
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy





class       _snake_case					(     snake_case				):
			UpperCamelCase__          =    'EncodecFeatureExtractor'
			UpperCamelCase__          =    ('T5Tokenizer', 'T5TokenizerFast')







			def __init__(				self						,   _a						,   _a		):
				super().__init__(_a						,   _a		)
				__magic_name__		:		int	   =     self.feature_extractor
				__magic_name__		:		List[str]	   =     False







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a=None						,   _a=None						,   _a=True		):
				return self.tokenizer.get_decoder_prompt_ids(task=_a						,   language=_a						,   no_timestamps=_a		)







			def __call__(				self						,   *_a						,   **_a		):
				# For backward compatibility
				if self._in_target_context_manager:
					return self.current_processor(*_a						,   **_a		)

				__magic_name__		:		Union[str, Any]	   =     kwargs.pop("audio"						,   _a		)
				__magic_name__		:		List[str]	   =     kwargs.pop("sampling_rate"						,   _a		)
				__magic_name__		:		Tuple	   =     kwargs.pop("text"						,   _a		)
				if len(_a		) > 0:
					__magic_name__		:		Union[str, Any]	   =     args[0]
					__magic_name__		:		str	   =     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:
					__magic_name__		:		Dict	   =     self.tokenizer(_a						,   **_a		)

				if audio is not None:
					__magic_name__		:		Any	   =     self.feature_extractor(_a						,   *_a						,   sampling_rate=_a						,   **_a		)

				if audio is None:
					return inputs

				elif text is None:
					return audio_inputs

				else:
					__magic_name__		:		Union[str, Any]	   =     audio_inputs["input_values"]
					if "padding_mask" in audio_inputs:
						__magic_name__		:		Optional[Any]	   =     audio_inputs["padding_mask"]
					return inputs







			def 			SCREAMING_SNAKE_CASE    (				self						,   *_a						,   **_a		):
				__magic_name__		:		Union[str, Any]	   =     kwargs.pop("audio"						,   _a		)
				__magic_name__		:		List[Any]	   =     kwargs.pop("padding_mask"						,   _a		)

				if len(_a		) > 0:
					__magic_name__		:		List[str]	   =     args[0]
					__magic_name__		:		List[Any]	   =     args[1:]

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







			def 			SCREAMING_SNAKE_CASE    (				self						,   *_a						,   **_a		):
				return self.tokenizer.decode(*_a						,   **_a		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		Optional[Any]	   =     to_numpy(_a		)
				__magic_name__   ,    __magic_name__   ,    __magic_name__		:		Union[str, Any]	   =     audio_values.shape

				if padding_mask is None:
					return list(_a		)

				__magic_name__		:		Tuple	   =     to_numpy(_a		)

				# 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)
				__magic_name__		:		str	   =     seq_len - padding_mask.shape[-1]
				__magic_name__		:		str	   =     1 - self.feature_extractor.padding_value
				__magic_name__		:		List[Any]	   =     np.pad(_a						,   ((0, 0), (0, difference))						,   "constant"						,   constant_values=_a		)

				__magic_name__		:		Tuple	   =     audio_values.tolist()
				for i in range(_a		):
					__magic_name__		:		Dict	   =     np.asarray(audio_values[i]		)[
					    padding_mask[i][None, :] != self.feature_extractor.padding_value
					]
					__magic_name__		:		List[str]	   =     sliced_audio.reshape(_a						,   -1		)

				return audio_values


from __future__ import annotations





class       _snake_case					:







			def __init__(				self						,   _a		):
				__magic_name__		:		Optional[Any]	   =     data
				__magic_name__		:		Node | None	   =     None
				__magic_name__		:		Node | None	   =     None







def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		None:  # In Order traversal of the tree







	'''simple docstring'''


	if tree:
		display(tree.left   )
		print(tree.data   )
		display(tree.right   )






def   lowerCAmelCase_						(					_snake_case      :  Node | None   )				->		int:







	'''simple docstring'''


	return 1 + max(depth_of_tree(tree.left   )      ,   depth_of_tree(tree.right   )   ) if tree else 0






def   lowerCAmelCase_						(					_snake_case      :  Node   )				->		bool:







	'''simple docstring'''


	if not tree:
		return True
	if tree.left and tree.right:
		return is_full_binary_tree(tree.left   ) and is_full_binary_tree(tree.right   )
	else:
		return not tree.left and not tree.right






def   lowerCAmelCase_						(					)				->		None:  # Main function for testing.







	'''simple docstring'''


	__magic_name__		:		int	   =     Node(1   )
	__magic_name__		:		Union[str, Any]	   =     Node(2   )
	__magic_name__		:		Tuple	   =     Node(3   )
	__magic_name__		:		Optional[Any]	   =     Node(4   )
	__magic_name__		:		Union[str, Any]	   =     Node(5   )
	__magic_name__		:		Any	   =     Node(6   )
	__magic_name__		:		int	   =     Node(7   )
	__magic_name__		:		List[str]	   =     Node(8   )
	__magic_name__		:		Union[str, Any]	   =     Node(9   )

	print(is_full_binary_tree(_snake_case   )   )
	print(depth_of_tree(_snake_case   )   )
	print("Tree is: "   )
	display(_snake_case   )


if __name__ == "__main__":
				main()
