from statistics import mean, stdev



def A__  (			lowercase:     list,		lowercase:     int = 3					)				->		list:
       A						:     int         =min(lowercase					)
       A						:     int         =max(lowercase					)
       # normalize data
       return [round((x - x_min) / (x_max - x_min),		lowercase					) for x in data]



def A__  (			lowercase:     list,		lowercase:     int = 3					)				->		list:
       A						:     Optional[int]         =mean(lowercase					)
       A						:     Union[str, Any]         =stdev(lowercase					)
       # standardize data
       return [round((x - mu) / (sigma),		lowercase					) for x in data]







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


_lowercase							:      List[Any]      							=logging.get_logger(__name__)



class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  def __init__(				self    :					Optional[int]      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					int      ,	SCREAMING_SNAKE_CASE__    :					float      ,	**SCREAMING_SNAKE_CASE__    :					List[Any]     )    ->				int:
         A						:     Tuple         =feature_size
         A						:     int         =sampling_rate
         A						:     List[str]         =padding_value

         A						:     Tuple         =kwargs.pop('padding_side'      ,	'right'     )
         A						:     str         =kwargs.pop('return_attention_mask'      ,	SCREAMING_SNAKE_CASE__     )

         super().__init__(**SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Union[
          BatchFeature,
          List[BatchFeature],
          Dict[str, BatchFeature],
          Dict[str, List[BatchFeature]],
          List[Dict[str, BatchFeature]],
      ]      ,	SCREAMING_SNAKE_CASE__    :					Union[bool, str, PaddingStrategy] = True      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					bool = False      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[Union[str, TensorType]] = 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(SCREAMING_SNAKE_CASE__      ,	(list, tuple)     ) and isinstance(processed_features[0]      ,	(dict, BatchFeature)     ):
                A						:     Tuple         ={
                    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()     )}'     )

         A						:     Dict         =processed_features[self.model_input_names[0]]
         A						:     int         =(
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

         if len(SCREAMING_SNAKE_CASE__     ) == 0:
                if return_attention_mask:
                       A						:     List[Any]         =[]
                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

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

         if return_tensors is None:
                if is_tf_tensor(SCREAMING_SNAKE_CASE__     ):
                       A						:     List[Any]         ='tf'
                elif is_torch_tensor(SCREAMING_SNAKE_CASE__     ):
                       A						:     Optional[int]         ='pt'
                elif isinstance(SCREAMING_SNAKE_CASE__      ,	(int, float, list, tuple, np.ndarray)     ):
                       A						:     Union[str, Any]         ='np'
                else:
                       raise ValueError(
                           f'type of {first_element} unknown: {type(SCREAMING_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)     ):
                       A						:     int         =to_numpy(SCREAMING_SNAKE_CASE__     )
                else:
                       A						:     List[Any]         =[to_numpy(SCREAMING_SNAKE_CASE__     ) for v in value]

        # Convert padding_strategy in PaddingStrategy
         A						:     List[Any]         =self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE__      ,	max_length=SCREAMING_SNAKE_CASE__     )

         A						:     Optional[int]         =processed_features[self.model_input_names[0]]

         A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     )
         if not all(len(SCREAMING_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.'     )

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

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

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

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

         return BatchFeature(SCREAMING_SNAKE_CASE__      ,	tensor_type=SCREAMING_SNAKE_CASE__     )







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Union[Dict[str, np.ndarray], BatchFeature]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	)    ->				dict:
         A						:     Optional[int]         =processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
                A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     )

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

         A						:     int         =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE__     ) < max_length

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

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

         return processed_features







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any      ,	SCREAMING_SNAKE_CASE__    :					Union[Dict[str, np.ndarray], BatchFeature]      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[int] = None      ,	SCREAMING_SNAKE_CASE__    :					Optional[bool] = None      ,	)    ->				Optional[Any]:
         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.'     )

         A						:     Tuple         =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):
                A						:     Any         =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         A						:     List[str]         =len(SCREAMING_SNAKE_CASE__     ) > max_length

         if needs_to_be_truncated:
                A						:     Union[str, Any]         =processed_features[self.model_input_names[0]][:max_length]
                if "attention_mask" in processed_features:
                       A						:     Dict         =processed_features['attention_mask'][:max_length]

         return processed_features







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Any=False      ,	SCREAMING_SNAKE_CASE__    :					Dict=None     )    ->				Union[str, Any]:

         # Get padding strategy
         if padding is not False:
                if padding is True:
                       A						:     List[Any]         =PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                elif not isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                       A						:     Tuple         =PaddingStrategy(SCREAMING_SNAKE_CASE__     )
                elif isinstance(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__     ):
                       A						:     Optional[int]         =padding
         else:
                A						:     List[str]         =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






