



'''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:      int		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):







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

      a		:      Any       =  kwargs.pop('padding_side'		,    'right'			)
      a		:      Tuple       =  kwargs.pop('return_attention_mask'		,    __snake_case			)

      super().__init__(**__snake_case			)







    def     lowercase_ (   self				:						Tuple		,    __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		,    ):
      # 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)			):
        a		:      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()			)}"""			)

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

      if len(__snake_case			) == 0:
        if return_attention_mask:
          a		:      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

      a		:      Optional[int]       =  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.
        a		:      Dict       =  0
        while len(required_input[index]			) == 0:
          index += 1
        if index < len(__snake_case			):
          a		:      Tuple       =  required_input[index][0]

      if return_tensors is None:
        if is_tf_tensor(__snake_case			):
          a		:      Any       =  'tf'
        elif is_torch_tensor(__snake_case			):
          a		:      Optional[Any]       =  'pt'
        elif isinstance(__snake_case		,    (int, float, list, tuple, np.ndarray)			):
          a		:      List[Any]       =  '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)			):
          a		:      List[Any]       =  to_numpy(__snake_case			)
        else:
          a		:      Tuple       =  [to_numpy(__snake_case			) for v in value]

        # Convert padding_strategy in PaddingStrategy
      a		:      Any       =  self._get_padding_strategies(padding=__snake_case		,    max_length=__snake_case			)

      a		:      int       =  processed_features[self.model_input_names[0]]

      a		:      Dict       =  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.'			)

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

      a		:      int       =  {}
      for i in range(__snake_case			):
        # padding
        a		:      int       =  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:
            a		:      int       =  []
          if value.dtype is np.dtype(np.floataa			):
            a		:      List[str]       =  value.astype(np.floataa			)
          batch_outputs[key].append(__snake_case			)

      return BatchFeature(__snake_case		,    tensor_type=__snake_case			)







    def     lowercase_ (   self				:						List[str]		,    __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		,    ):
      a		:      List[str]       =  processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
        a		:      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):
        a		:      Dict       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      a		:      List[Any]       =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case			) < max_length

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

      if needs_to_be_padded:
        a		:      Tuple       =  max_length - len(__snake_case			)
        if self.padding_side == "right":
          if return_attention_mask:
            a		:      Any       =  np.pad(
                processed_features['attention_mask']		,    (0, difference)			)
          a		:      Optional[Any]       =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
          a		:      Optional[int]       =  np.pad(
              __snake_case		,    __snake_case		,    'constant'		,    constant_values=self.padding_value			)
        elif self.padding_side == "left":
          if return_attention_mask:
            a		:      Optional[int]       =  np.pad(
                processed_features['attention_mask']		,    (difference, 0)			)
          a		:      Optional[Any]       =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
          a		:      List[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     lowercase_ (   self				:						List[str]		,    __snake_case				:						Union[Dict[str, np.ndarray], BatchFeature]		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						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.'			)

      a		:      Dict       =  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		:      Optional[Any]       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      a		:      str       =  len(__snake_case			) > max_length

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

      return processed_features







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Optional[Any]=False		,    __snake_case				:						Optional[int]=None			):

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






from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

lowerCAmelCase:      List[str]		 =		TypeVar('T')
def    lowerCamelCase__ (		_A       ):
  return (position - 1) // 2
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 1
def    lowerCamelCase__ (		_A       ):
  return (2 * position) + 2

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[Any]			):
      a		:      list[tuple[T, int]]       =  []
      a		:      dict[T, int]       =  {}
      a		:      int       =  0







    def __len__(   self				:						Union[str, Any]			):
      return self.elements







    def __repr__(   self				:						Any			):
      return str(self.heap			)







    def     lowercase_ (   self				:						Optional[int]			):
      # Check if the priority queue is empty
      return self.elements == 0







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an element with given priority to the queue
      self.heap.append((elem, weight)			)
      a		:      int       =  self.elements
      self.elements += 1
      self._bubble_up(__snake_case			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      # Remove and return the element with lowest weight (highest priority)
      if self.elements > 1:
        self._swap_nodes(0		,    self.elements - 1			)
      a     ,			a		:      str       =  self.heap.pop()
      del self.position_map[elem]
      self.elements -= 1
      if self.elements > 0:
        a     ,			a		:      List[Any]       =  self.heap[0]
        self._bubble_down(__snake_case			)
      return elem







