




import pytest

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




@pytest.mark.parametrize('''dataset_size'''	,					[None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize('''input_in_memory_max_size'''	,					['''default''', 0, 100 * 2**20, 900 * 2**20]				)
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Any				)  ->							Optional[Any]:
    if input_in_memory_max_size != "default":
        monkeypatch.setattr(datasets.config	,					'''IN_MEMORY_MAX_SIZE'''	,					__UpperCamelCase				)
    UpperCAmelCase_										=					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:
        UpperCAmelCase_										=					dataset_size < in_memory_max_size
    else:
        UpperCAmelCase_										=					False
    UpperCAmelCase_										=					is_small_dataset(__UpperCamelCase				)
    assert result == expected











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  ['input_values', 'padding_mask']
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      int = 1 ,   __snake_case       :      int = 2_40_00 ,   __snake_case       :      float = 0.0 ,   __snake_case       :      float = None ,   __snake_case       :      float = None ,   **__snake_case       :      Dict ,   ):
         super().__init__(feature_size=__snake_case ,   sampling_rate=__snake_case ,   padding_value=__snake_case ,   **__snake_case    )
         UpperCAmelCase_										=					chunk_length_s
         UpperCAmelCase_										=					overlap
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1 ,   int((1.0 - self.overlap) * self.chunk_length    )    )




     def __call__(     self       :      List[str] ,   __snake_case       :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,   __snake_case       :      Optional[Union[bool, str, PaddingStrategy]] = None ,   __snake_case       :      Optional[bool] = False ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Optional[int] = None ,   ):
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                     F' {self.sampling_rate} and not {sampling_rate}.'    )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''    )

         if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
         elif padding is None:
             # by default let's pad the inputs
             UpperCAmelCase_										=					True

         UpperCAmelCase_										=					bool(
             isinstance(__snake_case ,   (list, tuple)    ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)    ))    )

         if is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case ,   dtype=np.floataa    ).T for audio in raw_audio]
         elif not is_batched and not isinstance(__snake_case ,   np.ndarray    ):
             UpperCAmelCase_										=					np.asarray(__snake_case ,   dtype=np.floataa    )
         elif isinstance(__snake_case ,   np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             UpperCAmelCase_										=					raw_audio.astype(np.floataa    )

         # always return batch
         if not is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case    ).T]

         # verify inputs are valid
         for idx, example in enumerate(__snake_case    ):
             if example.ndim > 2:
                 raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'    )
             if self.feature_size == 1 and example.ndim != 1:
                 raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                 raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'    )

         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					BatchFeature({'''input_values''': raw_audio}    )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                 UpperCAmelCase_										=					min(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.floor(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                 UpperCAmelCase_										=					max(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.ceil(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                 UpperCAmelCase_										=					'''max_length'''
             else:
                 UpperCAmelCase_										=					input_values

        # normal padding on batch
         if padded_inputs is None:
             UpperCAmelCase_										=					self.pad(
                 __snake_case ,   max_length=__snake_case ,   truncation=__snake_case ,   padding=__snake_case ,   return_attention_mask=__snake_case ,   )
             if padding:
                 UpperCAmelCase_										=					padded_inputs.pop('''attention_mask'''    )

         UpperCAmelCase_										=					[]
         for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                 UpperCAmelCase_										=					example[..., None]
             input_values.append(example.T    )

         UpperCAmelCase_										=					input_values
         if return_tensors is not None:
             UpperCAmelCase_										=					padded_inputs.convert_to_tensors(__snake_case    )

         return padded_inputs





