


'''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


lowercase							=logging.get_logger(__name__)






class  __magic_name__							(		lowerCAmelCase    ):






       def __init__(      self  ,							snake_case  ,							snake_case  ,							snake_case  ,							**snake_case)						->  Optional[int]:




          '''simple docstring'''
          _UpperCAmelCase       :       Dict							   =feature_size
          _UpperCAmelCase       :       Optional[int]							   =sampling_rate
          _UpperCAmelCase       :       Dict							   =padding_value

          _UpperCAmelCase       :       Any							   =kwargs.pop('padding_side'  ,							'right')
          _UpperCAmelCase       :       List[Any]							   =kwargs.pop('return_attention_mask'  ,							snake_case)

          super().__init__(**snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = True  ,							snake_case = None  ,							snake_case = False  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							)						->  BatchFeature:




          '''simple docstring'''
          # 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       :       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())}")

          _UpperCAmelCase       :       Optional[int]							   =processed_features[self.model_input_names[0]]
          _UpperCAmelCase       :       Union[str, Any]							   =(
              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       :       Tuple							   =[]
             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       :       Union[str, Any]							   =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       :       List[Any]							   =0
             while len(required_input[index]) == 0:
                index += 1
             if index < len(snake_case):
                _UpperCAmelCase       :       Dict							   =required_input[index][0]

          if return_tensors is None:
             if is_tf_tensor(snake_case):
                _UpperCAmelCase       :       Optional[Any]							   ='tf'
             elif is_torch_tensor(snake_case):
                _UpperCAmelCase       :       Tuple							   ='pt'
             elif isinstance(snake_case  ,							(int, float, list, tuple, np.ndarray)):
                _UpperCAmelCase       :       Union[str, Any]							   ='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       :       Union[str, Any]							   =to_numpy(snake_case)
             else:
                _UpperCAmelCase       :       Optional[Any]							   =[to_numpy(snake_case) for v in value]

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

          _UpperCAmelCase       :       Tuple							   =processed_features[self.model_input_names[0]]

          _UpperCAmelCase       :       Union[str, Any]							   =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       :       int							   =[]
          for i in range(snake_case):
             _UpperCAmelCase       :       str							   ={k: v[i] for k, v in processed_features.items()}
             # truncation
             _UpperCAmelCase       :       Union[str, Any]							   =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       :       List[Any]							   =max(len(input_slice[self.model_input_names[0]]) for input_slice in truncated_inputs)
             _UpperCAmelCase       :       Optional[Any]							   =PaddingStrategy.MAX_LENGTH

          _UpperCAmelCase       :       List[Any]							   ={}
          for i in range(snake_case):
             # padding
             _UpperCAmelCase       :       Union[str, Any]							   =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       :       Union[str, Any]							   =[]
                if value.dtype is np.dtype(np.floataa):
                   _UpperCAmelCase       :       Optional[int]							   =value.astype(np.floataa)
                batch_outputs[key].append(snake_case)

          return BatchFeature(snake_case  ,							tensor_type=snake_case)






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case = None  ,							snake_case = PaddingStrategy.DO_NOT_PAD  ,							snake_case = None  ,							snake_case = None  ,							)						->  dict:




          '''simple docstring'''
          _UpperCAmelCase       :       int							   =processed_features[self.model_input_names[0]]

          if padding_strategy == PaddingStrategy.LONGEST:
             _UpperCAmelCase       :       Tuple							   =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       :       List[Any]							   =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          _UpperCAmelCase       :       str							   =padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case) < max_length

          if return_attention_mask and "attention_mask" not in processed_features:
             _UpperCAmelCase       :       Optional[Any]							   =np.ones(len(snake_case)  ,							dtype=np.intaa)

          if needs_to_be_padded:
             _UpperCAmelCase       :       int							   =max_length - len(snake_case)
             if self.padding_side == "right":
                if return_attention_mask:
                   _UpperCAmelCase       :       List[Any]							   =np.pad(
                       processed_features['attention_mask']  ,							(0, difference))
                _UpperCAmelCase       :       Dict							   =((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
                _UpperCAmelCase       :       List[str]							   =np.pad(
                    snake_case  ,							snake_case  ,							'constant'  ,							constant_values=self.padding_value)
             elif self.padding_side == "left":
                if return_attention_mask:
                   _UpperCAmelCase       :       Tuple							   =np.pad(
                       processed_features['attention_mask']  ,							(difference, 0))
                _UpperCAmelCase       :       int							   =((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
                _UpperCAmelCase       :       Optional[Any]							   =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  ,							snake_case  ,							snake_case = None  ,							snake_case = None  ,							snake_case = None  ,							)						->  Any:




          '''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.')

          _UpperCAmelCase       :       Any							   =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       :       List[str]							   =((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          _UpperCAmelCase       :       Union[str, Any]							   =len(snake_case) > max_length

          if needs_to_be_truncated:
             _UpperCAmelCase       :       Tuple							   =processed_features[self.model_input_names[0]][:max_length]
             if "attention_mask" in processed_features:
                _UpperCAmelCase       :       Tuple							   =processed_features['attention_mask'][:max_length]

          return processed_features






       def       lowerCAmelCase		(      self  ,							snake_case=False  ,							snake_case=None)						->  Optional[int]:




          '''simple docstring'''

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






from __future__ import annotations





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]							):







   '''simple docstring'''




   if not nums:
      return 0
   _UpperCAmelCase       :       Tuple							   =nums[0]
   _UpperCAmelCase       :       int							   =0
   for num in nums[1:]:
      _UpperCAmelCase   ,	_UpperCAmelCase       :       Optional[int]							   =(
          max_excluding + num,
          max(__lowerCamelCase	,   __lowerCamelCase							),
      )
   return max(__lowerCamelCase	,   __lowerCamelCase							)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

