




'''simple docstring'''





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   _a							(		__lowerCAmelCase				):
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		["""input_values""", """padding_mask"""]






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






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






     @property
     def 							_lowercase      (					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   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = False   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = None   ,_SCREAMING_SNAKE_CASE = 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				= True

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

          if is_batched:
               _snake_case				= [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				= 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				= raw_audio.astype(np.floataa       )

          # always return batch
          if not is_batched:
               _snake_case				= [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				= None
          _snake_case				= 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				= min(array.shape[0] for array in raw_audio       )
                    _snake_case				= int(np.floor(max_length / self.chunk_stride       )       )
                    _snake_case				= (nb_step - 1) * self.chunk_stride + self.chunk_length
               elif padding:
                    _snake_case				= max(array.shape[0] for array in raw_audio       )
                    _snake_case				= int(np.ceil(max_length / self.chunk_stride       )       )
                    _snake_case				= (nb_step - 1) * self.chunk_stride + self.chunk_length
                    _snake_case				= "max_length"
               else:
                    _snake_case				= input_values

        # normal padding on batch
          if padded_inputs is None:
               _snake_case				= 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				= padded_inputs.pop("attention_mask"       )

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

          _snake_case				= input_values
          if return_tensors is not None:
               _snake_case				= 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       )


