
import numpy as np

lowercase  :					List[Any]						    =							[
    ["""a""", """b""", """c""", """d""", """e"""],
    ["""f""", """g""", """h""", """i""", """k"""],
    ["""l""", """m""", """n""", """o""", """p"""],
    ["""q""", """r""", """s""", """t""", """u"""],
    ["""v""", """w""", """x""", """y""", """z"""],
]

class __snake_case		:
    def __init__(  self      ):



           '''simple docstring'''







           lowercase							:     int       	=      np.array(snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase	,			lowercase							:     Tuple       	=      np.where(letter == self.SQUARE      )
           lowercase							:     List[str]       	=      np.concatenate([indexa + 1, indexa + 1]      )
           return indexes
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''simple docstring'''







           lowercase							:     str       	=      self.SQUARE[indexa - 1, indexa - 1]
           return letter
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     List[str]       	=      message.lower()
           lowercase							:     str       	=      message.replace(""" """		,""""""      )
           lowercase							:     Tuple       	=      message.replace("""j"""		,"""i"""      )

           lowercase							:     List[str]       	=      np.empty((2, len(snake_case      ))      )
           for letter_index in range(len(snake_case      )      ):
                  lowercase							:     List[str]       	=      self.letter_to_numbers(message[letter_index]      )

                  lowercase							:     List[str]       	=      numbers[0]
                  lowercase							:     Union[str, Any]       	=      numbers[1]

           lowercase							:     str       	=      first_step.reshape(2 * len(snake_case      )      )
           lowercase							:     Dict       	=      """"""
           for numbers_index in range(len(snake_case      )      ):
                  lowercase							:     Any       	=      int(second_step[numbers_index * 2]      )
                  lowercase							:     Optional[int]       	=      int(second_step[(numbers_index * 2) + 1]      )
                  lowercase							:     List[str]       	=      self.numbers_to_letter(snake_case		,snake_case      )
                  lowercase							:     Tuple       	=      encoded_message + letter

           return encoded_message
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      message.lower()
           message.replace(""" """		,""""""      )
           lowercase							:     Dict       	=      np.empty(2 * len(snake_case      )      )
           for letter_index in range(len(snake_case      )      ):
                  lowercase							:     Optional[int]       	=      self.letter_to_numbers(message[letter_index]      )
                  lowercase							:     List[str]       	=      numbers[0]
                  lowercase							:     Any       	=      numbers[1]

           lowercase							:     Optional[int]       	=      first_step.reshape((2, len(snake_case      ))      )
           lowercase							:     Tuple       	=      """"""
           for numbers_index in range(len(snake_case      )      ):
                  lowercase							:     Any       	=      int(second_step[0, numbers_index]      )
                  lowercase							:     Optional[int]       	=      int(second_step[1, numbers_index]      )
                  lowercase							:     List[str]       	=      self.numbers_to_letter(snake_case		,snake_case      )
                  lowercase							:     Any       	=      decoded_message + letter

           return decoded_message


# 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		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "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."
    )
    _a		:						Dict=				"text_reader"
    _a		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

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

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

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







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