



"""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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    **_lowercase   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : Optional[int]						=			feature_size
								snake_case_   : Optional[int]						=			sampling_rate
								snake_case_   : Dict						=			padding_value

								snake_case_   : Any						=			kwargs.pop("""padding_side"""							,    """right"""   )
								snake_case_   : Dict						=			kwargs.pop("""return_attention_mask"""							,    _lowercase   )

								super().__init__(**_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = True							,    _lowercase = None							,    _lowercase = False							,    _lowercase = None							,    _lowercase = None							,    _lowercase = None							,    ) ->						BatchFeature:







								'''simple docstring'''

								if isinstance(_lowercase							,    (list, tuple)   ) and isinstance(processed_features[0]							,    (dict, BatchFeature)   ):
														snake_case_   : Optional[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()   )}'   )

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

								if len(_lowercase   ) == 0:
														if return_attention_mask:
																				snake_case_   : 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

								snake_case_   : Union[str, Any]						=			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.
														snake_case_   : Any						=			0
														while len(required_input[index]   ) == 0:
																				index += 1
														if index < len(_lowercase   ):
																				snake_case_   : Any						=			required_input[index][0]

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

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

								snake_case_   : List[Any]						=			processed_features[self.model_input_names[0]]

								snake_case_   : str						=			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."""   )

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

								snake_case_   : List[Any]						=			{}
								for i in range(_lowercase   ):
														# padding
														snake_case_   : str						=			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:
																										snake_case_   : Optional[Any]						=			[]
																				if value.dtype is np.dtype(np.floataa   ):
																										snake_case_   : List[Any]						=			value.astype(np.floataa   )
																				batch_outputs[key].append(_lowercase   )

								return BatchFeature(_lowercase							,    tensor_type=_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None							,    _lowercase = PaddingStrategy.DO_NOT_PAD							,    _lowercase = None							,    _lowercase = None							,    ) ->						dict:







								'''simple docstring'''

								snake_case_   : List[Any]						=			processed_features[self.model_input_names[0]]

								if padding_strategy == PaddingStrategy.LONGEST:
														snake_case_   : Dict						=			len(_lowercase   )

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

								snake_case_   : str						=			padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowercase   ) < max_length

								if return_attention_mask and "attention_mask" not in processed_features:
														snake_case_   : Any						=			np.ones(len(_lowercase   )							,    dtype=np.intaa   )

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

								return processed_features





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase = None							,    _lowercase = None							,    _lowercase = None							,    ) ->						str:







								'''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."""   )

								snake_case_   : Union[str, Any]						=			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):
														snake_case_   : Union[str, Any]						=			((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

								snake_case_   : List[str]						=			len(_lowercase   ) > max_length

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

								return processed_features





		def    UpperCAmelCase__							(					self							,    _lowercase=False							,    _lowercase=None   ) ->						Tuple:







								'''simple docstring'''

								if padding is not False:
														if padding is True:
																				snake_case_   : Any						=			PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
														elif not isinstance(_lowercase							,    _lowercase   ):
																				snake_case_   : Union[str, Any]						=			PaddingStrategy(_lowercase   )
														elif isinstance(_lowercase							,    _lowercase   ):
																				snake_case_   : Dict						=			padding
								else:
														snake_case_   : 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 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						snake_case_   : int						=			[0] * len(__UpperCamelCase    )
						snake_case_   : List[str]						=			[]
						snake_case_   : Any						=			[1] * len(__UpperCamelCase    )

						for values in graph.values():
												for i in values:
																		indegree[i] += 1

						for i in range(len(__UpperCamelCase    )    ):
												if indegree[i] == 0:
																		queue.append(__UpperCamelCase    )

						while queue:
												snake_case_   : Optional[int]						=			queue.pop(0    )
												for x in graph[vertex]:
																		indegree[x] -= 1

																		if long_dist[vertex] + 1 > long_dist[x]:
																								snake_case_   : Union[str, Any]						=			long_dist[vertex] + 1

																		if indegree[x] == 0:
																								queue.append(__UpperCamelCase    )

						print(max(__UpperCamelCase    )    )


# Adjacency list of Graph
__lowerCAmelCase       :					str      					=					{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}






longest_distance(graph)

