





'''simple docstring'''






from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

lowerCAmelCase       		=      typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
lowerCAmelCase       		=      typing.Union[np.floataa, int, float]  # noqa: UP007






def      __A						(							a_						:  Vector      ,a_						:  Vector	):
		return np.sqrt(np.sum((np.asarray(a_	) - np.asarray(a_	)) ** 2	)	)






def      __A						(							a_						:  Vector      ,a_						:  Vector	):
		return sum((va - va) ** 2 for va, va in zip(a_      ,a_	)	) ** (1 / 2)


if __name__ == "__main__":






					def      __A						(							):
							from timeit import timeit

							print("Without Numpy"	)
							print(
							    timeit(
							        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"      ,number=1_0_0_0_0      ,globals=globals()      ,)	)
							print("With Numpy"	)
							print(
							    timeit(
							        "euclidean_distance([1, 2, 3], [4, 5, 6])"      ,number=1_0_0_0_0      ,globals=globals()      ,)	)

					benchmark()









'''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 		lowerCamelCase  (      _A       ):
	snake_case_      =   "microsoft/speecht5_tts"
	snake_case_      =   (
	    "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."
	)
	snake_case_      =   "text_reader"
	snake_case_      =   SpeechTaProcessor
	snake_case_      =   SpeechTaForTextToSpeech
	snake_case_      =   SpeechTaHifiGan

	snake_case_      =   ["text"]
	snake_case_      =   ["audio"]



	def   _lowerCamelCase    (   self	):
			if self.post_processor is None:
					lowerCAmelCase     :    str						=   "microsoft/speecht5_hifigan"
			super().setup()



	def   _lowerCamelCase    (   self		,      a_		,      a_=None	):
			lowerCAmelCase     :    str						=   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     :    Tuple						=   load_dataset("Matthijs/cmu-arctic-xvectors"		,      split="validation"	)
					lowerCAmelCase     :    Union[str, Any]						=   torch.tensor(embeddings_dataset[7_305]["xvector"]	).unsqueeze(0	)

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



	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.model.generate_speech(**a_	)





	def   _lowerCamelCase    (   self		,      a_	):
			with torch.no_grad():
					return self.post_processor(a_	).cpu().detach()



