
"""simple docstring"""

import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput

class        lowercase      (    __lowercase):







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				Union[str, Any]	   =		dataset
					A_  :				List[str]	   =		process
					A_  :				List[Any]	   =		params







				def __len__(							self   :					str       ):



					"""simple docstring"""

					return len(self.dataset       )







				def __getitem__(							self   :					Tuple      ,     _lowerCamelCase   :					Union[str, Any]       ):



					"""simple docstring"""

					A_  :				List[str]	   =		self.dataset[i]
					A_  :				Union[str, Any]	   =		self.process(_lowerCamelCase      ,     **self.params       )
					return processed

class        lowercase      (    __lowercase):







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					List[Any]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Optional[int]      ,     _lowerCamelCase   :					Dict=None       ):



					"""simple docstring"""

					A_  :				int	   =		loader
					A_  :				Optional[int]	   =		infer
					A_  :				Any	   =		params
					if loader_batch_size == 1:
						# Let's spare some time by deactivating altogether
						A_  :				Optional[int]	   =		None
					A_  :				Any	   =		loader_batch_size

					# Internal bookkeeping
					A_  :				Optional[Any]	   =		None
					A_  :				Union[str, Any]	   =		None







				def __len__(							self   :					int       ):



					"""simple docstring"""

					return len(self.loader       )







				def __iter__(							self   :					List[Any]       ):



					"""simple docstring"""

					A_  :				str	   =		iter(self.loader       )
					return self







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					if isinstance(self._loader_batch_data      ,     torch.Tensor       ):
						# Batch data is simple tensor, just fetch the slice
						A_  :				List[Any]	   =		self._loader_batch_data[self._loader_batch_index]
					else:
						# Batch data is assumed to be BaseModelOutput (or dict)
						A_  :				Tuple	   =		{}
						for k, element in self._loader_batch_data.items():
							if isinstance(_lowerCamelCase      ,     _lowerCamelCase       ):
								# Convert ModelOutput to tuple first
								A_  :				List[str]	   =		element.to_tuple()
								if isinstance(element[0]      ,     torch.Tensor       ):
									A_  :				Optional[int]	   =		tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
								elif isinstance(element[0]      ,     np.ndarray       ):
									A_  :				str	   =		tuple(np.expand_dims(el[self._loader_batch_index]      ,     0       ) for el in element       )
								continue
							if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(_lowerCamelCase      ,     _lowerCamelCase       ):
								# Those are stored as lists of tensors so need specific unbatching.
								if isinstance(element[0]      ,     torch.Tensor       ):
									A_  :				Optional[Any]	   =		tuple(el[self._loader_batch_index].unsqueeze(0       ) for el in element       )
								elif isinstance(element[0]      ,     np.ndarray       ):
									A_  :				int	   =		tuple(np.expand_dims(el[self._loader_batch_index]      ,     0       ) for el in element       )
								continue
							if element is None:
								# This can happen for optional data that get passed around
								A_  :				Optional[Any]	   =		None
							elif isinstance(element[self._loader_batch_index]      ,     torch.Tensor       ):
								# Take correct batch data, but make it looked like batch_size=1
								# For compatibility with other methods within transformers

								A_  :				int	   =		element[self._loader_batch_index].unsqueeze(0       )
							elif isinstance(element[self._loader_batch_index]      ,     np.ndarray       ):
								# Take correct batch data, but make it looked like batch_size=1
								# For compatibility with other methods within transformers
								A_  :				Tuple	   =		np.expand_dims(element[self._loader_batch_index]      ,     0       )
							else:
								# This is typically a list, so no need to `unsqueeze`.
								A_  :				str	   =		element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
						A_  :				Tuple	   =		self._loader_batch_data.__class__(_lowerCamelCase       )
					self._loader_batch_index += 1
					return result







				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
						# We are currently unrolling a batch so we just need to return
						# the current item within a batch
						return self.loader_batch_item()

					# We're out of items within a batch
					A_  :				Any	   =		next(self.iterator       )
					A_  :				Optional[int]	   =		self.infer(_lowerCamelCase      ,     **self.params       )
					# We now have a batch of "inferred things".
					if self.loader_batch_size is not None:
						# Try to infer the size of the batch
						if isinstance(_lowerCamelCase      ,     torch.Tensor       ):
							A_  :				Optional[Any]	   =		processed
						else:
							A_  :				Tuple	   =		list(processed.keys()       )[0]
							A_  :				List[str]	   =		processed[key]
						if isinstance(_lowerCamelCase      ,     _lowerCamelCase       ):
							A_  :				str	   =		len(_lowerCamelCase       )
						else:
							A_  :				Any	   =		first_tensor.shape[0]
						if 0 < observed_batch_size < self.loader_batch_size:
							# could be last batch so we can't unroll as many
							# elements.
							A_  :				int	   =		observed_batch_size
						# Setting internal index to unwrap the batch
						A_  :				Tuple	   =		processed
						A_  :				List[Any]	   =		0
						return self.loader_batch_item()
					else:
						# We're not unrolling batches
						return processed

class        lowercase      (    __lowercase):







				def __init__(							self   :					Union[str, Any]      ,     _lowerCamelCase   :					Tuple      ,     _lowerCamelCase   :					Union[str, Any]      ,     _lowerCamelCase   :					Any      ,     _lowerCamelCase   :					Dict=None       ):



