






'''simple docstring'''

def     lowercase__ (      __UpperCamelCase    )-> Union[str, Any]:
      UpperCamelCase				  =					0
      UpperCamelCase				  =					len(snake_case_    )

      for i in range(n - 1    ):
            for j in range(i + 1		,						snake_case_    ):
                  if arr[i] > arr[j]:
                        num_inversions += 1

      return num_inversions







def     lowercase__ (      __UpperCamelCase    )-> Optional[int]:







      if len(snake_case_    ) <= 1:
            return arr, 0
      UpperCamelCase				  =					len(snake_case_    ) // 2
      UpperCamelCase				  =					arr[0:mid]
      UpperCamelCase				  =					arr[mid:]

      UpperCamelCase				  =					count_inversions_recursive(snake_case_    )
      UpperCamelCase				  =					count_inversions_recursive(snake_case_    )
      UpperCamelCase				  =					_count_cross_inversions(snake_case_		,						snake_case_    )

      UpperCamelCase				  =					inversion_p + inversions_q + cross_inversions
      return c, num_inversions







def     lowercase__ (      __UpperCamelCase		,						__UpperCamelCase    )-> List[Any]:
      UpperCamelCase				  =					[]
      UpperCamelCase				  =					0
      while i < len(snake_case_    ) and j < len(snake_case_    ):
            if p[i] > q[j]:
                  # if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
                  # These are all inversions. The claim emerges from the
                  # property that P is sorted.
                  num_inversion += len(snake_case_    ) - i
                  r.append(q[j]    )
                  j += 1
            else:
                  r.append(p[i]    )
                  i += 1

      if i < len(snake_case_    ):
            r.extend(p[i:]    )
      else:
            r.extend(q[j:]    )

      return r, num_inversion







def     lowercase__ (      )-> Optional[int]:
      UpperCamelCase				  =					[10, 2, 1, 5, 5, 2, 11]

      # this arr has 8 inversions:
      # (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

      UpperCamelCase				  =					count_inversions_bf(snake_case_    )
      UpperCamelCase				  =					count_inversions_recursive(snake_case_    )

      assert num_inversions_bf == num_inversions_recursive == 8

      print("""number of inversions = """		,						snake_case_    )

      # testing an array with zero inversion (a sorted arr_1)

      arr_a.sort()
      UpperCamelCase				  =					count_inversions_bf(snake_case_    )
      UpperCamelCase				  =					count_inversions_recursive(snake_case_    )

      assert num_inversions_bf == num_inversions_recursive == 0
      print("""number of inversions = """		,						snake_case_    )

      # an empty list should also have zero inversions
      UpperCamelCase				  =					[]
      UpperCamelCase				  =					count_inversions_bf(snake_case_    )
      UpperCamelCase				  =					count_inversions_recursive(snake_case_    )

      assert num_inversions_bf == num_inversions_recursive == 0
      print("""number of inversions = """		,						snake_case_    )


if __name__ == "__main__":
     main()







"""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()




