



'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


a__    :      List[str]      				=      logging.get_logger(__name__)
class 							UpperCAmelCase__	(     SCREAMING_SNAKE_CASE_):



 def __init__(	self		,						lowercase		,						lowercase		,						lowercase		,						**lowercase      )	->				str:
  __UpperCamelCase					   =	feature_size
  __UpperCamelCase					   =	sampling_rate
  __UpperCamelCase					   =	padding_value

  __UpperCamelCase					   =	kwargs.pop("""padding_side"""		,						"""right"""      )
  __UpperCamelCase					   =	kwargs.pop("""return_attention_mask"""		,						__snake_case      )

  super().__init__(**__snake_case      )



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase = True		,						lowercase = None		,						lowercase = False		,						lowercase = None		,						lowercase = None		,						lowercase = None		,						)	->				BatchFeature:
  # If we have a list of dicts, let's convert it in a dict of lists
  # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
  if isinstance(__snake_case		,						(list, tuple)      ) and isinstance(processed_features[0]		,						(dict, BatchFeature)      ):
   __UpperCamelCase					   =	{
       key: [example[key] for example in processed_features] for key in processed_features[0].keys()
   }

  # The model's main input name, usually `input_values`, has be passed for padding
  if self.model_input_names[0] not in processed_features:
   raise ValueError(
       """You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"""
       f" to this method that includes {self.model_input_names[0]}, but you provided"
       f" {list(processed_features.keys()      )}"      )

  __UpperCamelCase					   =	processed_features[self.model_input_names[0]]
  __UpperCamelCase					   =	(
      return_attention_mask if return_attention_mask is not None else self.return_attention_mask
  )

  if len(__snake_case      ) == 0:
   if return_attention_mask:
    __UpperCamelCase					   =	[]
   return processed_features

  # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
  # and rebuild them afterwards if no return_tensors is specified
  # Note that we lose the specific device the tensor may be on for PyTorch

  __UpperCamelCase					   =	required_input[0]
  if isinstance(__snake_case		,						(list, tuple)      ):
   # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
   __UpperCamelCase					   =	0
   while len(required_input[index]      ) == 0:
    index += 1
   if index < len(__snake_case      ):
    __UpperCamelCase					   =	required_input[index][0]

  if return_tensors is None:
   if is_tf_tensor(__snake_case      ):
    __UpperCamelCase					   =	"""tf"""
   elif is_torch_tensor(__snake_case      ):
    __UpperCamelCase					   =	"""pt"""
   elif isinstance(__snake_case		,						(int, float, list, tuple, np.ndarray)      ):
    __UpperCamelCase					   =	"""np"""
   else:
    raise ValueError(
        f"type of {first_element} unknown: {type(__snake_case      )}. "
        """Should be one of a python, numpy, pytorch or tensorflow object."""      )

  for key, value in processed_features.items():
   if isinstance(value[0]		,						(int, float)      ):
    __UpperCamelCase					   =	to_numpy(__snake_case      )
   else:
    __UpperCamelCase					   =	[to_numpy(__snake_case      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
  __UpperCamelCase					   =	self._get_padding_strategies(padding=__snake_case		,						max_length=__snake_case      )

  __UpperCamelCase					   =	processed_features[self.model_input_names[0]]

  __UpperCamelCase					   =	len(__snake_case      )
  if not all(len(__snake_case      ) == batch_size for v in processed_features.values()      ):
   raise ValueError("""Some items in the output dictionary have a different batch size than others."""      )

  __UpperCamelCase					   =	[]
  for i in range(__snake_case      ):
   __UpperCamelCase					   =	{k: v[i] for k, v in processed_features.items()}
   # truncation
   __UpperCamelCase					   =	self._truncate(
       __snake_case		,						max_length=__snake_case		,						pad_to_multiple_of=__snake_case		,						truncation=__snake_case		,						)
   truncated_inputs.append(__snake_case      )

  if padding_strategy == PaddingStrategy.LONGEST:
   # make sure that `max_length` cannot be longer than the longest truncated length
   __UpperCamelCase					   =	max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
   __UpperCamelCase					   =	PaddingStrategy.MAX_LENGTH

  __UpperCamelCase					   =	{}
  for i in range(__snake_case      ):
   # padding
   __UpperCamelCase					   =	self._pad(
       truncated_inputs[i]		,						max_length=__snake_case		,						padding_strategy=__snake_case		,						pad_to_multiple_of=__snake_case		,						return_attention_mask=__snake_case		,						)

   for key, value in outputs.items():
    if key not in batch_outputs:
     __UpperCamelCase					   =	[]
    if value.dtype is np.dtype(np.floataa      ):
     __UpperCamelCase					   =	value.astype(np.floataa      )
    batch_outputs[key].append(__snake_case      )

  return BatchFeature(__snake_case		,						tensor_type=__snake_case      )



