






import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset

@pytest.mark.parametrize('''dataset_size''',  [None, 4_0_0 * 2**2_0, 6_0_0 * 2**2_0] )
@pytest.mark.parametrize('''input_in_memory_max_size''',  ['''default''', 0, 1_0_0 * 2**2_0, 9_0_0 * 2**2_0] )
def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if input_in_memory_max_size != "default":
								monkeypatch.setattr(datasets.config,  '''IN_MEMORY_MAX_SIZE''',  snake_case )
				__magic_name__		:Union[str, Any]        =      datasets.config.IN_MEMORY_MAX_SIZE
				if input_in_memory_max_size == "default":
								assert in_memory_max_size == 0
				else:
								assert in_memory_max_size == input_in_memory_max_size
				if dataset_size and in_memory_max_size:
								__magic_name__		:int        =      dataset_size < in_memory_max_size
				else:
								__magic_name__		:Dict        =      False
				__magic_name__		:List[str]        =      is_small_dataset(snake_case )
				assert result == expected














from ...processing_utils import ProcessorMixin
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''WhisperFeatureExtractor'''
				a__					       =  '''WhisperTokenizer'''







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__(__lowerCAmelCase       ,					__lowerCAmelCase     )
								__magic_name__		:int        =      self.feature_extractor
								__magic_name__		:int        =      False







				def 			A	(						self       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=True     ):
								"""simple docstring"""
								return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase       ,					language=__lowerCAmelCase       ,					no_timestamps=__lowerCAmelCase     )







				def __call__(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								# For backward compatibility
								if self._in_target_context_manager:
												return self.current_processor(*__lowerCAmelCase       ,					**__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      kwargs.pop('''audio'''       ,					__lowerCAmelCase     )
								__magic_name__		:Any        =      kwargs.pop('''sampling_rate'''       ,					__lowerCAmelCase     )
								__magic_name__		:str        =      kwargs.pop('''text'''       ,					__lowerCAmelCase     )
								if len(__lowerCAmelCase     ) > 0:
												__magic_name__		:Any        =      args[0]
												__magic_name__		:Union[str, Any]        =      args[1:]

								if audio is None and text is None:
												raise ValueError('''You need to specify either an `audio` or `text` input to process.'''     )

								if audio is not None:
												__magic_name__		:Optional[int]        =      self.feature_extractor(__lowerCAmelCase       ,					*__lowerCAmelCase       ,					sampling_rate=__lowerCAmelCase       ,					**__lowerCAmelCase     )
								if text is not None:
												__magic_name__		:Optional[int]        =      self.tokenizer(__lowerCAmelCase       ,					**__lowerCAmelCase     )

								if text is None:
												return inputs

								elif audio is None:
												return encodings
								else:
												__magic_name__		:str        =      encodings['''input_ids''']
												return inputs







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase="np"     ):
								"""simple docstring"""
								return self.tokenizer.get_prompt_ids(__lowerCAmelCase       ,					return_tensors=__lowerCAmelCase     )






