


# 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    Tuple ='openai/whisper-base'
      UpperCamelCase__				:    Tuple =(
          'This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '
          'transcribed text.'
      )
      UpperCamelCase__				:    Optional[Any] ='transcriber'
      UpperCamelCase__				:    str =WhisperProcessor
      UpperCamelCase__				:    int =WhisperForConditionalGeneration

      UpperCamelCase__				:    Tuple =['audio']
      UpperCamelCase__				:    Union[str, Any] =['text']






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             return self.pre_processor(lowercase_     ,  return_tensors='pt'					).input_features






      def 			lowerCamelCase			(   self		:	Dict     ,  lowercase_		:	int					)   ->		Dict:
             """simple docstring"""




             return self.model.generate(inputs=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple     ,  lowercase_		:	Tuple					)   ->		str:
             """simple docstring"""




             return self.pre_processor.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)[0]






import numpy as np




def       a_ (       SCREAMING_SNAKE_CASE__   : np.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       return np.where(vector > 0						,     SCREAMING_SNAKE_CASE__						,     (alpha * (np.exp(SCREAMING_SNAKE_CASE__	) - 1))	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


