



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_    :  Union[str, Any]						  =		logging.get_logger(__name__)





class  lowerCamelCase__	(    UpperCAmelCase_):
     """simple docstring"""



     def __init__(self					,      __a					,      __a					,      __a					,      **__a     ):






           '''simple docstring'''



           lowerCamelCase             =		feature_size
           lowerCamelCase             =		sampling_rate
           lowerCamelCase             =		padding_value

           lowerCamelCase             =		kwargs.pop("padding_side"					,      "right"     )
           lowerCamelCase             =		kwargs.pop("return_attention_mask"					,      __a     )

           super().__init__(**__a     )







     def     _a   (self					,      __a					,      __a = True					,      __a = None					,      __a = False					,      __a = None					,      __a = None					,      __a = None					,      ):






           '''simple docstring'''



           if isinstance(__a					,      (list, tuple)     ) and isinstance(processed_features[0]					,      (dict, BatchFeature)     ):
                 lowerCamelCase             =		{
                     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()     )}"""     )

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

           if len(__a     ) == 0:
                 if return_attention_mask:
                       lowerCamelCase             =		[]
                 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

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

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

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

        # Convert padding_strategy in PaddingStrategy
           lowerCamelCase             =		self._get_padding_strategies(padding=__a					,      max_length=__a     )

           lowerCamelCase             =		processed_features[self.model_input_names[0]]

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

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

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

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

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

           return BatchFeature(__a					,      tensor_type=__a     )







     def     _a   (self					,      __a					,      __a = None					,      __a = PaddingStrategy.DO_NOT_PAD					,      __a = None					,      __a = None					,      ):






           '''simple docstring'''



           lowerCamelCase             =		processed_features[self.model_input_names[0]]

           if padding_strategy == PaddingStrategy.LONGEST:
                 lowerCamelCase             =		len(__a     )

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

           lowerCamelCase             =		padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__a     ) < max_length

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

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

           return processed_features







     def     _a   (self					,      __a					,      __a = None					,      __a = None					,      __a = None					,      ):






           '''simple docstring'''



           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."     )

           lowerCamelCase             =		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):
                 lowerCamelCase             =		((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

           lowerCamelCase             =		len(__a     ) > max_length

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

           return processed_features







     def     _a   (self					,      __a=False					,      __a=None     ):






           '''simple docstring'''



           if padding is not False:
                 if padding is True:
                       lowerCamelCase             =		PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                 elif not isinstance(__a					,      __a     ):
                       lowerCamelCase             =		PaddingStrategy(__a     )
                 elif isinstance(__a					,      __a     ):
                       lowerCamelCase             =		padding
           else:
                 lowerCamelCase             =		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




from __future__ import annotations

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		2
      lowerCamelCase             =		[]
      while i * i <= n:
            if n % i:
                  i += 1
            else:
                  n //= i
                  factors.append(UpperCAmelCase__			)
      if n > 1:
            factors.append(UpperCAmelCase__			)
      return factors


if __name__ == "__main__":
     import doctest

     doctest.testmod()