




_SCREAMING_SNAKE_CASE    :      str          =			[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						List[Any]							,						__UpperCamelCase :						str							,						__UpperCamelCase :						int       ) ->       str:

				"""simple docstring"""







				A__  :	str         =							[False] * len(_UpperCAmelCase       )
				A__  :	Optional[Any]         =							[s]
				A__  :	int         =							True

				while queue:
								A__  :	str         =							queue.pop(0       )
								for ind in range(len(graph[u]       )       ):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(_UpperCAmelCase       )
																A__  :	Optional[int]         =							True
																A__  :	Tuple         =							u

				return visited[t]

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]							,						__UpperCamelCase :						Union[str, Any]							,						__UpperCamelCase :						Union[str, Any]       ) ->       str:

				"""simple docstring"""







				A__  :	Dict         =							[-1] * (len(_UpperCAmelCase       ))
				A__  :	Union[str, Any]         =							0
				A__  :	Optional[Any]         =							[]
				A__  :	str         =							[i[:] for i in graph]  # Record original cut, copy.
				while bfs(_UpperCAmelCase							,						_UpperCAmelCase							,						_UpperCAmelCase							,						_UpperCAmelCase       ):
								A__  :	Tuple         =							float('''Inf'''       )
								A__  :	Union[str, Any]         =							sink

								while s != source:
												# Find the minimum value in select path
												A__  :	Dict         =							min(_UpperCAmelCase							,						graph[parent[s]][s]       )
												A__  :	Optional[int]         =							parent[s]

								max_flow += path_flow
								A__  :	Tuple         =							sink

								while v != source:
												A__  :	Tuple         =							parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												A__  :	Tuple         =							parent[v]

				for i in range(len(_UpperCAmelCase       )       ):
								for j in range(len(graph[0]       )       ):
												if graph[i][j] == 0 and temp[i][j] > 0:
																res.append((i, j)       )

				return res


if __name__ == "__main__":
					print(mincut(test_graph, source=0, sink=5))

# 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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"microsoft/speecht5_tts"
	_lowerCAmelCase							       =				(
	    "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."
	)
	_lowerCAmelCase							       =				"text_reader"
	_lowerCAmelCase							       =				SpeechTaProcessor
	_lowerCAmelCase							       =				SpeechTaForTextToSpeech
	_lowerCAmelCase							       =				SpeechTaHifiGan

	_lowerCAmelCase							       =				["text"]
	_lowerCAmelCase							       =				["audio"]





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





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	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.'''		)

									A__  :	List[Any]         =							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   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.model.generate_speech(**UpperCamelCase__		)





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