





'''simple docstring'''



import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__magic_name__   :						Union[str, Any]    =			logging.getLogger(__name__)


@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



       '''simple docstring'''






       UpperCAmelCase__       :						bool      =   field(default=__UpperCamelCase					,    metadata={'''help''': '''Whether to use SortishSampler or not.'''}			)
       UpperCAmelCase__       :						bool      =   field(
           default=__UpperCamelCase					,    metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}			)
       UpperCAmelCase__       :						Optional[int]      =   field(
           default=__UpperCamelCase					,    metadata={
               '''help''': (
                   '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                   '''to the `max_length` value of the model configuration.'''
               )
           }					,    )
       UpperCAmelCase__       :						Optional[int]      =   field(
           default=__UpperCamelCase					,    metadata={
               '''help''': (
                   '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
                   '''to the `num_beams` value of the model configuration.'''
               )
           }					,    )
       UpperCAmelCase__       :						Optional[Union[str, Path, GenerationConfig]]      =   field(
           default=__UpperCamelCase					,    metadata={
               '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
           }					,    )

       def 	UpperCamelCase(					self	):
         _snake_case     		=				super().to_dict()
         for k, v in d.items():
           if isinstance(lowerCamelCase						,   lowerCamelCase	):
             _snake_case     		=				v.to_dict()
         return d









'''simple docstring'''



from __future__ import annotations

from PIL import Image

# Define glider example
__magic_name__   :						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
__magic_name__   :						List[Any]    =			[[0, 1, 0], [0, 1, 0], [0, 1, 0]]




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

    # Save cells to image
    for x in range(len(SCREAMING_SNAKE_CASE__       )       ):
      for y in range(len(cells[0]       )       ):
        _snake_case     		=				2_55 - cells[y][x] * 2_55
        _snake_case     		=				(colour, colour, colour)

        # Save image
    images.append(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				new_generation(SCREAMING_SNAKE_CASE__       )
  return images


if __name__ == "__main__":
 __magic_name__   :						Optional[Any]    =			generate_images(GLIDER, 16)
 images[0].save("""out.gif""", save_all=True, append_images=images[1:])


