



"""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    : Optional[Any]							=							logging.get_logger(__name__)






class      UpperCAmelCase_  (    A_						):



      def __init__(					self					:     Optional[Any]					,     A					:     int					,     A					:     int					,     A					:     float					,     **A					:     str						):
           _UpperCAmelCase      :     Tuple        =   feature_size
           _UpperCAmelCase      :     int        =   sampling_rate
           _UpperCAmelCase      :     Any        =   padding_value

           _UpperCAmelCase      :     Optional[int]        =   kwargs.pop("padding_side"					,     "right"						)
           _UpperCAmelCase      :     Optional[Any]        =   kwargs.pop("return_attention_mask"					,     _lowerCamelCase						)

           super().__init__(**_lowerCamelCase						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     Union[
              BatchFeature,
              List[BatchFeature],
              Dict[str, BatchFeature],
              Dict[str, List[BatchFeature]],
              List[Dict[str, BatchFeature]],
          ]					,     A					:     Union[bool, str, PaddingStrategy] = True					,     A					:     Optional[int] = None					,     A					:     bool = False					,     A					:     Optional[int] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Union[str, TensorType]] = None					,     ):
           # 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)						):
                _UpperCAmelCase      :     Union[str, Any]        =   {
                    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()						)}'						)

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

           if len(_lowerCamelCase						) == 0:
                if return_attention_mask:
                     _UpperCAmelCase      :     Dict        =   []
                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

           _UpperCAmelCase      :     Optional[Any]        =   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.
                _UpperCAmelCase      :     Tuple        =   0
                while len(required_input[index]						) == 0:
                     index += 1
                if index < len(_lowerCamelCase						):
                     _UpperCAmelCase      :     Any        =   required_input[index][0]

           if return_tensors is None:
                if is_tf_tensor(_lowerCamelCase						):
                     _UpperCAmelCase      :     Dict        =   "tf"
                elif is_torch_tensor(_lowerCamelCase						):
                     _UpperCAmelCase      :     Dict        =   "pt"
                elif isinstance(_lowerCamelCase					,     (int, float, list, tuple, np.ndarray)						):
                     _UpperCAmelCase      :     Union[str, Any]        =   "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)						):
                     _UpperCAmelCase      :     Tuple        =   to_numpy(_lowerCamelCase						)
                else:
                     _UpperCAmelCase      :     Tuple        =   [to_numpy(_lowerCamelCase						) for v in value]

        # Convert padding_strategy in PaddingStrategy
           _UpperCAmelCase      :     List[str]        =   self._get_padding_strategies(padding=_lowerCamelCase					,     max_length=_lowerCamelCase						)

           _UpperCAmelCase      :     str        =   processed_features[self.model_input_names[0]]

           _UpperCAmelCase      :     int        =   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."						)

           _UpperCAmelCase      :     int        =   []
           for i in range(_lowerCamelCase						):
                _UpperCAmelCase      :     int        =   {k: v[i] for k, v in processed_features.items()}
                # truncation
                _UpperCAmelCase      :     Union[str, Any]        =   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
                _UpperCAmelCase      :     str        =   max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
                _UpperCAmelCase      :     List[str]        =   PaddingStrategy.MAX_LENGTH

           _UpperCAmelCase      :     int        =   {}
           for i in range(_lowerCamelCase						):
                # padding
                _UpperCAmelCase      :     Optional[int]        =   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:
                          _UpperCAmelCase      :     int        =   []
                     if value.dtype is np.dtype(np.floataa						):
                          _UpperCAmelCase      :     int        =   value.astype(np.floataa						)
                     batch_outputs[key].append(_lowerCamelCase						)

           return BatchFeature(_lowerCamelCase					,     tensor_type=_lowerCamelCase						)



      def 						snake_case_ (					self					:     str					,     A					:     Union[Dict[str, np.ndarray], BatchFeature]					,     A					:     Optional[int] = None					,     A					:     PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,     A					:     Optional[int] = None					,     A					:     Optional[bool] = None					,     ):
           _UpperCAmelCase      :     List[Any]        =   processed_features[self.model_input_names[0]]

           if padding_strategy == PaddingStrategy.LONGEST:
                _UpperCAmelCase      :     List[Any]        =   len(_lowerCamelCase						)

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

           _UpperCAmelCase      :     Dict        =   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowerCamelCase						) < max_length

           if return_attention_mask and "attention_mask" not in processed_features:
                _UpperCAmelCase      :     Optional[int]        =   np.ones(len(_lowerCamelCase						)					,     dtype=np.intaa						)

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

           return processed_features



      def 						snake_case_ (					self					:     Any					,     A					:     Union[Dict[str, np.ndarray], BatchFeature]					,     A					:     Optional[int] = None					,     A					:     Optional[int] = None					,     A					:     Optional[bool] = None					,     ):
           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."						)

           _UpperCAmelCase      :     str        =   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):
                _UpperCAmelCase      :     Optional[int]        =   ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           _UpperCAmelCase      :     Dict        =   len(_lowerCamelCase						) > max_length

           if needs_to_be_truncated:
                _UpperCAmelCase      :     Any        =   processed_features[self.model_input_names[0]][:max_length]
                if "attention_mask" in processed_features:
                     _UpperCAmelCase      :     int        =   processed_features["attention_mask"][:max_length]

           return processed_features



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     Optional[int]=False					,     A					:     List[str]=None						):

           # Get padding strategy
           if padding is not False:
                if padding is True:
                     _UpperCAmelCase      :     Union[str, Any]        =   PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                elif not isinstance(_lowerCamelCase					,     _lowerCamelCase						):
                     _UpperCAmelCase      :     Tuple        =   PaddingStrategy(_lowerCamelCase						)
                elif isinstance(_lowerCamelCase					,     _lowerCamelCase						):
                     _UpperCAmelCase      :     Tuple        =   padding
           else:
                _UpperCAmelCase      :     List[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





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						list    )  ->       list:





     '''simple docstring'''

     if len(SCREAMING_SNAKE_CASE__    ) < 2:
          return collection

     def circle_sort_util(SCREAMING_SNAKE_CASE__     :						list						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						int    ) -> bool:
          _UpperCAmelCase      :     Dict        =   False

          if low == high:
               return swapped

          _UpperCAmelCase      :     str        =   low
          _UpperCAmelCase      :     Dict        =   high

          while left < right:
               if collection[left] > collection[right]:
                    _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   (
                        collection[right],
                        collection[left],
                    )
                    _UpperCAmelCase      :     Optional[int]        =   True

               left += 1
               right -= 1

          if left == right and collection[left] > collection[right + 1]:
               _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   (
                   collection[right + 1],
                   collection[left],
               )

               _UpperCAmelCase      :     Union[str, Any]        =   True

          _UpperCAmelCase      :     Dict        =   low + int((high - low) / 2    )
          _UpperCAmelCase      :     Any        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__						,  SCREAMING_SNAKE_CASE__    )
          _UpperCAmelCase      :     Optional[Any]        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  mid + 1						,  SCREAMING_SNAKE_CASE__    )

          return swapped or left_swap or right_swap

     _UpperCAmelCase      :     Tuple        =   True

     while is_not_sorted is True:
          _UpperCAmelCase      :     Dict        =   circle_sort_util(SCREAMING_SNAKE_CASE__						,  0						,  len(SCREAMING_SNAKE_CASE__    ) - 1    )

     return collection


if __name__ == "__main__":
 _lowerCAmelCase    : Dict							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase    : Optional[Any]							=							[int(item) for item in user_input.split(",")]
 print(circle_sort(unsorted))
