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


__lowerCamelCase    :				Optional[Any]							     =	logging.get_logger(__name__)







class __snake_case       (		lowerCamelCase_	):




   def __init__(					self    :       Any ,			_lowercase    :       int ,			_lowercase    :       int ,			_lowercase    :       float ,			**_lowercase    :       Optional[int]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   feature_size
     SCREAMING_SNAKE_CASE__    							=   sampling_rate
     SCREAMING_SNAKE_CASE__    							=   padding_value

     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""padding_side""" ,			"""right"""   )
     SCREAMING_SNAKE_CASE__    							=   kwargs.pop("""return_attention_mask""" ,			_lowercase   )

     super().__init__(**_lowercase   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Union[
           BatchFeature,
           List[BatchFeature],
           Dict[str, BatchFeature],
           Dict[str, List[BatchFeature]],
           List[Dict[str, BatchFeature]],
       ] ,			_lowercase    :       Union[bool, str, PaddingStrategy] = True ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       bool = False ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       Optional[bool] = None ,			_lowercase    :       Optional[Union[str, TensorType]] = None ,			):



     """simple docstring"""






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

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

     if len(_lowercase   ) == 0:
       if return_attention_mask:
         SCREAMING_SNAKE_CASE__    							=   []
       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

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

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

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

        # Convert padding_strategy in PaddingStrategy
     SCREAMING_SNAKE_CASE__    							=   self._get_padding_strategies(padding=_lowercase ,			max_length=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   processed_features[self.model_input_names[0]]

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

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

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

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

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

     return BatchFeature(_lowercase ,			tensor_type=_lowercase   )




   def 			__a		(					self    :       List[Any] ,			_lowercase    :       Union[Dict[str, np.ndarray], BatchFeature] ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       Optional[bool] = None ,			):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   processed_features[self.model_input_names[0]]

     if padding_strategy == PaddingStrategy.LONGEST:
       SCREAMING_SNAKE_CASE__    							=   len(_lowercase   )

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

     SCREAMING_SNAKE_CASE__    							=   padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowercase   ) < max_length

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

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

     return processed_features




   def 			__a		(					self    :       List[str] ,			_lowercase    :       Union[Dict[str, np.ndarray], BatchFeature] ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       Optional[int] = None ,			_lowercase    :       Optional[bool] = 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."""   )

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

     SCREAMING_SNAKE_CASE__    							=   len(_lowercase   ) > max_length

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

     return processed_features




   def 			__a		(					self    :       Any ,			_lowercase    :       List[Any]=False ,			_lowercase    :       List[Any]=None   ):



     """simple docstring"""






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

def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: list				,  __UpperCamelCase					: list				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int				,  __UpperCamelCase					: int			)							->      int:







  """simple docstring"""





  if index == number_of_items:
    return 0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   0
  SCREAMING_SNAKE_CASE__    							=   knapsack(__UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  index + 1			)
  if weights[index] <= max_weight:
    SCREAMING_SNAKE_CASE__    							=   values[index] + knapsack(
        __UpperCamelCase				,  __UpperCamelCase				,  __UpperCamelCase				,  max_weight - weights[index]				,  index + 1			)
  return max(__UpperCamelCase				,  __UpperCamelCase			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
