




from __future__ import annotations

from PIL import Image

# Define glider example
_lowerCAmelCase						:   str		=   [
    [0, 1, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0, 0],
    [1, 1, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0],
]

# Define blinker example
_lowerCAmelCase						:   List[str]		=   [[0, 1, 0], [0, 1, 0], [0, 1, 0]]



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 for i in range(len(_lowerCAmelCase	)	):
  UpperCAmelCase__							 =						[]
  for j in range(len(cells[i]	)	):
   # Get the number of live neighbours
   UpperCAmelCase__							 =						0
   if i > 0 and j > 0:
    neighbour_count += cells[i - 1][j - 1]
   if i > 0:
    neighbour_count += cells[i - 1][j]
   if i > 0 and j < len(cells[i]	) - 1:
    neighbour_count += cells[i - 1][j + 1]
   if j > 0:
    neighbour_count += cells[i][j - 1]
   if j < len(cells[i]	) - 1:
    neighbour_count += cells[i][j + 1]
   if i < len(_lowerCAmelCase	) - 1 and j > 0:
    neighbour_count += cells[i + 1][j - 1]
   if i < len(_lowerCAmelCase	) - 1:
    neighbour_count += cells[i + 1][j]
   if i < len(_lowerCAmelCase	) - 1 and j < len(cells[i]	) - 1:
    neighbour_count += cells[i + 1][j + 1]

   # Rules of the game of life (excerpt from Wikipedia):
   # 1. Any live cell with two or three live neighbours survives.
   # 2. Any dead cell with three live neighbours becomes a live cell.
   # 3. All other live cells die in the next generation.
   #    Similarly, all other dead cells stay dead.
   UpperCAmelCase__							 =						cells[i][j] == 1
   if (
       (alive and 2 <= neighbour_count <= 3)
       or not alive
       and neighbour_count == 3
   ):
    next_generation_row.append(1	)
   else:
    next_generation_row.append(0	)

  next_generation.append(_lowerCAmelCase	)
 return next_generation



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list[list[int]]						,							_lowerCAmelCase							:   int	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 for _ in range(_lowerCAmelCase	):
  # Create output image
  UpperCAmelCase__							 =						Image.new("RGB"						,							(len(cells[0]	), len(_lowerCAmelCase	))	)
  UpperCAmelCase__							 =						img.load()

  # Save cells to image
  for x in range(len(_lowerCAmelCase	)	):
   for y in range(len(cells[0]	)	):
    UpperCAmelCase__							 =						255 - cells[y][x] * 255
    UpperCAmelCase__							 =						(colour, colour, colour)

        # Save image
  images.append(_lowerCAmelCase	)
  UpperCAmelCase__							 =						new_generation(_lowerCAmelCase	)
 return images


if __name__ == "__main__":
  _lowerCAmelCase						:   int		=   generate_images(GLIDER, 1_6)
  images[0].save("out.gif", save_all=True, append_images=images[1:])











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						:   Any		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""input_values""", """padding_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :int = 1 ,      lowerCamelCase   :int = 2_4000 ,      lowerCamelCase   :float = 0.0 ,      lowerCamelCase   :float = None ,      lowerCamelCase   :float = None ,      **lowerCamelCase   :Optional[Any] ,      )     ->			str:
       super().__init__(feature_size=lowerCamelCase ,      sampling_rate=lowerCamelCase ,      padding_value=lowerCamelCase ,      **lowerCamelCase      )
       UpperCAmelCase__							 =						chunk_length_s
       UpperCAmelCase__							 =						overlap
      @property
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Optional[int]:
       if self.chunk_length_s is None:
        return None
       else:
        return int(self.chunk_length_s * self.sampling_rate      )
      @property
      def 	UpperCAmelCase_						(			self   :str      )     ->			Optional[int]:
       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   :List[Any] ,      lowerCamelCase   :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,      lowerCamelCase   :Optional[Union[bool, str, PaddingStrategy]] = None ,      lowerCamelCase   :Optional[bool] = False ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Optional[Union[str, TensorType]] = None ,      lowerCamelCase   :Optional[int] = None ,      )     ->			BatchFeature:
       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
        UpperCAmelCase__							 =						True

       UpperCAmelCase__							 =						bool(
           isinstance(lowerCamelCase ,      (list, tuple)      ) and (isinstance(raw_audio[0] ,      (np.ndarray, tuple, list)      ))      )

       if is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ).T for audio in raw_audio]
       elif not is_batched and not isinstance(lowerCamelCase ,      np.ndarray      ):
        UpperCAmelCase__							 =						np.asarray(lowerCamelCase ,      dtype=np.floataa      )
       elif isinstance(lowerCamelCase ,      np.ndarray      ) and raw_audio.dtype is np.dtype(np.floataa      ):
        UpperCAmelCase__							 =						raw_audio.astype(np.floataa      )

       # always return batch
       if not is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase      ).T]

       # verify inputs are valid
       for idx, example in enumerate(lowerCamelCase      ):
        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'''      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						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:
         UpperCAmelCase__							 =						min(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.floor(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
        elif padding:
         UpperCAmelCase__							 =						max(array.shape[0] for array in raw_audio      )
         UpperCAmelCase__							 =						int(np.ceil(max_length / self.chunk_stride      )      )
         UpperCAmelCase__							 =						(nb_step - 1) * self.chunk_stride + self.chunk_length
         UpperCAmelCase__							 =						"max_length"
        else:
         UpperCAmelCase__							 =						input_values

        # normal padding on batch
       if padded_inputs is None:
        UpperCAmelCase__							 =						self.pad(
            lowerCamelCase ,      max_length=lowerCamelCase ,      truncation=lowerCamelCase ,      padding=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      )
        if padding:
         UpperCAmelCase__							 =						padded_inputs.pop("attention_mask"      )

       UpperCAmelCase__							 =						[]
       for example in padded_inputs.pop("input_values"      ):
        if self.feature_size == 1:
         UpperCAmelCase__							 =						example[..., None]
        input_values.append(example.T      )

       UpperCAmelCase__							 =						input_values
       if return_tensors is not None:
        UpperCAmelCase__							 =						padded_inputs.convert_to_tensors(lowerCamelCase      )

       return padded_inputs





