



"""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


__a      =				logging.get_logger(__name__)
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __init__(       self:							List[Any]						,     snake_case:							int						,     snake_case:							int						,     snake_case:							float						,     **snake_case:							Optional[int]						)   ->	Optional[Any]:
											snake_case_ :List[Any]						=			feature_size
											snake_case_ :Tuple						=			sampling_rate
											snake_case_ :Optional[int]						=			padding_value

											snake_case_ :Dict						=			kwargs.pop("""padding_side"""						,     """right"""						)
											snake_case_ :List[Any]						=			kwargs.pop("""return_attention_mask"""						,     snake_case						)

											super().__init__(**snake_case						)






					def    lowerCAmelCase_				(       self:							List[str]						,     snake_case:							Union[
					        BatchFeature,
					        List[BatchFeature],
					        Dict[str, BatchFeature],
					        Dict[str, List[BatchFeature]],
					        List[Dict[str, BatchFeature]],
					    ]						,     snake_case:							Union[bool, str, PaddingStrategy] = True						,     snake_case:							Optional[int] = None						,     snake_case:							bool = False						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[bool] = None						,     snake_case:							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(snake_case						,     (list, tuple)						) and isinstance(processed_features[0]						,     (dict, BatchFeature)						):
																	snake_case_ :int						=			{
																	    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()						)}"""						)

											snake_case_ :Union[str, Any]						=			processed_features[self.model_input_names[0]]
											snake_case_ :Optional[int]						=			(
											    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
											)

											if len(snake_case						) == 0:
																	if return_attention_mask:
																							snake_case_ :Optional[int]						=			[]
																	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

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

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

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

        # Convert padding_strategy in PaddingStrategy
											snake_case_ :Union[str, Any]						=			self._get_padding_strategies(padding=snake_case						,     max_length=snake_case						)

											snake_case_ :List[Any]						=			processed_features[self.model_input_names[0]]

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

											snake_case_ :Optional[Any]						=			[]
											for i in range(snake_case						):
																	snake_case_ :List[Any]						=			{k: v[i] for k, v in processed_features.items()}
																	# truncation
																	snake_case_ :Union[str, Any]						=			self._truncate(
																	    snake_case						,     max_length=snake_case						,     pad_to_multiple_of=snake_case						,     truncation=snake_case						,     )
																	truncated_inputs.append(snake_case						)

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

											snake_case_ :List[Any]						=			{}
											for i in range(snake_case						):
																	# padding
																	snake_case_ :Any						=			self._pad(
																	    truncated_inputs[i]						,     max_length=snake_case						,     padding_strategy=snake_case						,     pad_to_multiple_of=snake_case						,     return_attention_mask=snake_case						,     )

																	for key, value in outputs.items():
																							if key not in batch_outputs:
																													snake_case_ :Optional[int]						=			[]
																							if value.dtype is np.dtype(np.floataa						):
																													snake_case_ :Tuple						=			value.astype(np.floataa						)
																							batch_outputs[key].append(snake_case						)

											return BatchFeature(snake_case						,     tensor_type=snake_case						)






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

											if padding_strategy == PaddingStrategy.LONGEST:
																	snake_case_ :Any						=			len(snake_case						)

											if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	snake_case_ :Optional[int]						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											snake_case_ :Tuple						=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case						) < max_length

											if return_attention_mask and "attention_mask" not in processed_features:
																	snake_case_ :Union[str, Any]						=			np.ones(len(snake_case						)						,     dtype=np.intaa						)

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

											return processed_features






					def    lowerCAmelCase_				(       self:							Optional[Any]						,     snake_case:							Union[Dict[str, np.ndarray], BatchFeature]						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[int] = None						,     snake_case:							Optional[bool] = None						,     )   ->	List[Any]:
											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."""						)

											snake_case_ :Any						=			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):
																	snake_case_ :str						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

											snake_case_ :Optional[Any]						=			len(snake_case						) > max_length

											if needs_to_be_truncated:
																	snake_case_ :Union[str, Any]						=			processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																							snake_case_ :Union[str, Any]						=			processed_features["""attention_mask"""][:max_length]

											return processed_features






					def    lowerCAmelCase_				(       self:							List[Any]						,     snake_case:							Tuple=False						,     snake_case:							Union[str, Any]=None						)   ->	Union[str, Any]:

											# Get padding strategy
											if padding is not False:
																	if padding is True:
																							snake_case_ :Optional[Any]						=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(snake_case						,     snake_case						):
																							snake_case_ :Dict						=			PaddingStrategy(snake_case						)
																	elif isinstance(snake_case						,     snake_case						):
																							snake_case_ :Optional[Any]						=			padding
											else:
																	snake_case_ :Optional[Any]						=			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 					snake_case_						(   lowerCAmelCase_				:  list					):

							if len(lowerCAmelCase_					) <= 1:
														return [tuple(lowerCAmelCase_					)]

							__lowercase					:							Any			=   []

							def generate(lowerCAmelCase_				:  int				,				lowerCAmelCase_				:  list					):
														if k == 1:
																					res.append(tuple(arr[:]					)					)
																					return

														generate(k - 1				,				lowerCAmelCase_					)

														for i in range(k - 1					):
																					if k % 2 == 0:  # k is even
																												__lowercase	,	__lowercase					:							List[str]			=   arr[k - 1], arr[i]
																					else:  # k is odd
																												__lowercase	,	__lowercase					:							Any			=   arr[k - 1], arr[0]
																					generate(k - 1				,				lowerCAmelCase_					)

							generate(len(lowerCAmelCase_					)				,				lowerCAmelCase_					)
							return res


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