






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         =	logging.get_logger(__name__)



class 							_UpperCamelCase			(A_			):


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

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

           super().__init__(**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = True					,						__UpperCamelCase = None					,						__UpperCamelCase = False					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						)->	Optional[int]:
           # 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(__UpperCamelCase					,						(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(__UpperCamelCase	) == 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(__UpperCamelCase					,						(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(__UpperCamelCase	):
                     __lowerCAmelCase								=					required_input[index][0]

           if return_tensors is None:
                if is_tf_tensor(__UpperCamelCase	):
                     __lowerCAmelCase								=					"""tf"""
                elif is_torch_tensor(__UpperCamelCase	):
                     __lowerCAmelCase								=					"""pt"""
                elif isinstance(__UpperCamelCase					,						(int, float, list, tuple, np.ndarray)	):
                     __lowerCAmelCase								=					"""np"""
                else:
                     raise ValueError(
                         F"""type of {first_element} unknown: {type(__UpperCamelCase	)}. """
                         "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(__UpperCamelCase	)
                else:
                     __lowerCAmelCase								=					[to_numpy(__UpperCamelCase	) for v in value]

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

           __lowerCAmelCase								=					processed_features[self.model_input_names[0]]

           __lowerCAmelCase								=					len(__UpperCamelCase	)
           if not all(len(__UpperCamelCase	) == 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(__UpperCamelCase	):
                __lowerCAmelCase								=					{k: v[i] for k, v in processed_features.items()}
                # truncation
                __lowerCAmelCase								=					self._truncate(
                    __UpperCamelCase					,						max_length=__UpperCamelCase					,						pad_to_multiple_of=__UpperCamelCase					,						truncation=__UpperCamelCase					,						)
                truncated_inputs.append(__UpperCamelCase	)

           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(__UpperCamelCase	):
                # padding
                __lowerCAmelCase								=					self._pad(
                    truncated_inputs[i]					,						max_length=__UpperCamelCase					,						padding_strategy=__UpperCamelCase					,						pad_to_multiple_of=__UpperCamelCase					,						return_attention_mask=__UpperCamelCase					,						)

                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(__UpperCamelCase	)

           return BatchFeature(__UpperCamelCase					,						tensor_type=__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase = PaddingStrategy.DO_NOT_PAD					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						)->	List[Any]:
           __lowerCAmelCase								=					processed_features[self.model_input_names[0]]

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

           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(__UpperCamelCase	) < max_length

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

           if needs_to_be_padded:
                __lowerCAmelCase								=					max_length - len(__UpperCamelCase	)
                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(
                         __UpperCamelCase					,						__UpperCamelCase					,						"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(
                         __UpperCamelCase					,						__UpperCamelCase					,						"constant"					,						constant_values=self.padding_value	)
                else:
                     raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

           return processed_features


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						)->	Optional[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."	)

           __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(__UpperCamelCase	) > 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  __UpperCAmelCase		(	self					,						__UpperCamelCase=False					,						__UpperCamelCase=None	)->	Union[str, Any]:

           # Get padding strategy
           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(__UpperCamelCase					,						__UpperCamelCase	):
                     __lowerCAmelCase								=					PaddingStrategy(__UpperCamelCase	)
                elif isinstance(__UpperCamelCase					,						__UpperCamelCase	):
                     __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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):
     __lowerCAmelCase								=					list(range(len(__snake_case       )       )       )
     __lowerCAmelCase								=					[v / w for v, w in zip(__snake_case					, __snake_case       )]
     index.sort(key=lambda __snake_case       : ratio[i]					, reverse=__snake_case       )

     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					[0] * len(__snake_case       )
     for i in index:
          if weight[i] <= capacity:
               __lowerCAmelCase								=					1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __lowerCAmelCase								=					capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
 import doctest

 doctest.testmod()






