






import numpy as np


class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  (0, 0)
												lowerCAmelCase__        =  None
												lowerCAmelCase__        =  0
												lowerCAmelCase__        =  0
												lowerCAmelCase__        =  0






						def __eq__(		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.position == cell.position







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												print(self.position				)




class       __SCREAMING_SNAKE_CASE   :






						def __init__(		self       ,  _UpperCamelCase=(5, 5)				):






												"""simple docstring"""
												lowerCAmelCase__        =  np.zeros(_UpperCamelCase				)
												lowerCAmelCase__        =  world_size[0]
												lowerCAmelCase__        =  world_size[1]






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												print(self.w				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  [
												    (-1, -1),
												    (-1, 0),
												    (-1, 1),
												    (0, -1),
												    (0, 1),
												    (1, -1),
												    (1, 0),
												    (1, 1),
												]
												lowerCAmelCase__        =  cell.position[0]
												lowerCAmelCase__        =  cell.position[1]
												lowerCAmelCase__        =  []
												for n in neughbour_cord:
																		lowerCAmelCase__        =  current_x + n[0]
																		lowerCAmelCase__        =  current_y + n[1]
																		if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																								lowerCAmelCase__        =  Cell()
																								lowerCAmelCase__        =  (x, y)
																								lowerCAmelCase__        =  cell
																								neighbours.append(_UpperCamelCase				)
												return neighbours







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   List[str]						)					->				Tuple:

						"""simple docstring"""



						lowerCAmelCase__        =  []
						lowerCAmelCase__        =  []
						_open.append(UpperCamelCase_						)

						while _open:
												lowerCAmelCase__        =  np.argmin([n.f for n in _open]						)
												lowerCAmelCase__        =  _open[min_f]
												_closed.append(_open.pop(UpperCamelCase_						)						)
												if current == goal:
																		break
												for n in world.get_neigbours(UpperCamelCase_						):
																		for c in _closed:
																								if c == n:
																														continue
																		lowerCAmelCase__        =  current.g + 1
																		lowerCAmelCase__  ,    lowerCAmelCase__        =  n.position
																		lowerCAmelCase__  ,    lowerCAmelCase__        =  goal.position
																		lowerCAmelCase__        =  (ya - ya) ** 2 + (xa - xa) ** 2
																		lowerCAmelCase__        =  n.h + n.g

																		for c in _open:
																								if c == n and c.f < n.f:
																														continue
																		_open.append(UpperCamelCase_						)
						lowerCAmelCase__        =  []
						while current.parent is not None:
												path.append(current.position						)
												lowerCAmelCase__        =  current.parent
						path.append(current.position						)
						return path[::-1]


if __name__ == "__main__":
					__snake_case      :      Any             =       Gridworld()
					# Start position and goal
					__snake_case      :      Union[str, Any]             =       Cell()
					__snake_case      :      str             =       (0, 0)
					__snake_case      :      Optional[Any]             =       Cell()
					__snake_case      :      Tuple             =       (4, 4)
					print(f'path from {start.position} to {goal.position}')
					__snake_case      :      str             =       astar(world, start, goal)
					#   Just for visual reasons.
					for i in s:
										__snake_case      :      Tuple             =       1
					print(world.w)













# 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   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''microsoft/speecht5_tts'''
						_SCREAMING_SNAKE_CASE		:    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.'''
						)
						_SCREAMING_SNAKE_CASE		:    int						=   '''text_reader'''
						_SCREAMING_SNAKE_CASE		:    List[str]						=   SpeechTaProcessor
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   SpeechTaForTextToSpeech
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   SpeechTaHifiGan

						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   ['''text''']
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   ['''audio''']






						def  UpperCamelCase__ (		self				):






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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=None				):






												"""simple docstring"""
												lowerCAmelCase__        =  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__        =  load_dataset('Matthijs/cmu-arctic-xvectors'       ,  split='validation'				)
																		lowerCAmelCase__        =  torch.tensor(embeddings_dataset[73_05]['xvector']				).unsqueeze(0				)

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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.model.generate_speech(**_UpperCamelCase				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												with torch.no_grad():
																		return self.post_processor(_UpperCamelCase				).cpu().detach()





