




from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCAmelCase_					    = logging.get_logger(__name__)






class  _lowerCAmelCase     (  _lowercase	):
      A__		     =		['input_values', 'padding_mask']

      def __init__(   self      ,				__UpperCAmelCase = 1      ,				__UpperCAmelCase = 2_4000      ,				__UpperCAmelCase = 0.0      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				**__UpperCAmelCase      ,				):
        super().__init__(feature_size=__UpperCAmelCase      ,				sampling_rate=__UpperCAmelCase      ,				padding_value=__UpperCAmelCase      ,				**__UpperCAmelCase		)
        lowerCAmelCase__				:  Dict		       =     chunk_length_s
        lowerCAmelCase__				:  int		       =     overlap

      @property
      def 	__magic_name__(   self		):
        if self.chunk_length_s is None:
          return None
        else:
          return int(self.chunk_length_s * self.sampling_rate		)

      @property
      def 	__magic_name__(   self		):
        if self.chunk_length_s is None or self.overlap is None:
          return None
        else:
          return max(1      ,				int((1.0 - self.overlap) * self.chunk_length		)		)







      def __call__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = False      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				__UpperCAmelCase = None      ,				):
        if sampling_rate is not None:
          if sampling_rate != self.sampling_rate:
            raise ValueError(
                f"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                f""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                f""" {self.sampling_rate} and not {sampling_rate}."""		)
        else:
          logger.warning(
              '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
              '''Failing to do so can result in silent errors that might be hard to debug.'''		)

        if padding and truncation:
          raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''		)
        elif padding is None:
          # by default let's pad the inputs
          lowerCAmelCase__				:  List[Any]		       =     True

        lowerCAmelCase__				:  str		       =     bool(
            isinstance(__UpperCAmelCase      ,				(list, tuple)		) and (isinstance(raw_audio[0]      ,				(np.ndarray, tuple, list)		))		)

        if is_batched:
          lowerCAmelCase__				:  Tuple		       =     [np.asarray(__UpperCAmelCase      ,				dtype=np.floataa		).T for audio in raw_audio]
        elif not is_batched and not isinstance(__UpperCAmelCase      ,				np.ndarray		):
          lowerCAmelCase__				:  Dict		       =     np.asarray(__UpperCAmelCase      ,				dtype=np.floataa		)
        elif isinstance(__UpperCAmelCase      ,				np.ndarray		) and raw_audio.dtype is np.dtype(np.floataa		):
          lowerCAmelCase__				:  Optional[int]		       =     raw_audio.astype(np.floataa		)

        # always return batch
        if not is_batched:
          lowerCAmelCase__				:  Optional[int]		       =     [np.asarray(__UpperCAmelCase		).T]

        # verify inputs are valid
        for idx, example in enumerate(__UpperCAmelCase		):
          if example.ndim > 2:
            raise ValueError(f"""Expected input shape (channels, length) but got shape {example.shape}"""		)
          if self.feature_size == 1 and example.ndim != 1:
            raise ValueError(f"""Expected mono audio but example has {example.shape[-1]} channels"""		)
          if self.feature_size == 2 and example.shape[-1] != 2:
            raise ValueError(f"""Expected stereo audio but example has {example.shape[-1]} channels"""		)

        lowerCAmelCase__				:  Any		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     BatchFeature({'''input_values''': raw_audio}		)
        if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
          if truncation:
            lowerCAmelCase__				:  Optional[Any]		       =     min(array.shape[0] for array in raw_audio		)
            lowerCAmelCase__				:  Any		       =     int(np.floor(max_length / self.chunk_stride		)		)
            lowerCAmelCase__				:  int		       =     (nb_step - 1) * self.chunk_stride + self.chunk_length
          elif padding:
            lowerCAmelCase__				:  Any		       =     max(array.shape[0] for array in raw_audio		)
            lowerCAmelCase__				:  List[str]		       =     int(np.ceil(max_length / self.chunk_stride		)		)
            lowerCAmelCase__				:  Dict		       =     (nb_step - 1) * self.chunk_stride + self.chunk_length
            lowerCAmelCase__				:  List[str]		       =     '''max_length'''
          else:
            lowerCAmelCase__				:  Union[str, Any]		       =     input_values

