






'''simple docstring'''

def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase    )-> int:
      return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase=0    )-> Dict:
      return sorted(snake_case_		,						key=lambda __UpperCamelCase    : x[column]    )







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=float("""inf"""    )    )-> int:







      for i in range(points_counts - 1    ):
            for j in range(i + 1		,						snake_case_    ):
                  UpperCamelCase				  =					euclidean_distance_sqr(points[i]		,						points[j]    )
                  if current_dis < min_dis:
                        UpperCamelCase				  =					current_dis
      return min_dis







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase=float("""inf"""    )    )-> Dict:







      for i in range(min(6		,						points_counts - 1    )		,						snake_case_    ):
            for j in range(max(0		,						i - 6    )		,						snake_case_    ):
                  UpperCamelCase				  =					euclidean_distance_sqr(points[i]		,						points[j]    )
                  if current_dis < min_dis:
                        UpperCamelCase				  =					current_dis
      return min_dis







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase    )-> Any:







      if points_counts <= 3:
            return dis_between_closest_pair(snake_case_		,						snake_case_    )

      # recursion
      UpperCamelCase				  =					points_counts // 2
      UpperCamelCase				  =					closest_pair_of_points_sqr(
          snake_case_		,						points_sorted_on_y[:mid]		,						snake_case_    )
      UpperCamelCase				  =					closest_pair_of_points_sqr(
          snake_case_		,						points_sorted_on_y[mid:]		,						points_counts - mid    )
      UpperCamelCase				  =					min(snake_case_		,						snake_case_    )
      UpperCamelCase				  =					[]
      for point in points_sorted_on_x:
            if abs(point[0] - points_sorted_on_x[mid][0]    ) < closest_pair_dis:
                  cross_strip.append(snake_case_    )

      UpperCamelCase				  =					dis_between_closest_in_strip(
          snake_case_		,						len(snake_case_    )		,						snake_case_    )
      return min(snake_case_		,						snake_case_    )







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase    )-> List[str]:
      UpperCamelCase				  =					column_based_sort(snake_case_		,						column=0    )
      UpperCamelCase				  =					column_based_sort(snake_case_		,						column=1    )
      return (
          closest_pair_of_points_sqr(
              snake_case_		,						snake_case_		,						snake_case_    )
      ) ** 0.5


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__      =   [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
     print('Distance:', closest_pair_of_points(points, len(points)))







"""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 		__snake_case					(		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 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.post_processor is None:
         __A    :						List[str]						 =     '''microsoft/speecht5_hifigan'''
     super().setup()

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



     '''simple docstring'''




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

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.model.generate_speech(**__lowerCamelCase  )







 def 					UpperCamelCase__(    self					,						__lowerCamelCase  ):



     '''simple docstring'''




     with torch.no_grad():
         return self.post_processor(__lowerCamelCase  ).cpu().detach()




