
# 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()

'''simple docstring'''




class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				Any						,					A						:				str						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Optional[int]							      =    None
             _UpperCAmelCase     :  Any							      =    graph

             self._normalize_graph(A						,					A       )
             _UpperCAmelCase     :  List[str]							      =    len(A       )
             _UpperCAmelCase     :  Tuple							      =    None




      def      _A	(       self						:				Any						,					A						:				List[Any]						,					A						:				str       ):
             if sources is int:
                    _UpperCAmelCase     :  List[Any]							      =    [sources]
             if sinks is int:
                    _UpperCAmelCase     :  List[Any]							      =    [sinks]

             if len(A       ) == 0 or len(A       ) == 0:
                    return

             _UpperCAmelCase     :  str							      =    sources[0]
             _UpperCAmelCase     :  Union[str, Any]							      =    sinks[0]

             # make fake vertex if there are more
             # than one source or sink
             if len(A       ) > 1 or len(A       ) > 1:
                    _UpperCAmelCase     :  Dict							      =    0
                    for i in sources:
                           max_input_flow += sum(self.graph[i]       )

                    _UpperCAmelCase     :  str							      =    len(self.graph       ) + 1
                    for room in self.graph:
                           room.insert(0						,					0       )
                    self.graph.insert(0						,					[0] * size       )
                    for i in sources:
                           _UpperCAmelCase     :  Optional[Any]							      =    max_input_flow
                    _UpperCAmelCase     :  List[str]							      =    0

                    _UpperCAmelCase     :  str							      =    len(self.graph       ) + 1
                    for room in self.graph:
                           room.append(0       )
                    self.graph.append([0] * size       )
                    for i in sinks:
                           _UpperCAmelCase     :  Dict							      =    max_input_flow
                    _UpperCAmelCase     :  List[Any]							      =    size - 1




      def      _A	(       self						:				Union[str, Any]       ):
             if self.maximum_flow_algorithm is None:
                    raise Exception("You need to set maximum flow algorithm before."       )
             if self.source_index is None or self.sink_index is None:
                    return 0

             self.maximum_flow_algorithm.execute()
             return self.maximum_flow_algorithm.getMaximumFlow()





      def      _A	(       self						:				Tuple						,					A						:				Dict       ):
             _UpperCAmelCase     :  str							      =    algorithm(self       )

class 	lowerCamelCase_	:
      '''simple docstring'''

      def __init__(       self						:				Any						,					A						:				str       ):
             _UpperCAmelCase     :  Optional[int]							      =    flow_network
             _UpperCAmelCase     :  Any							      =    flow_network.verticesCount
             _UpperCAmelCase     :  List[str]							      =    flow_network.sourceIndex
             _UpperCAmelCase     :  Union[str, Any]							      =    flow_network.sinkIndex
             # it's just a reference, so you shouldn't change
             # it in your algorithms, use deep copy before doing that
             _UpperCAmelCase     :  Any							      =    flow_network.graph
             _UpperCAmelCase     :  Union[str, Any]							      =    False




      def      _A	(       self						:				List[str]       ):
             if not self.executed:
                    self._algorithm()
                    _UpperCAmelCase     :  int							      =    True





      def      _A	(       self						:				List[Any]       ):
             pass

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Union[str, Any]       ):
             super().__init__(A       )
             # use this to save your result
             _UpperCAmelCase     :  Any							      =    -1





      def      _A	(       self						:				Union[str, Any]       ):
             if not self.executed:
                    raise Exception("You should execute algorithm before using its result!"       )

             return self.maximum_flow

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Tuple						,					A						:				int       ):
             super().__init__(A       )

             _UpperCAmelCase     :  List[str]							      =    [[0] * self.verticies_count for i in range(self.verticies_count       )]

             _UpperCAmelCase     :  Union[str, Any]							      =    [0] * self.verticies_count
             _UpperCAmelCase     :  int							      =    [0] * self.verticies_count




      def      _A	(       self						:				Dict       ):
             _UpperCAmelCase     :  Dict							      =    self.verticies_count

             # push some substance to graph
             for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]       ):
                    self.preflow[self.source_index][nextvertex_index] += bandwidth
                    self.preflow[nextvertex_index][self.source_index] -= bandwidth
                    self.excesses[nextvertex_index] += bandwidth

             # Relabel-to-front selection rule
             _UpperCAmelCase     :  Optional[int]							      =    [
                 i
                 for i in range(self.verticies_count       )
                 if i != self.source_index and i != self.sink_index
             ]

             # move through list
             _UpperCAmelCase     :  Any							      =    0
             while i < len(A       ):
                    _UpperCAmelCase     :  int							      =    vertices_list[i]
                    _UpperCAmelCase     :  int							      =    self.heights[vertex_index]
                    self.process_vertex(A       )
                    if self.heights[vertex_index] > previous_height:
                           # if it was relabeled, swap elements
                           # and start from 0 index
                           vertices_list.insert(0						,					vertices_list.pop(A       )       )
                           _UpperCAmelCase     :  Union[str, Any]							      =    0
                    else:
                           i += 1

             _UpperCAmelCase     :  List[Any]							      =    sum(self.preflow[self.source_index]       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				str       ):
             while self.excesses[vertex_index] > 0:
                    for neighbour_index in range(self.verticies_count       ):
                           # if it's neighbour and current vertex is higher
                           if (
                               self.graph[vertex_index][neighbour_index]
                               - self.preflow[vertex_index][neighbour_index]
                               > 0
                               and self.heights[vertex_index] > self.heights[neighbour_index]
                           ):
                                  self.push(A						,					A       )

                    self.relabel(A       )




      def      _A	(       self						:				int						,					A						:				Dict						,					A						:				List[str]       ):
             _UpperCAmelCase     :  int							      =    min(
                 self.excesses[from_index]						,					self.graph[from_index][to_index] - self.preflow[from_index][to_index]						,					)
             self.preflow[from_index][to_index] += preflow_delta
             self.preflow[to_index][from_index] -= preflow_delta
             self.excesses[from_index] -= preflow_delta
             self.excesses[to_index] += preflow_delta





      def      _A	(       self						:				Optional[int]						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    None
             for to_index in range(self.verticies_count       ):
                    if (
                        self.graph[vertex_index][to_index]
                        - self.preflow[vertex_index][to_index]
                        > 0
                    ) and (min_height is None or self.heights[to_index] < min_height):
                           _UpperCAmelCase     :  Tuple							      =    self.heights[to_index]

             if min_height is not None:
                    _UpperCAmelCase     :  Optional[Any]							      =    min_height + 1


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE       :     Optional[int]     =  [0]
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  [3]
  # graph = [
  #     [0, 0, 4, 6, 0, 0],
  #     [0, 0, 5, 2, 0, 0],
  #     [0, 0, 0, 0, 4, 4],
  #     [0, 0, 0, 0, 6, 6],
  #     [0, 0, 0, 0, 0, 0],
  #     [0, 0, 0, 0, 0, 0],
  # ]
  __SCREAMING_SNAKE_CASE       :     List[Any]     =  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

  # prepare our network
  __SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  FlowNetwork(graph, entrances, exits)
  # set algorithm
  flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
  # and calculate
  __SCREAMING_SNAKE_CASE       :     Optional[Any]     =  flow_network.find_maximum_flow()

  print(F'maximum flow is {maximum_flow}')