        # normal padding on batch
        if padded_inputs is None:
          lowerCAmelCase__				:  Any		       =     self.pad(
              __UpperCAmelCase      ,				max_length=__UpperCAmelCase      ,				truncation=__UpperCAmelCase      ,				padding=__UpperCAmelCase      ,				return_attention_mask=__UpperCAmelCase      ,				)
          if padding:
            lowerCAmelCase__				:  Dict		       =     padded_inputs.pop('''attention_mask'''		)

        lowerCAmelCase__				:  Optional[int]		       =     []
        for example in padded_inputs.pop('''input_values'''		):
          if self.feature_size == 1:
            lowerCAmelCase__				:  str		       =     example[..., None]
          input_values.append(example.T		)

        lowerCAmelCase__				:  Optional[int]		       =     input_values
        if return_tensors is not None:
          lowerCAmelCase__				:  int		       =     padded_inputs.convert_to_tensors(__UpperCAmelCase		)

        return padded_inputs







import numpy as np






class  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  List[Any]		       =     (0, 0)
        lowerCAmelCase__				:  Optional[int]		       =     None
        lowerCAmelCase__				:  Optional[Any]		       =     0
        lowerCAmelCase__				:  Optional[int]		       =     0
        lowerCAmelCase__				:  Tuple		       =     0

      def __eq__(   self      ,				__UpperCAmelCase		):
        return self.position == cell.position







      def 	__magic_name__(   self		):
        print(self.position		)






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase=(5, 5)		):
        lowerCAmelCase__				:  List[str]		       =     np.zeros(__UpperCAmelCase		)
        lowerCAmelCase__				:  List[Any]		       =     world_size[0]
        lowerCAmelCase__				:  str		       =     world_size[1]

      def 	__magic_name__(   self		):
        print(self.w		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     [
            (-1, -1),
            (-1, 0),
            (-1, 1),
            (0, -1),
            (0, 1),
            (1, -1),
            (1, 0),
            (1, 1),
        ]
        lowerCAmelCase__				:  Tuple		       =     cell.position[0]
        lowerCAmelCase__				:  Optional[int]		       =     cell.position[1]
        lowerCAmelCase__				:  Any		       =     []
        for n in neughbour_cord:
          lowerCAmelCase__				:  int		       =     current_x + n[0]
          lowerCAmelCase__				:  Dict		       =     current_y + n[1]
          if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
            lowerCAmelCase__				:  Optional[int]		       =     Cell()
            lowerCAmelCase__				:  Optional[int]		       =     (x, y)
            lowerCAmelCase__				:  Any		       =     cell
            neighbours.append(__UpperCAmelCase		)
        return neighbours



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	Union[str, Any]:
  lowerCAmelCase__				:  int		       =     []
  lowerCAmelCase__				:  int		       =     []
  _open.append(UpperCamelCase      )

  while _open:
    lowerCAmelCase__				:  Dict		       =     np.argmin([n.f for n in _open]      )
    lowerCAmelCase__				:  Dict		       =     _open[min_f]
    _closed.append(_open.pop(UpperCamelCase      )      )
    if current == goal:
      break
    for n in world.get_neigbours(UpperCamelCase      ):
      for c in _closed:
        if c == n:
          continue
      lowerCAmelCase__				:  Any		       =     current.g + 1
      lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     n.position
      lowerCAmelCase__       ,			lowerCAmelCase__				:  Union[str, Any]		       =     goal.position
      lowerCAmelCase__				:  Tuple		       =     (ya - ya) ** 2 + (xa - xa) ** 2
      lowerCAmelCase__				:  Optional[Any]		       =     n.h + n.g

      for c in _open:
        if c == n and c.f < n.f:
          continue
      _open.append(UpperCamelCase      )
  lowerCAmelCase__				:  List[Any]		       =     []
  while current.parent is not None:
    path.append(current.position      )
    lowerCAmelCase__				:  List[Any]		       =     current.parent
  path.append(current.position      )
  return path[::-1]


if __name__ == "__main__":
     lowerCAmelCase_					    = Gridworld()
     # Start position and goal
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (0, 0)
     lowerCAmelCase_					    = Cell()
     lowerCAmelCase_					    = (4, 4)
     print(F"""path from {start.position} to {goal.position}""")
     lowerCAmelCase_					    = astar(world, start, goal)
     #   Just for visual reasons.
     for i in s:
          lowerCAmelCase_					    = 1
     print(world.w)

