


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
			from datasets import load_dataset


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      Optional[int]  				=			'''microsoft/speecht5_tts'''
							_lowercase     :      Union[str, Any]  				=			(
							    '''This is a tool that reads an English text out loud. It takes an input named `text` which should contain the '''
							    '''text to read (in English) and returns a waveform object containing the sound.'''
							)
							_lowercase     :      Any  				=			'''text_reader'''
							_lowercase     :      List[str]  				=			SpeechTaProcessor
							_lowercase     :      Dict  				=			SpeechTaForTextToSpeech
							_lowercase     :      Tuple  				=			SpeechTaHifiGan

							_lowercase     :      List[Any]  				=			['''text''']
							_lowercase     :      List[str]  				=			['''audio''']

							def 				lowerCamelCase_			(							self:     str							)      ->       int:





									"""simple docstring"""







									if self.post_processor is None:
											lowercase__						=				'''microsoft/speecht5_hifigan'''
									super().setup()

							def 				lowerCamelCase_			(							self:     str   ,						UpperCamelCase_:     int   ,						UpperCamelCase_:     Any=None							)      ->       str:





									"""simple docstring"""







									lowercase__						=				self.pre_processor(text=UpperCamelCase_   ,						return_tensors='''pt'''   ,						truncation=UpperCamelCase_							)

									if speaker_embeddings is None:
											if not is_datasets_available():
													raise ImportError('''Datasets needs to be installed if not passing speaker embeddings.'''							)

											lowercase__						=				load_dataset('''Matthijs/cmu-arctic-xvectors'''   ,						split='''validation'''							)
											lowercase__						=				torch.tensor(embeddings_dataset[7_305]['''xvector''']							).unsqueeze(0							)

									return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}

							def 				lowerCamelCase_			(							self:     List[str]   ,						UpperCamelCase_:     Any							)      ->       Dict:





									"""simple docstring"""







									with torch.no_grad():
											return self.model.generate_speech(**UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Dict   ,						UpperCamelCase_:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									with torch.no_grad():
											return self.post_processor(UpperCamelCase_							).cpu().detach()






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class 	_a							:
							_lowercase     :      int
							_lowercase     :      TreeNode | None  				=			None
							_lowercase     :      TreeNode | None  				=			None
lowerCAmelCase	=							namedtuple('CoinsDistribResult', 'moves excess')
def       _a			(				SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




		if root is None:
				return 0

		# Validation
		def count_nodes(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_nodes(node.left					) + count_nodes(node.right					) + 1

		def count_coins(SCREAMING_SNAKE_CASE					) -> int:
				if node is None:
						return 0

				return count_coins(node.left					) + count_coins(node.right					) + node.data

		if count_nodes(SCREAMING_SNAKE_CASE					) != count_coins(SCREAMING_SNAKE_CASE					):
				raise ValueError('''The nodes number should be same as the number of coins'''					)

		# Main calculation
		def get_distrib(SCREAMING_SNAKE_CASE					) -> CoinsDistribResult:

				if node is None:
						return CoinsDistribResult(0						,    1					)

				lowercase__					,		lowercase__						=				get_distrib(node.left					)
				lowercase__					,		lowercase__						=				get_distrib(node.right					)

				lowercase__						=				1 - left_distrib_excess
				lowercase__						=				1 - right_distrib_excess

				lowercase__						=				(
				    left_distrib_moves
				    + right_distrib_moves
				    + abs(SCREAMING_SNAKE_CASE					)
				    + abs(SCREAMING_SNAKE_CASE					)
				)
				lowercase__						=				node.data - coins_to_left - coins_to_right

				return CoinsDistribResult(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)

		return get_distrib(SCREAMING_SNAKE_CASE					)[0]


if __name__ == "__main__":
			import doctest

			doctest.testmod()


