

'''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__											=	logging.get_logger(__name__)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  def __init__(      self   :     Tuple					,lowercase__   :     int					,lowercase__   :     int					,lowercase__   :     float					,**lowercase__   :     str			):
   __lowercase							  = feature_size
   __lowercase							  = sampling_rate
   __lowercase							  = padding_value

   __lowercase							  = kwargs.pop('''padding_side'''					,'''right'''			)
   __lowercase							  = kwargs.pop('''return_attention_mask'''					,lowercase__			)

   super().__init__(**lowercase__			)






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

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

   if len(lowercase__			) == 0:
    if return_attention_mask:
     __lowercase							  = []
    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

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

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

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

        # Convert padding_strategy in PaddingStrategy
   __lowercase							  = self._get_padding_strategies(padding=lowercase__					,max_length=lowercase__			)

   __lowercase							  = processed_features[self.model_input_names[0]]

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

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

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

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

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

   return BatchFeature(lowercase__					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     Union[Dict[str, np.ndarray], BatchFeature]					,lowercase__   :     Optional[int] = None					,lowercase__   :     PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[bool] = None					,):
   __lowercase							  = processed_features[self.model_input_names[0]]

   if padding_strategy == PaddingStrategy.LONGEST:
    __lowercase							  = len(lowercase__			)

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

   __lowercase							  = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowercase__			) < max_length

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

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

   return processed_features






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     Union[Dict[str, np.ndarray], BatchFeature]					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[int] = None					,lowercase__   :     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.'''			)

   __lowercase							  = 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):
    __lowercase							  = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

   __lowercase							  = len(lowercase__			) > max_length

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

   return processed_features






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     Dict=False					,lowercase__   :     Dict=None			):

   # Get padding strategy
   if padding is not False:
    if padding is True:
     __lowercase							  = PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
    elif not isinstance(lowercase__					,lowercase__			):
     __lowercase							  = PaddingStrategy(lowercase__			)
    elif isinstance(lowercase__					,lowercase__			):
     __lowercase							  = padding
   else:
    __lowercase							  = 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 random import random
from typing import Generic, TypeVar

lowerCAmelCase__											=	TypeVar('''KT''')
lowerCAmelCase__											=	TypeVar('''VT''')






class 			lowercase_	(Generic[KT, VT]      ):




  """simple docstring"""




  def __init__(      self   :     Optional[Any]					,lowercase__   :     KT | str = "root"					,lowercase__   :     VT | None = None			):
   __lowercase							  = key
   __lowercase							  = value
   __lowercase							  = []






  def __repr__(      self   :     Tuple			):
   return F"Node({self.key}: {self.value})"






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   return len(self.forward			)






class 			lowercase_	(Generic[KT, VT]      ):




  """simple docstring"""




  def __init__(      self   :     int					,lowercase__   :     float = 0.5					,lowercase__   :     int = 1_6			):
   __lowercase							  = Node[KT, VT]()
   __lowercase							  = 0
   __lowercase							  = p
   __lowercase							  = max_level






  def __str__(      self   :     List[str]			):
   __lowercase							  = list(self			)

   if len(lowercase__			) == 0:
    return F"SkipList(level={self.level})"

   __lowercase							  = max((len(str(lowercase__			)			) for item in items)					,default=4			)
   __lowercase							  = max(lowercase__					,4			) + 4

   __lowercase							  = self.head
   __lowercase							  = []

   __lowercase							  = node.forward.copy()
   lines.append(F"[{node.key}]".ljust(lowercase__					,'''-'''			) + '''* ''' * len(lowercase__			)			)
   lines.append(''' ''' * label_size + '''| ''' * len(lowercase__			)			)

   while len(node.forward			) != 0:
    __lowercase							  = node.forward[0]

    lines.append(
        F"[{node.key}]".ljust(lowercase__					,'''-'''			)
        + ''' '''.join(str(n.key			) if n.key == node.key else '''|''' for n in forwards			)			)
    lines.append(''' ''' * label_size + '''| ''' * len(lowercase__			)			)
    __lowercase							  = node.forward

   lines.append('''None'''.ljust(lowercase__			) + '''* ''' * len(lowercase__			)			)
   return F"SkipList(level={self.level})\n" + "\n".join(lowercase__			)






  def __iter__(      self   :     List[str]			):
   __lowercase							  = self.head

   while len(node.forward			) != 0:
    yield node.forward[0].key
    __lowercase							  = node.forward[0]






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = 1
   while random() < self.p and level < self.max_level:
    level += 1

   return level






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     str			):
   __lowercase							  = []

   __lowercase							  = self.head

   for i in reversed(range(self.level			)			):
    # i < node.level - When node level is lesser than `i` decrement `i`.
    # node.forward[i].key < key - Jumping to node with key value higher
    #                             or equal to searched key would result
    #                             in skipping searched key.
    while i < node.level and node.forward[i].key < key:
     __lowercase							  = node.forward[i]
    # Each leftmost node (relative to searched node) will potentially have to
    # be updated.
    update_vector.append(lowercase__			)

   update_vector.reverse()  # Note that we were inserting values in reverse order.

