


def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					Optional[Any]    =				len(SCREAMING_SNAKE_CASE_	)
  lowerCamelCase :					List[Any]    =				sum(SCREAMING_SNAKE_CASE_	)

  lowerCamelCase :					Tuple    =				[[False for x in range(s + 1	)] for y in range(n + 1	)]

  for i in range(1      ,						n + 1	):
    lowerCamelCase :					List[str]    =				True

  for i in range(1      ,						s + 1	):
    lowerCamelCase :					Dict    =				False

  for i in range(1      ,						n + 1	):
    for j in range(1      ,						s + 1	):
      lowerCamelCase :					Tuple    =				dp[i][j - 1]

      if arr[i - 1] <= j:
        lowerCamelCase :					Dict    =				dp[i][j] or dp[i - 1][j - arr[i - 1]]

  for j in range(int(s / 2	)      ,						-1      ,						-1	):
    if dp[n][j] is True:
      lowerCamelCase :					Any    =				s - 2 * j
      break

  return diff







# 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 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"openai/whisper-base"
   __A :      str     		=		(
       "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
       "transcribed text."
   )
   __A :      Any     		=		"transcriber"
   __A :      Any     		=		WhisperProcessor
   __A :      int     		=		WhisperForConditionalGeneration

   __A :      Any     		=		["audio"]
   __A :      List[str]     		=		["text"]






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.pre_processor(__A						,				return_tensors="pt" ).input_features






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





     return self.model.generate(inputs=__A )






   def _snake_case    (		self						,				__A ):






     """simple docstring"""





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



