






'''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 	SCREAMING_SNAKE_CASE	(      SCREAMING_SNAKE_CASE_  ):
       snake_case__    :     Union[str, Any]   		=			'microsoft/speecht5_tts'
       snake_case__    :     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.'
       )
       snake_case__    :     List[Any]   		=			'text_reader'
       snake_case__    :     Optional[int]   		=			SpeechTaProcessor
       snake_case__    :     Tuple   		=			SpeechTaForTextToSpeech
       snake_case__    :     Optional[int]   		=			SpeechTaHifiGan

       snake_case__    :     Optional[int]   		=			['text']
       snake_case__    :     Optional[int]   		=			['audio']




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Optional[int]	,    A__      : Optional[int]	,    A__      : Tuple=None					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				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."""					)

                 __lowerCamelCase						:       List[Any]				   =				load_dataset("""Matthijs/cmu-arctic-xvectors"""	,    split="""validation"""					)
                 __lowerCamelCase						:       Dict				   =				torch.tensor(embeddings_dataset[7305]["""xvector"""]					).unsqueeze(0					)

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




       def 					a_					(					self      : Optional[int]	,    A__      : Optional[int]					):


            """simple docstring"""







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




       def 					a_					(					self      : int	,    A__      : List[Any]					):


            """simple docstring"""







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











'''simple docstring'''

from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

UpperCAmelCase__						:List[str]          =			typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
UpperCAmelCase__						:Optional[int]          =			typing.Union[np.floataa, int, float]  # noqa: UP007
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return np.sqrt(np.sum((np.asarray(_lowercase     ) - np.asarray(_lowercase     )) ** 2     )     )
def     __lowercase  (_lowercase,						_lowercase     )   ->     VectorOut:


     """simple docstring"""
     return sum((va - va) ** 2 for va, va in zip(_lowercase,						_lowercase     )     ) ** (1 / 2)


if __name__ == "__main__":
   def     __lowercase  ()   ->     None:


        """simple docstring"""
        from timeit import timeit

        print("""Without Numpy"""     )
        print(
            timeit(
                """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
        print("""With Numpy"""     )
        print(
            timeit(
                """euclidean_distance([1, 2, 3], [4, 5, 6])""",						number=10_000,						globals=globals(),						)     )
   benchmark()



