


"""simple docstring"""





import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def      _snake_case						(						_snake_case				:			Union[str, Any] ,   _snake_case				:			int			):
      # Load checkpoint
      lowerCAmelCase					:	Tuple       =   torch.load(_snake_case ,   map_location='''cpu'''			)

      lowerCAmelCase					:	Optional[Any]       =   chkpt['''model''']

      # We have the base model one level deeper than the original XLM repository
      lowerCAmelCase					:	Tuple       =   {}
      for k, v in state_dict.items():
            if "pred_layer" in k:
                  lowerCAmelCase					:	List[str]       =   v
            else:
                  lowerCAmelCase					:	List[Any]       =   v

      lowerCAmelCase					:	List[str]       =   chkpt['''params''']
      lowerCAmelCase					:	List[str]       =   {n: v for n, v in config.items() if not isinstance(_snake_case ,   (torch.FloatTensor, numpy.ndarray)			)}

      lowerCAmelCase					:	Optional[Any]       =   chkpt['''dico_word2id''']
      lowerCAmelCase					:	Tuple       =   {s + '''</w>''' if s.find('''@@'''			) == -1 and i > 13 else s.replace('''@@''' ,   ''''''			): i for s, i in vocab.items()}

      # Save pytorch-model
      lowerCAmelCase					:	str       =   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
      lowerCAmelCase					:	Dict       =   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
      lowerCAmelCase					:	List[Any]       =   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

      print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''			)
      torch.save(_snake_case ,   _snake_case			)

      print(f'''Save configuration file to {pytorch_config_dump_path}'''			)
      with open(_snake_case ,   '''w''' ,   encoding='''utf-8'''			) as f:
            f.write(json.dumps(_snake_case ,   indent=2			) + '''\n'''			)

      print(f'''Save vocab file to {pytorch_config_dump_path}'''			)
      with open(_snake_case ,   '''w''' ,   encoding='''utf-8'''			) as f:
            f.write(json.dumps(_snake_case ,   indent=2			) + '''\n'''			)


if __name__ == "__main__":
 snake_case__		:     List[str]    				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 snake_case__		:     Optional[Any]    				=				parser.parse_args()
 convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define glider example
snake_case__		:     int    				=				[
    [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
snake_case__		:     Any    				=				[[0, 1, 0], [0, 1, 0], [0, 1, 0]]


def      _snake_case						(						_snake_case				:			list[list[int]]			):
      lowerCAmelCase					:	Union[str, Any]       =   []
      for i in range(len(_snake_case			)			):
            lowerCAmelCase					:	Any       =   []
            for j in range(len(cells[i]			)			):
                  # Get the number of live neighbours
                  lowerCAmelCase					:	Optional[int]       =   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(_snake_case			) - 1 and j > 0:
                        neighbour_count += cells[i + 1][j - 1]
                  if i < len(_snake_case			) - 1:
                        neighbour_count += cells[i + 1][j]
                  if i < len(_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.
                  lowerCAmelCase					:	str       =   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(_snake_case			)
      return next_generation


def      _snake_case						(						_snake_case				:			list[list[int]] ,   _snake_case				:			int			):
      lowerCAmelCase					:	int       =   []
      for _ in range(_snake_case			):
            # Create output image
            lowerCAmelCase					:	Union[str, Any]       =   Image.new('''RGB''' ,   (len(cells[0]			), len(_snake_case			))			)
            lowerCAmelCase					:	Union[str, Any]       =   img.load()

            # Save cells to image
            for x in range(len(_snake_case			)			):
                  for y in range(len(cells[0]			)			):
                        lowerCAmelCase					:	Optional[int]       =   255 - cells[y][x] * 255
                        lowerCAmelCase					:	List[Any]       =   (colour, colour, colour)

        # Save image
            images.append(_snake_case			)
            lowerCAmelCase					:	Union[str, Any]       =   new_generation(_snake_case			)
      return images


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


