





__UpperCamelCase:	str = 6_5_5_2_1


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int			)   ->							int:




		'''simple docstring'''
		lowercase__					:      Optional[int]      =		1
		lowercase__					:      List[Any]      =		0
		for plain_chr in plain_text:
				lowercase__					:      List[Any]      =		(a + ord(__UpperCAmelCase			)) % MOD_ADLER
				lowercase__					:      Optional[int]      =		(b + a) % MOD_ADLER
		return (b << 16) | a







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_      :      Optional[int]    =     logging.get_logger(__name__)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= feature_size
  a__												= sampling_rate
  a__												= padding_value

  a__												= kwargs.pop('''padding_side'''		,				'''right'''      )
  a__												= kwargs.pop('''return_attention_mask'''		,				SCREAMING_SNAKE_CASE      )

  super().__init__(**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = True		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = False		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			BatchFeature:
  # 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)      ):
   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(SCREAMING_SNAKE_CASE      ) == 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(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.
   a__												= 0
   while len(required_input[index]      ) == 0:
    index += 1
   if index < len(SCREAMING_SNAKE_CASE      ):
    a__												= required_input[index][0]

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

        # Convert padding_strategy in PaddingStrategy
  a__												= self._get_padding_strategies(padding=SCREAMING_SNAKE_CASE		,				max_length=SCREAMING_SNAKE_CASE      )

  a__												= processed_features[self.model_input_names[0]]

  a__												= 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.'''      )

  a__												= []
  for i in range(SCREAMING_SNAKE_CASE      ):
   a__												= {k: v[i] for k, v in processed_features.items()}
   # truncation
   a__												= 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
   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(SCREAMING_SNAKE_CASE      ):
   # padding
   a__												= 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:
     a__												= []
    if value.dtype is np.dtype(np.floataa      ):
     a__												= value.astype(np.floataa      )
    batch_outputs[key].append(SCREAMING_SNAKE_CASE      )

  return BatchFeature(SCREAMING_SNAKE_CASE		,				tensor_type=SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = PaddingStrategy.DO_NOT_PAD		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			dict:
  a__												= processed_features[self.model_input_names[0]]

  if padding_strategy == PaddingStrategy.LONGEST:
   a__												= 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):
   a__												= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

  a__												= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(SCREAMING_SNAKE_CASE      ) < max_length

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

  if needs_to_be_padded:
   a__												= max_length - len(SCREAMING_SNAKE_CASE      )
   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(
        SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				'''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(
        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		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				) ->			Optional[Any]:
  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(SCREAMING_SNAKE_CASE      ) > 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		,				SCREAMING_SNAKE_CASE=False		,				SCREAMING_SNAKE_CASE=None      ) ->			Optional[Any]:

  # 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(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
    a__												= PaddingStrategy(SCREAMING_SNAKE_CASE      )
   elif isinstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ):
    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





