


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


a			:			Tuple												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["input_values", "padding_mask"]






					def __init__(					self			,							snake_case_ = 1			,							snake_case_ = 2_4_0_0_0			,							snake_case_ = 0.0			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      int:







								'''simple docstring'''

								super().__init__(feature_size=snake_case_			,							sampling_rate=snake_case_			,							padding_value=snake_case_			,							**snake_case_    )
								__lowercase			       = chunk_length_s
								__lowercase			       = overlap






					@property
					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								if self.chunk_length_s is None:
											return None
								else:
											return int(self.chunk_length_s * self.sampling_rate    )






					@property
					def A   (					self    ) ->      Optional[int]:







								'''simple docstring'''

								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			,							snake_case_			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							) ->      BatchFeature:







								'''simple docstring'''

								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
											__lowercase			       = True

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

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

								# always return batch
								if not is_batched:
											__lowercase			       = [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'    )

								__lowercase			       = None
								__lowercase			       = 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:
														__lowercase			       = min(array.shape[0] for array in raw_audio    )
														__lowercase			       = int(np.floor(max_length / self.chunk_stride    )    )
														__lowercase			       = (nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
														__lowercase			       = max(array.shape[0] for array in raw_audio    )
														__lowercase			       = int(np.ceil(max_length / self.chunk_stride    )    )
														__lowercase			       = (nb_step - 1) * self.chunk_stride + self.chunk_length
														__lowercase			       = '''max_length'''
											else:
														__lowercase			       = input_values

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

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

								__lowercase			       = input_values
								if return_tensors is not None:
											__lowercase			       = padded_inputs.convert_to_tensors(snake_case_    )

								return padded_inputs






from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							**snake_case_			,							) ->      Dict:







								'''simple docstring'''

								super().__init__(
								    snake_case_			,							split=snake_case_			,							features=snake_case_			,							cache_dir=snake_case_			,							keep_in_memory=snake_case_			,							streaming=snake_case_			,							num_proc=snake_case_			,							**snake_case_			,							)
								__lowercase			       = path_or_paths if isinstance(snake_case_			,							snake_case_    ) else {self.split: path_or_paths}
								__lowercase			       = Text(
								    cache_dir=snake_case_			,							data_files=snake_case_			,							features=snake_case_			,							**snake_case_			,							)






					def A   (					self    ) ->      int:







								'''simple docstring'''

								if self.streaming:
											__lowercase			       = self.builder.as_streaming_dataset(split=self.split    )
								# Build regular (map-style) dataset
								else:
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None
											__lowercase			       = None

											self.builder.download_and_prepare(
											    download_config=snake_case_			,							download_mode=snake_case_			,							verification_mode=snake_case_			,							base_path=snake_case_			,							num_proc=self.num_proc			,							)
											__lowercase			       = self.builder.as_dataset(
											    split=self.split			,							verification_mode=snake_case_			,							in_memory=self.keep_in_memory    )
								return dataset


