




'''simple docstring'''





# 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  _snake_case		(	UpperCamelCase_							):
			lowerCAmelCase_     :      List[str]						       =    "microsoft/speecht5_tts"
			lowerCAmelCase_     :      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."
			)
			lowerCAmelCase_     :      Optional[int]						       =    "text_reader"
			lowerCAmelCase_     :      str						       =    SpeechTaProcessor
			lowerCAmelCase_     :      Union[str, Any]						       =    SpeechTaForTextToSpeech
			lowerCAmelCase_     :      Optional[int]						       =    SpeechTaHifiGan

			lowerCAmelCase_     :      Optional[Any]						       =    ["text"]
			lowerCAmelCase_     :      int						       =    ["audio"]




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

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							a__				,							a__=None							)				->				int:

										'''simple docstring'''




										snake_case_						=      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."							)

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

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




			def    lowerCAmelCase__				(					self				,							a__							)				->				str:

										'''simple docstring'''




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




			def    lowerCAmelCase__				(					self				,							a__							)				->				int:

										'''simple docstring'''




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





def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