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Update the weight of the given key
      a		:      Any       =  self.position_map[elem]
      a		:      Union[str, Any]       =  (elem, weight)
      if position > 0:
        a		:      Optional[Any]       =  get_parent_position(__snake_case			)
        a     ,			a		:      Union[str, Any]       =  self.heap[parent_position]
        if parent_weight > weight:
          self._bubble_up(__snake_case			)
        else:
          self._bubble_down(__snake_case			)
      else:
        self._bubble_down(__snake_case			)







    def     lowercase_ (   self				:						int		,    __snake_case				:						T			):
      # Place a node at the proper position (upward movement) [to be used internally
      # only]
      a		:      Union[str, Any]       =  self.position_map[elem]
      if curr_pos == 0:
        return None
      a		:      Union[str, Any]       =  get_parent_position(__snake_case			)
      a     ,			a		:      Tuple       =  self.heap[curr_pos]
      a     ,			a		:      List[Any]       =  self.heap[parent_position]
      if parent_weight > weight:
        self._swap_nodes(__snake_case		,    __snake_case			)
        return self._bubble_up(__snake_case			)
      return None







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						T			):
      # Place a node at the proper position (downward movement) [to be used
      # internally only]
      a		:      str       =  self.position_map[elem]
      a     ,			a		:      List[Any]       =  self.heap[curr_pos]
      a		:      int       =  get_child_left_position(__snake_case			)
      a		:      Dict       =  get_child_right_position(__snake_case			)
      if child_left_position < self.elements and child_right_position < self.elements:
        a     ,			a		:      int       =  self.heap[child_left_position]
        a     ,			a		:      Optional[Any]       =  self.heap[child_right_position]
        if child_right_weight < child_left_weight and child_right_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      if child_left_position < self.elements:
        a     ,			a		:      Dict       =  self.heap[child_left_position]
        if child_left_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      else:
        return None
      if child_right_position < self.elements:
        a     ,			a		:      List[Any]       =  self.heap[child_right_position]
        if child_right_weight < weight:
          self._swap_nodes(__snake_case		,    __snake_case			)
          return self._bubble_down(__snake_case			)
      return None







    def     lowercase_ (   self				:						List[Any]		,    __snake_case				:						int		,    __snake_case				:						int			):
      # Swap the nodes at the given positions
      a		:      Optional[int]       =  self.heap[nodea_pos][0]
      a		:      int       =  self.heap[nodea_pos][0]
      a     ,			a		:      List[str]       =  (
          self.heap[nodea_pos],
          self.heap[nodea_pos],
      )
      a		:      Tuple       =  nodea_pos
      a		:      Any       =  nodea_pos

class 							a__(      Generic[T]			):







    def __init__(   self				:						Optional[int]			):
      a		:      dict[T, dict[T, int]]       =  {}
      a		:      int       =  0







    def __repr__(   self				:						List[str]			):
      return str(self.connections			)







    def __len__(   self				:						str			):
      return self.nodes







    def     lowercase_ (   self				:						int		,    __snake_case				:						T			):
      # Add a node in the graph if it is not in the graph
      if node not in self.connections:
        a		:      Optional[int]       =  {}
        self.nodes += 1







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						T		,    __snake_case				:						T		,    __snake_case				:						int			):
      # Add an edge between 2 nodes in the graph
      self.add_node(__snake_case			)
      self.add_node(__snake_case			)
      a		:      Any       =  weight
      a		:      Dict       =  weight
def    lowerCamelCase__ (		_A				,    ):
  a		:      dict[T, int]       =  {node: maxsize for node in graph.connections}
  a		:      dict[T, T | None]       =  {node: None for node in graph.connections}

  a		:      MinPriorityQueue[T]       =  MinPriorityQueue()
  for node, weight in dist.items():
    priority_queue.push(_A				,    _A       )

  if priority_queue.is_empty():
    return dist, parent

  # initialization
  a		:      int       =  priority_queue.extract_min()
  a		:      Dict       =  0
  for neighbour in graph.connections[node]:
    if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
      a		:      Tuple       =  dist[node] + graph.connections[node][neighbour]
      priority_queue.update_key(_A				,    dist[neighbour]       )
      a		:      Dict       =  node

    # running prim's algorithm
  while not priority_queue.is_empty():
    a		:      List[str]       =  priority_queue.extract_min()
    for neighbour in graph.connections[node]:
      if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
        a		:      int       =  dist[node] + graph.connections[node][neighbour]
        priority_queue.update_key(_A				,    dist[neighbour]       )
        a		:      Optional[int]       =  node
  return dist, parent