






'''simple docstring'''


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			__a							:							Optional[int]					     =					"EncodecFeatureExtractor"
			__a							:							Union[str, Any]					     =					("T5Tokenizer", "T5TokenizerFast")






			def __init__(   self							:						Any ,      lowercase							:						Any ,      lowercase							:						str			)							->    Dict:

									'''simple docstring'''







									super().__init__(lowercase ,      lowercase			)
									UpperCamelCase__         = self.feature_extractor
									UpperCamelCase__         = False






			def 				A      (   self							:						str ,      lowercase							:						Tuple=None ,      lowercase							:						Any=None ,      lowercase							:						int=True			)							->    Dict:

									'''simple docstring'''







									return self.tokenizer.get_decoder_prompt_ids(task=lowercase ,      language=lowercase ,      no_timestamps=lowercase			)






			def __call__(   self							:						Tuple ,      *lowercase							:						Optional[Any] ,      **lowercase							:						Union[str, Any]			)							->    List[Any]:

									'''simple docstring'''







									if self._in_target_context_manager:
															return self.current_processor(*lowercase ,      **lowercase			)

									UpperCamelCase__         = kwargs.pop("""audio""" ,      lowercase			)
									UpperCamelCase__         = kwargs.pop("""sampling_rate""" ,      lowercase			)
									UpperCamelCase__         = kwargs.pop("""text""" ,      lowercase			)
									if len(lowercase			) > 0:
															UpperCamelCase__         = args[0]
															UpperCamelCase__         = args[1:]

									if audio is None and text is None:
															raise ValueError("""You need to specify either an `audio` or `text` input to process."""			)

									if text is not None:
															UpperCamelCase__         = self.tokenizer(lowercase ,      **lowercase			)

									if audio is not None:
															UpperCamelCase__         = self.feature_extractor(lowercase ,      *lowercase ,      sampling_rate=lowercase ,      **lowercase			)

									if audio is None:
															return inputs

									elif text is None:
															return audio_inputs

									else:
															UpperCamelCase__         = audio_inputs["""input_values"""]
															if "padding_mask" in audio_inputs:
																					UpperCamelCase__         = audio_inputs["""padding_mask"""]
															return inputs






			def 				A      (   self							:						List[str] ,      *lowercase							:						Optional[int] ,      **lowercase							:						int			)							->    int:

									'''simple docstring'''







									UpperCamelCase__         = kwargs.pop("""audio""" ,      lowercase			)
									UpperCamelCase__         = kwargs.pop("""padding_mask""" ,      lowercase			)

									if len(lowercase			) > 0:
															UpperCamelCase__         = args[0]
															UpperCamelCase__         = args[1:]

									if audio_values is not None:
															return self._decode_audio(lowercase ,      padding_mask=lowercase			)
									else:
															return self.tokenizer.batch_decode(*lowercase ,      **lowercase			)






			def 				A      (   self							:						Optional[int] ,      *lowercase							:						Tuple ,      **lowercase							:						int			)							->    Tuple:

									'''simple docstring'''







									return self.tokenizer.decode(*lowercase ,      **lowercase			)







			def 				A      (   self							:						int ,      lowercase							:						int ,      lowercase							:						Optional = None			)							->    List[np.ndarray]:

									'''simple docstring'''







									UpperCamelCase__         = to_numpy(lowercase			)
									UpperCamelCase__    ,   UpperCamelCase__    ,   UpperCamelCase__         = audio_values.shape

									if padding_mask is None:
															return list(lowercase			)

									UpperCamelCase__         = to_numpy(lowercase			)

									# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
									# token (so that the generated audio values are **not** treated as padded tokens)
									UpperCamelCase__         = seq_len - padding_mask.shape[-1]
									UpperCamelCase__         = 1 - self.feature_extractor.padding_value
									UpperCamelCase__         = np.pad(lowercase ,      ((0, 0), (0, difference)) ,      """constant""" ,      constant_values=lowercase			)

									UpperCamelCase__         = audio_values.tolist()
									for i in range(lowercase			):
															UpperCamelCase__         = np.asarray(audio_values[i]			)[
															    padding_mask[i][None, :] != self.feature_extractor.padding_value
															]
															UpperCamelCase__         = sliced_audio.reshape(lowercase ,      -1			)

									return audio_values








'''simple docstring'''


from collections import defaultdict







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = 1
						UpperCamelCase__         = True
						for v in tree[start]:
												if v not in visited:
																		ret += dfs(_A					)
						if ret % 2 == 0:
												cuts.append(_A					)
						return ret







def 	__magic_name__( ):
						'''simple docstring'''





						dfs(1					)


if __name__ == "__main__":
					lowerCamelCase_	,     lowerCamelCase_				: int    	=							10, 9
					lowerCamelCase_				: int    	=							defaultdict(list)
					lowerCamelCase_				: dict[int, bool]    	=							{}
					lowerCamelCase_				: list[int]    	=							[]
					lowerCamelCase_				: Union[str, Any]    	=							0
					lowerCamelCase_				: Tuple    	=							[(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
					for u, v in edges:
										tree[u].append(v)
										tree[v].append(u)
					even_tree()
					print(len(cuts) - 1)
