


# 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        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[Any]			       =		"microsoft/speecht5_tts"
		UpperCamelCase     :					Optional[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."
		)
		UpperCamelCase     :					Optional[int]			       =		"text_reader"
		UpperCamelCase     :					Optional[int]			       =		SpeechTaProcessor
		UpperCamelCase     :					Optional[int]			       =		SpeechTaForTextToSpeech
		UpperCamelCase     :					str			       =		SpeechTaHifiGan

		UpperCamelCase     :					List[Any]			       =		["text"]
		UpperCamelCase     :					int			       =		["audio"]







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					if self.post_processor is None:
								lowerCamelCase    		=  """microsoft/speecht5_hifigan"""
					super().setup()







		def 					__A   ( self		,      A		,      A=None	)  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  self.pre_processor(text=A		,      return_tensors="""pt"""		,      truncation=A	)

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

								lowerCamelCase    		=  load_dataset("""Matthijs/cmu-arctic-xvectors"""		,      split="""validation"""	)
								lowerCamelCase    		=  torch.tensor(embeddings_dataset[73_05]["""xvector"""]	).unsqueeze(0	)

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







		def 					__A   ( self		,      A	)  -> List[Any]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







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





UpperCAmelCase   :    Any     =		[0, 2, 4, 6, 8]
UpperCAmelCase   :    Optional[Any]     =		[1, 3, 5, 7, 9]






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						list[int]  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			if remaining_length == 0:
						if digits[0] == 0 or digits[-1] == 0:
									return 0

						for i in range(length // 2 - 1  ,							-1  ,							-1      ):
									remainder += digits[i] + digits[length - i - 1]

									if remainder % 2 == 0:
												return 0

									remainder //= 10

						return 1

			if remaining_length == 1:
						if remainder % 2 == 0:
									return 0

						lowerCamelCase    		=  0
						for digit in range(10      ):
									lowerCamelCase    		=  digit
									result += reversible_numbers(
									    0  ,							(remainder + 2 * digit) // 10  ,							lowerCamelCase__  ,							lowerCamelCase__      )
						return result

			lowerCamelCase    		=  0
			for digita in range(10      ):
						lowerCamelCase    		=  digita

						if (remainder + digita) % 2 == 0:
									lowerCamelCase    		=  ODD_DIGITS
						else:
									lowerCamelCase    		=  EVEN_DIGITS

						for digita in other_parity_digits:
									lowerCamelCase    		=  digita
									result += reversible_numbers(
									    remaining_length - 2  ,							(remainder + digita + digita) // 10  ,							lowerCamelCase__  ,							lowerCamelCase__  ,							)
			return result






def 	__lowerCamelCase						(							lowerCamelCase__		:						int = 9      ):





			'''simple docstring'''




			lowerCamelCase    		=  0
			for length in range(1  ,							max_power + 1      ):
						result += reversible_numbers(lowerCamelCase__  ,							0  ,							[0] * length  ,							lowerCamelCase__      )
			return result


if __name__ == "__main__":
		print(f"""{solution() = }""")

