

'''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


A_       						=							logging.get_logger(__name__)





class 							_snake_case (       _a						):
   def __init__( self			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      int     ,SCREAMING_SNAKE_CASE__			:      float     ,**SCREAMING_SNAKE_CASE__			:      str							):
     SCREAMING_SNAKE_CASE:Tuple						      =					feature_size
     SCREAMING_SNAKE_CASE:Dict						      =					sampling_rate
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					padding_value

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					kwargs.pop("padding_side"     ,"right"							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					kwargs.pop("return_attention_mask"     ,SCREAMING_SNAKE_CASE__							)

     super().__init__(**SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( 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     ,):
     # 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)							):
       SCREAMING_SNAKE_CASE:Any						      =					{
           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:str						      =					processed_features[self.model_input_names[0]]
     SCREAMING_SNAKE_CASE:Optional[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:
         SCREAMING_SNAKE_CASE: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

     SCREAMING_SNAKE_CASE:Optional[int]						      =					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.
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0
       while len(required_input[index]							) == 0:
         index += 1
       if index < len(SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:List[Any]						      =					required_input[index][0]

     if return_tensors is None:
       if is_tf_tensor(SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:str						      =					"tf"
       elif is_torch_tensor(SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:List[str]						      =					"pt"
       elif isinstance(SCREAMING_SNAKE_CASE__     ,(int, float, list, tuple, np.ndarray)							):
         SCREAMING_SNAKE_CASE: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)							):
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					to_numpy(SCREAMING_SNAKE_CASE__							)
       else:
         SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[to_numpy(SCREAMING_SNAKE_CASE__							) for v in value]

        # Convert padding_strategy in PaddingStrategy
     SCREAMING_SNAKE_CASE:Dict						      =					self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE__     ,max_length=SCREAMING_SNAKE_CASE__							)

     SCREAMING_SNAKE_CASE:str						      =					processed_features[self.model_input_names[0]]

     SCREAMING_SNAKE_CASE:Tuple						      =					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."							)

     SCREAMING_SNAKE_CASE:List[str]						      =					[]
     for i in range(SCREAMING_SNAKE_CASE__							):
       SCREAMING_SNAKE_CASE:Dict						      =					{k: v[i] for k, v in processed_features.items()}
       # truncation
       SCREAMING_SNAKE_CASE: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
       SCREAMING_SNAKE_CASE:Tuple						      =					max(len(input_slice[self.model_input_names[0]]							) for input_slice in truncated_inputs							)
       SCREAMING_SNAKE_CASE:int						      =					PaddingStrategy.MAX_LENGTH

     SCREAMING_SNAKE_CASE:Dict						      =					{}
     for i in range(SCREAMING_SNAKE_CASE__							):
       # padding
       SCREAMING_SNAKE_CASE:List[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:
           SCREAMING_SNAKE_CASE:Dict						      =					[]
         if value.dtype is np.dtype(np.floataa							):
           SCREAMING_SNAKE_CASE:List[str]						      =					value.astype(np.floataa							)
         batch_outputs[key].append(SCREAMING_SNAKE_CASE__							)

     return BatchFeature(SCREAMING_SNAKE_CASE__     ,tensor_type=SCREAMING_SNAKE_CASE__							)
   def     __UpperCamelCase						( 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     ,):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					processed_features[self.model_input_names[0]]

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

     SCREAMING_SNAKE_CASE:Tuple						      =					padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE__							) < max_length

     if return_attention_mask and "attention_mask" not in processed_features:
       SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.ones(len(SCREAMING_SNAKE_CASE__							)     ,dtype=np.intaa							)

     if needs_to_be_padded:
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					max_length - len(SCREAMING_SNAKE_CASE__							)
       if self.padding_side == "right":
         if return_attention_mask:
           SCREAMING_SNAKE_CASE:Tuple						      =					np.pad(
               processed_features["attention_mask"]     ,(0, difference)							)
         SCREAMING_SNAKE_CASE:Dict						      =					((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
         SCREAMING_SNAKE_CASE:int						      =					np.pad(
             SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__     ,"constant"     ,constant_values=self.padding_value							)
       elif self.padding_side == "left":
         if return_attention_mask:
           SCREAMING_SNAKE_CASE:Union[str, Any]						      =					np.pad(
               processed_features["attention_mask"]     ,(difference, 0)							)
         SCREAMING_SNAKE_CASE:Any						      =					((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
         SCREAMING_SNAKE_CASE:str						      =					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     __UpperCamelCase						( self			:      str     ,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     ,):
     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:Optional[int]						      =					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:Union[str, Any]						      =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					len(SCREAMING_SNAKE_CASE__							) > max_length

     if needs_to_be_truncated:
       SCREAMING_SNAKE_CASE:List[Any]						      =					processed_features[self.model_input_names[0]][:max_length]
       if "attention_mask" in processed_features:
         SCREAMING_SNAKE_CASE:Any						      =					processed_features["attention_mask"][:max_length]

     return processed_features







   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      Optional[int]=False     ,SCREAMING_SNAKE_CASE__			:      str=None							):

     # Get padding strategy
     if padding is not False:
       if padding is True:
         SCREAMING_SNAKE_CASE:Tuple						      =					PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
       elif not isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:Any						      =					PaddingStrategy(SCREAMING_SNAKE_CASE__							)
       elif isinstance(SCREAMING_SNAKE_CASE__     ,SCREAMING_SNAKE_CASE__							):
         SCREAMING_SNAKE_CASE:Dict						      =					padding
     else:
       SCREAMING_SNAKE_CASE:Union[str, 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 ..utils import DummyObject, requires_backends





class 							_snake_case (       metaclass=_a						):
   _A       :     Any       =					['''torch''', '''torchsde''']
   def __init__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Tuple							):
     requires_backends(self     ,["torch", "torchsde"]							)
   @classmethod
   def     __UpperCamelCase						( cls			:      Dict     ,*SCREAMING_SNAKE_CASE__			:      Dict     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)







   @classmethod
   def     __UpperCamelCase						( cls			:      List[str]     ,*SCREAMING_SNAKE_CASE__			:      int     ,**SCREAMING_SNAKE_CASE__			:      Optional[Any]							):
     requires_backends(cls     ,["torch", "torchsde"]							)






