



"""simple docstring"""







from __future__ import annotations

from PIL import Image

# Define glider example
A    =			[
    [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
A    =			[[0, 1, 0], [0, 1, 0], [0, 1, 0]]

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							list[list[int]]:
       A         =					[]
       for i in range(len(lowerCamelCase__			)			):
              A         =					[]
              for j in range(len(cells[i]			)			):
                     # Get the number of live neighbours
                     A         =					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.
                     A         =					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__				,      lowerCamelCase__			) ->							list[Image.Image]:
       A         =					[]
       for _ in range(lowerCamelCase__			):
              # Create output image
              A         =					Image.new('RGB'				,      (len(cells[0]			), len(lowerCamelCase__			))			)
              A         =					img.load()

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

        # Save image
              images.append(lowerCamelCase__			)
              A         =					new_generation(lowerCamelCase__			)
       return images


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








"""simple docstring"""







import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
A    =			logging.get_logger(__name__)

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							Union[str, Any]:
							A         =					MobileNetVaConfig(layer_norm_eps=0.0_01			)

							if "_quant" in model_name:
														raise ValueError('Quantized models are not supported.'			)

							A         =					re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'				,      lowerCamelCase__			)
							if matches:
														A         =					float(matches[1]			)
														A         =					int(matches[2]			)

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							A         =					1001
							A         =					'imagenet-1k-id2label.json'
							A         =					'huggingface/label-files'
							A         =					json.load(open(hf_hub_download(lowerCamelCase__				,      lowerCamelCase__				,      repo_type='dataset'			)				,      'r'			)			)
							A         =					{int(lowerCamelCase__			) + 1: v for k, v in idalabel.items()}
							A         =					'background'
							A         =					idalabel
							A         =					{v: k for k, v in idalabel.items()}

							return config

def   lowerCAmelCase__			(     ) ->							List[Any]:
							A         =					'http://images.cocodataset.org/val2017/000000039769.jpg'
							A         =					Image.open(requests.get(lowerCamelCase__				,      stream=lowerCamelCase__			).raw			)
							return im

@torch.no_grad()
def   lowerCAmelCase__			(     lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__=False			) ->							Dict:
							A         =					get_mobilenet_va_config(lowerCamelCase__			)

							# Load 🤗 model
							A         =					MobileNetVaForImageClassification(lowerCamelCase__			).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__			)

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							A         =					MobileNetVaImageProcessor(
							    crop_size={'width': config.image_size, 'height': config.image_size}				,      size={'shortest_edge': config.image_size + 32}				,      )
							A         =					image_processor(images=prepare_img()				,      return_tensors='pt'			)
							A         =					model(**lowerCamelCase__			)
							A         =					outputs.logits

							assert logits.shape == (1, 1001)

							if model_name == "mobilenet_v1_1.0_224":
														A         =					torch.tensor([-4.17_39, -1.12_33, 3.12_05]			)
							elif model_name == "mobilenet_v1_0.75_192":
														A         =					torch.tensor([-3.94_40, -2.31_41, -0.33_33]			)
							else:
														A         =					None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]				,      lowerCamelCase__				,      atol=1E-4			)

							Path(lowerCamelCase__			).mkdir(exist_ok=lowerCamelCase__			)
							print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""			)
							model.save_pretrained(lowerCamelCase__			)
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""			)
							image_processor.save_pretrained(lowerCamelCase__			)

							if push_to_hub:
														print('Pushing to the hub...'			)
														A         =					'google/' + model_name
														image_processor.push_to_hub(lowerCamelCase__			)
														model.push_to_hub(lowerCamelCase__			)


if __name__ == "__main__":
							A    =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--model_name',
							    default='mobilenet_v1_1.0_224',
							    type=str,
							    help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
							)
							parser.add_argument(
							    '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
							)
							parser.add_argument(
							    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
							)

							A    =			parser.parse_args()
							convert_movilevit_checkpoint(
							    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)



