


from __future__ import annotations

from PIL import Image

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






def   _lowercase(					__a				:						list[list[int]]     ):
							a__      =[]
							for i in range(len(__a     )     ):
														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(__a     ) - 1 and j > 0:
																												neighbour_count += cells[i + 1][j - 1]
																					if i < len(__a     ) - 1:
																												neighbour_count += cells[i + 1][j]
																					if i < len(__a     ) - 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(__a     )
							return next_generation






def   _lowercase(					__a				:						list[list[int]]    ,     __a				:						int     ):
							a__      =[]
							for _ in range(__a     ):
														# Create output image
														a__      =Image.new('RGB'    ,     (len(cells[0]     ), len(__a     ))     )
														a__      =img.load()

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

        # Save image
														images.append(__a     )
														a__      =new_generation(__a     )
							return images


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





import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase:						List[str]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Tuple   				=       torch.device('cpu')






def   _lowercase(					):
							a__      ='http://images.cocodataset.org/val2017/000000039769.jpg'
							a__      =Image.open(requests.get(__a    ,     stream=__a     ).raw     )
							return im






def   _lowercase(					__a				:						Optional[Any]     ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]     )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]     )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]     )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]     )






def   _lowercase(					__a				:						int    ,     __a				:						int    ,     __a				:						Optional[Any]     ):
							a__      =dct.pop(__a     )
							a__      =val






def   _lowercase(					__a				:						Optional[Any]     ):
							a__      =[]
							for k in state_dict.keys():
														a__      =k
														if ".pwconv" in k:
																					a__      =k_new.replace('.pwconv'    ,     '.point_wise_conv'     )
														if ".dwconv" in k:
																					a__      =k_new.replace('.dwconv'    ,     '.depth_wise_conv'     )
														if ".Proj." in k:
																					a__      =k_new.replace('.Proj.'    ,     '.proj.'     )
														if "patch_embed" in k_new:
																					a__      =k_new.replace('patch_embed'    ,     'swiftformer.patch_embed.patch_embedding'     )
														if "network" in k_new:
																					a__      =k_new.split('.'     )
																					if ls[2].isdigit():
																												a__      ='swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
																					else:
																												a__      =k_new.replace('network'    ,     'swiftformer.encoder.network'     )
														rename_keys.append((k, k_new)     )
							return rename_keys






@torch.no_grad()
def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						int    ,     __a				:						str     ):
							a__      =SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__      =1000
							a__      ='huggingface/label-files'
							a__      ='imagenet-1k-id2label.json'
							a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}
							a__      =idalabel
							a__      ={v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__      =[3, 3, 6, 4]
														a__      =[48, 56, 112, 220]

							elif swiftformer_name == "swiftformer_s":
														a__      =[3, 3, 9, 6]
														a__      =[48, 64, 168, 224]

							elif swiftformer_name == "swiftformer_l1":
														a__      =[4, 3, 10, 5]
														a__      =[48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														a__      =[4, 4, 12, 6]
														a__      =[64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'     ):
																					a__      =torch.hub.load_state_dict_from_url(__a    ,     map_location='cpu'    ,     check_hash=__a     )
														else:
																					a__      =torch.load(__a    ,     map_location='cpu'     )
							a__      =checkpoint

							a__      =create_rename_keys(__a     )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__a    ,     __a    ,     __a     )

							# load HuggingFace model
							a__      =SwiftFormerForImageClassification(__a     ).eval()
							hf_model.load_state_dict(__a     )

							# prepare test inputs
							a__      =prepare_img()
							a__      =ViTImageProcessor.from_pretrained('preprocessor_config'     )
							a__      =processor(images=__a    ,     return_tensors='pt'     )

							# compare outputs from both models
							a__      =get_expected_output(__a     )
							a__      =hf_model(inputs['pixel_values']     ).logits

							assert hf_logits.shape == torch.Size([1, 1000]     )
							assert torch.allclose(hf_logits[0, 0:5]    ,     __a    ,     atol=1e-3     )

							Path(__a     ).mkdir(exist_ok=__a     )
							print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""     )
							hf_model.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Optional[Any]   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swiftformer_name',
	    default='swiftformer_xs',
	    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
	    type=str,
	    help='Name of the SwiftFormer model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='./converted_outputs/',
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

	_lowerCAmelCase:						Optional[int]   				=       parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