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase = None		,						lowercase = PaddingStrategy.DO_NOT_PAD		,						lowercase = None		,						lowercase = None		,						)	->				dict:
  __UpperCamelCase					   =	processed_features[self.model_input_names[0]]

  if padding_strategy == PaddingStrategy.LONGEST:
   __UpperCamelCase					   =	len(__snake_case      )

  if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
   __UpperCamelCase					   =	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  __UpperCamelCase					   =	padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case      ) < max_length

  if return_attention_mask and "attention_mask" not in processed_features:
   __UpperCamelCase					   =	np.ones(len(__snake_case      )		,						dtype=np.intaa      )

  if needs_to_be_padded:
   __UpperCamelCase					   =	max_length - len(__snake_case      )
   if self.padding_side == "right":
    if return_attention_mask:
     __UpperCamelCase					   =	np.pad(
         processed_features["""attention_mask"""]		,						(0, difference)      )
    __UpperCamelCase					   =	((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
    __UpperCamelCase					   =	np.pad(
        __snake_case		,						__snake_case		,						"""constant"""		,						constant_values=self.padding_value      )
   elif self.padding_side == "left":
    if return_attention_mask:
     __UpperCamelCase					   =	np.pad(
         processed_features["""attention_mask"""]		,						(difference, 0)      )
    __UpperCamelCase					   =	((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
    __UpperCamelCase					   =	np.pad(
        __snake_case		,						__snake_case		,						"""constant"""		,						constant_values=self.padding_value      )
   else:
    raise ValueError("""Invalid padding strategy:""" + str(self.padding_side      )      )

  return processed_features



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase = None		,						lowercase = None		,						lowercase = None		,						)	->				int:
  if not truncation:
   return processed_features
  elif truncation and max_length is None:
   raise ValueError("""When setting ``truncation=True``, make sure that ``max_length`` is defined."""      )

  __UpperCamelCase					   =	processed_features[self.model_input_names[0]]

  # find `max_length` that fits `pad_to_multiple_of`
  if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
   __UpperCamelCase					   =	((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  __UpperCamelCase					   =	len(__snake_case      ) > max_length

  if needs_to_be_truncated:
   __UpperCamelCase					   =	processed_features[self.model_input_names[0]][:max_length]
   if "attention_mask" in processed_features:
    __UpperCamelCase					   =	processed_features["""attention_mask"""][:max_length]

  return processed_features




 def  __lowerCamelCase					(	self		,						lowercase=False		,						lowercase=None      )	->				Optional[Any]:

  # Get padding strategy
  if padding is not False:
   if padding is True:
    __UpperCamelCase					   =	PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
   elif not isinstance(__snake_case		,						__snake_case      ):
    __UpperCamelCase					   =	PaddingStrategy(__snake_case      )
   elif isinstance(__snake_case		,						__snake_case      ):
    __UpperCamelCase					   =	padding
  else:
   __UpperCamelCase					   =	PaddingStrategy.DO_NOT_PAD

  # Set max length if needed
  if max_length is None:
   if padding_strategy == PaddingStrategy.MAX_LENGTH:
    raise ValueError(
        f"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
  if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
   raise ValueError(
       """Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"""
       """ as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."""      )

  return padding_strategy













"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def   a    (     __UpperCAmelCase      :    bytes      ,   __UpperCAmelCase      :    int  )    ->							np.array:
						__magic_name__:	Optional[int]							=       f'{sampling_rate}'
						__magic_name__:	Union[str, Any]							=       """1"""
						__magic_name__:	Tuple							=       """f32le"""
						__magic_name__:	Optional[Any]							=       [
						    """ffmpeg""",
						    """-i""",
						    """pipe:0""",
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]

						try:
												with subprocess.Popen(__UpperCAmelCase      ,   stdin=subprocess.PIPE      ,   stdout=subprocess.PIPE  ) as ffmpeg_process:
																		__magic_name__:	Any							=       ffmpeg_process.communicate(__UpperCAmelCase  )
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""  ) from error
						__magic_name__:	Optional[Any]							=       output_stream[0]
						__magic_name__:	Optional[int]							=       np.frombuffer(__UpperCAmelCase      ,   np.floataa  )
						if audio.shape[0] == 0:
												raise ValueError("""Malformed soundfile"""  )
						return audio



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							Optional[int]:
						__magic_name__:	List[Any]							=       f'{sampling_rate}'
						__magic_name__:	Optional[int]							=       """1"""
						if format_for_conversion == "s16le":
												__magic_name__:	Dict							=       2
						elif format_for_conversion == "f32le":
												__magic_name__:	List[str]							=       4
						else:
												raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'  )