					"""simple docstring"""

					super().__init__(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       )







				def __iter__(							self   :					Dict       ):



					"""simple docstring"""

					A_  :				Optional[Any]	   =		iter(self.loader       )
					A_  :				Optional[int]	   =		None
					return self







				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					if self.subiterator is None:
						A_  :				Union[str, Any]	   =		self.infer(next(self.iterator       )      ,     **self.params       )
					try:
						# Try to return next item
						A_  :				List[str]	   =		next(self.subiterator       )
					except StopIteration:
						# When a preprocess iterator ends, we can start lookig at the next item
						# ChunkIterator will keep feeding until ALL elements of iterator
						# all have created their subiterator and have been iterating against.
						#
						# Another way to look at it, is we're basically flattening lists of lists
						# into a single list, but with generators
						A_  :				Union[str, Any]	   =		self.infer(next(self.iterator       )      ,     **self.params       )
						A_  :				List[Any]	   =		next(self.subiterator       )
					return processed

class        lowercase      (    __lowercase):







				def __iter__(							self   :					Tuple       ):



					"""simple docstring"""

					A_  :				List[str]	   =		iter(self.loader       )
					return self







				def 					a_  (							self   :					str       ):



					"""simple docstring"""

					A_  :				str	   =		False
					A_  :				List[Any]	   =		[]
					if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
						while self._loader_batch_index < self.loader_batch_size:
							A_  :				Optional[Any]	   =		self.loader_batch_item()
							A_  :				Union[str, Any]	   =		item.pop('''is_last'''       )
							accumulator.append(_lowerCamelCase       )
							if is_last:
								return accumulator

					while not is_last:
						A_  :				Any	   =		self.infer(next(self.iterator       )      ,     **self.params       )
						if self.loader_batch_size is not None:
							if isinstance(_lowerCamelCase      ,     torch.Tensor       ):
								A_  :				str	   =		processed
							else:
								A_  :				Union[str, Any]	   =		list(processed.keys()       )[0]
								A_  :				Tuple	   =		processed[key]
							if isinstance(_lowerCamelCase      ,     _lowerCamelCase       ):
								A_  :				str	   =		len(_lowerCamelCase       )
							else:
								A_  :				Optional[int]	   =		first_tensor.shape[0]
							if 0 < observed_batch_size < self.loader_batch_size:
								# could be last batch so we can't unroll as many
								# elements.
								A_  :				Optional[int]	   =		observed_batch_size
							A_  :				Tuple	   =		processed
							A_  :				List[str]	   =		0
							while self._loader_batch_index < self.loader_batch_size:
								A_  :				List[str]	   =		self.loader_batch_item()
								A_  :				str	   =		item.pop('''is_last'''       )
								accumulator.append(_lowerCamelCase       )
								if is_last:
									return accumulator
						else:
							A_  :				Optional[int]	   =		processed
							A_  :				List[Any]	   =		item.pop('''is_last'''       )
							accumulator.append(_lowerCamelCase       )
					return accumulator

class        lowercase      (    __lowercase):







				def __init__(							self   :					Any      ,     _lowerCamelCase   :					Dataset      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				Optional[int]	   =		dataset
					A_  :				Union[str, Any]	   =		key







				def __len__(							self   :					Optional[int]       ):



					"""simple docstring"""

					return len(self.dataset       )







				def __getitem__(							self   :					Dict      ,     _lowerCamelCase   :					int       ):



					"""simple docstring"""

					return self.dataset[i][self.key]

class        lowercase      (    __lowercase):







				def __init__(							self   :					int      ,     _lowerCamelCase   :					Dataset      ,     _lowerCamelCase   :					str      ,     _lowerCamelCase   :					str       ):



					"""simple docstring"""

					A_  :				int	   =		dataset
					A_  :				Optional[int]	   =		keya
					A_  :				Dict	   =		keya







				def __len__(							self   :					Tuple       ):



					"""simple docstring"""

					return len(self.dataset       )







				def __getitem__(							self   :					str      ,     _lowerCamelCase   :					Dict       ):



					"""simple docstring"""

					return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}











"""simple docstring"""

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		len(_UpperCAmelCase	)
	for i in range(length - 1	):
		A_  :				str	   =		i
		for k in range(i + 1  ,      _UpperCAmelCase	):
			if collection[k] < collection[least]:
				A_  :				Tuple	   =		k
		if least != i:
			A_   , A_  :				Optional[int]	   =		(collection[i], collection[least])
	return collection


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





