






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


__UpperCAmelCase				: Tuple						    =	logging.get_logger(__name__)


class        _snake_case    (      __lowerCAmelCase			):
	_A 							= ["""input_values""", """padding_mask"""]






	def __init__(		self       ,UpperCamelCase = 1       ,UpperCamelCase = 24_000       ,UpperCamelCase = 0.0       ,UpperCamelCase = None       ,UpperCamelCase = None       ,**UpperCamelCase       ,)    ->     Optional[int]:
					super().__init__(feature_size=_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,padding_value=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE							)
					snake_case__   :str       = chunk_length_s
					snake_case__   :Union[str, Any]       = overlap






	@property
	def  lowerCAmelCase_				(		self							)    ->     Optional[int]:
					if self.chunk_length_s is None:
									return None
					else:
									return int(self.chunk_length_s * self.sampling_rate							)






	@property
	def  lowerCAmelCase_				(		self							)    ->     Optional[int]:
					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       ,UpperCamelCase       ,UpperCamelCase = None       ,UpperCamelCase = False       ,UpperCamelCase = None       ,UpperCamelCase = None       ,UpperCamelCase = None       ,)    ->     BatchFeature:
					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
									snake_case__   :str       = True

					snake_case__   :Optional[Any]       = bool(
					    isinstance(_SCREAMING_SNAKE_CASE       ,(list, tuple)							) and (isinstance(raw_audio[0]       ,(np.ndarray, tuple, list)							))							)

					if is_batched:
									snake_case__   :List[str]       = [np.asarray(_SCREAMING_SNAKE_CASE       ,dtype=np.floataa							).T for audio in raw_audio]
					elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE       ,np.ndarray							):
									snake_case__   :Optional[Any]       = np.asarray(_SCREAMING_SNAKE_CASE       ,dtype=np.floataa							)
					elif isinstance(_SCREAMING_SNAKE_CASE       ,np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
									snake_case__   :int       = raw_audio.astype(np.floataa							)

					# always return batch
					if not is_batched:
									snake_case__   :List[str]       = [np.asarray(_SCREAMING_SNAKE_CASE							).T]

					# verify inputs are valid
					for idx, example in enumerate(_SCREAMING_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'							)

					snake_case__   :int       = None
					snake_case__   :Optional[Any]       = 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:
													snake_case__   :Optional[int]       = min(array.shape[0] for array in raw_audio							)
													snake_case__   :Union[str, Any]       = int(np.floor(max_length / self.chunk_stride							)							)
													snake_case__   :Optional[int]       = (nb_step - 1) * self.chunk_stride + self.chunk_length
									elif padding:
													snake_case__   :Optional[Any]       = max(array.shape[0] for array in raw_audio							)
													snake_case__   :Dict       = int(np.ceil(max_length / self.chunk_stride							)							)
													snake_case__   :int       = (nb_step - 1) * self.chunk_stride + self.chunk_length
													snake_case__   :str       = "max_length"
									else:
													snake_case__   :Union[str, Any]       = input_values

        # normal padding on batch
					if padded_inputs is None:
									snake_case__   :List[Any]       = self.pad(
									    _SCREAMING_SNAKE_CASE       ,max_length=_SCREAMING_SNAKE_CASE       ,truncation=_SCREAMING_SNAKE_CASE       ,padding=_SCREAMING_SNAKE_CASE       ,return_attention_mask=_SCREAMING_SNAKE_CASE       ,)
									if padding:
													snake_case__   :List[str]       = padded_inputs.pop("attention_mask"							)

					snake_case__   :Dict       = []
					for example in padded_inputs.pop("input_values"							):
									if self.feature_size == 1:
													snake_case__   :Optional[Any]       = example[..., None]
									input_values.append(example.T							)

					snake_case__   :str       = input_values
					if return_tensors is not None:
									snake_case__   :str       = padded_inputs.convert_to_tensors(_SCREAMING_SNAKE_CASE							)

					return padded_inputs





'''simple docstring'''





from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version





def       __a      (  _UpperCamelCase:       str   ,		_UpperCamelCase:       str   ,		_UpperCamelCase:       Optional[str] = None       )		->					str:

     """simple docstring"""




     if version.parse(hfh.__version__       ).release < version.parse("0.11.0"       ).release:
          # old versions of hfh don't url-encode the file path
          _snake_case				= quote(_UpperCamelCase       )
     return hfh.hf_hub_url(_UpperCamelCase   ,		_UpperCamelCase   ,		repo_type="dataset"   ,		revision=_UpperCamelCase       )


