=    {}
							for i in range(__lowerCamelCase							):
										# padding
										a					=    self._pad(
										    truncated_inputs[i]      ,   max_length=__lowerCamelCase      ,   padding_strategy=__lowerCamelCase      ,   pad_to_multiple_of=__lowerCamelCase      ,   return_attention_mask=__lowerCamelCase      ,   )

										for key, value in outputs.items():
													if key not in batch_outputs:
																a					=    []
													if value.dtype is np.dtype(np.floataa							):
																a					=    value.astype(np.floataa							)
													batch_outputs[key].append(__lowerCamelCase							)

							return BatchFeature(__lowerCamelCase      ,   tensor_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Union[Dict[str, np.ndarray], BatchFeature]      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  Optional[bool] = None      ,   )      ->  dict:
							a					=    processed_features[self.model_input_names[0]]

							if padding_strategy == PaddingStrategy.LONGEST:
										a					=    len(__lowerCamelCase							)

							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
										a					=    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							a					=    padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__lowerCamelCase							) < max_length

							if return_attention_mask and "attention_mask" not in processed_features:
										a					=    np.ones(len(__lowerCamelCase							)      ,   dtype=np.intaa							)

							if needs_to_be_padded:
										a					=    max_length - len(__lowerCamelCase							)
										if self.padding_side == "right":
													if return_attention_mask:
																a					=    np.pad(
																    processed_features["attention_mask"]      ,   (0, difference)							)
													a					=    ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
													a					=    np.pad(
													    __lowerCamelCase      ,   __lowerCamelCase      ,   "constant"      ,   constant_values=self.padding_value							)
										elif self.padding_side == "left":
													if return_attention_mask:
																a					=    np.pad(
																    processed_features["attention_mask"]      ,   (difference, 0)							)
													a					=    ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
													a					=    np.pad(
													    __lowerCamelCase      ,   __lowerCamelCase      ,   "constant"      ,   constant_values=self.padding_value							)
										else:
													raise ValueError("Invalid padding strategy:" + str(self.padding_side							)							)

							return processed_features





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  Union[Dict[str, np.ndarray], BatchFeature]      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  Optional[int] = None      ,   __lowerCamelCase       :  Optional[bool] = None      ,   )      ->  List[str]:
							if not truncation:
										return processed_features
							elif truncation and max_length is None:
										raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."							)

							a					=    processed_features[self.model_input_names[0]]

							# find `max_length` that fits `pad_to_multiple_of`
							if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
										a					=    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

							a					=    len(__lowerCamelCase							) > max_length

							if needs_to_be_truncated:
										a					=    processed_features[self.model_input_names[0]][:max_length]
										if "attention_mask" in processed_features:
													a					=    processed_features["attention_mask"][:max_length]

							return processed_features





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]      ,   __lowerCamelCase       :  List[str]=False      ,   __lowerCamelCase       :  int=None							)      ->  int:

							# Get padding strategy
							if padding is not False:
										if padding is True:
													a					=    PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
										elif not isinstance(__lowerCamelCase      ,   __lowerCamelCase							):
													a					=    PaddingStrategy(__lowerCamelCase							)
										elif isinstance(__lowerCamelCase      ,   __lowerCamelCase							):
													a					=    padding
							else:
										a					=    PaddingStrategy.DO_NOT_PAD

							# Set max length if needed
							if max_length is None:
										if padding_strategy == PaddingStrategy.MAX_LENGTH:
													raise ValueError(
													    f"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""							)

        # Test if we have a padding value
							if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
										raise ValueError(
										    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
										    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."							)

							return padding_strategy












def     __magic_name__       (				A	:      list     ):






			'''simple docstring'''







			for i in range(len(A     ) - 1,						0,						-1     ):
						a					=    False

						for j in range(A,						0,						-1     ):
									if unsorted[j] < unsorted[j - 1]:
												a					,	a					=    unsorted[j - 1], unsorted[j]
												a					=    True

						for j in range(A     ):
									if unsorted[j] > unsorted[j + 1]:
												a					,	a					=    unsorted[j + 1], unsorted[j]
												a					=    True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__lowerCAmelCase	:       Tuple     =	input('Enter numbers separated by a comma:\n').strip()
		__lowerCAmelCase	:       List[Any]     =	[int(item) for item in user_input.split(',')]
		print(F'''{cocktail_shaker_sort(unsorted) = }''')