						__magic_name__:	str							=       platform.system()
						if system == "Linux":
												__magic_name__:	Optional[Any]							=       """alsa"""
												__magic_name__:	Optional[Any]							=       """default"""
						elif system == "Darwin":
												__magic_name__:	Union[str, Any]							=       """avfoundation"""
												__magic_name__:	Dict							=       """:0"""
						elif system == "Windows":
												__magic_name__:	Any							=       """dshow"""
												__magic_name__:	Union[str, Any]							=       """default"""

						__magic_name__:	Union[str, Any]							=       [
						    """ffmpeg""",
						    """-f""",
						    format_,
						    """-i""",
						    input_,
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-fflags""",
						    """nobuffer""",
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]
						__magic_name__:	Tuple							=       int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
						__magic_name__:	Union[str, Any]							=       _ffmpeg_stream(__UpperCAmelCase      ,   __UpperCAmelCase  )
						for item in iterator:
												yield item



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    Optional[int] = None      ,   __UpperCAmelCase      :    Optional[Union[Tuple[float, float], float]] = None      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							List[Any]:
						if stream_chunk_s is not None:
												__magic_name__:	Optional[int]							=       stream_chunk_s
						else:
												__magic_name__:	List[str]							=       chunk_length_s

						__magic_name__:	Optional[int]							=       ffmpeg_microphone(__UpperCAmelCase      ,   __UpperCAmelCase      ,   format_for_conversion=__UpperCAmelCase  )
						if format_for_conversion == "s16le":
												__magic_name__:	str							=       np.intaa
												__magic_name__:	str							=       2
						elif format_for_conversion == "f32le":
												__magic_name__:	Optional[Any]							=       np.floataa
												__magic_name__:	Dict							=       4
						else:
												raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'  )

						if stride_length_s is None:
												__magic_name__:	List[Any]							=       chunk_length_s / 6
						__magic_name__:	Optional[Any]							=       int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
						if isinstance(__UpperCAmelCase      ,   (int, float)  ):
												__magic_name__:	Union[str, Any]							=       [stride_length_s, stride_length_s]

						__magic_name__:	List[str]							=       int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
						__magic_name__:	List[Any]							=       int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
						__magic_name__:	List[str]							=       datetime.datetime.now()
						__magic_name__:	Optional[Any]							=       datetime.timedelta(seconds=__UpperCAmelCase  )
						for item in chunk_bytes_iter(__UpperCAmelCase      ,   __UpperCAmelCase      ,   stride=(stride_left, stride_right)      ,   stream=__UpperCAmelCase  ):
												# Put everything back in numpy scale
												__magic_name__:	int							=       np.frombuffer(item["""raw"""]      ,   dtype=__UpperCAmelCase  )
												__magic_name__:	int							=       (
												    item["""stride"""][0] // size_of_sample,
												    item["""stride"""][1] // size_of_sample,
												)
												__magic_name__:	Union[str, Any]							=       sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 1_0 * delta:
																		# We're late !! SKIP
																		continue
												yield item



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    Tuple[int, int]      ,   __UpperCAmelCase      :    bool = False  )    ->							Union[str, Any]:
						__magic_name__:	Tuple							=       B""""""
						__magic_name__,			__magic_name__:	int							=       stride
						if stride_left + stride_right >= chunk_len:
												raise ValueError(
												    f'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'  )
						__magic_name__:	int							=       0
						for raw in iterator:
												acc += raw
												if stream and len(__UpperCAmelCase  ) < chunk_len:
																		__magic_name__:	Optional[int]							=       (_stride_left, 0)
																		yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
												else:
																		while len(__UpperCAmelCase  ) >= chunk_len:
																								# We are flushing the accumulator
																								__magic_name__:	Union[str, Any]							=       (_stride_left, stride_right)
																								__magic_name__:	Any							=       {"""raw""": acc[:chunk_len], """stride""": stride}
																								if stream:
																														__magic_name__:	str							=       False
																								yield item
																								__magic_name__:	str							=       stride_left
																								__magic_name__:	Optional[Any]							=       acc[chunk_len - stride_left - stride_right :]
    # Last chunk
						if len(__UpperCAmelCase  ) > stride_left:
												__magic_name__:	List[Any]							=       {"""raw""": acc, """stride""": (_stride_left, 0)}
												if stream:
																		__magic_name__:	Union[str, Any]							=       False
												yield item



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    int  )    ->							List[Any]:
						__magic_name__:	str							=       2**2_4  # 16Mo
						try:
												with subprocess.Popen(__UpperCAmelCase      ,   stdout=subprocess.PIPE      ,   bufsize=__UpperCAmelCase  ) as ffmpeg_process:
																		while True:
																								__magic_name__:	Dict							=       ffmpeg_process.stdout.read(__UpperCAmelCase  )
																								if raw == b"":
																														break
																								yield raw
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""  ) from error



