





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout




def     A_      (	lowercase_	)      ->    Union[str, Any]:
       _snake_case   :				Optional[Any]  					=    FileLock(str(tmpdir / '''foo.lock'''	)	)
       _snake_case   :				Union[str, Any]  					=    FileLock(str(tmpdir / '''foo.lock'''	)	)
       _snake_case   :				List[Any]  					=    0.01
       with locka.acquire():
              with pytest.raises(__A	):
                     _snake_case   :				List[Any]  					=    time.time()
                     locka.acquire(__A	)
                     assert time.time() - _start > timeout




def     A_      (	lowercase_	)      ->    Optional[Any]:
       _snake_case   :				Tuple  					=    '''a''' * 1000 + '''.lock'''
       _snake_case   :				List[Any]  					=    FileLock(str(tmpdir / filename	)	)
       assert locka._lock_file.endswith('''.lock'''	)
       assert not locka._lock_file.endswith(__A	)
       assert len(os.path.basename(locka._lock_file	)	) <= 255
       _snake_case   :				Optional[int]  					=    FileLock(tmpdir / filename	)
       with locka.acquire():
              with pytest.raises(__A	):
                     locka.acquire(0	)














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





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"openai/whisper-base"
			snake_case__	=					(
			    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
			    "transcribed text."
			)
			snake_case__	=					"transcriber"
			snake_case__	=					WhisperProcessor
			snake_case__	=					WhisperForConditionalGeneration

			snake_case__	=					["audio"]
			snake_case__	=					["text"]






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							return self.pre_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:
							return self.model.generate(inputs=__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Optional[Any]:
							return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )[0]




