from collections.abc import Callable


class 						lowercase    :




							def __init__(						self   :					List[str]	,							_UpperCamelCase   :					Callable | None = None      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       []
												# Stores indexes of each item for supporting updates and deletion.
												SCREAMING_SNAKE_CASE    			=       {}
												# Stores current size of heap.
												SCREAMING_SNAKE_CASE    			=       0
												# Stores function used to evaluate the score of an item on which basis ordering
												# will be done.
												SCREAMING_SNAKE_CASE    			=       key or (lambda _UpperCamelCase      : x)




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					int      )					->				int | None:



												'''simple docstring'''

												return int((i - 1) / 2      ) if i > 0 else None




							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					int      )					->				int | None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       int(2 * i + 1      )
												return left if 0 < left < self.size else None




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					int      )					->				int | None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       int(2 * i + 2      )
												return right if 0 < right < self.size else None




							def 	__snake_case(						self   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       (
												    self.pos_map[self.arr[j][0]],
												    self.pos_map[self.arr[i][0]],
												)
												# Then swap the items in the list.
												SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       self.arr[j], self.arr[i]




							def 	__snake_case(						self   :					List[str]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int      )					->				bool:



												'''simple docstring'''

												return self.arr[i][1] < self.arr[j][1]




							def 	__snake_case(						self   :					Any	,							_UpperCamelCase   :					int      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self._left(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       self._right(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       i

												if left is not None and not self._cmp(_UpperCamelCase	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       left
												if right is not None and not self._cmp(_UpperCamelCase	,							_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       right

												return valid_parent




							def 	__snake_case(						self   :					Optional[int]	,							_UpperCamelCase   :					int      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self._parent(_UpperCamelCase      )
												while parent is not None and not self._cmp(_UpperCamelCase	,							_UpperCamelCase      ):
																	self._swap(_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       parent, self._parent(_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					int      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self._get_valid_parent(_UpperCamelCase      )
												while valid_parent != index:
																	self._swap(_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       valid_parent, self._get_valid_parent(_UpperCamelCase      )




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int      )					->				None:



												'''simple docstring'''

												if item not in self.pos_map:
																	return
												SCREAMING_SNAKE_CASE    			=       self.pos_map[item]
												SCREAMING_SNAKE_CASE    			=       [item, self.key(_UpperCamelCase      )]
												# Make sure heap is right in both up and down direction.
												# Ideally only one of them will make any change.
												self._heapify_up(_UpperCamelCase      )
												self._heapify_down(_UpperCamelCase      )




							def 	__snake_case(						self   :					str	,							_UpperCamelCase   :					int      )					->				None:



												'''simple docstring'''

												if item not in self.pos_map:
																	return
												SCREAMING_SNAKE_CASE    			=       self.pos_map[item]
												del self.pos_map[item]
												SCREAMING_SNAKE_CASE    			=       self.arr[self.size - 1]
												SCREAMING_SNAKE_CASE    			=       index
												self.size -= 1
												# Make sure heap is right in both up and down direction. Ideally only one
												# of them will make any change- so no performance loss in calling both.
												if self.size > index:
																	self._heapify_up(_UpperCamelCase      )
																	self._heapify_down(_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int      )					->				None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       len(self.arr      )
												if arr_len == self.size:
																	self.arr.append([item, self.key(_UpperCamelCase      )]      )
												else:
																	SCREAMING_SNAKE_CASE    			=       [item, self.key(_UpperCamelCase      )]
												SCREAMING_SNAKE_CASE    			=       self.size
												self.size += 1
												self._heapify_up(self.size - 1      )




							def 	__snake_case(						self   :					List[Any]      )					->				tuple | None:



												'''simple docstring'''

												return self.arr[0] if self.size else None





							def 	__snake_case(						self   :					int      )					->				tuple | None:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.get_top()
												if top_item_tuple:
																	self.delete_item(top_item_tuple[0]      )
												return top_item_tuple





def 	__lowerCamelCase							():
					pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()





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 						lowercase    (     a     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					float	,							**_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       feature_size
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       padding_value

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("padding_side"	,							"right"      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("return_attention_mask"	,							_UpperCamelCase      )

												super().__init__(**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = True	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if isinstance(_UpperCamelCase	,							(list, tuple)      ) and isinstance(processed_features[0]	,							(dict, BatchFeature)      ):
																	SCREAMING_SNAKE_CASE    			=       {
																	    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()      )}"      )

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

												if len(_UpperCamelCase      ) == 0:
																	if return_attention_mask:
																						SCREAMING_SNAKE_CASE    			=       []
																	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

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

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

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

        # Convert padding_strategy in PaddingStrategy
												SCREAMING_SNAKE_CASE    			=       self._get_padding_strategies(padding=_UpperCamelCase	,							max_length=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

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

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

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

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

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

												return BatchFeature(_UpperCamelCase	,							tensor_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )

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

												SCREAMING_SNAKE_CASE    			=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCamelCase      ) < max_length

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

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

												return processed_features




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				Optional[int]:



												'''simple docstring'''

												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."      )

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

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) > max_length

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

												return processed_features





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					Tuple=None      )					->				Tuple:



												'''simple docstring'''

												if padding is not False:
																	if padding is True:
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy(_UpperCamelCase      )
																	elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       padding
												else:
																	SCREAMING_SNAKE_CASE    			=       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




