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 		_lowercase						(					snake_case_       ):

 def __init__( self	:							List[Any]   ,      snake_case	:							int   ,      snake_case	:							int   ,      snake_case	:							float   ,      **snake_case	:							int			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					feature_size
  UpperCamelCase_					:						Dict =					sampling_rate
  UpperCamelCase_					:						str =					padding_value

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

  super().__init__(**snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Union[
         BatchFeature,
         List[BatchFeature],
         Dict[str, BatchFeature],
         Dict[str, List[BatchFeature]],
         List[Dict[str, BatchFeature]],
     ]   ,      snake_case	:							Union[bool, str, PaddingStrategy] = True   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							bool = False   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[bool] = None   ,      snake_case	:							Optional[Union[str, TensorType]] = None   ,      )   ->						BatchFeature:

  """simple docstring"""





  if isinstance(snake_case   ,      (list, tuple)			) and isinstance(processed_features[0]   ,      (dict, BatchFeature)			):
   UpperCamelCase_					:						Optional[int] =					{
       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_					:						str =					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_					:						str =					[]
   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_					:						Optional[int] =					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_					:						int =					0
   while len(required_input[index]			) == 0:
    index += 1
   if index < len(snake_case			):
    UpperCamelCase_					:						List[str] =					required_input[index][0]

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

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

  UpperCamelCase_					:						Dict =					processed_features[self.model_input_names[0]]

  UpperCamelCase_					:						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_					:						Any =					[]
  for i in range(snake_case			):
   UpperCamelCase_					:						Union[str, Any] =					{k: v[i] for k, v in processed_features.items()}
   # truncation
   UpperCamelCase_					:						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_					:						int =					PaddingStrategy.MAX_LENGTH

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

  return BatchFeature(snake_case   ,      tensor_type=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Union[Dict[str, np.ndarray], BatchFeature]   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							PaddingStrategy = PaddingStrategy.DO_NOT_PAD   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[bool] = None   ,      )   ->						dict:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					processed_features[self.model_input_names[0]]

  if padding_strategy == PaddingStrategy.LONGEST:
   UpperCamelCase_					:						Optional[int] =					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[str] =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  UpperCamelCase_					:						Tuple =					padding_strategy != PaddingStrategy.DO_NOT_PAD and len(snake_case			) < max_length

  if return_attention_mask and "attention_mask" not in processed_features:
   UpperCamelCase_					:						str =					np.ones(len(snake_case			)   ,      dtype=np.intaa			)

  if needs_to_be_padded:
   UpperCamelCase_					:						str =					max_length - len(snake_case			)
   if self.padding_side == "right":
    if return_attention_mask:
     UpperCamelCase_					:						Tuple =					np.pad(
         processed_features['attention_mask']   ,      (0, difference)			)
    UpperCamelCase_					:						str =					((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
    UpperCamelCase_					:						List[Any] =					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_					:						List[Any] =					((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
    UpperCamelCase_					:						Dict =					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 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Union[Dict[str, np.ndarray], BatchFeature]   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[int] = None   ,      snake_case	:							Optional[bool] = 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_					:						str =					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_					:						Optional[Any] =					((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  UpperCamelCase_					:						Optional[Any] =					len(snake_case			) > max_length

  if needs_to_be_truncated:
   UpperCamelCase_					:						str =					processed_features[self.model_input_names[0]][:max_length]
   if "attention_mask" in processed_features:
    UpperCamelCase_					:						Optional[int] =					processed_features['attention_mask'][:max_length]

  return processed_features


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							str=False   ,      snake_case	:							Dict=None			)   ->						int:

  """simple docstring"""





  if padding is not False:
   if padding is True:
    UpperCamelCase_					:						Any =					PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
   elif not isinstance(snake_case   ,      snake_case			):
    UpperCamelCase_					:						List[Any] =					PaddingStrategy(snake_case			)
   elif isinstance(snake_case   ,      snake_case			):
    UpperCamelCase_					:						Optional[int] =					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




def    __lowercase     (						lowerCamelCase					:     str	):
 UpperCamelCase_					:						Dict =					0
 for ch in input_str:
  UpperCamelCase_					:						Tuple =					ord(lowerCamelCase	)
  UpperCamelCase_					:						str =					pow(2  ,  lowerCamelCase	)

  # If we already turned on bit for current character's unicode
  if bitmap >> ch_unicode & 1 == 1:
   return False
  bitmap |= ch_bit_index_on
 return True


if __name__ == "__main__":
   import doctest

   doctest.testmod()