   # len(node.forward) != 0 - If current node doesn't contain any further
   #                          references then searched key is not present.
   # node.forward[0].key == key - Next node key should be equal to search key
   #                              if key is present.
   if len(node.forward			) != 0 and node.forward[0].key == key:
    return node.forward[0], update_vector
   else:
    return None, update_vector






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     KT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)

   if node is not None:
    for i, update_node in enumerate(lowercase__			):
     # Remove or replace all references to removed node.
     if update_node.level > i and update_node.forward[i].key == key:
      if node.level > i:
       __lowercase							  = node.forward[i]
      else:
       __lowercase							  = update_node.forward[:i]






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     KT					,lowercase__   :     VT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)
   if node is not None:
    __lowercase							  = value
   else:
    __lowercase							  = self.random_level()

    if level > self.level:
     # After level increase we have to add additional nodes to head.
     for _ in range(self.level - 1					,lowercase__			):
      update_vector.append(self.head			)
     __lowercase							  = level

    __lowercase							  = Node(lowercase__					,lowercase__			)

    for i, update_node in enumerate(update_vector[:level]			):
     # Change references to pass through new node.
     if update_node.level > i:
      new_node.forward.append(update_node.forward[i]			)

     if update_node.level < i + 1:
      update_node.forward.append(lowercase__			)
     else:
      __lowercase							  = new_node






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     VT			):
   __lowercase  ,	__lowercase							  = self._locate_node(lowercase__			)

   if node is not None:
    return node.value

   return None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert('''Key1'''     ,				3      )
 skip_list.insert('''Key2'''     ,				12      )
 skip_list.insert('''Key3'''     ,				41      )
 skip_list.insert('''Key4'''     ,				-19      )

 __lowercase							  = skip_list.head
 __lowercase							  = {}
 while node.level != 0:
  __lowercase							  = node.forward[0]
  __lowercase							  = node.value

 assert len(A__      ) == 4
 assert all_values["Key1"] == 3
 assert all_values["Key2"] == 12
 assert all_values["Key3"] == 41
 assert all_values["Key4"] == -19


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert('''Key1'''     ,				10      )
 skip_list.insert('''Key1'''     ,				12      )

 skip_list.insert('''Key5'''     ,				7      )
 skip_list.insert('''Key7'''     ,				10      )
 skip_list.insert('''Key10'''     ,				5      )

 skip_list.insert('''Key7'''     ,				7      )
 skip_list.insert('''Key5'''     ,				5      )
 skip_list.insert('''Key10'''     ,				10      )

 __lowercase							  = skip_list.head
 __lowercase							  = {}
 while node.level != 0:
  __lowercase							  = node.forward[0]
  __lowercase							  = node.value

 if len(A__      ) != 4:
  print()
 assert len(A__      ) == 4
 assert all_values["Key1"] == 12
 assert all_values["Key7"] == 7
 assert all_values["Key5"] == 5
 assert all_values["Key10"] == 10


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 assert skip_list.find('''Some key'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key2'''     ,				20      )
 assert skip_list.find('''Key2'''      ) == 20

 skip_list.insert('''Some Key'''     ,				10      )
 skip_list.insert('''Key2'''     ,				8      )
 skip_list.insert('''V'''     ,				13      )

 assert skip_list.find('''Y'''      ) is None
 assert skip_list.find('''Key2'''      ) == 8
 assert skip_list.find('''Some Key'''      ) == 10
 assert skip_list.find('''V'''      ) == 13


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.delete('''Some key'''      )

 assert len(skip_list.head.forward      ) == 0


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				14      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''V'''      )
 skip_list.delete('''Key2'''      )

 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''Key2'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				14      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''V'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) == 14
 assert skip_list.find('''Key1'''      ) == 12
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''X'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) == 12
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''Key1'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) is None
 assert skip_list.find('''Key2'''      ) == 15

 skip_list.delete('''Key2'''      )
 assert skip_list.find('''V'''      ) is None
 assert skip_list.find('''X'''      ) is None
 assert skip_list.find('''Key1'''      ) is None
 assert skip_list.find('''Key2'''      ) is None


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()

 skip_list.insert('''Key1'''     ,				12      )
 skip_list.insert('''V'''     ,				13      )
 skip_list.insert('''X'''     ,				142      )
 skip_list.insert('''Key2'''     ,				15      )

 skip_list.delete('''X'''      )

 def traverse_keys(A__      ):
  yield node.key
  for forward_node in node.forward:
   yield from traverse_keys(A__      )

 assert len(set(traverse_keys(skip_list.head      )      )      ) == 4


def 		_A     (    ):






 """simple docstring"""



 def is_sorted(A__      ):
  return all(next_item >= item for item, next_item in zip(A__     ,				lst[1:]      )      )

 __lowercase							  = SkipList()
 for i in range(10      ):
  skip_list.insert(A__     ,				A__      )
 assert is_sorted(list(A__      )      )
 skip_list.delete(5      )
 skip_list.delete(8      )
 skip_list.delete(2      )
 assert is_sorted(list(A__      )      )
 skip_list.insert(-12     ,				-12      )
 skip_list.insert(77     ,				77      )
 assert is_sorted(list(A__      )      )


def 		_A     (    ):






 """simple docstring"""



 for _ in range(100      ):
  # Repeat test 100 times due to the probabilistic nature of skip list
  # random values == random bugs
  test_insert()
  test_insert_overrides_existing_value()

  test_searching_empty_list_returns_none()
  test_search()

  test_deleting_item_from_empty_list_do_nothing()
  test_deleted_items_are_not_founded_by_find_method()
  test_delete_removes_only_given_key()
  test_delete_doesnt_leave_dead_nodes()

  test_iter_always_yields_sorted_values()


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = SkipList()
 skip_list.insert(2     ,				'''2'''      )
 skip_list.insert(4     ,				'''4'''      )
 skip_list.insert(6     ,				'''4'''      )
 skip_list.insert(4     ,				'''5'''      )
 skip_list.insert(8     ,				'''4'''      )
 skip_list.insert(9     ,				'''4'''      )

 skip_list.delete(4      )

 print(A__      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()

