

# 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    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       int  							=	'''microsoft/speecht5_tts'''
					UpperCAmelCase__:       List[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.'''
					)
					UpperCAmelCase__:       int  							=	'''text_reader'''
					UpperCAmelCase__:       List[str]  							=	SpeechTaProcessor
					UpperCAmelCase__:       Optional[Any]  							=	SpeechTaForTextToSpeech
					UpperCAmelCase__:       int  							=	SpeechTaHifiGan

					UpperCAmelCase__:       List[str]  							=	['''text''']
					UpperCAmelCase__:       List[Any]  							=	['''audio''']






					def   __A      (			self      ):
						if self.post_processor is None:
							A__							:   Union[str, Any]					       =		"""microsoft/speecht5_hifigan"""
						super().setup()






					def   __A      (			self   ,	A__   ,	A__=None      ):
						A__							:   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."""      )

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

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






					def   __A      (			self   ,	A__      ):
						with torch.no_grad():
							return self.model.generate_speech(**A__      )






					def   __A      (			self   ,	A__      ):
						with torch.no_grad():
							return self.post_processor(A__      ).cpu().detach()






from queue import PriorityQueue
from typing import Any

import numpy as np

def 						UpperCamelCase (lowercase_:			dict   ,				lowercase_:			str   ,				lowercase_:			set   ,				lowercase_:			set   ,				lowercase_:			dict   ,				lowercase_:			dict   ,				lowercase_:			PriorityQueue   ,				lowercase_:			dict   ,				lowercase_:			float | int   ,				)					->				float | int:
	for nxt, d in graph[v]:
		if nxt in visited_forward:
			continue
		A__							:   Any					       =		cst_fwd.get(lowercase_   ,				np.inf )
		A__							:   List[Any]					       =		cst_fwd[v] + d
		if new_cost_f < old_cost_f:
			queue.put((new_cost_f, nxt) )
			A__							:   Tuple					       =		new_cost_f
			A__							:   Any					       =		v
		if nxt in visited_backward:
			if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
				A__							:   Optional[int]					       =		cst_fwd[v] + d + cst_bwd[nxt]
	return shortest_distance

def 						UpperCamelCase (lowercase_:			str   ,				lowercase_:			str   ,				lowercase_:			dict   ,				lowercase_:			dict )					->				int:
	A__							:   Dict					       =		-1

	A__							:   List[Any]					       =		set()
	A__							:   Union[str, Any]					       =		set()
	A__							:   Optional[Any]					       =		{source: 0}
	A__							:   int					       =		{destination: 0}
	A__							:   Optional[Any]					       =		{source: None}
	A__							:   Union[str, Any]					       =		{destination: None}
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()
	A__							:   PriorityQueue[Any]					       =		PriorityQueue()

	A__							:   List[Any]					       =		np.inf

	queue_forward.put((0, source) )
	queue_backward.put((0, destination) )

	if source == destination:
		return 0

	while not queue_forward.empty() and not queue_backward.empty():
		A__       ,       A__							:   Tuple					       =		queue_forward.get()
		visited_forward.add(lowercase_ )

		A__       ,       A__							:   Optional[Any]					       =		queue_backward.get()
		visited_backward.add(lowercase_ )

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		A__							:   List[Any]					       =		pass_and_relaxation(
		    lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				lowercase_   ,				)

		if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
			break

	if shortest_distance != np.inf:
		A__							:   int					       =		shortest_distance
	return shortest_path_distance


A_	:			List[Any]  	=      {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A_	:			Optional[int]  	=      {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
	import doctest

	doctest.testmod()



