
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


__magic_name__            =  logging.get_logger(__name__)




class 					_SCREAMING_SNAKE_CASE  (      _lowerCAmelCase ):
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	**lowerCamelCase ):
          snake_case__						=				feature_size
          snake_case__						=				sampling_rate
          snake_case__						=				padding_value

          snake_case__						=				kwargs.pop("padding_side"		,	"right" )
          snake_case__						=				kwargs.pop("return_attention_mask"		,	_lowerCAmelCase )

          super().__init__(**_lowerCAmelCase )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = True		,	lowerCamelCase = None		,	lowerCamelCase = False		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = None		,	):







          if isinstance(_lowerCAmelCase		,	(list, tuple) ) and isinstance(processed_features[0]		,	(dict, BatchFeature) ):
              snake_case__						=				{
                  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() )}""" )

          snake_case__						=				processed_features[self.model_input_names[0]]
          snake_case__						=				(
              return_attention_mask if return_attention_mask is not None else self.return_attention_mask
          )

          if len(_lowerCAmelCase ) == 0:
              if return_attention_mask:
                  snake_case__						=				[]
              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

          snake_case__						=				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.
              snake_case__						=				0
              while len(required_input[index] ) == 0:
                  index += 1
              if index < len(_lowerCAmelCase ):
                  snake_case__						=				required_input[index][0]

          if return_tensors is None:
              if is_tf_tensor(_lowerCAmelCase ):
                  snake_case__						=				"tf"
              elif is_torch_tensor(_lowerCAmelCase ):
                  snake_case__						=				"pt"
              elif isinstance(_lowerCAmelCase		,	(int, float, list, tuple, np.ndarray) ):
                  snake_case__						=				"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) ):
                  snake_case__						=				to_numpy(_lowerCAmelCase )
              else:
                  snake_case__						=				[to_numpy(_lowerCAmelCase ) for v in value]

        # Convert padding_strategy in PaddingStrategy
          snake_case__						=				self._get_padding_strategies(padding=_lowerCAmelCase		,	max_length=_lowerCAmelCase )

          snake_case__						=				processed_features[self.model_input_names[0]]

          snake_case__						=				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." )

          snake_case__						=				[]
          for i in range(_lowerCAmelCase ):
              snake_case__						=				{k: v[i] for k, v in processed_features.items()}
              # truncation
              snake_case__						=				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
              snake_case__						=				max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
              snake_case__						=				PaddingStrategy.MAX_LENGTH

          snake_case__						=				{}
          for i in range(_lowerCAmelCase ):
              # padding
              snake_case__						=				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:
                      snake_case__						=				[]
                  if value.dtype is np.dtype(np.floataa ):
                      snake_case__						=				value.astype(np.floataa )
                  batch_outputs[key].append(_lowerCAmelCase )

          return BatchFeature(_lowerCAmelCase		,	tensor_type=_lowerCAmelCase )
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = PaddingStrategy.DO_NOT_PAD		,	lowerCamelCase = None		,	lowerCamelCase = None		,	):
          snake_case__						=				processed_features[self.model_input_names[0]]

          if padding_strategy == PaddingStrategy.LONGEST:
              snake_case__						=				len(_lowerCAmelCase )

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

          snake_case__						=				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowerCAmelCase ) < max_length

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

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

          return processed_features
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = None		,	lowerCamelCase = 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." )

          snake_case__						=				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):
              snake_case__						=				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

          snake_case__						=				len(_lowerCAmelCase ) > max_length

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

          return processed_features



      def A_			(			self		,	lowerCamelCase=False		,	lowerCamelCase=None ):







          if padding is not False:
              if padding is True:
                  snake_case__						=				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
              elif not isinstance(_lowerCAmelCase		,	_lowerCAmelCase ):
                  snake_case__						=				PaddingStrategy(_lowerCAmelCase )
              elif isinstance(_lowerCAmelCase		,	_lowerCAmelCase ):
                  snake_case__						=				padding
          else:
              snake_case__						=				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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



