






'''simple docstring'''



from random import randint, random



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	bool = False,					SCREAMING_SNAKE_CASE__:	bool = False,					SCREAMING_SNAKE_CASE__:	int = 5,					)  ->			list:

   """simple docstring"""
   __a				     =    [[-1] * number_of_cells]  # Create a highway without any car
   __a				     =    0
   __a				     =    max(SCREAMING_SNAKE_CASE__,					0    )
   while i < number_of_cells:
      __a				     =    (
          randint(0,					SCREAMING_SNAKE_CASE__    ) if random_speed else initial_speed
      )  # Place the cars
      i += (
          randint(1,					max_speed * 2    ) if random_frequency else frequency
      )  # Arbitrary number, may need tuning
   return highway



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   __a				     =    0
   __a				     =    highway_now[car_index + 1 :]
   for cell in range(len(SCREAMING_SNAKE_CASE__    )    ):  # May need a better name for this
      if cells[cell] != -1:  # If the cell is not empty then
         return distance  # we have the distance we wanted
      distance += 1
   # Here if the car is near the end of the highway
   return distance + get_distance(SCREAMING_SNAKE_CASE__,					-1    )



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	float,					SCREAMING_SNAKE_CASE__:	int    )  ->			list:

   """simple docstring"""
   __a				     =    len(SCREAMING_SNAKE_CASE__    )
   # Beforce calculations, the highway is empty
   __a				     =    [-1] * number_of_cells

   for car_index in range(SCREAMING_SNAKE_CASE__    ):
      if highway_now[car_index] != -1:
         # Add 1 to the current speed of the car and cap the speed
         __a				     =    min(highway_now[car_index] + 1,					SCREAMING_SNAKE_CASE__    )
         # Number of empty cell before the next car
         __a				     =    get_distance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ) - 1
         # We can't have the car causing an accident
         __a				     =    min(next_highway[car_index],					SCREAMING_SNAKE_CASE__    )
         if random() < probability:
            # Randomly, a driver will slow down
            __a				     =    max(next_highway[car_index] - 1,					0    )
   return next_highway



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	list,					SCREAMING_SNAKE_CASE__:	int,					SCREAMING_SNAKE_CASE__:	float,					SCREAMING_SNAKE_CASE__:	int    )  ->			list:

   """simple docstring"""
   __a				     =    len(highway[0]    )

   for i in range(SCREAMING_SNAKE_CASE__    ):
      __a				     =    update(highway[i],					SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    )
      __a				     =    [-1] * number_of_cells

      for car_index in range(SCREAMING_SNAKE_CASE__    ):
         __a				     =    next_speeds_calculated[car_index]
         if speed != -1:
            # Change the position based on the speed (with % to create the loop)
            __a				     =    (car_index + speed) % number_of_cells
            # Commit the change of position
            __a				     =    speed
      highway.append(SCREAMING_SNAKE_CASE__    )

   return highway


if __name__ == "__main__":
 import doctest

 doctest.testmod()







'''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       (							_lowerCAmelCase     ):
      __a  					="microsoft/speecht5_tts"
      __a  					=(
          "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  					="text_reader"
      __a  					=SpeechTaProcessor
      __a  					=SpeechTaForTextToSpeech
      __a  					=SpeechTaHifiGan

      __a  					=["text"]
      __a  					=["audio"]




      def  __UpperCamelCase							(  self      )							->str:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase=None      )							->str:



         '''simple docstring'''
         __a				     =    self.pre_processor(text=lowerCamelCase ,   return_tensors='pt' ,   truncation=lowerCamelCase      )

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

            __a				     =    load_dataset('Matthijs/cmu-arctic-xvectors' ,   split='validation'      )
            __a				     =    torch.tensor(embeddings_dataset[7305]['xvector']      ).unsqueeze(0      )

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




      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.model.generate_speech(**lowerCamelCase      )






      def  __UpperCamelCase							(  self ,   lowerCamelCase      )							->List[str]:



         '''simple docstring'''
         with torch.no_grad():
            return self.post_processor(lowerCamelCase      ).cpu().detach()