






'''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


__UpperCamelCase      :       Optional[int]      			=      logging.get_logger(__name__)
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase ,   **lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    feature_size
         __a				     =    sampling_rate
         __a				     =    padding_value

         __a				     =    kwargs.pop('padding_side' ,   'right'      )
         __a				     =    kwargs.pop('return_attention_mask' ,   lowerCamelCase      )

         super().__init__(**lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = True ,   lowerCamelCase = None ,   lowerCamelCase = False ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = 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(lowerCamelCase ,   (list, tuple)      ) and isinstance(processed_features[0] ,   (dict, BatchFeature)      ):
            __a				     =    {
                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				     =    processed_features[self.model_input_names[0]]
         __a				     =    (
             return_attention_mask if return_attention_mask is not None else self.return_attention_mask
         )

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

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

        # Convert padding_strategy in PaddingStrategy
         __a				     =    self._get_padding_strategies(padding=lowerCamelCase ,   max_length=lowerCamelCase      )

         __a				     =    processed_features[self.model_input_names[0]]

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

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

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

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

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

         return BatchFeature(lowerCamelCase ,   tensor_type=lowerCamelCase      )




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = PaddingStrategy.DO_NOT_PAD ,   lowerCamelCase = None ,   lowerCamelCase = None ,   )							->dict:



         '''simple docstring'''
         __a				     =    processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
            __a				     =    len(lowerCamelCase      )

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

         __a				     =    padding_strategy != PaddingStrategy.DO_NOT_PAD and len(lowerCamelCase      ) < max_length

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

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

         return processed_features




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase = None ,   lowerCamelCase = None ,   lowerCamelCase = 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.'      )

         __a				     =    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				     =    ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

         __a				     =    len(lowerCamelCase      ) > max_length

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

         return processed_features






      def  __UpperCamelCase							(  self ,   lowerCamelCase=False ,   lowerCamelCase=None      )							->List[Any]:



         '''simple docstring'''

         # Get padding strategy
         if padding is not False:
            if padding is True:
               __a				     =    PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
            elif not isinstance(lowerCamelCase ,   lowerCamelCase      ):
               __a				     =    PaddingStrategy(lowerCamelCase      )
            elif isinstance(lowerCamelCase ,   lowerCamelCase      ):
               __a				     =    padding
         else:
            __a				     =    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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCamelCase      :       Optional[int]      			=      {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Dict      			=      ["""DebertaTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Tuple      			=      [
     """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """DebertaForMaskedLM""",
     """DebertaForQuestionAnswering""",
     """DebertaForSequenceClassification""",
     """DebertaForTokenClassification""",
     """DebertaModel""",
     """DebertaPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __UpperCamelCase      :       Optional[Any]      			=      [
     """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFDebertaForMaskedLM""",
     """TFDebertaForQuestionAnswering""",
     """TFDebertaForSequenceClassification""",
     """TFDebertaForTokenClassification""",
     """TFDebertaModel""",
     """TFDebertaPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
 from .tokenization_deberta import DebertaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_deberta_fast import DebertaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_deberta import (
      DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
      DebertaPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_deberta import (
      TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
      TFDebertaPreTrainedModel,
  )


else:
 import sys

 __UpperCamelCase      :       List[Any]      			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)