






'''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__						:Any          =			logging.get_logger(__name__)







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : int	,    A__      : int	,    A__      : int	,    A__      : float	,    **A__      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				feature_size
            __lowerCamelCase						:       List[str]				   =				sampling_rate
            __lowerCamelCase						:       Union[str, Any]				   =				padding_value

            __lowerCamelCase						:       Optional[int]				   =				kwargs.pop("""padding_side"""	,    """right"""					)
            __lowerCamelCase						:       Optional[int]				   =				kwargs.pop("""return_attention_mask"""	,    A__					)

            super().__init__(**A__					)




       def 					a_					(					self      : List[str]	,    A__      : Union[
               BatchFeature,
               List[BatchFeature],
               Dict[str, BatchFeature],
               Dict[str, List[BatchFeature]],
               List[Dict[str, BatchFeature]],
           ]	,    A__      : Union[bool, str, PaddingStrategy] = True	,    A__      : Optional[int] = None	,    A__      : bool = False	,    A__      : Optional[int] = None	,    A__      : Optional[bool] = None	,    A__      : Optional[Union[str, TensorType]] = None	,    ):


            """simple docstring"""







            if isinstance(A__	,    (list, tuple)					) and isinstance(processed_features[0]	,    (dict, BatchFeature)					):
                 __lowerCamelCase						:       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()					)}"					)

            __lowerCamelCase						:       Tuple				   =				processed_features[self.model_input_names[0]]
            __lowerCamelCase						:       Dict				   =				(
                return_attention_mask if return_attention_mask is not None else self.return_attention_mask
            )

            if len(A__					) == 0:
                 if return_attention_mask:
                      __lowerCamelCase						:       Union[str, 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

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

            if return_tensors is None:
                 if is_tf_tensor(A__					):
                      __lowerCamelCase						:       str				   =				"""tf"""
                 elif is_torch_tensor(A__					):
                      __lowerCamelCase						:       Dict				   =				"""pt"""
                 elif isinstance(A__	,    (int, float, list, tuple, np.ndarray)					):
                      __lowerCamelCase						:       int				   =				"""np"""
                 else:
                      raise ValueError(
                          f"type of {first_element} unknown: {type(A__					)}. "
                          """Should be one of a python, numpy, pytorch or tensorflow object."""					)

            for key, value in processed_features.items():
                 if isinstance(value[0]	,    (int, float)					):
                      __lowerCamelCase						:       List[str]				   =				to_numpy(A__					)
                 else:
                      __lowerCamelCase						:       int				   =				[to_numpy(A__					) for v in value]

        # Convert padding_strategy in PaddingStrategy
            __lowerCamelCase						:       Optional[int]				   =				self._get_padding_strategies(padding=A__	,    max_length=A__					)

            __lowerCamelCase						:       Any				   =				processed_features[self.model_input_names[0]]

            __lowerCamelCase						:       List[str]				   =				len(A__					)
            if not all(len(A__					) == batch_size for v in processed_features.values()					):
                 raise ValueError("""Some items in the output dictionary have a different batch size than others."""					)

            __lowerCamelCase						:       List[Any]				   =				[]
            for i in range(A__					):
                 __lowerCamelCase						:       str				   =				{k: v[i] for k, v in processed_features.items()}
                 # truncation
                 __lowerCamelCase						:       Dict				   =				self._truncate(
                     A__	,    max_length=A__	,    pad_to_multiple_of=A__	,    truncation=A__	,    )
                 truncated_inputs.append(A__					)

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

            __lowerCamelCase						:       Tuple				   =				{}
            for i in range(A__					):
                 # padding
                 __lowerCamelCase						:       Optional[int]				   =				self._pad(
                     truncated_inputs[i]	,    max_length=A__	,    padding_strategy=A__	,    pad_to_multiple_of=A__	,    return_attention_mask=A__	,    )

                 for key, value in outputs.items():
                      if key not in batch_outputs:
                           __lowerCamelCase						:       Any				   =				[]
                      if value.dtype is np.dtype(np.floataa					):
                           __lowerCamelCase						:       Union[str, Any]				   =				value.astype(np.floataa					)
                      batch_outputs[key].append(A__					)

            return BatchFeature(A__	,    tensor_type=A__					)




       def 					a_					(					self      : Optional[int]	,    A__      : Union[Dict[str, np.ndarray], BatchFeature]	,    A__      : Optional[int] = None	,    A__      : PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,    A__      : Optional[int] = None	,    A__      : Optional[bool] = None	,    ):


            """simple docstring"""







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

            if padding_strategy == PaddingStrategy.LONGEST:
                 __lowerCamelCase						:       Optional[Any]				   =				len(A__					)

            if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
                 __lowerCamelCase						:       Optional[Any]				   =				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

            __lowerCamelCase						:       int				   =				padding_strategy != PaddingStrategy.DO_NOT_PAD and len(A__					) < max_length

            if return_attention_mask and "attention_mask" not in processed_features:
                 __lowerCamelCase						:       Any				   =				np.ones(len(A__					)	,    dtype=np.intaa					)

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

            return processed_features




       def 					a_					(					self      : Any	,    A__      : Union[Dict[str, np.ndarray], BatchFeature]	,    A__      : Optional[int] = None	,    A__      : Optional[int] = None	,    A__      : Optional[bool] = None	,    ):


            """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."""					)

            __lowerCamelCase						:       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):
                 __lowerCamelCase						:       Optional[int]				   =				((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

            __lowerCamelCase						:       List[str]				   =				len(A__					) > max_length

            if needs_to_be_truncated:
                 __lowerCamelCase						:       Union[str, Any]				   =				processed_features[self.model_input_names[0]][:max_length]
                 if "attention_mask" in processed_features:
                      __lowerCamelCase						:       List[str]				   =				processed_features["""attention_mask"""][:max_length]

            return processed_features




       def 					a_					(					self      : Tuple	,    A__      : Optional[Any]=False	,    A__      : List[Any]=None					):


            """simple docstring"""







            if padding is not False:
                 if padding is True:
                      __lowerCamelCase						:       Union[str, Any]				   =				PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
                 elif not isinstance(A__	,    A__					):
                      __lowerCamelCase						:       Optional[Any]				   =				PaddingStrategy(A__					)
                 elif isinstance(A__	,    A__					):
                      __lowerCamelCase						:       Optional[Any]				   =				padding
            else:
                 __lowerCamelCase						:       Dict				   =				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 __future__ import annotations

from typing import Any
def     __lowercase  (_lowercase     )   ->     int:


     """simple docstring"""
     if not postfix_notation:
          return 0

     __lowerCamelCase						:       Optional[int]				   =				{"""+""", """-""", """*""", """/"""}
     __lowerCamelCase						:       list[Any]				   =				[]

     for token in postfix_notation:
          if token in operations:
               __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				stack.pop(), stack.pop()
               if token == "+":
                    stack.append(a + b     )
               elif token == "-":
                    stack.append(a - b     )
               elif token == "*":
                    stack.append(a * b     )
               else:
                    if a * b < 0 and a % b != 0:
                         stack.append(a // b + 1     )
                    else:
                         stack.append(a // b     )
          else:
               stack.append(int(_lowercase     )     )

     return stack.pop()


if __name__ == "__main__":
   import doctest

   doctest.testmod()



